Distributed HUC-based modeling with SUMMA for ensemble streamflow forecasting over large regional domains.

NASA Astrophysics Data System (ADS)

Saharia, M.; Wood, A.; Clark, M. P.; Bennett, A.; Nijssen, B.; Clark, E.; Newman, A. J.

2017-12-01

Most operational streamflow forecasting systems rely on a forecaster-in-the-loop approach in which some parts of the forecast workflow require an experienced human forecaster. But this approach faces challenges surrounding process reproducibility, hindcasting capability, and extension to large domains. The operational hydrologic community is increasingly moving towards `over-the-loop' (completely automated) large-domain simulations yet recent developments indicate a widespread lack of community knowledge about the strengths and weaknesses of such systems for forecasting. A realistic representation of land surface hydrologic processes is a critical element for improving forecasts, but often comes at the substantial cost of forecast system agility and efficiency. While popular grid-based models support the distributed representation of land surface processes, intermediate-scale Hydrologic Unit Code (HUC)-based modeling could provide a more efficient and process-aligned spatial discretization, reducing the need for tradeoffs between model complexity and critical forecasting requirements such as ensemble methods and comprehensive model calibration. The National Center for Atmospheric Research is collaborating with the University of Washington, the Bureau of Reclamation and the USACE to implement, assess, and demonstrate real-time, over-the-loop distributed streamflow forecasting for several large western US river basins and regions. In this presentation, we present early results from short to medium range hydrologic and streamflow forecasts for the Pacific Northwest (PNW). We employ a real-time 1/16th degree daily ensemble model forcings as well as downscaled Global Ensemble Forecasting System (GEFS) meteorological forecasts. These datasets drive an intermediate-scale configuration of the Structure for Unifying Multiple Modeling Alternatives (SUMMA) model, which represents the PNW using over 11,700 HUCs. The system produces not only streamflow forecasts (using the MizuRoute channel routing tool) but also distributed model states such as soil moisture and snow water equivalent. We also describe challenges in distributed model-based forecasting, including the application and early results of real-time hydrologic data assimilation.

A comparison of the stochastic and machine learning approaches in hydrologic time series forecasting

NASA Astrophysics Data System (ADS)

Kim, T.; Joo, K.; Seo, J.; Heo, J. H.

2016-12-01

Hydrologic time series forecasting is an essential task in water resources management and it becomes more difficult due to the complexity of runoff process. Traditional stochastic models such as ARIMA family has been used as a standard approach in time series modeling and forecasting of hydrological variables. Due to the nonlinearity in hydrologic time series data, machine learning approaches has been studied with the advantage of discovering relevant features in a nonlinear relation among variables. This study aims to compare the predictability between the traditional stochastic model and the machine learning approach. Seasonal ARIMA model was used as the traditional time series model, and Random Forest model which consists of decision tree and ensemble method using multiple predictor approach was applied as the machine learning approach. In the application, monthly inflow data from 1986 to 2015 of Chungju dam in South Korea were used for modeling and forecasting. In order to evaluate the performances of the used models, one step ahead and multi-step ahead forecasting was applied. Root mean squared error and mean absolute error of two models were compared.

HEPEX - achievements and challenges!

NASA Astrophysics Data System (ADS)

Pappenberger, Florian; Ramos, Maria-Helena; Thielen, Jutta; Wood, Andy; Wang, Qj; Duan, Qingyun; Collischonn, Walter; Verkade, Jan; Voisin, Nathalie; Wetterhall, Fredrik; Vuillaume, Jean-Francois Emmanuel; Lucatero Villasenor, Diana; Cloke, Hannah L.; Schaake, John; van Andel, Schalk-Jan

2014-05-01

HEPEX is an international initiative bringing together hydrologists, meteorologists, researchers and end-users to develop advanced probabilistic hydrological forecast techniques for improved flood, drought and water management. HEPEX was launched in 2004 as an independent, cooperative international scientific activity. During the first meeting, the overarching goal was defined as: "to develop and test procedures to produce reliable hydrological ensemble forecasts, and to demonstrate their utility in decision making related to the water, environmental and emergency management sectors." The applications of hydrological ensemble predictions span across large spatio-temporal scales, ranging from short-term and localized predictions to global climate change and regional modeling. Within the HEPEX community, information is shared through its blog (www.hepex.org), meetings, testbeds and intercompaison experiments, as well as project reportings. Key questions of HEPEX are: * What adaptations are required for meteorological ensemble systems to be coupled with hydrological ensemble systems? * How should the existing hydrological ensemble prediction systems be modified to account for all sources of uncertainty within a forecast? * What is the best way for the user community to take advantage of ensemble forecasts and to make better decisions based on them? This year HEPEX celebrates its 10th year anniversary and this poster will present a review of the main operational and research achievements and challenges prepared by Hepex contributors on data assimilation, post-processing of hydrologic predictions, forecast verification, communication and use of probabilistic forecasts in decision-making. Additionally, we will present the most recent activities implemented by Hepex and illustrate how everyone can join the community and participate to the development of new approaches in hydrologic ensemble prediction.

The state of the art of flood forecasting - Hydrological Ensemble Prediction Systems

NASA Astrophysics Data System (ADS)

Thielen-Del Pozo, J.; Pappenberger, F.; Salamon, P.; Bogner, K.; Burek, P.; de Roo, A.

2010-09-01

Flood forecasting systems form a key part of ‘preparedness' strategies for disastrous floods and provide hydrological services, civil protection authorities and the public with information of upcoming events. Provided the warning leadtime is sufficiently long, adequate preparatory actions can be taken to efficiently reduce the impacts of the flooding. Because of the specific characteristics of each catchment, varying data availability and end-user demands, the design of the best flood forecasting system may differ from catchment to catchment. However, despite the differences in concept and data needs, there is one underlying issue that spans across all systems. There has been an growing awareness and acceptance that uncertainty is a fundamental issue of flood forecasting and needs to be dealt with at the different spatial and temporal scales as well as the different stages of the flood generating processes. Today, operational flood forecasting centres change increasingly from single deterministic forecasts to probabilistic forecasts with various representations of the different contributions of uncertainty. The move towards these so-called Hydrological Ensemble Prediction Systems (HEPS) in flood forecasting represents the state of the art in forecasting science, following on the success of the use of ensembles for weather forecasting (Buizza et al., 2005) and paralleling the move towards ensemble forecasting in other related disciplines such as climate change predictions. The use of HEPS has been internationally fostered by initiatives such as "The Hydrologic Ensemble Prediction Experiment" (HEPEX), created with the aim to investigate how best to produce, communicate and use hydrologic ensemble forecasts in hydrological short-, medium- und long term prediction of hydrological processes. The advantages of quantifying the different contributions of uncertainty as well as the overall uncertainty to obtain reliable and useful flood forecasts also for extreme events, has become evident. However, despite the demonstrated advantages, worldwide the incorporation of HEPS in operational flood forecasting is still limited. The applicability of HEPS for smaller river basins was tested in MAP D-Phase, an acronym for "Demonstration of Probabilistic Hydrological and Atmospheric Simulation of flood Events in the Alpine region" which was launched in 2005 as a Forecast Demonstration Project of World Weather Research Programme of WMO, and entered a pre-operational and still active testing phase in 2007. In Europe, a comparatively high number of EPS driven systems for medium-large rivers exist. National flood forecasting centres of Sweden, Finland and the Netherlands, have already implemented HEPS in their operational forecasting chain, while in other countries including France, Germany, Czech Republic and Hungary, hybrids or experimental chains have been installed. As an example of HEPS, the European Flood Alert System (EFAS) is being presented. EFAS provides medium-range probabilistic flood forecasting information for large trans-national river basins. It incorporates multiple sets of weather forecast including different types of EPS and deterministic forecasts from different providers. EFAS products are evaluated and visualised as exceedance of critical levels only - both in forms of maps and time series. Different sources of uncertainty and its impact on the flood forecasting performance for every grid cell has been tested offline but not yet incorporated operationally into the forecasting chain for computational reasons. However, at stations where real-time discharges are available, a hydrological uncertainty processor is being applied to estimate the total predictive uncertainty from the hydrological and input uncertainties. Research on long-term EFAS results has shown the need for complementing statistical analysis with case studies for which examples will be shown.

Improving flood forecasting capability of physically based distributed hydrological model by parameter optimization

NASA Astrophysics Data System (ADS)

Chen, Y.; Li, J.; Xu, H.

2015-10-01

Physically based distributed hydrological models discrete the terrain of the whole catchment into a number of grid cells at fine resolution, and assimilate different terrain data and precipitation to different cells, and are regarded to have the potential to improve the catchment hydrological processes simulation and prediction capability. In the early stage, physically based distributed hydrological models are assumed to derive model parameters from the terrain properties directly, so there is no need to calibrate model parameters, but unfortunately, the uncertanties associated with this model parameter deriving is very high, which impacted their application in flood forecasting, so parameter optimization may also be necessary. There are two main purposes for this study, the first is to propose a parameter optimization method for physically based distributed hydrological models in catchment flood forecasting by using PSO algorithm and to test its competence and to improve its performances, the second is to explore the possibility of improving physically based distributed hydrological models capability in cathcment flood forecasting by parameter optimization. In this paper, based on the scalar concept, a general framework for parameter optimization of the PBDHMs for catchment flood forecasting is first proposed that could be used for all PBDHMs. Then, with Liuxihe model as the study model, which is a physically based distributed hydrological model proposed for catchment flood forecasting, the improverd Particle Swarm Optimization (PSO) algorithm is developed for the parameter optimization of Liuxihe model in catchment flood forecasting, the improvements include to adopt the linear decreasing inertia weight strategy to change the inertia weight, and the arccosine function strategy to adjust the acceleration coefficients. This method has been tested in two catchments in southern China with different sizes, and the results show that the improved PSO algorithm could be used for Liuxihe model parameter optimization effectively, and could improve the model capability largely in catchment flood forecasting, thus proven that parameter optimization is necessary to improve the flood forecasting capability of physically based distributed hydrological model. It also has been found that the appropriate particle number and the maximum evolution number of PSO algorithm used for Liuxihe model catchment flood forcasting is 20 and 30, respectively.

Insights on the impact of systematic model errors on data assimilation performance in changing catchments

NASA Astrophysics Data System (ADS)

Pathiraja, S.; Anghileri, D.; Burlando, P.; Sharma, A.; Marshall, L.; Moradkhani, H.

2018-03-01

The global prevalence of rapid and extensive land use change necessitates hydrologic modelling methodologies capable of handling non-stationarity. This is particularly true in the context of Hydrologic Forecasting using Data Assimilation. Data Assimilation has been shown to dramatically improve forecast skill in hydrologic and meteorological applications, although such improvements are conditional on using bias-free observations and model simulations. A hydrologic model calibrated to a particular set of land cover conditions has the potential to produce biased simulations when the catchment is disturbed. This paper sheds new light on the impacts of bias or systematic errors in hydrologic data assimilation, in the context of forecasting in catchments with changing land surface conditions and a model calibrated to pre-change conditions. We posit that in such cases, the impact of systematic model errors on assimilation or forecast quality is dependent on the inherent prediction uncertainty that persists even in pre-change conditions. Through experiments on a range of catchments, we develop a conceptual relationship between total prediction uncertainty and the impacts of land cover changes on the hydrologic regime to demonstrate how forecast quality is affected when using state estimation Data Assimilation with no modifications to account for land cover changes. This work shows that systematic model errors as a result of changing or changed catchment conditions do not always necessitate adjustments to the modelling or assimilation methodology, for instance through re-calibration of the hydrologic model, time varying model parameters or revised offline/online bias estimation.

The application of a Grey Markov Model to forecasting annual maximum water levels at hydrological stations

NASA Astrophysics Data System (ADS)

Dong, Sheng; Chi, Kun; Zhang, Qiyi; Zhang, Xiangdong

2012-03-01

Compared with traditional real-time forecasting, this paper proposes a Grey Markov Model (GMM) to forecast the maximum water levels at hydrological stations in the estuary area. The GMM combines the Grey System and Markov theory into a higher precision model. The GMM takes advantage of the Grey System to predict the trend values and uses the Markov theory to forecast fluctuation values, and thus gives forecast results involving two aspects of information. The procedure for forecasting annul maximum water levels with the GMM contains five main steps: 1) establish the GM (1, 1) model based on the data series; 2) estimate the trend values; 3) establish a Markov Model based on relative error series; 4) modify the relative errors caused in step 2, and then obtain the relative errors of the second order estimation; 5) compare the results with measured data and estimate the accuracy. The historical water level records (from 1960 to 1992) at Yuqiao Hydrological Station in the estuary area of the Haihe River near Tianjin, China are utilized to calibrate and verify the proposed model according to the above steps. Every 25 years' data are regarded as a hydro-sequence. Eight groups of simulated results show reasonable agreement between the predicted values and the measured data. The GMM is also applied to the 10 other hydrological stations in the same estuary. The forecast results for all of the hydrological stations are good or acceptable. The feasibility and effectiveness of this new forecasting model have been proved in this paper.

Multimodel hydrological ensemble forecasts for the Baskatong catchment in Canada using the TIGGE database.

NASA Astrophysics Data System (ADS)

Tito Arandia Martinez, Fabian

2014-05-01

Adequate uncertainty assessment is an important issue in hydrological modelling. An important issue for hydropower producers is to obtain ensemble forecasts which truly grasp the uncertainty linked to upcoming streamflows. If properly assessed, this uncertainty can lead to optimal reservoir management and energy production (ex. [1]). The meteorological inputs to the hydrological model accounts for an important part of the total uncertainty in streamflow forecasting. Since the creation of the THORPEX initiative and the TIGGE database, access to meteorological ensemble forecasts from nine agencies throughout the world have been made available. This allows for hydrological ensemble forecasts based on multiple meteorological ensemble forecasts. Consequently, both the uncertainty linked to the architecture of the meteorological model and the uncertainty linked to the initial condition of the atmosphere can be accounted for. The main objective of this work is to show that a weighted combination of meteorological ensemble forecasts based on different atmospheric models can lead to improved hydrological ensemble forecasts, for horizons from one to ten days. This experiment is performed for the Baskatong watershed, a head subcatchment of the Gatineau watershed in the province of Quebec, in Canada. Baskatong watershed is of great importance for hydro-power production, as it comprises the main reservoir for the Gatineau watershed, on which there are six hydropower plants managed by Hydro-Québec. Since the 70's, they have been using pseudo ensemble forecast based on deterministic meteorological forecasts to which variability derived from past forecasting errors is added. We use a combination of meteorological ensemble forecasts from different models (precipitation and temperature) as the main inputs for hydrological model HSAMI ([2]). The meteorological ensembles from eight of the nine agencies available through TIGGE are weighted according to their individual performance and combined to form a grand ensemble. Results show that the hydrological forecasts derived from the grand ensemble perform better than the pseudo ensemble forecasts actually used operationally at Hydro-Québec. References: [1] M. Verbunt, A. Walser, J. Gurtz et al., "Probabilistic flood forecasting with a limited-area ensemble prediction system: Selected case studies," Journal of Hydrometeorology, vol. 8, no. 4, pp. 897-909, Aug, 2007. [2] N. Evora, Valorisation des prévisions météorologiques d'ensemble, Institu de recherceh d'Hydro-Québec 2005. [3] V. Fortin, Le modèle météo-apport HSAMI: historique, théorie et application, Institut de recherche d'Hydro-Québec, 2000.

Improving flood forecasting capability of physically based distributed hydrological models by parameter optimization

NASA Astrophysics Data System (ADS)

Chen, Y.; Li, J.; Xu, H.

2016-01-01

Physically based distributed hydrological models (hereafter referred to as PBDHMs) divide the terrain of the whole catchment into a number of grid cells at fine resolution and assimilate different terrain data and precipitation to different cells. They are regarded to have the potential to improve the catchment hydrological process simulation and prediction capability. In the early stage, physically based distributed hydrological models are assumed to derive model parameters from the terrain properties directly, so there is no need to calibrate model parameters. However, unfortunately the uncertainties associated with this model derivation are very high, which impacted their application in flood forecasting, so parameter optimization may also be necessary. There are two main purposes for this study: the first is to propose a parameter optimization method for physically based distributed hydrological models in catchment flood forecasting by using particle swarm optimization (PSO) algorithm and to test its competence and to improve its performances; the second is to explore the possibility of improving physically based distributed hydrological model capability in catchment flood forecasting by parameter optimization. In this paper, based on the scalar concept, a general framework for parameter optimization of the PBDHMs for catchment flood forecasting is first proposed that could be used for all PBDHMs. Then, with the Liuxihe model as the study model, which is a physically based distributed hydrological model proposed for catchment flood forecasting, the improved PSO algorithm is developed for the parameter optimization of the Liuxihe model in catchment flood forecasting. The improvements include adoption of the linearly decreasing inertia weight strategy to change the inertia weight and the arccosine function strategy to adjust the acceleration coefficients. This method has been tested in two catchments in southern China with different sizes, and the results show that the improved PSO algorithm could be used for the Liuxihe model parameter optimization effectively and could improve the model capability largely in catchment flood forecasting, thus proving that parameter optimization is necessary to improve the flood forecasting capability of physically based distributed hydrological models. It also has been found that the appropriate particle number and the maximum evolution number of PSO algorithm used for the Liuxihe model catchment flood forecasting are 20 and 30 respectively.

Impact of number of realizations on the suitability of simulated weather data for hydrologic and environmental applications

USDA-ARS?s Scientific Manuscript database

Stochastic weather generators are widely used in hydrological, environmental, and agricultural applications to simulate and forecast weather time series. However, such stochastic processes usually produce random outputs hence the question on how representative the generated data are if obtained fro...

Short-term ensemble radar rainfall forecasts for hydrological applications

NASA Astrophysics Data System (ADS)

Codo de Oliveira, M.; Rico-Ramirez, M. A.

2016-12-01

Flooding is a very common natural disaster around the world, putting local population and economy at risk. Forecasting floods several hours ahead and issuing warnings are of main importance to permit proper response in emergency situations. However, it is important to know the uncertainties related to the rainfall forecasting in order to produce more reliable forecasts. Nowcasting models (short-term rainfall forecasts) are able to produce high spatial and temporal resolution predictions that are useful in hydrological applications. Nonetheless, they are subject to uncertainties mainly due to the nowcasting model used, errors in radar rainfall estimation, temporal development of the velocity field and to the fact that precipitation processes such as growth and decay are not taken into account. In this study an ensemble generation scheme using rain gauge data as a reference to estimate radars errors is used to produce forecasts with up to 3h lead-time. The ensembles try to assess in a realistic way the residual uncertainties that remain even after correction algorithms are applied in the radar data. The ensembles produced are compered to a stochastic ensemble generator. Furthermore, the rainfall forecast output was used as an input in a hydrodynamic sewer network model and also in hydrological model for catchments of different sizes in north England. A comparative analysis was carried of how was carried out to assess how the radar uncertainties propagate into these models. The first named author is grateful to CAPES - Ciencia sem Fronteiras for funding this PhD research.

A Diagnostics Tool to detect ensemble forecast system anomaly and guide operational decisions

NASA Astrophysics Data System (ADS)

Park, G. H.; Srivastava, A.; Shrestha, E.; Thiemann, M.; Day, G. N.; Draijer, S.

2017-12-01

The hydrologic community is moving toward using ensemble forecasts to take uncertainty into account during the decision-making process. The New York City Department of Environmental Protection (DEP) implements several types of ensemble forecasts in their decision-making process: ensemble products for a statistical model (Hirsch and enhanced Hirsch); the National Weather Service (NWS) Advanced Hydrologic Prediction Service (AHPS) forecasts based on the classical Ensemble Streamflow Prediction (ESP) technique; and the new NWS Hydrologic Ensemble Forecasting Service (HEFS) forecasts. To remove structural error and apply the forecasts to additional forecast points, the DEP post processes both the AHPS and the HEFS forecasts. These ensemble forecasts provide mass quantities of complex data, and drawing conclusions from these forecasts is time-consuming and difficult. The complexity of these forecasts also makes it difficult to identify system failures resulting from poor data, missing forecasts, and server breakdowns. To address these issues, we developed a diagnostic tool that summarizes ensemble forecasts and provides additional information such as historical forecast statistics, forecast skill, and model forcing statistics. This additional information highlights the key information that enables operators to evaluate the forecast in real-time, dynamically interact with the data, and review additional statistics, if needed, to make better decisions. We used Bokeh, a Python interactive visualization library, and a multi-database management system to create this interactive tool. This tool compiles and stores data into HTML pages that allows operators to readily analyze the data with built-in user interaction features. This paper will present a brief description of the ensemble forecasts, forecast verification results, and the intended applications for the diagnostic tool.

A Bayesian modelling method for post-processing daily sub-seasonal to seasonal rainfall forecasts from global climate models and evaluation for 12 Australian catchments

NASA Astrophysics Data System (ADS)

Schepen, Andrew; Zhao, Tongtiegang; Wang, Quan J.; Robertson, David E.

2018-03-01

Rainfall forecasts are an integral part of hydrological forecasting systems at sub-seasonal to seasonal timescales. In seasonal forecasting, global climate models (GCMs) are now the go-to source for rainfall forecasts. For hydrological applications however, GCM forecasts are often biased and unreliable in uncertainty spread, and calibration is therefore required before use. There are sophisticated statistical techniques for calibrating monthly and seasonal aggregations of the forecasts. However, calibration of seasonal forecasts at the daily time step typically uses very simple statistical methods or climate analogue methods. These methods generally lack the sophistication to achieve unbiased, reliable and coherent forecasts of daily amounts and seasonal accumulated totals. In this study, we propose and evaluate a Rainfall Post-Processing method for Seasonal forecasts (RPP-S), which is based on the Bayesian joint probability modelling approach for calibrating daily forecasts and the Schaake Shuffle for connecting the daily ensemble members of different lead times. We apply the method to post-process ACCESS-S forecasts for 12 perennial and ephemeral catchments across Australia and for 12 initialisation dates. RPP-S significantly reduces bias in raw forecasts and improves both skill and reliability. RPP-S forecasts are also more skilful and reliable than forecasts derived from ACCESS-S forecasts that have been post-processed using quantile mapping, especially for monthly and seasonal accumulations. Several opportunities to improve the robustness and skill of RPP-S are identified. The new RPP-S post-processed forecasts will be used in ensemble sub-seasonal to seasonal streamflow applications.

The case for probabilistic forecasting in hydrology

NASA Astrophysics Data System (ADS)

Krzysztofowicz, Roman

2001-08-01

That forecasts should be stated in probabilistic, rather than deterministic, terms has been argued from common sense and decision-theoretic perspectives for almost a century. Yet most operational hydrological forecasting systems produce deterministic forecasts and most research in operational hydrology has been devoted to finding the 'best' estimates rather than quantifying the predictive uncertainty. This essay presents a compendium of reasons for probabilistic forecasting of hydrological variates. Probabilistic forecasts are scientifically more honest, enable risk-based warnings of floods, enable rational decision making, and offer additional economic benefits. The growing demand for information about risk and the rising capability to quantify predictive uncertainties create an unparalleled opportunity for the hydrological profession to dramatically enhance the forecasting paradigm.

A Four-Stage Hybrid Model for Hydrological Time Series Forecasting

PubMed Central

Di, Chongli; Yang, Xiaohua; Wang, Xiaochao

2014-01-01

Hydrological time series forecasting remains a difficult task due to its complicated nonlinear, non-stationary and multi-scale characteristics. To solve this difficulty and improve the prediction accuracy, a novel four-stage hybrid model is proposed for hydrological time series forecasting based on the principle of ‘denoising, decomposition and ensemble’. The proposed model has four stages, i.e., denoising, decomposition, components prediction and ensemble. In the denoising stage, the empirical mode decomposition (EMD) method is utilized to reduce the noises in the hydrological time series. Then, an improved method of EMD, the ensemble empirical mode decomposition (EEMD), is applied to decompose the denoised series into a number of intrinsic mode function (IMF) components and one residual component. Next, the radial basis function neural network (RBFNN) is adopted to predict the trend of all of the components obtained in the decomposition stage. In the final ensemble prediction stage, the forecasting results of all of the IMF and residual components obtained in the third stage are combined to generate the final prediction results, using a linear neural network (LNN) model. For illustration and verification, six hydrological cases with different characteristics are used to test the effectiveness of the proposed model. The proposed hybrid model performs better than conventional single models, the hybrid models without denoising or decomposition and the hybrid models based on other methods, such as the wavelet analysis (WA)-based hybrid models. In addition, the denoising and decomposition strategies decrease the complexity of the series and reduce the difficulties of the forecasting. With its effective denoising and accurate decomposition ability, high prediction precision and wide applicability, the new model is very promising for complex time series forecasting. This new forecast model is an extension of nonlinear prediction models. PMID:25111782

A four-stage hybrid model for hydrological time series forecasting.

PubMed

Di, Chongli; Yang, Xiaohua; Wang, Xiaochao

2014-01-01

Hydrological time series forecasting remains a difficult task due to its complicated nonlinear, non-stationary and multi-scale characteristics. To solve this difficulty and improve the prediction accuracy, a novel four-stage hybrid model is proposed for hydrological time series forecasting based on the principle of 'denoising, decomposition and ensemble'. The proposed model has four stages, i.e., denoising, decomposition, components prediction and ensemble. In the denoising stage, the empirical mode decomposition (EMD) method is utilized to reduce the noises in the hydrological time series. Then, an improved method of EMD, the ensemble empirical mode decomposition (EEMD), is applied to decompose the denoised series into a number of intrinsic mode function (IMF) components and one residual component. Next, the radial basis function neural network (RBFNN) is adopted to predict the trend of all of the components obtained in the decomposition stage. In the final ensemble prediction stage, the forecasting results of all of the IMF and residual components obtained in the third stage are combined to generate the final prediction results, using a linear neural network (LNN) model. For illustration and verification, six hydrological cases with different characteristics are used to test the effectiveness of the proposed model. The proposed hybrid model performs better than conventional single models, the hybrid models without denoising or decomposition and the hybrid models based on other methods, such as the wavelet analysis (WA)-based hybrid models. In addition, the denoising and decomposition strategies decrease the complexity of the series and reduce the difficulties of the forecasting. With its effective denoising and accurate decomposition ability, high prediction precision and wide applicability, the new model is very promising for complex time series forecasting. This new forecast model is an extension of nonlinear prediction models.

Stochastic Simulation and Forecast of Hydrologic Time Series Based on Probabilistic Chaos Expansion

NASA Astrophysics Data System (ADS)

Li, Z.; Ghaith, M.

2017-12-01

Hydrological processes are characterized by many complex features, such as nonlinearity, dynamics and uncertainty. How to quantify and address such complexities and uncertainties has been a challenging task for water engineers and managers for decades. To support robust uncertainty analysis, an innovative approach for the stochastic simulation and forecast of hydrologic time series is developed is this study. Probabilistic Chaos Expansions (PCEs) are established through probabilistic collocation to tackle uncertainties associated with the parameters of traditional hydrological models. The uncertainties are quantified in model outputs as Hermite polynomials with regard to standard normal random variables. Sequentially, multivariate analysis techniques are used to analyze the complex nonlinear relationships between meteorological inputs (e.g., temperature, precipitation, evapotranspiration, etc.) and the coefficients of the Hermite polynomials. With the established relationships between model inputs and PCE coefficients, forecasts of hydrologic time series can be generated and the uncertainties in the future time series can be further tackled. The proposed approach is demonstrated using a case study in China and is compared to a traditional stochastic simulation technique, the Markov-Chain Monte-Carlo (MCMC) method. Results show that the proposed approach can serve as a reliable proxy to complicated hydrological models. It can provide probabilistic forecasting in a more computationally efficient manner, compared to the traditional MCMC method. This work provides technical support for addressing uncertainties associated with hydrological modeling and for enhancing the reliability of hydrological modeling results. Applications of the developed approach can be extended to many other complicated geophysical and environmental modeling systems to support the associated uncertainty quantification and risk analysis.

A national-scale seasonal hydrological forecast system: development and evaluation over Britain

NASA Astrophysics Data System (ADS)

Bell, Victoria A.; Davies, Helen N.; Kay, Alison L.; Brookshaw, Anca; Scaife, Adam A.

2017-09-01

Skilful winter seasonal predictions for the North Atlantic circulation and northern Europe have now been demonstrated and the potential for seasonal hydrological forecasting in the UK is now being explored. One of the techniques being used combines seasonal rainfall forecasts provided by operational weather forecast systems with hydrological modelling tools to provide estimates of seasonal mean river flows up to a few months ahead. The work presented here shows how spatial information contained in a distributed hydrological model typically requiring high-resolution (daily or better) rainfall data can be used to provide an initial condition for a much simpler forecast model tailored to use low-resolution monthly rainfall forecasts. Rainfall forecasts (hindcasts) from the GloSea5 model (1996 to 2009) are used to provide the first assessment of skill in these national-scale flow forecasts. The skill in the combined modelling system is assessed for different seasons and regions of Britain, and compared to what might be achieved using other approaches such as use of an ensemble of historical rainfall in a hydrological model, or a simple flow persistence forecast. The analysis indicates that only limited forecast skill is achievable for Spring and Summer seasonal hydrological forecasts; however, Autumn and Winter flows can be reasonably well forecast using (ensemble mean) rainfall forecasts based on either GloSea5 forecasts or historical rainfall (the preferred type of forecast depends on the region). Flow forecasts using ensemble mean GloSea5 rainfall perform most consistently well across Britain, and provide the most skilful forecasts overall at the 3-month lead time. Much of the skill (64 %) in the 1-month ahead seasonal flow forecasts can be attributed to the hydrological initial condition (particularly in regions with a significant groundwater contribution to flows), whereas for the 3-month ahead lead time, GloSea5 forecasts account for ˜ 70 % of the forecast skill (mostly in areas of high rainfall to the north and west) and only 30 % of the skill arises from hydrological memory (typically groundwater-dominated areas). Given the high spatial heterogeneity in typical patterns of UK rainfall and evaporation, future development of skilful spatially distributed seasonal forecasts could lead to substantial improvements in seasonal flow forecast capability, potentially benefitting practitioners interested in predicting hydrological extremes, not only in the UK but also across Europe.

Towards an Australian ensemble streamflow forecasting system for flood prediction and water management

NASA Astrophysics Data System (ADS)

Bennett, J.; David, R. E.; Wang, Q.; Li, M.; Shrestha, D. L.

2016-12-01

Flood forecasting in Australia has historically relied on deterministic forecasting models run only when floods are imminent, with considerable forecaster input and interpretation. These now co-existed with a continually available 7-day streamflow forecasting service (also deterministic) aimed at operational water management applications such as environmental flow releases. The 7-day service is not optimised for flood prediction. We describe progress on developing a system for ensemble streamflow forecasting that is suitable for both flood prediction and water management applications. Precipitation uncertainty is handled through post-processing of Numerical Weather Prediction (NWP) output with a Bayesian rainfall post-processor (RPP). The RPP corrects biases, downscales NWP output, and produces reliable ensemble spread. Ensemble precipitation forecasts are used to force a semi-distributed conceptual rainfall-runoff model. Uncertainty in precipitation forecasts is insufficient to reliably describe streamflow forecast uncertainty, particularly at shorter lead-times. We characterise hydrological prediction uncertainty separately with a 4-stage error model. The error model relies on data transformation to ensure residuals are homoscedastic and symmetrically distributed. To ensure streamflow forecasts are accurate and reliable, the residuals are modelled using a mixture-Gaussian distribution with distinct parameters for the rising and falling limbs of the forecast hydrograph. In a case study of the Murray River in south-eastern Australia, we show ensemble predictions of floods generally have lower errors than deterministic forecasting methods. We also discuss some of the challenges in operationalising short-term ensemble streamflow forecasts in Australia, including meeting the needs for accurate predictions across all flow ranges and comparing forecasts generated by event and continuous hydrological models.

Evaluation of medium-range ensemble flood forecasting based on calibration strategies and ensemble methods in Lanjiang Basin, Southeast China

NASA Astrophysics Data System (ADS)

Liu, Li; Gao, Chao; Xuan, Weidong; Xu, Yue-Ping

2017-11-01

Ensemble flood forecasts by hydrological models using numerical weather prediction products as forcing data are becoming more commonly used in operational flood forecasting applications. In this study, a hydrological ensemble flood forecasting system comprised of an automatically calibrated Variable Infiltration Capacity model and quantitative precipitation forecasts from TIGGE dataset is constructed for Lanjiang Basin, Southeast China. The impacts of calibration strategies and ensemble methods on the performance of the system are then evaluated. The hydrological model is optimized by the parallel programmed ε-NSGA II multi-objective algorithm. According to the solutions by ε-NSGA II, two differently parameterized models are determined to simulate daily flows and peak flows at each of the three hydrological stations. Then a simple yet effective modular approach is proposed to combine these daily and peak flows at the same station into one composite series. Five ensemble methods and various evaluation metrics are adopted. The results show that ε-NSGA II can provide an objective determination on parameter estimation, and the parallel program permits a more efficient simulation. It is also demonstrated that the forecasts from ECMWF have more favorable skill scores than other Ensemble Prediction Systems. The multimodel ensembles have advantages over all the single model ensembles and the multimodel methods weighted on members and skill scores outperform other methods. Furthermore, the overall performance at three stations can be satisfactory up to ten days, however the hydrological errors can degrade the skill score by approximately 2 days, and the influence persists until a lead time of 10 days with a weakening trend. With respect to peak flows selected by the Peaks Over Threshold approach, the ensemble means from single models or multimodels are generally underestimated, indicating that the ensemble mean can bring overall improvement in forecasting of flows. For peak values taking flood forecasts from each individual member into account is more appropriate.

A Local Forecast of Land Surface Wetness Conditions, Drought, and St. Louis Encephalitis Virus Transmission Derived from Seasonal Climate Predictions

NASA Astrophysics Data System (ADS)

Shaman, J.; Stieglitz, M.; Zebiak, S.; Cane, M.; Day, J. F.

2002-12-01

We present an ensemble local hydrologic forecast derived from the seasonal forecasts of the International Research Institute (IRI) for Climate Prediction. Three- month seasonal forecasts were used to resample historical meteorological conditions and generate ensemble forcing datasets for a TOPMODEL-based hydrology model. Eleven retrospective forecasts were run at a Florida and New York site. Forecast skill was assessed for mean area modeled water table depth (WTD), i.e. near surface soil wetness conditions, and compared with WTD simulated with observed data. Hydrology model forecast skill was evident at the Florida site but not at the New York site. At the Florida site, persistence of hydrologic conditions and local skill of the IRI seasonal forecast contributed to the local hydrologic forecast skill. This forecast will permit probabilistic prediction of future hydrologic conditions. At the Florida site, we have also quantified the link between modeled WTD (i.e. drought) and the amplification and transmission of St. Louis Encephalitis virus (SLEV). We derive an empirical relationship between modeled land surface wetness and levels of SLEV transmission associated with human clinical cases. We then combine the seasonal forecasts of local, modeled WTD with this empirical relationship and produce retrospective probabilistic seasonal forecasts of epidemic SLEV transmission in Florida. Epidemic SLEV transmission forecast skill is demonstrated. These findings will permit real-time forecast of drought and resultant SLEV transmission in Florida.

Application of remote sensing to hydrology. [for the formulation of watershed behavior models

NASA Technical Reports Server (NTRS)

Ambaruch, R.; Simmons, J. W.

1973-01-01

Streamflow forecasting and hydrologic modelling are considered in a feasibility assessment of using the data produced by remote observation from space and/or aircraft to reduce the time and expense normally involved in achieving the ability to predict the hydrological behavior of an ungaged watershed. Existing watershed models are described, and both stochastic and parametric techniques are discussed towards the selection of a suitable simulation model. Technical progress and applications are reported and recommendations are made for additional research.

Assessment of Hydrologic Response to Variable Precipitation Forcing: Russian River Case Study

NASA Astrophysics Data System (ADS)

Cifelli, R.; Hsu, C.; Johnson, L. E.

2014-12-01

NOAA Hydrometeorology Testbed (HMT) activities in California have involved deployment of advanced sensor networks to better track atmospheric river (AR) dynamics and inland penetration of high water vapor air masses. Numerical weather prediction models and decision support tools have been developed to provide forecasters a better basis for forecasting heavy precipitation and consequent flooding. The HMT also involves a joint project with California Department of Water Resources (CA-DWR) and the Scripps Institute for Oceanography (SIO) as part of CA-DWR's Enhanced Flood Response and Emergency Preparedness (EFREP) program. The HMT activities have included development and calibration of a distributed hydrologic model, the NWS Office of Hydrologic Development's (OHD) Research Distributed Hydrologic Model (RDHM), to prototype the distributed approach for flood and other water resources applications. HMT has applied RDHM to the Russian-Napa watersheds for research assessment of gap-filling weather radars for precipitation and hydrologic forecasting and for establishing a prototype to inform both the NWS Monterey Forecast Office and the California Nevada River Forecast Center (CNRFC) of RDHM capabilities. In this presentation, a variety of precipitation forcings generated with and without gap filling radar and rain gauge data are used as input to RDHM to assess the hydrologic response for selected case study events. Both the precipitation forcing and hydrologic model are run at different spatial and temporal resolution in order to examine the sensitivity of runoff to the precipitation inputs. Based on the timing of the events and the variations of spatial and temporal resolution, the parameters which dominate the hydrologic response are identified. The assessment is implemented at two USGS stations (Ukiah near Russian River and Austin Creek near Cazadero) that are minimally influenced by managed flows and objective evaluation can thus be derived. The results are assessed using statistical metrics, including daily Nash scores, Pearson Correlation, and sub daily timing errors.

NWS Operational Requirements for Ensemble-Based Hydrologic Forecasts

NASA Astrophysics Data System (ADS)

Hartman, R. K.

2008-12-01

Ensemble-based hydrologic forecasts have been developed and issued by National Weather Service (NWS) staff at River Forecast Centers (RFCs) for many years. Used principally for long-range water supply forecasts, only the uncertainty associated with weather and climate have been traditionally considered. As technology and societal expectations of resource managers increase, the use and desire for risk-based decision support tools has also increased. These tools require forecast information that includes reliable uncertainty estimates across all time and space domains. The development of reliable uncertainty estimates associated with hydrologic forecasts is being actively pursued within the United States and internationally. This presentation will describe the challenges, components, and requirements for operational hydrologic ensemble-based forecasts from the perspective of a NOAA/NWS River Forecast Center.

Analog-Based Postprocessing of Navigation-Related Hydrological Ensemble Forecasts

NASA Astrophysics Data System (ADS)

Hemri, S.; Klein, B.

2017-11-01

Inland waterway transport benefits from probabilistic forecasts of water levels as they allow to optimize the ship load and, hence, to minimize the transport costs. Probabilistic state-of-the-art hydrologic ensemble forecasts inherit biases and dispersion errors from the atmospheric ensemble forecasts they are driven with. The use of statistical postprocessing techniques like ensemble model output statistics (EMOS) allows for a reduction of these systematic errors by fitting a statistical model based on training data. In this study, training periods for EMOS are selected based on forecast analogs, i.e., historical forecasts that are similar to the forecast to be verified. Due to the strong autocorrelation of water levels, forecast analogs have to be selected based on entire forecast hydrographs in order to guarantee similar hydrograph shapes. Custom-tailored measures of similarity for forecast hydrographs comprise hydrological series distance (SD), the hydrological matching algorithm (HMA), and dynamic time warping (DTW). Verification against observations reveals that EMOS forecasts for water level at three gauges along the river Rhine with training periods selected based on SD, HMA, and DTW compare favorably with reference EMOS forecasts, which are based on either seasonal training periods or on training periods obtained by dividing the hydrological forecast trajectories into runoff regimes.

Communicating uncertainty in hydrological forecasts: mission impossible?

NASA Astrophysics Data System (ADS)

Ramos, Maria-Helena; Mathevet, Thibault; Thielen, Jutta; Pappenberger, Florian

2010-05-01

Cascading uncertainty in meteo-hydrological modelling chains for forecasting and integrated flood risk assessment is an essential step to improve the quality of hydrological forecasts. Although the best methodology to quantify the total predictive uncertainty in hydrology is still debated, there is a common agreement that one must avoid uncertainty misrepresentation and miscommunication, as well as misinterpretation of information by users. Several recent studies point out that uncertainty, when properly explained and defined, is no longer unwelcome among emergence response organizations, users of flood risk information and the general public. However, efficient communication of uncertain hydro-meteorological forecasts is far from being a resolved issue. This study focuses on the interpretation and communication of uncertain hydrological forecasts based on (uncertain) meteorological forecasts and (uncertain) rainfall-runoff modelling approaches to decision-makers such as operational hydrologists and water managers in charge of flood warning and scenario-based reservoir operation. An overview of the typical flow of uncertainties and risk-based decisions in hydrological forecasting systems is presented. The challenges related to the extraction of meaningful information from probabilistic forecasts and the test of its usefulness in assisting operational flood forecasting are illustrated with the help of two case-studies: 1) a study on the use and communication of probabilistic flood forecasting within the European Flood Alert System; 2) a case-study on the use of probabilistic forecasts by operational forecasters from the hydroelectricity company EDF in France. These examples show that attention must be paid to initiatives that promote or reinforce the active participation of expert forecasters in the forecasting chain. The practice of face-to-face forecast briefings, focusing on sharing how forecasters interpret, describe and perceive the model output forecasted scenarios, is essential. We believe that the efficient communication of uncertainty in hydro-meteorological forecasts is not a mission impossible. Questions remaining unanswered in probabilistic hydrological forecasting should not neutralize the goal of such a mission, and the suspense kept should instead act as a catalyst for overcoming the remaining challenges.

Applying a coupled hydrometeorological simulation system to flash flood forecasting over the Korean Peninsula

NASA Astrophysics Data System (ADS)

Ryu, Young; Lim, Yoon-Jin; Ji, Hee-Sook; Park, Hyun-Hee; Chang, Eun-Chul; Kim, Baek-Jo

2017-11-01

In flash flood forecasting, it is necessary to consider not only traditional meteorological variables such as precipitation, evapotranspiration, and soil moisture, but also hydrological components such as streamflow. To address this challenge, the application of high resolution coupled atmospheric-hydrological models is emerging as a promising alternative. This study demonstrates the feasibility of linking a coupled atmospheric-hydrological model (WRF/WRFHydro) with 150-m horizontal grid spacing for flash flood forecasting in Korea. The study area is the Namgang Dam basin in Southern Korea, a mountainous area located downstream of Jiri Mountain (1915 m in height). Under flash flood conditions, the simulated precipitation over the entire basin is comparable to the domain-averaged precipitation, but discharge data from WRF-Hydro shows some differences in the total available water and the temporal distribution of streamflow (given by the timing of the streamflow peak following precipitation), compared to observations. On the basis of sensitivity tests, the parameters controlling the infiltration of excess precipitation and channel roughness depending on stream order are refined and their influence on temporal distribution of streamflow is addressed with intent to apply WRF-Hydro to flash flood forecasting in the Namgang Dam basin. The simulation results from the WRF-Hydro model with optimized parameters demonstrate the potential utility of a coupled atmospheric-hydrological model for forecasting heavy rain-induced flash flooding over the Korean Peninsula.

Integrating observation and statistical forecasts over sub-Saharan Africa to support Famine Early Warning

USGS Publications Warehouse

Funk, Chris; Verdin, James P.; Husak, Gregory

2007-01-01

Famine early warning in Africa presents unique challenges and rewards. Hydrologic extremes must be tracked and anticipated over complex and changing climate regimes. The successful anticipation and interpretation of hydrologic shocks can initiate effective government response, saving lives and softening the impacts of droughts and floods. While both monitoring and forecast technologies continue to advance, discontinuities between monitoring and forecast systems inhibit effective decision making. Monitoring systems typically rely on high resolution satellite remote-sensed normalized difference vegetation index (NDVI) and rainfall imagery. Forecast systems provide information on a variety of scales and formats. Non-meteorologists are often unable or unwilling to connect the dots between these disparate sources of information. To mitigate these problem researchers at UCSB's Climate Hazard Group, NASA GIMMS and USGS/EROS are implementing a NASA-funded integrated decision support system that combines the monitoring of precipitation and NDVI with statistical one-to-three month forecasts. We present the monitoring/forecast system, assess its accuracy, and demonstrate its application in food insecure sub-Saharan Africa.

Short-term Forecasting Tools for Agricultural Nutrient Management.

PubMed

Easton, Zachary M; Kleinman, Peter J A; Buda, Anthony R; Goering, Dustin; Emberston, Nichole; Reed, Seann; Drohan, Patrick J; Walter, M Todd; Guinan, Pat; Lory, John A; Sommerlot, Andrew R; Sharpley, Andrew

2017-11-01

The advent of real-time, short-term farm management tools is motivated by the need to protect water quality above and beyond the general guidance offered by existing nutrient management plans. Advances in high-performance computing and hydrologic or climate modeling have enabled rapid dissemination of real-time information that can assist landowners and conservation personnel with short-term management planning. This paper reviews short-term decision support tools for agriculture that are under various stages of development and implementation in the United States: (i) Wisconsin's Runoff Risk Advisory Forecast (RRAF) System, (ii) New York's Hydrologically Sensitive Area Prediction Tool, (iii) Virginia's Saturated Area Forecast Model, (iv) Pennsylvania's Fertilizer Forecaster, (v) Washington's Application Risk Management (ARM) System, and (vi) Missouri's Design Storm Notification System. Although these decision support tools differ in their underlying model structure, the resolution at which they are applied, and the hydroclimates to which they are relevant, all provide forecasts (range 24-120 h) of runoff risk or soil moisture saturation derived from National Weather Service Forecast models. Although this review highlights the need for further development of robust and well-supported short-term nutrient management tools, their potential for adoption and ultimate utility requires an understanding of the appropriate context of application, the strategic and operational needs of managers, access to weather forecasts, scales of application (e.g., regional vs. field level), data requirements, and outreach communication structure. Copyright © by the American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America, Inc.

Development of a software framework for data assimilation and its applications for streamflow forecasting in Japan

NASA Astrophysics Data System (ADS)

Noh, S. J.; Tachikawa, Y.; Shiiba, M.; Yorozu, K.; Kim, S.

2012-04-01

Data assimilation methods have received increased attention to accomplish uncertainty assessment and enhancement of forecasting capability in various areas. Despite of their potentials, applicable software frameworks to probabilistic approaches and data assimilation are still limited because the most of hydrologic modeling software are based on a deterministic approach. In this study, we developed a hydrological modeling framework for sequential data assimilation, so called MPI-OHyMoS. MPI-OHyMoS allows user to develop his/her own element models and to easily build a total simulation system model for hydrological simulations. Unlike process-based modeling framework, this software framework benefits from its object-oriented feature to flexibly represent hydrological processes without any change of the main library. Sequential data assimilation based on the particle filters is available for any hydrologic models based on MPI-OHyMoS considering various sources of uncertainty originated from input forcing, parameters and observations. The particle filters are a Bayesian learning process in which the propagation of all uncertainties is carried out by a suitable selection of randomly generated particles without any assumptions about the nature of the distributions. In MPI-OHyMoS, ensemble simulations are parallelized, which can take advantage of high performance computing (HPC) system. We applied this software framework for short-term streamflow forecasting of several catchments in Japan using a distributed hydrologic model. Uncertainty of model parameters and remotely-sensed rainfall data such as X-band or C-band radar is estimated and mitigated in the sequential data assimilation.

Residual uncertainty estimation using instance-based learning with applications to hydrologic forecasting

NASA Astrophysics Data System (ADS)

Wani, Omar; Beckers, Joost V. L.; Weerts, Albrecht H.; Solomatine, Dimitri P.

2017-08-01

A non-parametric method is applied to quantify residual uncertainty in hydrologic streamflow forecasting. This method acts as a post-processor on deterministic model forecasts and generates a residual uncertainty distribution. Based on instance-based learning, it uses a k nearest-neighbour search for similar historical hydrometeorological conditions to determine uncertainty intervals from a set of historical errors, i.e. discrepancies between past forecast and observation. The performance of this method is assessed using test cases of hydrologic forecasting in two UK rivers: the Severn and Brue. Forecasts in retrospect were made and their uncertainties were estimated using kNN resampling and two alternative uncertainty estimators: quantile regression (QR) and uncertainty estimation based on local errors and clustering (UNEEC). Results show that kNN uncertainty estimation produces accurate and narrow uncertainty intervals with good probability coverage. Analysis also shows that the performance of this technique depends on the choice of search space. Nevertheless, the accuracy and reliability of uncertainty intervals generated using kNN resampling are at least comparable to those produced by QR and UNEEC. It is concluded that kNN uncertainty estimation is an interesting alternative to other post-processors, like QR and UNEEC, for estimating forecast uncertainty. Apart from its concept being simple and well understood, an advantage of this method is that it is relatively easy to implement.

Uncertainty in flood forecasting: A distributed modeling approach in a sparse data catchment

NASA Astrophysics Data System (ADS)

Mendoza, Pablo A.; McPhee, James; Vargas, Ximena

2012-09-01

Data scarcity has traditionally precluded the application of advanced hydrologic techniques in developing countries. In this paper, we evaluate the performance of a flood forecasting scheme in a sparsely monitored catchment based on distributed hydrologic modeling, discharge assimilation, and numerical weather predictions with explicit validation uncertainty analysis. For the hydrologic component of our framework, we apply TopNet to the Cautin River basin, located in southern Chile, using a fully distributed a priori parameterization based on both literature-suggested values and data gathered during field campaigns. Results obtained from this step indicate that the incremental effort spent in measuring directly a set of model parameters was insufficient to represent adequately the most relevant hydrologic processes related to spatiotemporal runoff patterns. Subsequent uncertainty validation performed over a six month ensemble simulation shows that streamflow uncertainty is better represented during flood events, due to both the increase of state perturbation introduced by rainfall and the flood-oriented calibration strategy adopted here. Results from different assimilation configurations suggest that the upper part of the basin is the major source of uncertainty in hydrologic process representation and hint at the usefulness of interpreting assimilation results in terms of model input and parameterization inadequacy. Furthermore, in this case study the violation of Markovian state properties by the Ensemble Kalman filter did affect the numerical results, showing that an explicit treatment of the time delay between the generation of surface runoff and the arrival at the basin outlet is required in the assimilation scheme. Peak flow forecasting results demonstrate that there is a major problem with the Weather Research and Forecasting model outputs, which systematically overestimate precipitation over the catchment. A final analysis performed for a large flooding event that occurred in July 2006 shows that, in the absence of bias introduced by an incorrect model calibration, the updating of both model states and meteorological forecasts contributes to a better representation of streamflow uncertainty and to better hydrologic forecasts.

Comparison of ensemble post-processing approaches, based on empirical and dynamical error modelisation of rainfall-runoff model forecasts

NASA Astrophysics Data System (ADS)

Chardon, J.; Mathevet, T.; Le Lay, M.; Gailhard, J.

2012-04-01

In the context of a national energy company (EDF : Electricité de France), hydro-meteorological forecasts are necessary to ensure safety and security of installations, meet environmental standards and improve water ressources management and decision making. Hydrological ensemble forecasts allow a better representation of meteorological and hydrological forecasts uncertainties and improve human expertise of hydrological forecasts, which is essential to synthesize available informations, coming from different meteorological and hydrological models and human experience. An operational hydrological ensemble forecasting chain has been developed at EDF since 2008 and is being used since 2010 on more than 30 watersheds in France. This ensemble forecasting chain is characterized ensemble pre-processing (rainfall and temperature) and post-processing (streamflow), where a large human expertise is solicited. The aim of this paper is to compare 2 hydrological ensemble post-processing methods developed at EDF in order improve ensemble forecasts reliability (similar to Monatanari &Brath, 2004; Schaefli et al., 2007). The aim of the post-processing methods is to dress hydrological ensemble forecasts with hydrological model uncertainties, based on perfect forecasts. The first method (called empirical approach) is based on a statistical modelisation of empirical error of perfect forecasts, by streamflow sub-samples of quantile class and lead-time. The second method (called dynamical approach) is based on streamflow sub-samples of quantile class and streamflow variation, and lead-time. On a set of 20 watersheds used for operational forecasts, results show that both approaches are necessary to ensure a good post-processing of hydrological ensemble, allowing a good improvement of reliability, skill and sharpness of ensemble forecasts. The comparison of the empirical and dynamical approaches shows the limits of the empirical approach which is not able to take into account hydrological dynamic and processes, i. e. sample heterogeneity. For a same streamflow range corresponds different processes such as rising limbs or recession, where uncertainties are different. The dynamical approach improves reliability, skills and sharpness of forecasts and globally reduces confidence intervals width. When compared in details, the dynamical approach allows a noticeable reduction of confidence intervals during recessions where uncertainty is relatively lower and a slight increase of confidence intervals during rising limbs or snowmelt where uncertainty is greater. The dynamic approach, validated by forecaster's experience that considered the empirical approach not discriminative enough, improved forecaster's confidence and communication of uncertainties. Montanari, A. and Brath, A., (2004). A stochastic approach for assessing the uncertainty of rainfall-runoff simulations. Water Resources Research, 40, W01106, doi:10.1029/2003WR002540. Schaefli, B., Balin Talamba, D. and Musy, A., (2007). Quantifying hydrological modeling errors through a mixture of normal distributions. Journal of Hydrology, 332, 303-315.

Sensitivity of monthly streamflow forecasts to the quality of rainfall forcing: When do dynamical climate forecasts outperform the Ensemble Streamflow Prediction (ESP) method?

NASA Astrophysics Data System (ADS)

Tanguy, M.; Prudhomme, C.; Harrigan, S.; Smith, K. A.; Parry, S.

2017-12-01

Forecasting hydrological extremes is challenging, especially at lead times over 1 month for catchments with limited hydrological memory and variable climates. One simple way to derive monthly or seasonal hydrological forecasts is to use historical climate data to drive hydrological models using the Ensemble Streamflow Prediction (ESP) method. This gives a range of possible future streamflow given known initial hydrologic conditions alone. The degree of skill of ESP depends highly on the forecast initialisation month and catchment type. Using dynamic rainfall forecasts as driving data instead of historical data could potentially improve streamflow predictions. A lot of effort is being invested within the meteorological community to improve these forecasts. However, while recent progress shows promise (e.g. NAO in winter), the skill of these forecasts at monthly to seasonal timescales is generally still limited, and the extent to which they might lead to improved hydrological forecasts is an area of active research. Additionally, these meteorological forecasts are currently being produced at 1 month or seasonal time-steps in the UK, whereas hydrological models require forcings at daily or sub-daily time-steps. Keeping in mind these limitations of available rainfall forecasts, the objectives of this study are to find out (i) how accurate monthly dynamical rainfall forecasts need to be to outperform ESP, and (ii) how the method used to disaggregate monthly rainfall forecasts into daily rainfall time series affects results. For the first objective, synthetic rainfall time series were created by increasingly degrading observed data (proxy for a `perfect forecast') from 0 % to +/-50 % error. For the second objective, three different methods were used to disaggregate monthly rainfall data into daily time series. These were used to force a simple lumped hydrological model (GR4J) to generate streamflow predictions at a one-month lead time for over 300 catchments representative of the range of UK's hydro-climatic conditions. These forecasts were then benchmarked against the traditional ESP method. It is hoped that the results of this work will help the meteorological community to identify where to focus their efforts in order to increase the usefulness of their forecasts within hydrological forecasting systems.

Explicitly integrating parameter, input, and structure uncertainties into Bayesian Neural Networks for probabilistic hydrologic forecasting

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, Xuesong; Liang, Faming; Yu, Beibei

2011-11-09

Estimating uncertainty of hydrologic forecasting is valuable to water resources and other relevant decision making processes. Recently, Bayesian Neural Networks (BNNs) have been proved powerful tools for quantifying uncertainty of streamflow forecasting. In this study, we propose a Markov Chain Monte Carlo (MCMC) framework to incorporate the uncertainties associated with input, model structure, and parameter into BNNs. This framework allows the structure of the neural networks to change by removing or adding connections between neurons and enables scaling of input data by using rainfall multipliers. The results show that the new BNNs outperform the BNNs that only consider uncertainties associatedmore » with parameter and model structure. Critical evaluation of posterior distribution of neural network weights, number of effective connections, rainfall multipliers, and hyper-parameters show that the assumptions held in our BNNs are not well supported. Further understanding of characteristics of different uncertainty sources and including output error into the MCMC framework are expected to enhance the application of neural networks for uncertainty analysis of hydrologic forecasting.« less

L-band Microwave Remote Sensing and Land Data Assimilation Improve the Representation of Prestorm Soil Moisture Conditions for Hydrologic Forecasting

NASA Technical Reports Server (NTRS)

Crow, W. T.; Chen, F.; Reichle, R. H.; Liu, Q.

2017-01-01

Recent advances in remote sensing and land data assimilation purport to improve the quality of antecedent soil moisture information available for operational hydrologic forecasting. We objectively validate this claim by calculating the strength of the relationship between storm-scale runoff ratio (i.e., total stream flow divided by total rainfall accumulation in depth units) and pre-storm surface soil moisture estimates from a range of surface soil moisture data products. Results demonstrate that both satellite-based, L-band microwave radiometry and the application of land data assimilation techniques have significantly improved the utility of surface soil moisture data sets for forecasting stream flow response to future rainfall events.

L-band microwave remote sensing and land data assimilation improve the representation of pre-storm soil moisture conditions for hydrologic forecasting.

PubMed

Crow, W T; Chen, F; Reichle, R H; Liu, Q

2017-06-16

Recent advances in remote sensing and land data assimilation purport to improve the quality of antecedent soil moisture information available for operational hydrologic forecasting. We objectively validate this claim by calculating the strength of the relationship between storm-scale runoff ratio (i.e., total stream flow divided by total rainfall accumulation in depth units) and pre-storm surface soil moisture estimates from a range of surface soil moisture data products. Results demonstrate that both satellite-based, L-band microwave radiometry and the application of land data assimilation techniques have significantly improved the utility of surface soil moisture data sets for forecasting stream flow response to future rainfall events.

L-band microwave remote sensing and land data assimilation improve the representation of pre-storm soil moisture conditions for hydrologic forecasting

PubMed Central

Crow, W.T.; Chen, F.; Reichle, R.H.; Liu, Q.

2018-01-01

Recent advances in remote sensing and land data assimilation purport to improve the quality of antecedent soil moisture information available for operational hydrologic forecasting. We objectively validate this claim by calculating the strength of the relationship between storm-scale runoff ratio (i.e., total stream flow divided by total rainfall accumulation in depth units) and pre-storm surface soil moisture estimates from a range of surface soil moisture data products. Results demonstrate that both satellite-based, L-band microwave radiometry and the application of land data assimilation techniques have significantly improved the utility of surface soil moisture data sets for forecasting stream flow response to future rainfall events. PMID:29657342

Variance analysis of forecasted streamflow maxima in a wet temperate climate

NASA Astrophysics Data System (ADS)

Al Aamery, Nabil; Fox, James F.; Snyder, Mark; Chandramouli, Chandra V.

2018-05-01

Coupling global climate models, hydrologic models and extreme value analysis provides a method to forecast streamflow maxima, however the elusive variance structure of the results hinders confidence in application. Directly correcting the bias of forecasts using the relative change between forecast and control simulations has been shown to marginalize hydrologic uncertainty, reduce model bias, and remove systematic variance when predicting mean monthly and mean annual streamflow, prompting our investigation for maxima streamflow. We assess the variance structure of streamflow maxima using realizations of emission scenario, global climate model type and project phase, downscaling methods, bias correction, extreme value methods, and hydrologic model inputs and parameterization. Results show that the relative change of streamflow maxima was not dependent on systematic variance from the annual maxima versus peak over threshold method applied, albeit we stress that researchers strictly adhere to rules from extreme value theory when applying the peak over threshold method. Regardless of which method is applied, extreme value model fitting does add variance to the projection, and the variance is an increasing function of the return period. Unlike the relative change of mean streamflow, results show that the variance of the maxima's relative change was dependent on all climate model factors tested as well as hydrologic model inputs and calibration. Ensemble projections forecast an increase of streamflow maxima for 2050 with pronounced forecast standard error, including an increase of +30(±21), +38(±34) and +51(±85)% for 2, 20 and 100 year streamflow events for the wet temperate region studied. The variance of maxima projections was dominated by climate model factors and extreme value analyses.

Supporting inland waterway transport on German waterways by operational forecasting services - water-levels, discharges, river ice

NASA Astrophysics Data System (ADS)

Meißner, Dennis; Klein, Bastian; Ionita, Monica; Hemri, Stephan; Rademacher, Silke

2017-04-01

Inland waterway transport (IWT) is an important commercial sector significantly vulnerable to hydrological impacts. River ice and floods limit the availability of the waterway network and may cause considerable damages to waterway infrastructure. Low flows significantly affect IWT's operation efficiency usually several months a year due to the close correlation of (low) water levels / water depths and (high) transport costs. Therefore "navigation-related" hydrological forecasts focussing on the specific requirements of water-bound transport (relevant forecast locations, target parameters, skill characteristics etc.) play a major role in order to mitigate IWT's vulnerability to hydro-meteorological impacts. In light of continuing transport growth within the European Union, hydrological forecasts for the waterways are essential to stimulate the use of the free capacity IWT still offers more consequently. An overview of the current operational and pre-operational forecasting systems for the German waterways predicting water levels, discharges and river ice thickness on various time-scales will be presented. While short-term (deterministic) forecasts have a long tradition in navigation-related forecasting, (probabilistic) forecasting services offering extended lead-times are not yet well-established and are still subject to current research and development activities (e.g. within the EU-projects EUPORIAS and IMPREX). The focus is on improving technical aspects as well as on exploring adequate ways of disseminating and communicating probabilistic forecast information. For the German stretch of the River Rhine, one of the most frequented inland waterways worldwide, the existing deterministic forecast scheme has been extended by ensemble forecasts combined with statistical post-processing modules applying EMOS (Ensemble Model Output Statistics) and ECC (Ensemble Copula Coupling) in order to generate water level predictions up to 10 days and to estimate its predictive uncertainty properly. Additionally for the key locations at the international waterways Rhine, Elbe and Danube three competing forecast approaches are currently tested in a pre-operational set-up in order to generate monthly to seasonal (up to 3 months) forecasts: (1) the well-known Ensemble Streamflow Prediction approach (ensemble based on historical meteorology), (2) coupling hydrological models with post-processed outputs from ECMWF's general circulation model (System 4), and (3) a purely statistical approach based on the stable relationship (teleconnection) of global or regional oceanic, climate and hydrological data with river flows. The current results, still pre-operational, reveal the existence of a valuable predictability of water levels and streamflow also at monthly up to seasonal time-scales along the larger rivers used as waterways in Germany. Last but not least insight into the technical set-up of the aforementioned forecasting systems operated at the Federal Institute of Hydrology, which are based on a Delft-FEWS application, will be given focussing on the step-wise extension of the former system by integrating new components in order to meet the growing needs of the customers and to improve and extend the forecast portfolio for waterway users.

Role-play games, experiments, workshops, blog posts: how community activities in HEPEX contribute to advance hydrologic ensemble prediction

NASA Astrophysics Data System (ADS)

Ramos, Maria-Helena; Wetterhall, Fredrik; Wood, Andy; Wang, Qj; Pappenberger, Florian; Verkade, Jan

2017-04-01

Since 2004, HEPEX (Hydrologic Ensemble Prediction Experiment) has been fostering a community of researchers and practitioners around the world. Through the years, it has contributed to establish a more integrative view of hydrological forecasting, where data assimilation, hydro-meteorological modelling chains, post-processing techniques, expert knowledge, and decision support systems are connected to enhance operational systems and water management applications. Here we present the community activities in HEPEX that have contributed to strengthening this unfunded/volunteer effort for more than a decade. It includes the organization of workshops, conference sessions, testbeds and inter-comparison experiments. More recently, HEPEX has also prompted the development of several publicly available role-play games and, since 2013, it has been running a blog portal (www.hepex.org), which is used as an intersection point for members. Through this website, members can continuously share their research, make announcements, report on workshops, projects and meetings, and hear about related research and operational challenges. It also creates a platform for early career scientists to become increasingly involved in hydrological forecasting science and applications.

Multivariate Statistical Postprocessing of Ensemble Forcasts of Precipitation and Temperature over four River Basins in California

NASA Astrophysics Data System (ADS)

Scheuerer, Michael; Hamill, Thomas M.; Whitin, Brett; He, Minxue; Henkel, Arthur

2017-04-01

Hydrological forecasts strongly rely on predictions of precipitation amounts and temperature as meteorological inputs to hydrological models. Ensemble weather predictions provide a number of different scenarios that reflect the uncertainty about these meteorological inputs, but are often biased and underdispersive, and therefore require statistical postprocessing. In hydrological applications it is crucial that spatial and temporal (i.e. between different forecast lead times) dependencies as well as dependence between the two weather variables is adequately represented by the recalibrated forecasts. We present a study with temperature and precipitation forecasts over four river basins over California that are postprocessed with a variant of the nonhomogeneous Gaussian regression method (Gneiting et al., 2005) and the censored, shifted gamma distribution approach (Scheuerer and Hamill, 2015) respectively. For modelling spatial, temporal and inter-variable dependence we propose a variant of the Schaake Shuffle (Clark et al., 2005) that uses spatio-temporal trajectories of observed temperture and precipitation as a dependence template, and chooses the historic dates in such a way that the divergence between the marginal distributions of these trajectories and the univariate forecast distributions is minimized. For the four river basins considered in our study, this new multivariate modelling technique consistently improves upon the Schaake Shuffle and yields reliable spatio-temporal forecast trajectories of temperature and precipitation that can be used to force hydrological forecast systems. References: Clark, M., Gangopadhyay, S., Hay, L., Rajagopalan, B., Wilby, R., 2004. The Schaake Shuffle: A method for reconstructing space-time variability in forecasted precipitation and temperature fields. Journal of Hydrometeorology, 5, pp.243-262. Gneiting, T., Raftery, A.E., Westveld, A.H., Goldman, T., 2005. Calibrated probabilistic forecasting using ensemble model output statistics and minimum CRPS. Monthly Weather Review, 133, pp.1098-1118. Scheuerer, M., Hamill, T.M., 2015. Statistical postprocessing of ensemble precipitation forecasts by fitting censored, shifted gamma distributions. Monthly Weather Review, 143, pp.4578-4596. Scheuerer, M., Hamill, T.M., Whitin, B., He, M., and Henkel, A., 2016: A method for preferential selection of dates in the Schaake shuffle approach to constructing spatio-temporal forecast fields of temperature and precipitation. Water Resources Research, submitted.

Statistical and Hydrological evaluation of precipitation forecasts from IMD MME and ECMWF numerical weather forecasts for Indian River basins

NASA Astrophysics Data System (ADS)

Mohite, A. R.; Beria, H.; Behera, A. K.; Chatterjee, C.; Singh, R.

2016-12-01

Flood forecasting using hydrological models is an important and cost-effective non-structural flood management measure. For forecasting at short lead times, empirical models using real-time precipitation estimates have proven to be reliable. However, their skill depreciates with increasing lead time. Coupling a hydrologic model with real-time rainfall forecasts issued from numerical weather prediction (NWP) systems could increase the lead time substantially. In this study, we compared 1-5 days precipitation forecasts from India Meteorological Department (IMD) Multi-Model Ensemble (MME) with European Center for Medium Weather forecast (ECMWF) NWP forecasts for over 86 major river basins in India. We then evaluated the hydrologic utility of these forecasts over Basantpur catchment (approx. 59,000 km2) of the Mahanadi River basin. Coupled MIKE 11 RR (NAM) and MIKE 11 hydrodynamic (HD) models were used for the development of flood forecast system (FFS). RR model was calibrated using IMD station rainfall data. Cross-sections extracted from SRTM 30 were used as input to the MIKE 11 HD model. IMD started issuing operational MME forecasts from the year 2008, and hence, both the statistical and hydrologic evaluation were carried out from 2008-2014. The performance of FFS was evaluated using both the NWP datasets separately for the year 2011, which was a large flood year in Mahanadi River basin. We will present figures and metrics for statistical (threshold based statistics, skill in terms of correlation and bias) and hydrologic (Nash Sutcliffe efficiency, mean and peak error statistics) evaluation. The statistical evaluation will be at pan-India scale for all the major river basins and the hydrologic evaluation will be for the Basantpur catchment of the Mahanadi River basin.

A New Approach in Generating Meteorological Forecasts for Ensemble Streamflow Forecasting using Multivariate Functions

NASA Astrophysics Data System (ADS)

Khajehei, S.; Madadgar, S.; Moradkhani, H.

2014-12-01

The reliability and accuracy of hydrological predictions are subject to various sources of uncertainty, including meteorological forcing, initial conditions, model parameters and model structure. To reduce the total uncertainty in hydrological applications, one approach is to reduce the uncertainty in meteorological forcing by using the statistical methods based on the conditional probability density functions (pdf). However, one of the requirements for current methods is to assume the Gaussian distribution for the marginal distribution of the observed and modeled meteorology. Here we propose a Bayesian approach based on Copula functions to develop the conditional distribution of precipitation forecast needed in deriving a hydrologic model for a sub-basin in the Columbia River Basin. Copula functions are introduced as an alternative approach in capturing the uncertainties related to meteorological forcing. Copulas are multivariate joint distribution of univariate marginal distributions, which are capable to model the joint behavior of variables with any level of correlation and dependency. The method is applied to the monthly forecast of CPC with 0.25x0.25 degree resolution to reproduce the PRISM dataset over 1970-2000. Results are compared with Ensemble Pre-Processor approach as a common procedure used by National Weather Service River forecast centers in reproducing observed climatology during a ten-year verification period (2000-2010).

Nonlinear Prediction Model for Hydrologic Time Series Based on Wavelet Decomposition

NASA Astrophysics Data System (ADS)

Kwon, H.; Khalil, A.; Brown, C.; Lall, U.; Ahn, H.; Moon, Y.

2005-12-01

Traditionally forecasting and characterizations of hydrologic systems is performed utilizing many techniques. Stochastic linear methods such as AR and ARIMA and nonlinear ones such as statistical learning theory based tools have been extensively used. The common difficulty to all methods is the determination of sufficient and necessary information and predictors for a successful prediction. Relationships between hydrologic variables are often highly nonlinear and interrelated across the temporal scale. A new hybrid approach is proposed for the simulation of hydrologic time series combining both the wavelet transform and the nonlinear model. The present model employs some merits of wavelet transform and nonlinear time series model. The Wavelet Transform is adopted to decompose a hydrologic nonlinear process into a set of mono-component signals, which are simulated by nonlinear model. The hybrid methodology is formulated in a manner to improve the accuracy of a long term forecasting. The proposed hybrid model yields much better results in terms of capturing and reproducing the time-frequency properties of the system at hand. Prediction results are promising when compared to traditional univariate time series models. An application of the plausibility of the proposed methodology is provided and the results conclude that wavelet based time series model can be utilized for simulating and forecasting of hydrologic variable reasonably well. This will ultimately serve the purpose of integrated water resources planning and management.

Hydrological Applications of a High-Resolution Radar Precipitation Data Base for Sweden

NASA Astrophysics Data System (ADS)

Olsson, Jonas; Berg, Peter; Norin, Lars; Simonsson, Lennart

2017-04-01

There is an increasing need for high-resolution observations of precipitation on local, regional, national and even continental level. Urbanization and other environmental changes often make societies more vulnerable to intense short-duration rainfalls (cloudbursts) and their consequences in terms of e.g. flooding and landslides. Impact and forecasting models of these hazards put very high demands on the rainfall input in terms of both resolution and accuracy. Weather radar systems obviously have a great potential in this context, but also limitations with respect to e.g. conversion algorithms and various error sources that may have a significant impact on the subsequent hydrological modelling. In Sweden, the national weather radar network has been in operation for nearly three decades, but until recently the hydrological applications have been very limited. This is mainly because of difficulties in managing the different errors and biases in the radar precipitation product, which made it hard to demonstrate any distinct added value as compared with gauge-based precipitation products. In the last years, however, in light of distinct progress in developing error correction procedures, substantial efforts have been made to develop a national gauge-adjusted radar precipitation product - HIPRAD (High-Resolution Precipitation from Gauge-Adjusted Weather Radar). In HIPRAD, the original radar precipitation data are scaled to match the monthly accumulations in a national grid (termed PTHBV) created by optimal interpolation of corrected daily gauge observations, with the intention to attain both a high spatio-temporal resolution and accurate long-term accumulations. At present, HIPRAD covers the period 2000-present with resolutions 15 min and 2×2 km2. A key motivation behind the development of HIPRAD is the intention to increase the temporal resolution in the national flood forecasting system from 1 day to 1 hour. Whereas a daily time step is sufficient to describe the rainfall-runoff process in large, slow river basins, which traditionally has been the main focus in the national forecasting, an hourly time step (or preferably even shorter) is required to simulate the flow in fast-responding basins. At the daily scale, the PTHBV product is used for model initialization prior to the forecasts but with its daily resolution it is not applicable at the hourly scale. For this purpose, a real-time version of HIPRAD has been developed which is currently running operationally. HIPRAD is also being used for historical simulations with an hourly time step, which is important for e.g. water quality assessment. Finally, we will use HIPRAD to gain an improved knowledge of the short-duration precipitation climate in Sweden. Currently there are many open issues with respect to e.g. geographical differences, spatial correlations and areal extremes. Here we will show and discuss selected results from the ongoing development and validation of HIPRAD as well as its various applications for hydrological forecasting and risk assessment. Further, web resources containing radar-based observation and forecasting for hydrological applications will be demonstrated. Finally, some future research directions will be outlined. Fast responding hydrological catchments require fine spatial and temporal resolution of the precipitation input data to provide realistic results.

Reducing streamflow forecast uncertainty: Application and qualitative assessment of the upper klamath river Basin, Oregon

USGS Publications Warehouse

Hay, L.E.; McCabe, G.J.; Clark, M.P.; Risley, J.C.

2009-01-01

The accuracy of streamflow forecasts depends on the uncertainty associated with future weather and the accuracy of the hydrologic model that is used to produce the forecasts. We present a method for streamflow forecasting where hydrologic model parameters are selected based on the climate state. Parameter sets for a hydrologic model are conditioned on an atmospheric pressure index defined using mean November through February (NDJF) 700-hectoPascal geopotential heights over northwestern North America [Pressure Index from Geopotential heights (PIG)]. The hydrologic model is applied in the Sprague River basin (SRB), a snowmelt-dominated basin located in the Upper Klamath basin in Oregon. In the SRB, the majority of streamflow occurs during March through May (MAM). Water years (WYs) 1980-2004 were divided into three groups based on their respective PIG values (high, medium, and low PIG). Low (high) PIG years tend to have higher (lower) than average MAM streamflow. Four parameter sets were calibrated for the SRB, each using a different set of WYs. The initial set used WYs 1995-2004 and the remaining three used WYs defined as high-, medium-, and low-PIG years. Two sets of March, April, and May streamflow volume forecasts were made using Ensemble Streamflow Prediction (ESP). The first set of ESP simulations used the initial parameter set. Because the PIG is defined using NDJF pressure heights, forecasts starting in March can be made using the PIG parameter set that corresponds with the year being forecasted. The second set of ESP simulations used the parameter set associated with the given PIG year. Comparison of the ESP sets indicates that more accuracy and less variability in volume forecasts may be possible when the ESP is conditioned using the PIG. This is especially true during the high-PIG years (low-flow years). ?? 2009 American Water Resources Association.

Artificial intelligence based models for stream-flow forecasting: 2000-2015

NASA Astrophysics Data System (ADS)

Yaseen, Zaher Mundher; El-shafie, Ahmed; Jaafar, Othman; Afan, Haitham Abdulmohsin; Sayl, Khamis Naba

2015-11-01

The use of Artificial Intelligence (AI) has increased since the middle of the 20th century as seen in its application in a wide range of engineering and science problems. The last two decades, for example, has seen a dramatic increase in the development and application of various types of AI approaches for stream-flow forecasting. Generally speaking, AI has exhibited significant progress in forecasting and modeling non-linear hydrological applications and in capturing the noise complexity in the dataset. This paper explores the state-of-the-art application of AI in stream-flow forecasting, focusing on defining the data-driven of AI, the advantages of complementary models, as well as the literature and their possible future application in modeling and forecasting stream-flow. The review also identifies the major challenges and opportunities for prospective research, including, a new scheme for modeling the inflow, a novel method for preprocessing time series frequency based on Fast Orthogonal Search (FOS) techniques, and Swarm Intelligence (SI) as an optimization approach.

Nationwide validation of ensemble streamflow forecasts from the Hydrologic Ensemble Forecast Service (HEFS) of the U.S. National Weather Service

NASA Astrophysics Data System (ADS)

Lee, H. S.; Liu, Y.; Ward, J.; Brown, J.; Maestre, A.; Herr, H.; Fresch, M. A.; Wells, E.; Reed, S. M.; Jones, E.

2017-12-01

The National Weather Service's (NWS) Office of Water Prediction (OWP) recently launched a nationwide effort to verify streamflow forecasts from the Hydrologic Ensemble Forecast Service (HEFS) for a majority of forecast locations across the 13 River Forecast Centers (RFCs). Known as the HEFS Baseline Validation (BV), the project involves a joint effort between the OWP and the RFCs. It aims to provide a geographically consistent, statistically robust validation, and a benchmark to guide the operational implementation of the HEFS, inform practical applications, such as impact-based decision support services, and to provide an objective framework for evaluating strategic investments in the HEFS. For the BV, HEFS hindcasts are issued once per day on a 12Z cycle for the period of 1985-2015 with a forecast horizon of 30 days. For the first two weeks, the hindcasts are forced with precipitation and temperature ensemble forecasts from the Global Ensemble Forecast System of the National Centers for Environmental Prediction, and by resampled climatology for the remaining period. The HEFS-generated ensemble streamflow hindcasts are verified using the Ensemble Verification System. Skill is assessed relative to streamflow hindcasts generated from NWS' current operational system, namely climatology-based Ensemble Streamflow Prediction. In this presentation, we summarize the results and findings to date.

Hydrologic and hydraulic flood forecasting constrained by remote sensing data

NASA Astrophysics Data System (ADS)

Li, Y.; Grimaldi, S.; Pauwels, V. R. N.; Walker, J. P.; Wright, A. J.

2017-12-01

Flooding is one of the most destructive natural disasters, resulting in many deaths and billions of dollars of damages each year. An indispensable tool to mitigate the effect of floods is to provide accurate and timely forecasts. An operational flood forecasting system typically consists of a hydrologic model, converting rainfall data into flood volumes entering the river system, and a hydraulic model, converting these flood volumes into water levels and flood extents. Such a system is prone to various sources of uncertainties from the initial conditions, meteorological forcing, topographic data, model parameters and model structure. To reduce those uncertainties, current forecasting systems are typically calibrated and/or updated using ground-based streamflow measurements, and such applications are limited to well-gauged areas. The recent increasing availability of spatially distributed remote sensing (RS) data offers new opportunities to improve flood forecasting skill. Based on an Australian case study, this presentation will discuss the use of 1) RS soil moisture to constrain a hydrologic model, and 2) RS flood extent and level to constrain a hydraulic model.The GRKAL hydrological model is calibrated through a joint calibration scheme using both ground-based streamflow and RS soil moisture observations. A lag-aware data assimilation approach is tested through a set of synthetic experiments to integrate RS soil moisture to constrain the streamflow forecasting in real-time.The hydraulic model is LISFLOOD-FP which solves the 2-dimensional inertial approximation of the Shallow Water Equations. Gauged water level time series and RS-derived flood extent and levels are used to apply a multi-objective calibration protocol. The effectiveness with which each data source or combination of data sources constrained the parameter space will be discussed.

Robust Hydrological Forecasting for High-resolution Distributed Models Using a Unified Data Assimilation Approach

NASA Astrophysics Data System (ADS)

Hernandez, F.; Liang, X.

2017-12-01

Reliable real-time hydrological forecasting, to predict important phenomena such as floods, is invaluable to the society. However, modern high-resolution distributed models have faced challenges when dealing with uncertainties that are caused by the large number of parameters and initial state estimations involved. Therefore, to rely on these high-resolution models for critical real-time forecast applications, considerable improvements on the parameter and initial state estimation techniques must be made. In this work we present a unified data assimilation algorithm called Optimized PareTo Inverse Modeling through Inverse STochastic Search (OPTIMISTS) to deal with the challenge of having robust flood forecasting for high-resolution distributed models. This new algorithm combines the advantages of particle filters and variational methods in a unique way to overcome their individual weaknesses. The analysis of candidate particles compares model results with observations in a flexible time frame, and a multi-objective approach is proposed which attempts to simultaneously minimize differences with the observations and departures from the background states by using both Bayesian sampling and non-convex evolutionary optimization. Moreover, the resulting Pareto front is given a probabilistic interpretation through kernel density estimation to create a non-Gaussian distribution of the states. OPTIMISTS was tested on a low-resolution distributed land surface model using VIC (Variable Infiltration Capacity) and on a high-resolution distributed hydrological model using the DHSVM (Distributed Hydrology Soil Vegetation Model). In the tests streamflow observations are assimilated. OPTIMISTS was also compared with a traditional particle filter and a variational method. Results show that our method can reliably produce adequate forecasts and that it is able to outperform those resulting from assimilating the observations using a particle filter or an evolutionary 4D variational method alone. In addition, our method is shown to be efficient in tackling high-resolution applications with robust results.

Post-processing of multi-model ensemble river discharge forecasts using censored EMOS

NASA Astrophysics Data System (ADS)

Hemri, Stephan; Lisniak, Dmytro; Klein, Bastian

2014-05-01

When forecasting water levels and river discharge, ensemble weather forecasts are used as meteorological input to hydrologic process models. As hydrologic models are imperfect and the input ensembles tend to be biased and underdispersed, the output ensemble forecasts for river runoff typically are biased and underdispersed, too. Thus, statistical post-processing is required in order to achieve calibrated and sharp predictions. Standard post-processing methods such as Ensemble Model Output Statistics (EMOS) that have their origins in meteorological forecasting are now increasingly being used in hydrologic applications. Here we consider two sub-catchments of River Rhine, for which the forecasting system of the Federal Institute of Hydrology (BfG) uses runoff data that are censored below predefined thresholds. To address this methodological challenge, we develop a censored EMOS method that is tailored to such data. The censored EMOS forecast distribution can be understood as a mixture of a point mass at the censoring threshold and a continuous part based on a truncated normal distribution. Parameter estimates of the censored EMOS model are obtained by minimizing the Continuous Ranked Probability Score (CRPS) over the training dataset. Model fitting on Box-Cox transformed data allows us to take account of the positive skewness of river discharge distributions. In order to achieve realistic forecast scenarios over an entire range of lead-times, there is a need for multivariate extensions. To this end, we smooth the marginal parameter estimates over lead-times. In order to obtain realistic scenarios of discharge evolution over time, the marginal distributions have to be linked with each other. To this end, the multivariate dependence structure can either be adopted from the raw ensemble like in Ensemble Copula Coupling (ECC), or be estimated from observations in a training period. The censored EMOS model has been applied to multi-model ensemble forecasts issued on a daily basis over a period of three years. For the two catchments considered, this resulted in well calibrated and sharp forecast distributions over all lead-times from 1 to 114 h. Training observations tended to be better indicators for the dependence structure than the raw ensemble.

Improving forecasting accuracy of medium and long-term runoff using artificial neural network based on EEMD decomposition.

PubMed

Wang, Wen-chuan; Chau, Kwok-wing; Qiu, Lin; Chen, Yang-bo

2015-05-01

Hydrological time series forecasting is one of the most important applications in modern hydrology, especially for the effective reservoir management. In this research, an artificial neural network (ANN) model coupled with the ensemble empirical mode decomposition (EEMD) is presented for forecasting medium and long-term runoff time series. First, the original runoff time series is decomposed into a finite and often small number of intrinsic mode functions (IMFs) and a residual series using EEMD technique for attaining deeper insight into the data characteristics. Then all IMF components and residue are predicted, respectively, through appropriate ANN models. Finally, the forecasted results of the modeled IMFs and residual series are summed to formulate an ensemble forecast for the original annual runoff series. Two annual reservoir runoff time series from Biuliuhe and Mopanshan in China, are investigated using the developed model based on four performance evaluation measures (RMSE, MAPE, R and NSEC). The results obtained in this work indicate that EEMD can effectively enhance forecasting accuracy and the proposed EEMD-ANN model can attain significant improvement over ANN approach in medium and long-term runoff time series forecasting. Copyright © 2015 Elsevier Inc. All rights reserved.

Using ensemble rainfall predictions in a countrywide flood forecasting model in Scotland

NASA Astrophysics Data System (ADS)

Cranston, M. D.; Maxey, R.; Tavendale, A. C. W.; Buchanan, P.

2012-04-01

Improving flood predictions for all sources of flooding is at the centre of flood risk management policy in Scotland. With the introduction of the Flood Risk Management (Scotland) Act providing a new statutory basis for SEPA's flood warning responsibilities, the pressures on delivering hydrological science developments in support of this legislation has increased. Specifically, flood forecasting capabilities need to develop in support of the need to reduce the impact of flooding through the provision of actively disseminated, reliable and timely flood warnings. Flood forecasting in Scotland has developed significantly in recent years (Cranston and Tavendale, 2012). The development of hydrological models to predict flooding at a catchment scale has relied upon the application of rainfall runoff models utilising raingauge, radar and quantitative precipitation forecasts in the short lead time (less than 6 hours). Single or deterministic forecasts based on highly uncertain rainfall predictions have led to the greatest operational difficulties when communicating flood risk with emergency responders, therefore the emergence of probability-based estimates offers the greatest opportunity for managing uncertain predictions. This paper presents operational application of a physical-conceptual distributed hydrological model on a countrywide basis across Scotland. Developed by CEH Wallingford for SEPA in 2011, Grid-to-Grid (G2G) principally runs in deterministic mode and employs radar and raingauge estimates of rainfall together with weather model predictions to produce forecast river flows, as gridded time-series at a resolution of 1km and for up to 5 days ahead (Cranston, et al., 2012). However the G2G model is now being run operationally using ensemble predictions of rainfall from the MOGREPS-R system to provide probabilistic flood forecasts. By presenting a range of flood predictions on a national scale through this approach, hydrologists are now able to consider an objective measure of the likelihood of flooding impacts to help with risk based emergency communication.

Stream-flow forecasting using extreme learning machines: A case study in a semi-arid region in Iraq

NASA Astrophysics Data System (ADS)

Yaseen, Zaher Mundher; Jaafar, Othman; Deo, Ravinesh C.; Kisi, Ozgur; Adamowski, Jan; Quilty, John; El-Shafie, Ahmed

2016-11-01

Monthly stream-flow forecasting can yield important information for hydrological applications including sustainable design of rural and urban water management systems, optimization of water resource allocations, water use, pricing and water quality assessment, and agriculture and irrigation operations. The motivation for exploring and developing expert predictive models is an ongoing endeavor for hydrological applications. In this study, the potential of a relatively new data-driven method, namely the extreme learning machine (ELM) method, was explored for forecasting monthly stream-flow discharge rates in the Tigris River, Iraq. The ELM algorithm is a single-layer feedforward neural network (SLFNs) which randomly selects the input weights, hidden layer biases and analytically determines the output weights of the SLFNs. Based on the partial autocorrelation functions of historical stream-flow data, a set of five input combinations with lagged stream-flow values are employed to establish the best forecasting model. A comparative investigation is conducted to evaluate the performance of the ELM compared to other data-driven models: support vector regression (SVR) and generalized regression neural network (GRNN). The forecasting metrics defined as the correlation coefficient (r), Nash-Sutcliffe efficiency (ENS), Willmott's Index (WI), root-mean-square error (RMSE) and mean absolute error (MAE) computed between the observed and forecasted stream-flow data are employed to assess the ELM model's effectiveness. The results revealed that the ELM model outperformed the SVR and the GRNN models across a number of statistical measures. In quantitative terms, superiority of ELM over SVR and GRNN models was exhibited by ENS = 0.578, 0.378 and 0.144, r = 0.799, 0.761 and 0.468 and WI = 0.853, 0.802 and 0.689, respectively and the ELM model attained lower RMSE value by approximately 21.3% (relative to SVR) and by approximately 44.7% (relative to GRNN). Based on the findings of this study, several recommendations were suggested for further exploration of the ELM model in hydrological forecasting problems.

Understanding and seasonal forecasting of hydrological drought in the Anthropocene

NASA Astrophysics Data System (ADS)

Yuan, Xing; Zhang, Miao; Wang, Linying; Zhou, Tian

2017-11-01

Hydrological drought is not only caused by natural hydroclimate variability but can also be directly altered by human interventions including reservoir operation, irrigation, groundwater exploitation, etc. Understanding and forecasting of hydrological drought in the Anthropocene are grand challenges due to complicated interactions among climate, hydrology and humans. In this paper, five decades (1961-2010) of naturalized and observed streamflow datasets are used to investigate hydrological drought characteristics in a heavily managed river basin, the Yellow River basin in north China. Human interventions decrease the correlation between hydrological and meteorological droughts, and make the hydrological drought respond to longer timescales of meteorological drought. Due to large water consumptions in the middle and lower reaches, there are 118-262 % increases in the hydrological drought frequency, up to 8-fold increases in the drought severity, 21-99 % increases in the drought duration and the drought onset is earlier. The non-stationarity due to anthropogenic climate change and human water use basically decreases the correlation between meteorological and hydrological droughts and reduces the effect of human interventions on hydrological drought frequency while increasing the effect on drought duration and severity. A set of 29-year (1982-2010) hindcasts from an established seasonal hydrological forecasting system are used to assess the forecast skill of hydrological drought. In the naturalized condition, the climate-model-based approach outperforms the climatology method in predicting the 2001 severe hydrological drought event. Based on the 29-year hindcasts, the former method has a Brier skill score of 11-26 % against the latter for the probabilistic hydrological drought forecasting. In the Anthropocene, the skill for both approaches increases due to the dominant influence of human interventions that have been implicitly incorporated by the hydrological post-processing, while the difference between the two predictions decreases. This suggests that human interventions can outweigh the climate variability for the hydrological drought forecasting in the Anthropocene, and the predictability for human interventions needs more attention.

Seamless hydrological predictions for a monsoon driven catchment in North-East India

NASA Astrophysics Data System (ADS)

Köhn, Lisei; Bürger, Gerd; Bronstert, Axel

2016-04-01

Improving hydrological forecasting systems on different time scales is interesting and challenging with regards to humanitarian as well as scientific aspects. In meteorological research, short-, medium-, and long-term forecasts are now being merged to form a system of seamless weather and climate predictions. Coupling of these meteorological forecasts with a hydrological model leads to seamless predictions of streamflow, ranging from one day to a season. While there are big efforts made to analyse the uncertainties of probabilistic streamflow forecasts, knowledge of the single uncertainty contributions from meteorological and hydrological modeling is still limited. The overarching goal of this project is to gain knowledge in this subject by decomposing and quantifying the overall predictive uncertainty into its single factors for the entire seamless forecast horizon. Our study area is the Mahanadi River Basin in North-East India, which is prone to severe floods and droughts. Improved streamflow forecasts on different time scales would contribute to early flood warning as well as better water management operations in the agricultural sector. Because of strong inter-annual monsoon variations in this region, which are, unlike the mid-latitudes, partly predictable from long-term atmospheric-oceanic oscillations, the Mahanadi catchment represents an ideal study site. Regionalized precipitation forecasts are obtained by applying the method of expanded downscaling to the ensemble prediction systems of ECMWF and NCEP. The semi-distributed hydrological model HYPSO-RR, which was developed in the Eco-Hydrological Simulation Environment ECHSE, is set up for several sub-catchments of the Mahanadi River Basin. The model is calibrated automatically using the Dynamically Dimensioned Search algorithm, with a modified Nash-Sutcliff efficiency as objective function. Meteorological uncertainty is estimated from the existing ensemble simulations, while the hydrological uncertainty is derived from a statistical post-processor. After running the hydrological model with the precipitation forecasts and applying the hydrological post-processor, the predictive uncertainty of the streamflow forecast can be analysed. The decomposition of total uncertainty is done using a two-way analysis of variance. In this contribution we present the model set-up and the first results of our hydrological forecasts with up to a 180 days lead time, which are derived by using 15 downscaled members of the ECMWF multi-model seasonal forecast ensemble as model input.

A two-stage method of quantitative flood risk analysis for reservoir real-time operation using ensemble-based hydrologic forecasts

NASA Astrophysics Data System (ADS)

Liu, P.

2013-12-01

Quantitative analysis of the risk for reservoir real-time operation is a hard task owing to the difficulty of accurate description of inflow uncertainties. The ensemble-based hydrologic forecasts directly depict the inflows not only the marginal distributions but also their persistence via scenarios. This motivates us to analyze the reservoir real-time operating risk with ensemble-based hydrologic forecasts as inputs. A method is developed by using the forecast horizon point to divide the future time into two stages, the forecast lead-time and the unpredicted time. The risk within the forecast lead-time is computed based on counting the failure number of forecast scenarios, and the risk in the unpredicted time is estimated using reservoir routing with the design floods and the reservoir water levels of forecast horizon point. As a result, a two-stage risk analysis method is set up to quantify the entire flood risks by defining the ratio of the number of scenarios that excessive the critical value to the total number of scenarios. The China's Three Gorges Reservoir (TGR) is selected as a case study, where the parameter and precipitation uncertainties are implemented to produce ensemble-based hydrologic forecasts. The Bayesian inference, Markov Chain Monte Carlo, is used to account for the parameter uncertainty. Two reservoir operation schemes, the real operated and scenario optimization, are evaluated for the flood risks and hydropower profits analysis. With the 2010 flood, it is found that the improvement of the hydrologic forecast accuracy is unnecessary to decrease the reservoir real-time operation risk, and most risks are from the forecast lead-time. It is therefore valuable to decrease the avarice of ensemble-based hydrologic forecasts with less bias for a reservoir operational purpose.

Development and application of an atmospheric-hydrologic-hydraulic flood forecasting model driven by TIGGE ensemble forecasts

NASA Astrophysics Data System (ADS)

Bao, Hongjun; Zhao, Linna

2012-02-01

A coupled atmospheric-hydrologic-hydraulic ensemble flood forecasting model, driven by The Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble (TIGGE) data, has been developed for flood forecasting over the Huaihe River. The incorporation of numerical weather prediction (NWP) information into flood forecasting systems may increase forecast lead time from a few hours to a few days. A single NWP model forecast from a single forecast center, however, is insufficient as it involves considerable non-predictable uncertainties and leads to a high number of false alarms. The availability of global ensemble NWP systems through TIGGE offers a new opportunity for flood forecast. The Xinanjiang model used for hydrological rainfall-runoff modeling and the one-dimensional unsteady flow model applied to channel flood routing are coupled with ensemble weather predictions based on the TIGGE data from the Canadian Meteorological Centre (CMC), the European Centre for Medium-Range Weather Forecasts (ECMWF), the UK Met Office (UKMO), and the US National Centers for Environmental Prediction (NCEP). The developed ensemble flood forecasting model is applied to flood forecasting of the 2007 flood season as a test case. The test case is chosen over the upper reaches of the Huaihe River above Lutaizi station with flood diversion and retarding areas. The input flood discharge hydrograph from the main channel to the flood diversion area is estimated with the fixed split ratio of the main channel discharge. The flood flow inside the flood retarding area is calculated as a reservoir with the water balance method. The Muskingum method is used for flood routing in the flood diversion area. A probabilistic discharge and flood inundation forecast is provided as the end product to study the potential benefits of using the TIGGE ensemble forecasts. The results demonstrate satisfactory flood forecasting with clear signals of probability of floods up to a few days in advance, and show that TIGGE ensemble forecast data are a promising tool for forecasting of flood inundation, comparable with that driven by raingauge observations.

Seasonal Forecasting of Reservoir Inflow for the Segura River Basin, Spain

NASA Astrophysics Data System (ADS)

de Tomas, Alberto; Hunink, Johannes

2017-04-01

A major threat to the agricultural sector in Europe is an increasing occurrence of low water availability for irrigation, affecting the local and regional food security and economies. Especially in the Mediterranean region, such as in the Segura river basin (Spain), drought epidodes are relatively frequent. Part of the irrigation water demand in this basin is met by a water transfer from the Tagus basin (central Spain), but also in this basin an increasing pressure on the water resources has reduced the water available to be transferred. Currently, Drought Management Plans in these Spanish basins are in place and mitigate the impact of drought periods to some extent. Drought indicators that are derived from the available water in the storage reservoirs impose a set of drought mitigation measures. Decisions on water transfers are dependent on a regression-based time series forecast from the reservoir inflows of the preceding months. This user-forecast has its limitations and can potentially be improved using more advanced techniques. Nowadays, seasonal climate forecasts have shown to have increasing skill for certain areas and for certain applications. So far, such forecasts have not been evaluated in a seasonal hydrologic forecasting system in the Spanish context. The objective of this work is to develop a prototype of a Seasonal Hydrologic Forecasting System and compare this with a reference forecast. The reference forecast in this case is the locally used regression-based forecast. Additionally, hydrological simulations derived from climatological reanalysis (ERA-Interim) are taken as a reference forecast. The Spatial Processes in Hydrology model (SPHY - http://www.sphy.nl/) forced with the ECMWF- SFS4 (15 ensembles) Seasonal Forecast Systems is used to predict reservoir inflows of the upper basins of the Segura and Tagus rivers. The system is evaluated for 4 seasons with a forecasting lead time of 3 months. First results show that only for certain initialization months and lead times, the developed system outperforms the reference forecast. This research is carried out within the European research project IMPREX (www.imprex.eu) that aims at investigating the value of improving predictions of hydro-meteorological extremes in a number of water sectors, including agriculture . The next step is to integrate improved seasonal forecasts into the system and evaluate these. This should finally lead to a more robust forecasting system that allows water managers and irrigators to better anticipate to drought episodes and putting into practice more effective water allocation and mitigation practices.

A study of application of remote sensing to river forecasting. Volume 1: Executive summary

NASA Technical Reports Server (NTRS)

1975-01-01

A project is described whose goal was to define, implement and evaluate a pilot demonstration test to show the practicability of applying remotely sensed data to operational river forecasting in gaged or previously ungaged watersheds. A secondary objective was to provide NASA with documentation describing the computer programs that comprise the streamflow forecasting simulation model used. A computer-based simulation model was adapted to a streamflow forecasting application and implemented in an IBM System/360 Model 44 computer, operating in a dedicated mode, with operator interactive control through a Model 2250 keyboard/graphic CRT terminal. The test site whose hydrologic behavior was simulated is a small basin (365 square kilometers) designated Town Creek near Geraldine, Alabama.

Using Meteorological Analogues for Reordering Postprocessed Precipitation Ensembles in Hydrological Forecasting

NASA Astrophysics Data System (ADS)

Bellier, Joseph; Bontron, Guillaume; Zin, Isabella

2017-12-01

Meteorological ensemble forecasts are nowadays widely used as input of hydrological models for probabilistic streamflow forecasting. These forcings are frequently biased and have to be statistically postprocessed, using most of the time univariate techniques that apply independently to individual locations, lead times and weather variables. Postprocessed ensemble forecasts therefore need to be reordered so as to reconstruct suitable multivariate dependence structures. The Schaake shuffle and ensemble copula coupling are the two most popular methods for this purpose. This paper proposes two adaptations of them that make use of meteorological analogues for reconstructing spatiotemporal dependence structures of precipitation forecasts. Performances of the original and adapted techniques are compared through a multistep verification experiment using real forecasts from the European Centre for Medium-Range Weather Forecasts. This experiment evaluates not only multivariate precipitation forecasts but also the corresponding streamflow forecasts that derive from hydrological modeling. Results show that the relative performances of the different reordering methods vary depending on the verification step. In particular, the standard Schaake shuffle is found to perform poorly when evaluated on streamflow. This emphasizes the crucial role of the precipitation spatiotemporal dependence structure in hydrological ensemble forecasting.

Verification of ECMWF System 4 for seasonal hydrological forecasting in a northern climate

NASA Astrophysics Data System (ADS)

Bazile, Rachel; Boucher, Marie-Amélie; Perreault, Luc; Leconte, Robert

2017-11-01

Hydropower production requires optimal dam and reservoir management to prevent flooding damage and avoid operation losses. In a northern climate, where spring freshet constitutes the main inflow volume, seasonal forecasts can help to establish a yearly strategy. Long-term hydrological forecasts often rely on past observations of streamflow or meteorological data. Another alternative is to use ensemble meteorological forecasts produced by climate models. In this paper, those produced by the ECMWF (European Centre for Medium-Range Forecast) System 4 are examined and bias is characterized. Bias correction, through the linear scaling method, improves the performance of the raw ensemble meteorological forecasts in terms of continuous ranked probability score (CRPS). Then, three seasonal ensemble hydrological forecasting systems are compared: (1) the climatology of simulated streamflow, (2) the ensemble hydrological forecasts based on climatology (ESP) and (3) the hydrological forecasts based on bias-corrected ensemble meteorological forecasts from System 4 (corr-DSP). Simulated streamflow computed using observed meteorological data is used as benchmark. Accounting for initial conditions is valuable even for long-term forecasts. ESP and corr-DSP both outperform the climatology of simulated streamflow for lead times from 1 to 5 months depending on the season and watershed. Integrating information about future meteorological conditions also improves monthly volume forecasts. For the 1-month lead time, a gain exists for almost all watersheds during winter, summer and fall. However, volume forecasts performance for spring varies from one watershed to another. For most of them, the performance is close to the performance of ESP. For longer lead times, the CRPS skill score is mostly in favour of ESP, even if for many watersheds, ESP and corr-DSP have comparable skill. Corr-DSP appears quite reliable but, in some cases, under-dispersion or bias is observed. A more complex bias-correction method should be further investigated to remedy this weakness and take more advantage of the ensemble forecasts produced by the climate model. Overall, in this study, bias-corrected ensemble meteorological forecasts appear to be an interesting source of information for hydrological forecasting for lead times up to 1 month. They could also complement ESP for longer lead times.

An Ensemble-Based Forecasting Framework to Optimize Reservoir Releases

NASA Astrophysics Data System (ADS)

Ramaswamy, V.; Saleh, F.

2017-12-01

Increasing frequency of extreme precipitation events are stressing the need to manage water resources on shorter timescales. Short-term management of water resources becomes proactive when inflow forecasts are available and this information can be effectively used in the control strategy. This work investigates the utility of short term hydrological ensemble forecasts for operational decision making during extreme weather events. An advanced automated hydrologic prediction framework integrating a regional scale hydrologic model, GIS datasets and the meteorological ensemble predictions from the European Center for Medium Range Weather Forecasting (ECMWF) was coupled to an implicit multi-objective dynamic programming model to optimize releases from a water supply reservoir. The proposed methodology was evaluated by retrospectively forecasting the inflows to the Oradell reservoir in the Hackensack River basin in New Jersey during the extreme hydrologic event, Hurricane Irene. Additionally, the flexibility of the forecasting framework was investigated by forecasting the inflows from a moderate rainfall event to provide important perspectives on using the framework to assist reservoir operations during moderate events. The proposed forecasting framework seeks to provide a flexible, assistive tool to alleviate the complexity of operational decision-making.

An assessment of a North American Multi-Model Ensemble (NMME) based global drought early warning forecast system

NASA Astrophysics Data System (ADS)

Wood, E. F.; Yuan, X.; Sheffield, J.; Pan, M.; Roundy, J.

2013-12-01

One of the key recommendations of the WCRP Global Drought Information System (GDIS) workshop is to develop an experimental real-time global monitoring and prediction system. While great advances has been made in global drought monitoring based on satellite observations and model reanalysis data, global drought forecasting has been stranded in part due to the limited skill both in climate forecast models and global hydrologic predictions. Having been working on drought monitoring and forecasting over USA for more than a decade, the Princeton land surface hydrology group is now developing an experimental global drought early warning system that is based on multiple climate forecast models and a calibrated global hydrologic model. In this presentation, we will test its capability in seasonal forecasting of meteorological, agricultural and hydrologic droughts over global major river basins, using precipitation, soil moisture and streamflow forecasts respectively. Based on the joint probability distribution between observations using Princeton's global drought monitoring system and model hindcasts and real-time forecasts from North American Multi-Model Ensemble (NMME) project, we (i) bias correct the monthly precipitation and temperature forecasts from multiple climate forecast models, (ii) downscale them to a daily time scale, and (iii) use them to drive the calibrated VIC model to produce global drought forecasts at a 1-degree resolution. A parallel run using the ESP forecast method, which is based on resampling historical forcings, is also carried out for comparison. Analysis is being conducted over global major river basins, with multiple drought indices that have different time scales and characteristics. The meteorological drought forecast does not have uncertainty from hydrologic models and can be validated directly against observations - making the validation an 'apples-to-apples' comparison. Preliminary results for the evaluation of meteorological drought onset hindcasts indicate that climate models increase drought detectability over ESP by 31%-81%. However, less than 30% of the global drought onsets can be detected by climate models. The missed drought events are associated with weak ENSO signals and lower potential predictability. Due to the high false alarms from climate models, the reliability is more important than sharpness for a skillful probabilistic drought onset forecast. Validations and skill assessments for agricultural and hydrologic drought forecasts are carried out using soil moisture and streamflow output from the VIC land surface model (LSM) forced by a global forcing data set. Given our previous drought forecasting experiences over USA and Africa, validating the hydrologic drought forecasting is a significant challenge for a global drought early warning system.

Operational flash flood forecasting platform based on grid technology

NASA Astrophysics Data System (ADS)

Thierion, V.; Ayral, P.-A.; Angelini, V.; Sauvagnargues-Lesage, S.; Nativi, S.; Payrastre, O.

2009-04-01

Flash flood events of south of France such as the 8th and 9th September 2002 in the Grand Delta territory caused important economic and human damages. Further to this catastrophic hydrological situation, a reform of flood warning services have been initiated (set in 2006). Thus, this political reform has transformed the 52 existing flood warning services (SAC) in 22 flood forecasting services (SPC), in assigning them territories more hydrological consistent and new effective hydrological forecasting mission. Furthermore, national central service (SCHAPI) has been created to ease this transformation and support local services in their new objectives. New functioning requirements have been identified: - SPC and SCHAPI carry the responsibility to clearly disseminate to public organisms, civil protection actors and population, crucial hydrologic information to better anticipate potential dramatic flood event, - a new effective hydrological forecasting mission to these flood forecasting services seems essential particularly for the flash floods phenomenon. Thus, models improvement and optimization was one of the most critical requirements. Initially dedicated to support forecaster in their monitoring mission, thanks to measuring stations and rainfall radar images analysis, hydrological models have to become more efficient in their capacity to anticipate hydrological situation. Understanding natural phenomenon occuring during flash floods mainly leads present hydrological research. Rather than trying to explain such complex processes, the presented research try to manage the well-known need of computational power and data storage capacities of these services. Since few years, Grid technology appears as a technological revolution in high performance computing (HPC) allowing large-scale resource sharing, computational power using and supporting collaboration across networks. Nowadays, EGEE (Enabling Grids for E-science in Europe) project represents the most important effort in term of grid technology development. This paper presents an operational flash flood forecasting platform which have been developed in the framework of CYCLOPS European project providing one of virtual organizations of EGEE project. This platform has been designed to enable multi-simulations processes to ease forecasting operations of several supervised watersheds on Grand Delta (SPC-GD) territory. Grid technology infrastructure, in providing multiple remote computing elements enables the processing of multiple rainfall scenarios, derived to the original meteorological forecasting transmitted by Meteo-France, and their respective hydrological simulations. First results show that from one forecasting scenario, this new presented approach can permit simulations of more than 200 different scenarios to support forecasters in their aforesaid mission and appears as an efficient hydrological decision-making tool. Although, this system seems operational, model validity has to be confirmed. So, further researches are necessary to improve models core to be more efficient in term of hydrological aspects. Finally, this platform could be an efficient tool for developing others modelling aspects as calibration or data assimilation in real time processing.

Drought Monitoring and Forecasting Using the Princeton/U Washington National Hydrologic Forecasting System

NASA Astrophysics Data System (ADS)

Wood, E. F.; Yuan, X.; Roundy, J. K.; Lettenmaier, D. P.; Mo, K. C.; Xia, Y.; Ek, M. B.

2011-12-01

Extreme hydrologic events in the form of droughts or floods are a significant source of social and economic damage in many parts of the world. Having sufficient warning of extreme events allows managers to prepare for and reduce the severity of their impacts. A hydrologic forecast system can give seasonal predictions that can be used by mangers to make better decisions; however there is still much uncertainty associated with such a system. Therefore it is important to understand the forecast skill of the system before transitioning to operational usage. Seasonal reforecasts (1982 - 2010) from the NCEP Climate Forecast System (both version 1 (CFS) and version 2 (CFSv2), Climate Prediction Center (CPC) outlooks and the European Seasonal Interannual Prediction (EUROSIP) system, are assessed for forecasting skill in drought prediction across the U.S., both singularly and as a multi-model system The Princeton/U Washington national hydrologic monitoring and forecast system is being implemented at NCEP/EMC via their Climate Test Bed as the experimental hydrological forecast system to support U.S. operational drought prediction. Using our system, the seasonal forecasts are biased corrected, downscaled and used to drive the Variable Infiltration Capacity (VIC) land surface model to give seasonal forecasts of hydrologic variables with lead times of up to six months. Results are presented for a number of events, with particular focus on the Apalachicola-Chattahoochee-Flint (ACF) River Basin in the South Eastern United States, which has experienced a number of severe droughts in recent years and is a pilot study basin for the National Integrated Drought Information System (NIDIS). The performance of the VIC land surface model is evaluated using observational forcing when compared to observed streamflow. The effectiveness of the forecast system to predict streamflow and soil moisture is evaluated when compared with observed streamflow and modeled soil moisture driven by observed atmospheric forcing. The forecast skills from the dynamical seasonal models (CFSv1, CFSv2, EUROSIP) and CPC are also compared with forecasts based on the Ensemble Streamflow Prediction (ESP) method, which uses initial conditions and historical forcings to generate seasonal forecasts. The skill of the system to predict drought, drought recovery and related hydrological conditions such as low-flows is assessed, along with quantified uncertainty.

How do I know if my forecasts are better? Using benchmarks in hydrological ensemble prediction

NASA Astrophysics Data System (ADS)

Pappenberger, F.; Ramos, M. H.; Cloke, H. L.; Wetterhall, F.; Alfieri, L.; Bogner, K.; Mueller, A.; Salamon, P.

2015-03-01

The skill of a forecast can be assessed by comparing the relative proximity of both the forecast and a benchmark to the observations. Example benchmarks include climatology or a naïve forecast. Hydrological ensemble prediction systems (HEPS) are currently transforming the hydrological forecasting environment but in this new field there is little information to guide researchers and operational forecasters on how benchmarks can be best used to evaluate their probabilistic forecasts. In this study, it is identified that the forecast skill calculated can vary depending on the benchmark selected and that the selection of a benchmark for determining forecasting system skill is sensitive to a number of hydrological and system factors. A benchmark intercomparison experiment is then undertaken using the continuous ranked probability score (CRPS), a reference forecasting system and a suite of 23 different methods to derive benchmarks. The benchmarks are assessed within the operational set-up of the European Flood Awareness System (EFAS) to determine those that are 'toughest to beat' and so give the most robust discrimination of forecast skill, particularly for the spatial average fields that EFAS relies upon. Evaluating against an observed discharge proxy the benchmark that has most utility for EFAS and avoids the most naïve skill across different hydrological situations is found to be meteorological persistency. This benchmark uses the latest meteorological observations of precipitation and temperature to drive the hydrological model. Hydrological long term average benchmarks, which are currently used in EFAS, are very easily beaten by the forecasting system and the use of these produces much naïve skill. When decomposed into seasons, the advanced meteorological benchmarks, which make use of meteorological observations from the past 20 years at the same calendar date, have the most skill discrimination. They are also good at discriminating skill in low flows and for all catchment sizes. Simpler meteorological benchmarks are particularly useful for high flows. Recommendations for EFAS are to move to routine use of meteorological persistency, an advanced meteorological benchmark and a simple meteorological benchmark in order to provide a robust evaluation of forecast skill. This work provides the first comprehensive evidence on how benchmarks can be used in evaluation of skill in probabilistic hydrological forecasts and which benchmarks are most useful for skill discrimination and avoidance of naïve skill in a large scale HEPS. It is recommended that all HEPS use the evidence and methodology provided here to evaluate which benchmarks to employ; so forecasters can have trust in their skill evaluation and will have confidence that their forecasts are indeed better.

Probabilistic hydrological nowcasting using radar based nowcasting techniques and distributed hydrological models: application in the Mediterranean area

NASA Astrophysics Data System (ADS)

Poletti, Maria Laura; Pignone, Flavio; Rebora, Nicola; Silvestro, Francesco

2017-04-01

The exposure of the urban areas to flash-floods is particularly significant to Mediterranean coastal cities, generally densely-inhabited. Severe rainfall events often associated to intense and organized thunderstorms produced, during the last century, flash-floods and landslides causing serious damages to urban areas and in the worst events led to human losses. The temporal scale of these events has been observed strictly linked to the size of the catchments involved: in the Mediterranean area a great number of catchments that pass through coastal cities have a small drainage area (less than 100 km2) and a corresponding hydrologic response timescale in the order of a few hours. A suitable nowcasting chain is essential for the on time forecast of this kind of events. In fact meteorological forecast systems are unable to predict precipitation at the scale of these events, small both at spatial (few km) and temporal (hourly) scales. Nowcasting models, covering the time interval of the following two hours starting from the observation try to extend the predictability limits of the forecasting models in support of real-time flood alert system operations. This work aims to present the use of hydrological models coupled with nowcasting techniques. The nowcasting model PhaSt furnishes an ensemble of equi-probable future precipitation scenarios on time horizons of 1-3 h starting from the most recent radar observations. The coupling of the nowcasting model PhaSt with the hydrological model Continuum allows to forecast the flood with a few hours in advance. In this way it is possible to generate different discharge prediction for the following hours and associated return period maps: these maps can be used as a support in the decisional process for the warning system.

The Rise of Complexity in Flood Forecasting: Opportunities, Challenges and Tradeoffs

NASA Astrophysics Data System (ADS)

Wood, A. W.; Clark, M. P.; Nijssen, B.

2017-12-01

Operational flood forecasting is currently undergoing a major transformation. Most national flood forecasting services have relied for decades on lumped, highly calibrated conceptual hydrological models running on local office computing resources, providing deterministic streamflow predictions at gauged river locations that are important to stakeholders and emergency managers. A variety of recent technological advances now make it possible to run complex, high-to-hyper-resolution models for operational hydrologic prediction over large domains, and the US National Weather Service is now attempting to use hyper-resolution models to create new forecast services and products. Yet other `increased-complexity' forecasting strategies also exist that pursue different tradeoffs between model complexity (i.e., spatial resolution, physics) and streamflow forecast system objectives. There is currently a pressing need for a greater understanding in the hydrology community of the opportunities, challenges and tradeoffs associated with these different forecasting approaches, and for a greater participation by the hydrology community in evaluating, guiding and implementing these approaches. Intermediate-resolution forecast systems, for instance, use distributed land surface model (LSM) physics but retain the agility to deploy ensemble methods (including hydrologic data assimilation and hindcast-based post-processing). Fully coupled numerical weather prediction (NWP) systems, another example, use still coarser LSMs to produce ensemble streamflow predictions either at the model scale or after sub-grid scale runoff routing. Based on the direct experience of the authors and colleagues in research and operational forecasting, this presentation describes examples of different streamflow forecast paradigms, from the traditional to the recent hyper-resolution, to illustrate the range of choices facing forecast system developers. We also discuss the degree to which the strengths and weaknesses of each strategy map onto the requirements for different types of forecasting services (e.g., flash flooding, river flooding, seasonal water supply prediction).

What is the relative role of initial hydrological conditions and meteorological forcing to the seasonal hydrological forecasting skill? Analysis along Europe's hydro-climatic gradient

NASA Astrophysics Data System (ADS)

Pechlivanidis, Ilias; Crochemore, Louise

2017-04-01

Recent advances in understanding and forecasting of climate have led into skilful seasonal meteorological predictions, which can consequently increase the confidence of hydrological prognosis. The majority of seasonal impact modelling has commonly been conducted at only one or a limited number of basins limiting the potential to understand large systems. Nevertheless, there is a necessity to develop operational seasonal forecasting services at the pan-European scale, capable of addressing the end-user needs. The skill of such forecasting services is subject to a number of sources of uncertainty, i.e. model structure, parameters, and forcing input. In here, we complement the "deep" knowledge from basin based modelling by investigating the relative contributions of initial hydrological conditions (IHCs) and meteorological forcing (MF) to the skill of a seasonal pan-European hydrological forecasting system. We use the Ensemble Streamflow Prediction (ESP) and reverse ESP (revESP) procedure to show a proxy of hydrological forecasting uncertainty due to MF and IHC uncertainties respectively. We further calculate the critical lead time (CLT), as a proxy of the river memory, after which the importance of MFs surpasses the importance of IHCs. We analyze these results in the context of prevailing hydro-climatic conditions for about 35000 European basins. Both model state initialisation (level in surface water, i.e. reservoirs, lakes and wetlands, soil moisture, snow depth) and provision of climatology are based on forcing input derived from the WFDEI product for the period 1981-2010. The analysis shows that the contribution of ICs and MFs to the hydrological forecasting skill varies considerably according to location, season and lead time. This analysis allows clustering of basins in which hydrological forecasting skill may be improved by better estimation of IHCs, e.g. via data assimilation of in-situ and/or satellite observations; whereas in other basins skill improvement depends on better MFs.

An experimental seasonal hydrological forecasting system over the Yellow River basin – Part 2: The added value from climate forecast models

DOE PAGES

Yuan, Xing

2016-06-22

This is the second paper of a two-part series on introducing an experimental seasonal hydrological forecasting system over the Yellow River basin in northern China. While the natural hydrological predictability in terms of initial hydrological conditions (ICs) is investigated in a companion paper, the added value from eight North American Multimodel Ensemble (NMME) climate forecast models with a grand ensemble of 99 members is assessed in this paper, with an implicit consideration of human-induced uncertainty in the hydrological models through a post-processing procedure. The forecast skill in terms of anomaly correlation (AC) for 2 m air temperature and precipitation does not necessarily decrease overmore » leads but is dependent on the target month due to a strong seasonality for the climate over the Yellow River basin. As there is more diversity in the model performance for the temperature forecasts than the precipitation forecasts, the grand NMME ensemble mean forecast has consistently higher skill than the best single model up to 6 months for the temperature but up to 2 months for the precipitation. The NMME climate predictions are downscaled to drive the variable infiltration capacity (VIC) land surface hydrological model and a global routing model regionalized over the Yellow River basin to produce forecasts of soil moisture, runoff and streamflow. And the NMME/VIC forecasts are compared with the Ensemble Streamflow Prediction method (ESP/VIC) through 6-month hindcast experiments for each calendar month during 1982–2010. As verified by the VIC offline simulations, the NMME/VIC is comparable to the ESP/VIC for the soil moisture forecasts, and the former has higher skill than the latter only for the forecasts at long leads and for those initialized in the rainy season. The forecast skill for runoff is lower for both forecast approaches, but the added value from NMME/VIC is more obvious, with an increase of the average AC by 0.08–0.2. To compare with the observed streamflow, both the hindcasts from NMME/VIC and ESP/VIC are post-processed through a linear regression model fitted by using VIC offline-simulated streamflow. The post-processed NMME/VIC reduces the root mean squared error (RMSE) from the post-processed ESP/VIC by 5–15 %. And the reduction occurs mostly during the transition from wet to dry seasons. As a result, with the consideration of the uncertainty in the hydrological models, the added value from climate forecast models is decreased especially at short leads, suggesting the necessity of improving the large-scale hydrological models in human-intervened river basins.« less

An experimental seasonal hydrological forecasting system over the Yellow River basin – Part 2: The added value from climate forecast models

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yuan, Xing

This is the second paper of a two-part series on introducing an experimental seasonal hydrological forecasting system over the Yellow River basin in northern China. While the natural hydrological predictability in terms of initial hydrological conditions (ICs) is investigated in a companion paper, the added value from eight North American Multimodel Ensemble (NMME) climate forecast models with a grand ensemble of 99 members is assessed in this paper, with an implicit consideration of human-induced uncertainty in the hydrological models through a post-processing procedure. The forecast skill in terms of anomaly correlation (AC) for 2 m air temperature and precipitation does not necessarily decrease overmore » leads but is dependent on the target month due to a strong seasonality for the climate over the Yellow River basin. As there is more diversity in the model performance for the temperature forecasts than the precipitation forecasts, the grand NMME ensemble mean forecast has consistently higher skill than the best single model up to 6 months for the temperature but up to 2 months for the precipitation. The NMME climate predictions are downscaled to drive the variable infiltration capacity (VIC) land surface hydrological model and a global routing model regionalized over the Yellow River basin to produce forecasts of soil moisture, runoff and streamflow. And the NMME/VIC forecasts are compared with the Ensemble Streamflow Prediction method (ESP/VIC) through 6-month hindcast experiments for each calendar month during 1982–2010. As verified by the VIC offline simulations, the NMME/VIC is comparable to the ESP/VIC for the soil moisture forecasts, and the former has higher skill than the latter only for the forecasts at long leads and for those initialized in the rainy season. The forecast skill for runoff is lower for both forecast approaches, but the added value from NMME/VIC is more obvious, with an increase of the average AC by 0.08–0.2. To compare with the observed streamflow, both the hindcasts from NMME/VIC and ESP/VIC are post-processed through a linear regression model fitted by using VIC offline-simulated streamflow. The post-processed NMME/VIC reduces the root mean squared error (RMSE) from the post-processed ESP/VIC by 5–15 %. And the reduction occurs mostly during the transition from wet to dry seasons. As a result, with the consideration of the uncertainty in the hydrological models, the added value from climate forecast models is decreased especially at short leads, suggesting the necessity of improving the large-scale hydrological models in human-intervened river basins.« less

Uncertainty analysis of neural network based flood forecasting models: An ensemble based approach for constructing prediction interval

NASA Astrophysics Data System (ADS)

Kasiviswanathan, K.; Sudheer, K.

2013-05-01

Artificial neural network (ANN) based hydrologic models have gained lot of attention among water resources engineers and scientists, owing to their potential for accurate prediction of flood flows as compared to conceptual or physics based hydrologic models. The ANN approximates the non-linear functional relationship between the complex hydrologic variables in arriving at the river flow forecast values. Despite a large number of applications, there is still some criticism that ANN's point prediction lacks in reliability since the uncertainty of predictions are not quantified, and it limits its use in practical applications. A major concern in application of traditional uncertainty analysis techniques on neural network framework is its parallel computing architecture with large degrees of freedom, which makes the uncertainty assessment a challenging task. Very limited studies have considered assessment of predictive uncertainty of ANN based hydrologic models. In this study, a novel method is proposed that help construct the prediction interval of ANN flood forecasting model during calibration itself. The method is designed to have two stages of optimization during calibration: at stage 1, the ANN model is trained with genetic algorithm (GA) to obtain optimal set of weights and biases vector, and during stage 2, the optimal variability of ANN parameters (obtained in stage 1) is identified so as to create an ensemble of predictions. During the 2nd stage, the optimization is performed with multiple objectives, (i) minimum residual variance for the ensemble mean, (ii) maximum measured data points to fall within the estimated prediction interval and (iii) minimum width of prediction interval. The method is illustrated using a real world case study of an Indian basin. The method was able to produce an ensemble that has an average prediction interval width of 23.03 m3/s, with 97.17% of the total validation data points (measured) lying within the interval. The derived prediction interval for a selected hydrograph in the validation data set is presented in Fig 1. It is noted that most of the observed flows lie within the constructed prediction interval, and therefore provides information about the uncertainty of the prediction. One specific advantage of the method is that when ensemble mean value is considered as a forecast, the peak flows are predicted with improved accuracy by this method compared to traditional single point forecasted ANNs. Fig. 1 Prediction Interval for selected hydrograph

Hybrid Forecasting of Daily River Discharges Considering Autoregressive Heteroscedasticity

NASA Astrophysics Data System (ADS)

Szolgayová, Elena Peksová; Danačová, Michaela; Komorniková, Magda; Szolgay, Ján

2017-06-01

It is widely acknowledged that in the hydrological and meteorological communities, there is a continuing need to improve the quality of quantitative rainfall and river flow forecasts. A hybrid (combined deterministic-stochastic) modelling approach is proposed here that combines the advantages offered by modelling the system dynamics with a deterministic model and a deterministic forecasting error series with a data-driven model in parallel. Since the processes to be modelled are generally nonlinear and the model error series may exhibit nonstationarity and heteroscedasticity, GARCH-type nonlinear time series models are considered here. The fitting, forecasting and simulation performance of such models have to be explored on a case-by-case basis. The goal of this paper is to test and develop an appropriate methodology for model fitting and forecasting applicable for daily river discharge forecast error data from the GARCH family of time series models. We concentrated on verifying whether the use of a GARCH-type model is suitable for modelling and forecasting a hydrological model error time series on the Hron and Morava Rivers in Slovakia. For this purpose we verified the presence of heteroscedasticity in the simulation error series of the KLN multilinear flow routing model; then we fitted the GARCH-type models to the data and compared their fit with that of an ARMA - type model. We produced one-stepahead forecasts from the fitted models and again provided comparisons of the model's performance.

Water Management Applications of Advanced Precipitation Products

NASA Astrophysics Data System (ADS)

Johnson, L. E.; Braswell, G.; Delaney, C.

2012-12-01

Advanced precipitation sensors and numerical models track storms as they occur and forecast the likelihood of heavy rain for time frames ranging from 1 to 8 hours, 1 day, and extended outlooks out to 3 to 7 days. Forecast skill decreases at the extended time frames but the outlooks have been shown to provide "situational awareness" which aids in preparation for flood mitigation and water supply operations. In California the California-Nevada River Forecast Centers and local Weather Forecast Offices provide precipitation products that are widely used to support water management and flood response activities of various kinds. The Hydrometeorology Testbed (HMT) program is being conducted to help advance the science of precipitation tracking and forecasting in support of the NWS. HMT high-resolution products have found applications for other non-federal water management activities as well. This presentation will describe water management applications of HMT advanced precipitation products, and characterization of benefits expected to accrue. Two case examples will be highlighted, 1) reservoir operations for flood control and water supply, and 2) urban stormwater management. Application of advanced precipitation products in support of reservoir operations is a focus of the Sonoma County Water Agency. Examples include: a) interfacing the high-resolution QPE products with a distributed hydrologic model for the Russian-Napa watersheds, b) providing early warning of in-coming storms for flood preparedness and water supply storage operations. For the stormwater case, San Francisco wastewater engineers are developing a plan to deploy high resolution gap-filling radars looking off shore to obtain longer lead times on approaching storms. A 4 to 8 hour lead time would provide opportunity to optimize stormwater capture and treatment operations, and minimize combined sewer overflows into the Bay.ussian River distributed hydrologic model.

Comparison of the performance and reliability of 18 lumped hydrological models driven by ECMWF rainfall ensemble forecasts: a case study on 29 French catchments

NASA Astrophysics Data System (ADS)

Velázquez, Juan Alberto; Anctil, François; Ramos, Maria-Helena; Perrin, Charles

2010-05-01

An ensemble forecasting system seeks to assess and to communicate the uncertainty of hydrological predictions by proposing, at each time step, an ensemble of forecasts from which one can estimate the probability distribution of the predictant (the probabilistic forecast), in contrast with a single estimate of the flow, for which no distribution is obtainable (the deterministic forecast). In the past years, efforts towards the development of probabilistic hydrological prediction systems were made with the adoption of ensembles of numerical weather predictions (NWPs). The additional information provided by the different available Ensemble Prediction Systems (EPS) was evaluated in a hydrological context on various case studies (see the review by Cloke and Pappenberger, 2009). For example, the European ECMWF-EPS was explored in case studies by Roulin et al. (2005), Bartholmes et al. (2005), Jaun et al. (2008), and Renner et al. (2009). The Canadian EC-EPS was also evaluated by Velázquez et al. (2009). Most of these case studies investigate the ensemble predictions of a given hydrological model, set up over a limited number of catchments. Uncertainty from weather predictions is assessed through the use of meteorological ensembles. However, uncertainty from the tested hydrological model and statistical robustness of the forecasting system when coping with different hydro-meteorological conditions are less frequently evaluated. The aim of this study is to evaluate and compare the performance and the reliability of 18 lumped hydrological models applied to a large number of catchments in an operational ensemble forecasting context. Some of these models were evaluated in a previous study (Perrin et al. 2001) for their ability to simulate streamflow. Results demonstrated that very simple models can achieve a level of performance almost as high (sometimes higher) as models with more parameters. In the present study, we focus on the ability of the hydrological models to provide reliable probabilistic forecasts of streamflow, based on ensemble weather predictions. The models were therefore adapted to run in a forecasting mode, i.e., to update initial conditions according to the last observed discharge at the time of the forecast, and to cope with ensemble weather scenarios. All models are lumped, i.e., the hydrological behavior is integrated over the spatial scale of the catchment, and run at daily time steps. The complexity of tested models varies between 3 and 13 parameters. The models are tested on 29 French catchments. Daily streamflow time series extend over 17 months, from March 2005 to July 2006. Catchment areas range between 1470 km2 and 9390 km2, and represent a variety of hydrological and meteorological conditions. The 12 UTC 10-day ECMWF rainfall ensemble (51 members) was used, which led to daily streamflow forecasts for a 9-day lead time. In order to assess the performance and reliability of the hydrological ensemble predictions, we computed the Continuous Ranked probability Score (CRPS) (Matheson and Winkler, 1976), as well as the reliability diagram (e.g. Wilks, 1995) and the rank histogram (Talagrand et al., 1999). Since the ECMWF deterministic forecasts are also available, the performance of the hydrological forecasting systems was also evaluated by comparing the deterministic score (MAE) with the probabilistic score (CRPS). The results obtained for the 18 hydrological models and the 29 studied catchments are discussed in the perspective of improving the operational use of ensemble forecasting in hydrology. References Bartholmes, J. and Todini, E.: Coupling meteorological and hydrological models for flood forecasting, Hydrol. Earth Syst. Sci., 9, 333-346, 2005. Cloke, H. and Pappenberger, F.: Ensemble Flood Forecasting: A Review. Journal of Hydrology 375 (3-4): 613-626, 2009. Jaun, S., Ahrens, B., Walser, A., Ewen, T., and Schär, C.: A probabilistic view on the August 2005 floods in the upper Rhine catchment, Nat. Hazards Earth Syst. Sci., 8, 281-291, 2008. Matheson, J. E. and Winkler, R. L.: Scoring rules for continuous probability distributions, Manage Sci., 22, 1087-1096, 1976. Perrin, C., Michel C. and Andréassian,V. Does a large number of parameters enhance model performance? Comparative assessment of common catchment model structures on 429 catchments, J. Hydrol., 242, 275-301, 2001. Renner, M., Werner, M. G. F., Rademacher, S., and Sprokkereef, E.: Verification of ensemble flow forecast for the River Rhine, J. Hydrol., 376, 463-475, 2009. Roulin, E. and Vannitsem, S.: Skill of medium-range hydrological ensemble predictions, J. Hydrometeorol., 6, 729-744, 2005. Talagrand, O., Vautard, R., and Strauss, B.: Evaluation of the probabilistic prediction systems, in: Proceedings, ECMWF Workshop on Predictability, Shinfield Park, Reading, Berkshire, ECMWF, 1-25, 1999. Velázquez, J.A., Petit, T., Lavoie, A., Boucher M.-A., Turcotte R., Fortin V., and Anctil, F. : An evaluation of the Canadian global meteorological ensemble prediction system for short-term hydrological forecasting, Hydrol. Earth Syst. Sci., 13, 2221-2231, 2009. Wilks, D. S.: Statistical Methods in the Atmospheric Sciences, Academic Press, San Diego, CA, 465 pp., 1995.

Large-watershed flood simulation and forecasting based on different-resolution distributed hydrological model

NASA Astrophysics Data System (ADS)

Li, J.

2017-12-01

Large-watershed flood simulation and forecasting is very important for a distributed hydrological model in the application. There are some challenges including the model's spatial resolution effect, model performance and accuracy and so on. To cope with the challenge of the model's spatial resolution effect, different model resolution including 1000m*1000m, 600m*600m, 500m*500m, 400m*400m, 200m*200m were used to build the distributed hydrological model—Liuxihe model respectively. The purpose is to find which one is the best resolution for Liuxihe model in Large-watershed flood simulation and forecasting. This study sets up a physically based distributed hydrological model for flood forecasting of the Liujiang River basin in south China. Terrain data digital elevation model (DEM), soil type and land use type are downloaded from the website freely. The model parameters are optimized by using an improved Particle Swarm Optimization(PSO) algorithm; And parameter optimization could reduce the parameter uncertainty that exists for physically deriving model parameters. The different model resolution (200m*200m—1000m*1000m ) are proposed for modeling the Liujiang River basin flood with the Liuxihe model in this study. The best model's spatial resolution effect for flood simulation and forecasting is 200m*200m.And with the model's spatial resolution reduction, the model performance and accuracy also become worse and worse. When the model resolution is 1000m*1000m, the flood simulation and forecasting result is the worst, also the river channel divided based on this resolution is differs from the actual one. To keep the model with an acceptable performance, minimum model spatial resolution is needed. The suggested threshold model spatial resolution for modeling the Liujiang River basin flood is a 500m*500m grid cell, but the model spatial resolution with a 200m*200m grid cell is recommended in this study to keep the model at a best performance.

Decadal predictability of river discharge with climate oscillations over the 20th and early 21st century

NASA Astrophysics Data System (ADS)

Wanders, Niko; Wada, Yoshihide

2015-12-01

Long-term hydrological forecasts are important to increase our resilience and preparedness to extreme hydrological events. The skill in these forecasts is still limited due to large uncertainties inherent in hydrological models and poor predictability of long-term meteorological conditions. Here we show that strong (lagged) correlations exist between four different major climate oscillation modes and modeled and observed discharge anomalies over a 100 year period. The strongest correlations are found between the El Niño-Southern Oscillation signal and river discharge anomalies all year round, while North Atlantic Oscillation and Antarctic Oscillation time series are strongly correlated with winter discharge anomalies. The correlation signal is significant for periods up to 5 years for some regions, indicating a high added value of this information for long-term hydrological forecasting. The results suggest that long-term hydrological forecasting could be significantly improved by including the climate oscillation signals and thus improve our preparedness for hydrological extremes in the near future.

Remote Sensing and River Discharge Forecasting for Major Rivers in South Asia (Invited)

NASA Astrophysics Data System (ADS)

Webster, P. J.; Hopson, T. M.; Hirpa, F. A.; Brakenridge, G. R.; De-Groeve, T.; Shrestha, K.; Gebremichael, M.; Restrepo, P. J.

2013-12-01

The South Asia is a flashpoint for natural disasters particularly flooding of the Indus, Ganges, and Brahmaputra has profound societal impacts for the region and globally. The 2007 Brahmaputra floods affecting India and Bangladesh, the 2008 avulsion of the Kosi River in India, the 2010 flooding of the Indus River in Pakistan and the 2013 Uttarakhand exemplify disasters on scales almost inconceivable elsewhere. Their frequent occurrence of floods combined with large and rapidly growing populations, high levels of poverty and low resilience, exacerbate the impact of the hazards. Mitigation of these devastating hazards are compounded by limited flood forecast capability, lack of rain/gauge measuring stations and forecast use within and outside the country, and transboundary data sharing on natural hazards. Here, we demonstrate the utility of remotely-derived hydrologic and weather products in producing skillful flood forecasting information without reliance on vulnerable in situ data sources. Over the last decade a forecast system has been providing operational probabilistic forecasts of severe flooding of the Brahmaputra and Ganges Rivers in Bangldesh was developed (Hopson and Webster 2010). The system utilizes ECMWF weather forecast uncertainty information and ensemble weather forecasts, rain gauge and satellite-derived precipitation estimates, together with the limited near-real-time river stage observations from Bangladesh. This system has been expanded to Pakistan and has successfully forecast the 2010-2012 flooding (Shrestha and Webster 2013). To overcome the in situ hydrological data problem, recent efforts in parallel with the numerical modeling have utilized microwave satellite remote sensing of river widths to generate operational discharge advective-based forecasts for the Ganges and Brahmaputra. More than twenty remotely locations upstream of Bangldesh were used to produce stand-alone river flow nowcasts and forecasts at 1-15 days lead time. showing that satellite-based flow estimates are a useful source of dynamical surface water information in data-scarce regions and that they could be used for model calibration and data assimilation purposes in near-time hydrologic forecast applications (Hirpa et al. 2013). More recent efforts during this year's monsoon season are optimally combining these different independent sources of river forecast information along with archived flood inundation imagery of the Dartmouth Flood Observatory to improve the visualization and overall skill of the ongoing CFAB ensemble weather forecast-based flood forecasting system within the unique context of the ongoing flood forecasting efforts for Bangladesh.

A versatile data-visualization application for the Norwegian flood forecasting service

NASA Astrophysics Data System (ADS)

Kobierska, Florian; Langsholt, Elin G.; Hamududu, Byman H.; Engeland, Kolbjørn

2017-04-01

- General motivation A graphical user interface has been developed to visualize multi-model hydrological forecasts at the flood forecasting service of the Norwegian water and energy directorate. It is based on the R 'shiny' package, with which interactive web applications can quickly be prototyped. The app queries multiple data sources, building a comprehensive infographics dashboard for the decision maker. - Main features of the app The visualization application comprises several tabs, each built with different functionality and focus. A map of forecast stations gives a rapid insight of the flood situation and serves, concurrently, as a map station selection (based on the 'leaflet' package). The map selection is linked to multi-panel forecast plots which can present input, state or runoff parameters. Another tab focuses on past model performance and calibration runs. - Software design choices The application was programmed with a focus on flexibility regarding data-sources. The parsing of text-based model results was explicitly separated from the app (in the separate R package 'NVEDATA'), so that it only loads standardized RData binary files. We focused on allowing re-usability in other contexts by structuring the app into specific 'shiny' modules. The code was bundled into an R package, which is available on GitHub. - Documentation efforts A documentation website is under development. For easier collaboration, we chose to host it on the 'GitHub Pages' branch of the repository and build it automatically with a continuous integration service. The aim is to gather all information about the flood forecasting methodology at NVE in one location. This encompasses details on each hydrological model used as well as the documentation of the data-visualization application. - Outlook for further development The ability to select a group of stations by filtering a table (i.e. past performance, past major flood events, catchment parameters) and exporting it to the forecast tab could be of interest for detailed model analysis. The design choices for this app were motivated by a need for extensibility and modularity and those qualities will be tested and improved as new datasets need integrating into this to​ol.

Self-organizing linear output map (SOLO): An artificial neural network suitable for hydrologic modeling and analysis

NASA Astrophysics Data System (ADS)

Hsu, Kuo-Lin; Gupta, Hoshin V.; Gao, Xiaogang; Sorooshian, Soroosh; Imam, Bisher

2002-12-01

Artificial neural networks (ANNs) can be useful in the prediction of hydrologic variables, such as streamflow, particularly when the underlying processes have complex nonlinear interrelationships. However, conventional ANN structures suffer from network training issues that significantly limit their widespread application. This paper presents a multivariate ANN procedure entitled self-organizing linear output map (SOLO), whose structure has been designed for rapid, precise, and inexpensive estimation of network structure/parameters and system outputs. More important, SOLO provides features that facilitate insight into the underlying processes, thereby extending its usefulness beyond forecast applications as a tool for scientific investigations. These characteristics are demonstrated using a classic rainfall-runoff forecasting problem. Various aspects of model performance are evaluated in comparison with other commonly used modeling approaches, including multilayer feedforward ANNs, linear time series modeling, and conceptual rainfall-runoff modeling.

The Regional Hydrologic Extremes Assessment System: A software framework for hydrologic modeling and data assimilation

PubMed Central

Das, Narendra; Stampoulis, Dimitrios; Ines, Amor; Fisher, Joshua B.; Granger, Stephanie; Kawata, Jessie; Han, Eunjin; Behrangi, Ali

2017-01-01

The Regional Hydrologic Extremes Assessment System (RHEAS) is a prototype software framework for hydrologic modeling and data assimilation that automates the deployment of water resources nowcasting and forecasting applications. A spatially-enabled database is a key component of the software that can ingest a suite of satellite and model datasets while facilitating the interfacing with Geographic Information System (GIS) applications. The datasets ingested are obtained from numerous space-borne sensors and represent multiple components of the water cycle. The object-oriented design of the software allows for modularity and extensibility, showcased here with the coupling of the core hydrologic model with a crop growth model. RHEAS can exploit multi-threading to scale with increasing number of processors, while the database allows delivery of data products and associated uncertainty through a variety of GIS platforms. A set of three example implementations of RHEAS in the United States and Kenya are described to demonstrate the different features of the system in real-world applications. PMID:28545077

The Regional Hydrologic Extremes Assessment System: A software framework for hydrologic modeling and data assimilation.

PubMed

Andreadis, Konstantinos M; Das, Narendra; Stampoulis, Dimitrios; Ines, Amor; Fisher, Joshua B; Granger, Stephanie; Kawata, Jessie; Han, Eunjin; Behrangi, Ali

2017-01-01

The Regional Hydrologic Extremes Assessment System (RHEAS) is a prototype software framework for hydrologic modeling and data assimilation that automates the deployment of water resources nowcasting and forecasting applications. A spatially-enabled database is a key component of the software that can ingest a suite of satellite and model datasets while facilitating the interfacing with Geographic Information System (GIS) applications. The datasets ingested are obtained from numerous space-borne sensors and represent multiple components of the water cycle. The object-oriented design of the software allows for modularity and extensibility, showcased here with the coupling of the core hydrologic model with a crop growth model. RHEAS can exploit multi-threading to scale with increasing number of processors, while the database allows delivery of data products and associated uncertainty through a variety of GIS platforms. A set of three example implementations of RHEAS in the United States and Kenya are described to demonstrate the different features of the system in real-world applications.

Improving hydrologic disaster forecasting and response for transportation by assimilating and fusing NASA and other data sets : final report.

DOT National Transportation Integrated Search

2017-04-15

In this 3-year project, the research team developed the Hydrologic Disaster Forecast and Response (HDFR) system, a set of integrated software tools for end users that streamlines hydrologic prediction workflows involving automated retrieval of hetero...

Development of a model-based flood emergency management system in Yujiang River Basin, South China

NASA Astrophysics Data System (ADS)

Zeng, Yong; Cai, Yanpeng; Jia, Peng; Mao, Jiansu

2014-06-01

Flooding is the most frequent disaster in China. It affects people's lives and properties, causing considerable economic loss. Flood forecast and operation of reservoirs are important in flood emergency management. Although great progress has been achieved in flood forecast and reservoir operation through using computer, network technology, and geographic information system technology in China, the prediction accuracy of models are not satisfactory due to the unavailability of real-time monitoring data. Also, real-time flood control scenario analysis is not effective in many regions and can seldom provide online decision support function. In this research, a decision support system for real-time flood forecasting in Yujiang River Basin, South China (DSS-YRB) is introduced in this paper. This system is based on hydrological and hydraulic mathematical models. The conceptual framework and detailed components of the proposed DSS-YRB is illustrated, which employs real-time rainfall data conversion, model-driven hydrologic forecasting, model calibration, data assimilation methods, and reservoir operational scenario analysis. Multi-tiered architecture offers great flexibility, portability, reusability, and reliability. The applied case study results show the development and application of a decision support system for real-time flood forecasting and operation is beneficial for flood control.

Inclusion of potential vorticity uncertainties into a hydrometeorological forecasting chain: application to a medium size basin of Mediterranean Spain

NASA Astrophysics Data System (ADS)

Amengual, A.; Romero, R.; Vich, M.; Alonso, S.

2009-06-01

The improvement of the short- and mid-range numerical runoff forecasts over the flood-prone Spanish Mediterranean area is a challenging issue. This work analyses four intense precipitation events which produced floods of different magnitude over the Llobregat river basin, a medium size catchment located in Catalonia, north-eastern Spain. One of them was a devasting flash flood - known as the "Montserrat" event - which produced 5 fatalities and material losses estimated at about 65 million euros. The characterization of the Llobregat basin's hydrological response to these floods is first assessed by using rain-gauge data and the Hydrologic Engineering Center's Hydrological Modeling System (HEC-HMS) runoff model. In second place, the non-hydrostatic fifth-generation Pennsylvania State University/NCAR mesoscale model (MM5) is nested within the ECMWF large-scale forecast fields in a set of 54 h period simulations to provide quantitative precipitation forecasts (QPFs) for each hydrometeorological episode. The hydrological model is forced with these QPFs to evaluate the reliability of the resulting discharge forecasts, while an ensemble prediction system (EPS) based on perturbed atmospheric initial and boundary conditions has been designed to test the value of a probabilistic strategy versus the previous deterministic approach. Specifically, a Potential Vorticity (PV) Inversion technique has been used to perturb the MM5 model initial and boundary states (i.e. ECMWF forecast fields). For that purpose, a PV error climatology has been previously derived in order to introduce realistic PV perturbations in the EPS. Results show the benefits of using a probabilistic approach in those cases where the deterministic QPF presents significant deficiencies over the Llobregat river basin in terms of the rainfall amounts, timing and localization. These deficiences in precipitation fields have a major impact on flood forecasts. Our ensemble strategy has been found useful to reduce the biases at different hydrometric sections along the watershed. Therefore, in an operational context, the devised methodology could be useful to expand the lead times associated with the prediction of similar future floods, helping to alleviate their possible hazardous consequences.

Inclusion of potential vorticity uncertainties into a hydrometeorological forecasting chain: application to a medium size basin of Mediterranean Spain

NASA Astrophysics Data System (ADS)

Amengual, A.; Romero, R.; Vich, M.; Alonso, S.

2009-01-01

The improvement of the short- and mid-range numerical runoff forecasts over the flood-prone Spanish Mediterranean area is a challenging issue. This work analyses four intense precipitation events which produced floods of different magnitude over the Llobregat river basin, a medium size catchment located in Catalonia, north-eastern Spain. One of them was a devasting flash flood - known as the "Montserrat" event - which produced 5 fatalities and material losses estimated at about 65 million euros. The characterization of the Llobregat basin's hydrological response to these floods is first assessed by using rain-gauge data and the Hydrologic Engineering Center's Hydrological Modeling System (HEC-HMS) runoff model. In second place, the non-hydrostatic fifth-generation Pennsylvania State University/NCAR mesoscale model (MM5) is nested within the ECMWF large-scale forecast fields in a set of 54 h period simulations to provide quantitative precipitation forecasts (QPFs) for each hydrometeorological episode. The hydrological model is forced with these QPFs to evaluate the reliability of the resulting discharge forecasts, while an ensemble prediction system (EPS) based on perturbed atmospheric initial and boundary conditions has been designed to test the value of a probabilistic strategy versus the previous deterministic approach. Specifically, a Potential Vorticity (PV) Inversion technique has been used to perturb the MM5 model initial and boundary states (i.e. ECMWF forecast fields). For that purpose, a PV error climatology has been previously derived in order to introduce realistic PV perturbations in the EPS. Results show the benefits of using a probabilistic approach in those cases where the deterministic QPF presents significant deficiencies over the Llobregat river basin in terms of the rainfall amounts, timing and localization. These deficiences in precipitation fields have a major impact on flood forecasts. Our ensemble strategy has been found useful to reduce the biases at different hydrometric sections along the watershed. Therefore, in an operational context, the devised methodology could be useful to expand the lead times associated with the prediction of similar future floods, helping to alleviate their possible hazardous consequences.

Bayesian flood forecasting methods: A review

NASA Astrophysics Data System (ADS)

Han, Shasha; Coulibaly, Paulin

2017-08-01

Over the past few decades, floods have been seen as one of the most common and largely distributed natural disasters in the world. If floods could be accurately forecasted in advance, then their negative impacts could be greatly minimized. It is widely recognized that quantification and reduction of uncertainty associated with the hydrologic forecast is of great importance for flood estimation and rational decision making. Bayesian forecasting system (BFS) offers an ideal theoretic framework for uncertainty quantification that can be developed for probabilistic flood forecasting via any deterministic hydrologic model. It provides suitable theoretical structure, empirically validated models and reasonable analytic-numerical computation method, and can be developed into various Bayesian forecasting approaches. This paper presents a comprehensive review on Bayesian forecasting approaches applied in flood forecasting from 1999 till now. The review starts with an overview of fundamentals of BFS and recent advances in BFS, followed with BFS application in river stage forecasting and real-time flood forecasting, then move to a critical analysis by evaluating advantages and limitations of Bayesian forecasting methods and other predictive uncertainty assessment approaches in flood forecasting, and finally discusses the future research direction in Bayesian flood forecasting. Results show that the Bayesian flood forecasting approach is an effective and advanced way for flood estimation, it considers all sources of uncertainties and produces a predictive distribution of the river stage, river discharge or runoff, thus gives more accurate and reliable flood forecasts. Some emerging Bayesian forecasting methods (e.g. ensemble Bayesian forecasting system, Bayesian multi-model combination) were shown to overcome limitations of single model or fixed model weight and effectively reduce predictive uncertainty. In recent years, various Bayesian flood forecasting approaches have been developed and widely applied, but there is still room for improvements. Future research in the context of Bayesian flood forecasting should be on assimilation of various sources of newly available information and improvement of predictive performance assessment methods.

Improving River Flow Predictions from the NOAA NCRFC Forecasting Model by Incorporating Satellite Observations

NASA Astrophysics Data System (ADS)

Tuttle, S. E.; Jacobs, J. M.; Restrepo, P. J.; Deweese, M. M.; Connelly, B.; Buan, S.

2016-12-01

The NOAA National Weather Service North Central River Forecast Center (NCRFC) is responsible for issuing river flow forecasts for parts of the Upper Mississippi, Great Lakes, and Hudson Bay drainages, including the Red River of the North basin (RRB). The NCRFC uses an operational hydrologic modeling infrastructure called the Community Hydrologic Prediction System (CHPS) for its operational forecasts, which currently links the SNOW-17 snow accumulation and ablation model, to the Sacramento-Soil Moisture Accounting (SAC-SMA) rainfall-runoff model, to a number of hydrologic and hydraulic flow routing models. The operational model is lumped and requires only area-averaged precipitation and air temperature as inputs. NCRFC forecasters use observational data of hydrological state variables as a source of supplemental information during forecasting, and can use professional judgment to modify the model states in real time. In a few recent years (e.g. 2009, 2013), the RRB exhibited unexpected anomalous hydrologic behavior, resulting in overestimation of peak flood discharge by up to 70% and highlighting the need for observations with high temporal and spatial coverage. Unfortunately, observations of hydrological states (e.g. soil moisture, snow water equivalent (SWE)) are relatively scarce in the RRB. Satellite remote sensing can fill this need. We use Minnesota's Buffalo River watershed within the RRB as a test case and update the operational CHPS model using modifications based on satellite observations, including AMSR-E SWE and SMOS soil moisture estimates. We evaluate the added forecasting skill of the satellite-enhanced model compared to measured streamflow using hindcasts from 2010-2013.

The Electrical Self-Potential Method as a Non-Intrusive Snow-Hydrological Sensor

NASA Astrophysics Data System (ADS)

Kulessa, B.; Thompson, S. S.; Luethi, M. P.; Essery, R.

2015-12-01

Building on growing momentum in the application of geophysical techniques to snow problems and, specifically, on new theory and an electrical geophysical snow hydrological model published recently; we demonstrate for the first time that the electrical self-potential geophysical technique can sense in-situ bulk meltwater fluxes. This has broad and immediate implications for snow measurement practice, modelling and operational snow forecasting. Our ability to measure, quantify and assimilate hydrological properties and processes of snow in operational models is disproportionally poor compared to the significance of seasonal snowmelt as a global water resource and major risk factor in flood and avalanche forecasting. Encouraged by recent theoretical, modelling and laboratory work, we show here that the diurnal evolution of aerially-distributed self-potential magnitudes closely track those of bulk meltwater fluxes in melting in-situ snowpacks at Rhone and Jungfraujoch glaciers, Switzerland. Numerical modelling infers temporally-evolving liquid water contents in the snowpacks on successive days in close agreement with snow-pit measurements. Muting previous concerns, the governing physical and chemical properties of snow and meltwater became temporally invariant for modelling purposes. Because measurement procedure is straightforward and readily automated for continuous monitoring over significant spatial scales, we conclude that the self-potential geophysical method is a highly-promising non-intrusive snow-hydrological sensor for measurement practice, modelling and operational snow forecasting.

Evaluating NMME Seasonal Forecast Skill for use in NASA SERVIR Hub Regions

NASA Technical Reports Server (NTRS)

Roberts, J. Brent; Roberts, Franklin R.

2013-01-01

The U.S. National Multi-Model Ensemble seasonal forecasting system is providing hindcast and real-time data streams to be used in assessing and improving seasonal predictive capacity. The coupled forecasts have numerous potential applications, both national and international in scope. The NASA / USAID SERVIR project, which leverages satellite and modeling-based resources for environmental decision making in developing nations, is focusing on the evaluation of NMME forecasts specifically for use in driving applications models in hub regions including East Africa, the Hindu Kush- Himalayan (HKH) region and Mesoamerica. A prerequisite for seasonal forecast use in application modeling (e.g. hydrology, agriculture) is bias correction and skill assessment. Efforts to address systematic biases and multi-model combination in support of NASA SERVIR impact modeling requirements will be highlighted. Specifically, quantilequantile mapping for bias correction has been implemented for all archived NMME hindcasts. Both deterministic and probabilistic skill estimates for raw, bias-corrected, and multi-model ensemble forecasts as a function of forecast lead will be presented for temperature and precipitation. Complementing this statistical assessment will be case studies of significant events, for example, the ability of the NMME forecasts suite to anticipate the 2010/2011 drought in the Horn of Africa and its relationship to evolving SST patterns.

Improving National Water Modeling: An Intercomparison of two High-Resolution, Continental Scale Models, CONUS-ParFlow and the National Water Model

NASA Astrophysics Data System (ADS)

Tijerina, D.; Gochis, D.; Condon, L. E.; Maxwell, R. M.

2017-12-01

Development of integrated hydrology modeling systems that couple atmospheric, land surface, and subsurface flow is growing trend in hydrologic modeling. Using an integrated modeling framework, subsurface hydrologic processes, such as lateral flow and soil moisture redistribution, are represented in a single cohesive framework with surface processes like overland flow and evapotranspiration. There is a need for these more intricate models in comprehensive hydrologic forecasting and water management over large spatial areas, specifically the Continental US (CONUS). Currently, two high-resolution, coupled hydrologic modeling applications have been developed for this domain: CONUS-ParFlow built using the integrated hydrologic model ParFlow and the National Water Model that uses the NCAR Weather Research and Forecasting hydrological extension package (WRF-Hydro). Both ParFlow and WRF-Hydro include land surface models, overland flow, and take advantage of parallelization and high-performance computing (HPC) capabilities; however, they have different approaches to overland subsurface flow and groundwater-surface water interactions. Accurately representing large domains remains a challenge considering the difficult task of representing complex hydrologic processes, computational expense, and extensive data needs; both models have accomplished this, but have differences in approach and continue to be difficult to validate. A further exploration of effective methodology to accurately represent large-scale hydrology with integrated models is needed to advance this growing field. Here we compare the outputs of CONUS-ParFlow and the National Water Model to each other and with observations to study the performance of hyper-resolution models over large domains. Models were compared over a range of scales for major watersheds within the CONUS with a specific focus on the Mississippi, Ohio, and Colorado River basins. We use a novel set of approaches and analysis for this comparison to better understand differences in process and bias. This intercomparison is a step toward better understanding how much water we have and interactions between surface and subsurface. Our goal is to advance our understanding and simulation of the hydrologic system and ultimately improve hydrologic forecasts.

Skill of a global seasonal ensemble streamflow forecasting system

NASA Astrophysics Data System (ADS)

Candogan Yossef, Naze; Winsemius, Hessel; Weerts, Albrecht; van Beek, Rens; Bierkens, Marc

2013-04-01

Forecasting of water availability and scarcity is a prerequisite for managing the risks and opportunities caused by the inter-annual variability of streamflow. Reliable seasonal streamflow forecasts are necessary to prepare for an appropriate response in disaster relief, management of hydropower reservoirs, water supply, agriculture and navigation. Seasonal hydrological forecasting on a global scale could be valuable especially for developing regions of the world, where effective hydrological forecasting systems are scarce. In this study, we investigate the forecasting skill of the global seasonal streamflow forecasting system FEWS-World, using the global hydrological model PCR-GLOBWB. FEWS-World has been setup within the European Commission 7th Framework Programme project Global Water Scarcity Information Service (GLOWASIS). Skill is assessed in historical simulation mode as well as retroactive forecasting mode. The assessment in historical simulation mode used a meteorological forcing based on observations from the Climate Research Unit of the University of East Anglia and the ERA-40 reanalysis of the European Center for Medium-Range Weather Forecasts (ECMWF). We assessed the skill of the global hydrological model PCR-GLOBWB in reproducing past discharge extremes in 20 large rivers of the world. This preliminary assessment concluded that the prospects for seasonal forecasting with PCR-GLOBWB or comparable models are positive. However this assessment did not include actual meteorological forecasts. Thus the meteorological forcing errors were not assessed. Yet, in a forecasting setup, the predictive skill of a hydrological forecasting system is affected by errors due to uncertainty from numerical weather prediction models. For the assessment in retroactive forecasting mode, the model is forced with actual ensemble forecasts from the seasonal forecast archives of ECMWF. Skill is assessed at 78 stations on large river basins across the globe, for all the months of the year and for lead times up to 6 months. The forecasted discharges are compared with observed monthly streamflow records using the ensemble verification measures Brier Skill Score (BSS) and Continuous Ranked Probability Score (CRPS). The eventual goal is to transfer FEWS-World to operational forecasting mode, where the system will use operational seasonal forecasts from ECMWF. The results will be disseminated on the internet, and hopefully provide information that is valuable for users in data and model-poor regions of the world.

Forecasting and Communicating Water-Related Disasters in Africa

NASA Astrophysics Data System (ADS)

Hong, Y.; Clark, R. A.; Mandl, D.; Gourley, J. J.; Flamig, Z.; Zhang, K.; Macharia, D.; Frye, S. W.; Cappelaere, P. G.; Handy, M.

2016-12-01

Accurate forecasting and communication of water and water-related hazards in developing regions could save untold lives and property. To this end, the CREST (Coupled Routing and Excess Storage) hydrologic model has been implemented over East Africa, and in dozens of other countries as a user-friendly, flexible, and highly extensible platform for monitoring water resources, floods, droughts, and landslides since 2009. We will present the updated CREST/EF5 hydrologic ensemble modeling framework with new model physics and better forecasts of streamflow, soil moisture, and other hydrologic states to RCMRD (the Regional Centre for Mapping of Resources for Development) and SERVIR global hub network. The central goal of this project is to develop an ensemble hydrologic prediction system, forced by weather and climate forecasts in a single continuum, to communicate forecasts on scales ranging from sub-daily to seasonal and in formats designed for better decision making about water and water-related disasters. The CREST/EF5 is a proven performer at getting researcher and officials in emerging regions excited about and confident in their ability to independently monitor, forecast, and understand water and water-related disasters, through a series of training workshops and capacity building activities in USA, Africa, Mesoamerica, and South Asia and is thus particularly well-suited for hydrologic capacity building in emerging countries.

Value of medium range weather forecasts in the improvement of seasonal hydrologic prediction skill

DOE Office of Scientific and Technical Information (OSTI.GOV)

Shukla, Shraddhanand; Voisin, Nathalie; Lettenmaier, D. P.

2012-08-15

We investigated the contribution of medium range weather forecasts with lead times up to 14 days to seasonal hydrologic prediction skill over the Conterminous United States (CONUS). Three different Ensemble Streamflow Prediction (ESP)-based experiments were performed for the period 1980-2003 using the Variable Infiltration Capacity (VIC) hydrology model to generate forecasts of monthly runoff and soil moisture (SM) at lead-1 (first month of the forecast period) to lead-3. The first experiment (ESP) used a resampling from the retrospective period 1980-2003 and represented full climatological uncertainty for the entire forecast period. In the second and third experiments, the first 14 daysmore » of each ESP ensemble member were replaced by either observations (perfect 14-day forecast) or by a deterministic 14-day weather forecast. We used Spearman rank correlations of forecasts and observations as the forecast skill score. We estimated the potential and actual improvement in baseline skill as the difference between the skill of experiments 2 and 3 relative to ESP, respectively. We found that useful runoff and SM forecast skill at lead-1 to -3 months can be obtained by exploiting medium range weather forecast skill in conjunction with the skill derived by the knowledge of initial hydrologic conditions. Potential improvement in baseline skill by using medium range weather forecasts, for runoff (SM) forecasts generally varies from 0 to 0.8 (0 to 0.5) as measured by differences in correlations, with actual improvement generally from 0 to 0.8 of the potential improvement. With some exceptions, most of the improvement in runoff is for lead-1 forecasts, although some improvement in SM was achieved at lead-2.« less

Interoperability challenges in river discharge modelling: A cross domain application scenario

NASA Astrophysics Data System (ADS)

Santoro, Mattia; Andres, Volker; Jirka, Simon; Koike, Toshio; Looser, Ulrich; Nativi, Stefano; Pappenberger, Florian; Schlummer, Manuela; Strauch, Adrian; Utech, Michael; Zsoter, Ervin

2018-06-01

River discharge is a critical water cycle variable, as it integrates all the processes (e.g. runoff and evapotranspiration) occurring within a river basin and provides a hydrological output variable that can be readily measured. Its prediction is of invaluable help for many water-related tasks including water resources assessment and management, flood protection, and disaster mitigation. Observations of river discharge are important to calibrate and validate hydrological or coupled land, atmosphere and ocean models. This requires using datasets from different scientific domains (Water, Weather, etc.). Typically, such datasets are provided using different technological solutions. This complicates the integration of new hydrological data sources into application systems. Therefore, a considerable effort is often spent on data access issues instead of the actual scientific question. This paper describes the work performed to address multidisciplinary interoperability challenges related to river discharge modeling and validation. This includes definition and standardization of domain specific interoperability standards for hydrological data sharing and their support in global frameworks such as the Global Earth Observation System of Systems (GEOSS). The research was developed in the context of the EU FP7-funded project GEOWOW (GEOSS Interoperability for Weather, Ocean and Water), which implemented a "River Discharge" application scenario. This scenario demonstrates the combination of river discharge observations data from the Global Runoff Data Centre (GRDC) database and model outputs produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) predicting river discharge based on weather forecast information in the context of the GEOSS.

Ensemble Bayesian forecasting system Part I: Theory and algorithms

NASA Astrophysics Data System (ADS)

Herr, Henry D.; Krzysztofowicz, Roman

2015-05-01

The ensemble Bayesian forecasting system (EBFS), whose theory was published in 2001, is developed for the purpose of quantifying the total uncertainty about a discrete-time, continuous-state, non-stationary stochastic process such as a time series of stages, discharges, or volumes at a river gauge. The EBFS is built of three components: an input ensemble forecaster (IEF), which simulates the uncertainty associated with random inputs; a deterministic hydrologic model (of any complexity), which simulates physical processes within a river basin; and a hydrologic uncertainty processor (HUP), which simulates the hydrologic uncertainty (an aggregate of all uncertainties except input). It works as a Monte Carlo simulator: an ensemble of time series of inputs (e.g., precipitation amounts) generated by the IEF is transformed deterministically through a hydrologic model into an ensemble of time series of outputs, which is next transformed stochastically by the HUP into an ensemble of time series of predictands (e.g., river stages). Previous research indicated that in order to attain an acceptable sampling error, the ensemble size must be on the order of hundreds (for probabilistic river stage forecasts and probabilistic flood forecasts) or even thousands (for probabilistic stage transition forecasts). The computing time needed to run the hydrologic model this many times renders the straightforward simulations operationally infeasible. This motivates the development of the ensemble Bayesian forecasting system with randomization (EBFSR), which takes full advantage of the analytic meta-Gaussian HUP and generates multiple ensemble members after each run of the hydrologic model; this auxiliary randomization reduces the required size of the meteorological input ensemble and makes it operationally feasible to generate a Bayesian ensemble forecast of large size. Such a forecast quantifies the total uncertainty, is well calibrated against the prior (climatic) distribution of predictand, possesses a Bayesian coherence property, constitutes a random sample of the predictand, and has an acceptable sampling error-which makes it suitable for rational decision making under uncertainty.

A GLM Post-processor to Adjust Ensemble Forecast Traces

NASA Astrophysics Data System (ADS)

Thiemann, M.; Day, G. N.; Schaake, J. C.; Draijer, S.; Wang, L.

2011-12-01

The skill of hydrologic ensemble forecasts has improved in the last years through a better understanding of climate variability, better climate forecasts and new data assimilation techniques. Having been extensively utilized for probabilistic water supply forecasting, interest is developing to utilize these forecasts in operational decision making. Hydrologic ensemble forecast members typically have inherent biases in flow timing and volume caused by (1) structural errors in the models used, (2) systematic errors in the data used to calibrate those models, (3) uncertain initial hydrologic conditions, and (4) uncertainties in the forcing datasets. Furthermore, hydrologic models have often not been developed for operational decision points and ensemble forecasts are thus not always available where needed. A statistical post-processor can be used to address these issues. The post-processor should (1) correct for systematic biases in flow timing and volume, (2) preserve the skill of the available raw forecasts, (3) preserve spatial and temporal correlation as well as the uncertainty in the forecasted flow data, (4) produce adjusted forecast ensembles that represent the variability of the observed hydrograph to be predicted, and (5) preserve individual forecast traces as equally likely. The post-processor should also allow for the translation of available ensemble forecasts to hydrologically similar locations where forecasts are not available. This paper introduces an ensemble post-processor (EPP) developed in support of New York City water supply operations. The EPP employs a general linear model (GLM) to (1) adjust available ensemble forecast traces and (2) create new ensembles for (nearby) locations where only historic flow observations are available. The EPP is calibrated by developing daily and aggregated statistical relationships form historical flow observations and model simulations. These are then used in operation to obtain the conditional probability density function (PDF) of the observations to be predicted, thus jointly adjusting individual ensemble members. These steps are executed in a normalized transformed space ('z'-space) to account for the strong non-linearity in the flow observations involved. A data window centered on each calibration date is used to minimize impacts from sampling errors and data noise. Testing on datasets from California and New York suggests that the EPP can successfully minimize biases in ensemble forecasts, while preserving the raw forecast skill in a 'days to weeks' forecast horizon and reproducing the variability of climatology for 'weeks to years' forecast horizons.

Assessing the skill of seasonal precipitation and streamflow forecasts in sixteen French catchments

NASA Astrophysics Data System (ADS)

Crochemore, Louise; Ramos, Maria-Helena; Pappenberger, Florian

2015-04-01

Meteorological centres make sustained efforts to provide seasonal forecasts that are increasingly skilful. Streamflow forecasting is one of the many applications than can benefit from these efforts. Seasonal flow forecasts generated using seasonal ensemble precipitation forecasts as input to a hydrological model can help to take anticipatory measures for water supply reservoir operation or drought risk management. The objective of the study is to assess the skill of seasonal precipitation and streamflow forecasts in France. First, we evaluated the skill of ECMWF SYS4 seasonal precipitation forecasts for streamflow forecasting in sixteen French catchments. Daily flow forecasts were produced using raw seasonal precipitation forecasts as input to the GR6J hydrological model. Ensemble forecasts are issued every month with 15 or 51 members according to the month of the year and evaluated for up to 90 days ahead. In a second step, we applied eight variants of bias correction approaches to the precipitation forecasts prior to generating the flow forecasts. The approaches were based on the linear scaling and the distribution mapping methods. The skill of the ensemble forecasts was assessed in accuracy (MAE), reliability (PIT Diagram) and overall performance (CRPS). The results show that, in most catchments, raw seasonal precipitation and streamflow forecasts are more skilful in terms of accuracy and overall performance than a reference prediction based on historic observed precipitation and watershed initial conditions at the time of forecast. Reliability is the only attribute that is not significantly improved. The skill of the forecasts is, in general, improved when applying bias correction. Two bias correction methods showed the best performance for the studied catchments: the simple linear scaling of monthly values and the empirical distribution mapping of daily values. L. Crochemore is funded by the Interreg IVB DROP Project (Benefit of governance in DROught adaPtation).

Delft-FEWS:A Decision Making Platform to Intergrate Data, Model, Algorithm for Large-Scale River Basin Water Management

NASA Astrophysics Data System (ADS)

Yang, T.; Welles, E.

2017-12-01

In this paper, we introduce a flood forecasting and decision making platform, named Delft-FEWS, which has been developed over years at the Delft Hydraulics and now at Deltares. The philosophy of Delft-FEWS is to provide water managers and operators with an open shell tool, which allows the integratation of a variety of hydrological, hydraulics, river routing, and reservoir models with hydrometerological forecasts data. Delft-FEWS serves as an powerful tool for both basin-scale and national-scale water resources management. The essential novelty of Delft-FEWS is to change the flood forecasting and water resources management from a single model or agency centric paradigm to a intergrated framework, in which different model, data, algorithm and stakeholders are strongly linked together. The paper will start with the challenges in water resources managment, and the concept and philosophy of Delft-FEWS. Then, the details of data handling and linkages of Delft-FEWS with different hydrological, hydraulic, and reservoir models, etc. Last, several cases studies and applications of Delft-FEWS will be demonstrated, including the National Weather Service and the Bonneville Power Administration in USA, and a national application in the water board in the Netherland.

Valuing year-to-go hydrologic forecast improvements for a peaking hydropower system in the Sierra Nevada

NASA Astrophysics Data System (ADS)

Rheinheimer, David E.; Bales, Roger C.; Oroza, Carlos A.; Lund, Jay R.; Viers, Joshua H.

2016-05-01

We assessed the potential value of hydrologic forecasting improvements for a snow-dominated high-elevation hydropower system in the Sierra Nevada of California, using a hydropower optimization model. To mimic different forecasting skill levels for inflow time series, rest-of-year inflows from regression-based forecasts were blended in different proportions with representative inflows from a spatially distributed hydrologic model. The statistical approach mimics the simpler, historical forecasting approach that is still widely used. Revenue was calculated using historical electricity prices, with perfect price foresight assumed. With current infrastructure and operations, perfect hydrologic forecasts increased annual hydropower revenue by 0.14 to 1.6 million, with lower values in dry years and higher values in wet years, or about $0.8 million (1.2%) on average, representing overall willingness-to-pay for perfect information. A second sensitivity analysis found a wider range of annual revenue gain or loss using different skill levels in snow measurement in the regression-based forecast, mimicking expected declines in skill as the climate warms and historical snow measurements no longer represent current conditions. The value of perfect forecasts was insensitive to storage capacity for small and large reservoirs, relative to average inflow, and modestly sensitive to storage capacity with medium (current) reservoir storage. The value of forecasts was highly sensitive to powerhouse capacity, particularly for the range of capacities in the northern Sierra Nevada. The approach can be extended to multireservoir, multipurpose systems to help guide investments in forecasting.

HEPS4Power - Extended-range Hydrometeorological Ensemble Predictions for Improved Hydropower Operations and Revenues

NASA Astrophysics Data System (ADS)

Bogner, Konrad; Monhart, Samuel; Liniger, Mark; Spririg, Christoph; Jordan, Fred; Zappa, Massimiliano

2015-04-01

In recent years large progresses have been achieved in the operational prediction of floods and hydrological drought with up to ten days lead time. Both the public and the private sectors are currently using probabilistic runoff forecast in order to monitoring water resources and take actions when critical conditions are to be expected. The use of extended-range predictions with lead times exceeding 10 days is not yet established. The hydropower sector in particular might have large benefits from using hydro meteorological forecasts for the next 15 to 60 days in order to optimize the operations and the revenues from their watersheds, dams, captions, turbines and pumps. The new Swiss Competence Centers in Energy Research (SCCER) targets at boosting research related to energy issues in Switzerland. The objective of HEPS4POWER is to demonstrate that operational extended-range hydro meteorological forecasts have the potential to become very valuable tools for fine tuning the production of energy from hydropower systems. The project team covers a specific system-oriented value chain starting from the collection and forecast of meteorological data (MeteoSwiss), leading to the operational application of state-of-the-art hydrological models (WSL) and terminating with the experience in data presentation and power production forecasts for end-users (e-dric.ch). The first task of the HEPS4POWER will be the downscaling and post-processing of ensemble extended-range meteorological forecasts (EPS). The goal is to provide well-tailored forecasts of probabilistic nature that should be reliable in statistical and localized at catchment or even station level. The hydrology related task will consist in feeding the post-processed meteorological forecasts into a HEPS using a multi-model approach by implementing models with different complexity. Also in the case of the hydrological ensemble predictions, post-processing techniques need to be tested in order to improve the quality of the forecasts against observed discharge. Analysis should be specifically oriented to the maximisation of hydroelectricity production. Thus, verification metrics should include economic measures like cost loss approaches. The final step will include the transfer of the HEPS system to several hydropower systems, the connection with the energy market prices and the development of probabilistic multi-reservoir production and management optimizations guidelines. The baseline model chain yielding three-days forecasts established for a hydropower system in southern-Switzerland will be presented alongside with the work-plan to achieve seasonal ensemble predictions.

Evaluating the extreme precipitation events using a mesoscale atmopshere model

NASA Astrophysics Data System (ADS)

Yucel, I.; Onen, A.

2012-04-01

Evidence is showing that global warming or climate change has a direct influence on changes in precipitation and the hydrological cycle. Extreme weather events such as heavy rainfall and flooding are projected to become much more frequent as climate warms. Mesoscale atmospheric models coupled with land surface models provide efficient forecasts for meteorological events in high lead time and therefore they should be used for flood forecasting and warning issues as they provide more continuous monitoring of precipitation over large areas. This study examines the performance of the Weather Research and Forecasting (WRF) model in producing the temporal and spatial characteristics of the number of extreme precipitation events observed in West Black Sea Region of Turkey. Extreme precipitation events usually resulted in flood conditions as an associated hydrologic response of the basin. The performance of the WRF system is further investigated by using the three dimensional variational (3D-VAR) data assimilation scheme within WRF. WRF performance with and without data assimilation at high spatial resolution (4 km) is evaluated by making comparison with gauge precipitation and satellite-estimated rainfall data from Multi Precipitation Estimates (MPE). WRF-derived precipitation showed capabilities in capturing the timing of the precipitation extremes and in some extent spatial distribution and magnitude of the heavy rainfall events. These precipitation characteristics are enhanced with the use of 3D-VAR scheme in WRF system. Data assimilation improved area-averaged precipitation forecasts by 9 percent and at some points there exists quantitative match in precipitation events, which are critical for hydrologic forecast application.

An operational hydrological ensemble prediction system for the city of Zurich (Switzerland): assessing the added value of probabilistic forecasts

NASA Astrophysics Data System (ADS)

Addor, N.; Jaun, S.; Fundel, F.; Zappa, M.

2012-04-01

The Sihl River flows through Zurich, Switzerland's most populated city, for which it represents the largest flood threat. To anticipate extreme discharge events and provide decision support in case of flood risk, a hydrometeorological ensemble prediction system (HEPS) was launched operationally in 2008. This model chain relies on deterministic (COSMO-7) and probabilistic (COSMO-LEPS) atmospheric forecasts, which are used to force a semi-distributed hydrological model (PREVAH) coupled to a hydraulic model (FLORIS). The resulting hydrological forecasts are eventually communicated to the stakeholders involved in the Sihl discharge management. This fully operational setting provides a real framework with which we assessed the potential of deterministic and probabilistic discharge forecasts for flood mitigation. To study the suitability of HEPS for small-scale basins and to quantify the added value conveyed by the probability information, a 31-month reforecast was produced for the Sihl catchment (336 km2). Several metrics support the conclusion that the performance gain is of up to 2 days lead time for the catchment considered. Brier skill scores show that probabilistic hydrological forecasts outperform their deterministic counterparts for all the lead times and event intensities considered. The small size of the Sihl catchment does not prevent skillful discharge forecasts, but makes them particularly dependent on correct precipitation forecasts. Our evaluation stresses that the capacity of the model to provide confident and reliable mid-term probability forecasts for high discharges is limited. We finally highlight challenges for making decisions on the basis of hydrological predictions, and discuss the need for a tool to be used in addition to forecasts to compare the different mitigation actions possible in the Sihl catchment.

Metric optimisation for analogue forecasting by simulated annealing

NASA Astrophysics Data System (ADS)

Bliefernicht, J.; Bárdossy, A.

2009-04-01

It is well known that weather patterns tend to recur from time to time. This property of the atmosphere is used by analogue forecasting techniques. They have a long history in weather forecasting and there are many applications predicting hydrological variables at the local scale for different lead times. The basic idea of the technique is to identify past weather situations which are similar (analogue) to the predicted one and to take the local conditions of the analogues as forecast. But the forecast performance of the analogue method depends on user-defined criteria like the choice of the distance function and the size of the predictor domain. In this study we propose a new methodology of optimising both criteria by minimising the forecast error with simulated annealing. The performance of the methodology is demonstrated for the probability forecast of daily areal precipitation. It is compared with a traditional analogue forecasting algorithm, which is used operational as an element of a hydrological forecasting system. The study is performed for several meso-scale catchments located in the Rhine basin in Germany. The methodology is validated by a jack-knife method in a perfect prognosis framework for a period of 48 years (1958-2005). The predictor variables are derived from the NCEP/NCAR reanalysis data set. The Brier skill score and the economic value are determined to evaluate the forecast skill and value of the technique. In this presentation we will present the concept of the optimisation algorithm and the outcome of the comparison. It will be also demonstrated how a decision maker should apply a probability forecast to maximise the economic benefit from it.

CEREF: A hybrid data-driven model for forecasting annual streamflow from a socio-hydrological system

NASA Astrophysics Data System (ADS)

Zhang, Hongbo; Singh, Vijay P.; Wang, Bin; Yu, Yinghao

2016-09-01

Hydrological forecasting is complicated by flow regime alterations in a coupled socio-hydrologic system, encountering increasingly non-stationary, nonlinear and irregular changes, which make decision support difficult for future water resources management. Currently, many hybrid data-driven models, based on the decomposition-prediction-reconstruction principle, have been developed to improve the ability to make predictions of annual streamflow. However, there exist many problems that require further investigation, the chief among which is the direction of trend components decomposed from annual streamflow series and is always difficult to ascertain. In this paper, a hybrid data-driven model was proposed to capture this issue, which combined empirical mode decomposition (EMD), radial basis function neural networks (RBFNN), and external forces (EF) variable, also called the CEREF model. The hybrid model employed EMD for decomposition and RBFNN for intrinsic mode function (IMF) forecasting, and determined future trend component directions by regression with EF as basin water demand representing the social component in the socio-hydrologic system. The Wuding River basin was considered for the case study, and two standard statistical measures, root mean squared error (RMSE) and mean absolute error (MAE), were used to evaluate the performance of CEREF model and compare with other models: the autoregressive (AR), RBFNN and EMD-RBFNN. Results indicated that the CEREF model had lower RMSE and MAE statistics, 42.8% and 7.6%, respectively, than did other models, and provided a superior alternative for forecasting annual runoff in the Wuding River basin. Moreover, the CEREF model can enlarge the effective intervals of streamflow forecasting compared to the EMD-RBFNN model by introducing the water demand planned by the government department to improve long-term prediction accuracy. In addition, we considered the high-frequency component, a frequent subject of concern in EMD-based forecasting, and results showed that removing high-frequency component is an effective measure to improve forecasting precision and is suggested for use with the CEREF model for better performance. Finally, the study concluded that the CEREF model can be used to forecast non-stationary annual streamflow change as a co-evolution of hydrologic and social systems with better accuracy. Also, the modification about removing high-frequency can further improve the performance of the CEREF model. It should be noted that the CEREF model is beneficial for data-driven hydrologic forecasting in complex socio-hydrologic systems, and as a simple data-driven socio-hydrologic forecasting model, deserves more attention.

The FAST-T approach for operational, real time, short term hydrological forecasting: Results from the Betania Hydropower Reservoir case study

NASA Astrophysics Data System (ADS)

Domínguez, Efraín; Angarita, Hector; Rosmann, Thomas; Mendez, Zulma; Angulo, Gustavo

2013-04-01

A viable quantitative hydrological forecasting service is a combination of technological elements, personnel and knowledge, working together to establish a stable operational cycle of forecasts emission, dissemination and assimilation; hence, the process for establishing such system usually requires significant resources and time to reach an adequate development and integration in order to produce forecasts with acceptable levels of performance. Here are presented the results of this process for the recently implemented Operational Forecast Service for the Betania's Hydropower Reservoir - or SPHEB, located at the Upper-Magdalena River Basin (Colombia). The current scope of the SPHEB includes forecasting of water levels and discharge for the three main streams affluent to the reservoir, for lead times between +1 to +57 hours, and +1 to +10 days. The core of the SPHEB is the Flexible, Adaptive, Simple and Transient Time forecasting approach, namely FAST-T. This comprises of a set of data structures, mathematical kernel, distributed computing and network infrastructure designed to provide seamless real-time operational forecast and automatic model adjustment in case of failures in data transmission or assimilation. Among FAST-T main features are: an autonomous evaluation and detection of the most relevant information for the later configuration of forecasting models; an adaptively linearized mathematical kernel, the optimal adaptive linear combination or OALC, which provides a computationally simple and efficient algorithm for real-time applications; and finally, a meta-model catalog, containing prioritized forecast models at given stream conditions. The SPHEB is at present feed by the fraction of hydrological monitoring network installed at the basin that has telemetric capabilities via NOAA-GOES satellites (8 stages, approximately 47%) with data availability of about a 90% at one hour intervals. However, there is a dense network of 'conventional' hydro-meteorological stages -read manually once or twice per day - that, despite not ideal in the context of real-time system, improve model performance significantly, and therefore are entered into the system by manual input. At its current configuration, the SPHEB performance objectives are fulfilled for 90% of the forecasts with lead times up to +2 days and +15 hours (using the predictability criteria of the Russian Hydrometeorological Center S/?Δ) and the average accuracy is in the range 70-99% ( r2 criteria). However, longer lead times are at present not satisfactory in terms of forecasts accuracy.

High-Resolution Hydrological Sub-Seasonal Forecasting for Water Resources Management Over Europe

NASA Astrophysics Data System (ADS)

Wood, E. F.; Wanders, N.; Pan, M.; Sheffield, J.; Samaniego, L. E.; Thober, S.; Kumar, R.; Prudhomme, C.; Houghton-Carr, H.

2017-12-01

For decision-making at the sub-seasonal and seasonal time scale, hydrological forecasts with a high temporal and spatial resolution are required by water managers. So far such forecasts have been unavailable due to 1) lack of availability of meteorological seasonal forecasts, 2) coarse temporal resolution of meteorological seasonal forecasts, requiring temporal downscaling, 3) lack of consistency between observations and seasonal forecasts, requiring bias-correction. The EDgE (End-to-end Demonstrator for improved decision making in the water sector in Europe) project commissioned by the ECMWF (C3S) created a unique dataset of hydrological seasonal forecasts derived from four global climate models (CanCM4, FLOR-B01, ECMF, LFPW) in combination with four global hydrological models (PCR-GLOBWB, VIC, mHM, Noah-MP), resulting in 208 forecasts for any given day. The forecasts provide a daily temporal and 5-km spatial resolution, and are bias corrected against E-OBS meteorological observations. The forecasts are communicated to stakeholders via Sectoral Climate Impact Indicators (SCIIs), created in collaboration with the end-user community of the EDgE project (e.g. the percentage of ensemble realizations above the 10th percentile of monthly river flow, or below the 90th). Results show skillful forecasts for discharge from 3 months to 6 months (latter for N Europe due to snow); for soil moisture up to three months due precipitation forecast skill and short initial condition memory; and for groundwater greater than 6 months (lowest skill in western Europe.) The SCIIs are effective in communicating both forecast skill and uncertainty. Overall the new system provides an unprecedented ensemble for seasonal forecasts with significant skill over Europe to support water management. The consistency in both the GCM forecasts and the LSM parameterization ensures a stable and reliable forecast framework and methodology, even if additional GCMs or LSMs are added in the future.

Ensemble hydrological forecast efficiency evolution over various issue dates and lead-time: case study for the Cheboksary reservoir (Volga River)

NASA Astrophysics Data System (ADS)

Gelfan, Alexander; Moreido, Vsevolod

2017-04-01

Ensemble hydrological forecasting allows for describing uncertainty caused by variability of meteorological conditions in the river basin for the forecast lead-time. At the same time, in snowmelt-dependent river basins another significant source of uncertainty relates to variability of initial conditions of the basin (snow water equivalent, soil moisture content, etc.) prior to forecast issue. Accurate long-term hydrological forecast is most crucial for large water management systems, such as the Cheboksary reservoir (the catchment area is 374 000 sq.km) located in the Middle Volga river in Russia. Accurate forecasts of water inflow volume, maximum discharge and other flow characteristics are of great value for this basin, especially before the beginning of the spring freshet season that lasts here from April to June. The semi-distributed hydrological model ECOMAG was used to develop long-term ensemble forecast of daily water inflow into the Cheboksary reservoir. To describe variability of the meteorological conditions and construct ensemble of possible weather scenarios for the lead-time of the forecast, two approaches were applied. The first one utilizes 50 weather scenarios observed in the previous years (similar to the ensemble streamflow prediction (ESP) procedure), the second one uses 1000 synthetic scenarios simulated by a stochastic weather generator. We investigated the evolution of forecast uncertainty reduction, expressed as forecast efficiency, over various consequent forecast issue dates and lead time. We analyzed the Nash-Sutcliffe efficiency of inflow hindcasts for the period 1982 to 2016 starting from 1st of March with 15 days frequency for lead-time of 1 to 6 months. This resulted in the forecast efficiency matrix with issue dates versus lead-time that allows for predictability identification of the basin. The matrix was constructed separately for observed and synthetic weather ensembles.

The End-to-end Demonstrator for improved decision making in the water sector in Europe (EDgE)

NASA Astrophysics Data System (ADS)

Wood, Eric; Wanders, Niko; Pan, Ming; Sheffield, Justin; Samaniego, Luis; Thober, Stephan; Kumar, Rohinni; Prudhomme, Christel; Houghton-Carr, Helen

2017-04-01

High-resolution simulations of water resources from hydrological models are vital to supporting important climate services. Apart from a high level of detail, both spatially and temporally, it is important to provide simulations that consistently cover a range of timescales, from historical reanalysis to seasonal forecast and future projections. In the new EDgE project commissioned by the ECMWF (C3S) we try to fulfill these requirements. EDgE is a proof-of-concept project which combines climate data and state-of-the-art hydrological modelling to demonstrate a water-oriented information system implemented through a web application. EDgE is working with key European stakeholders representative of private and public sectors to jointly develop and tailor approaches and techniques. With these tools, stakeholders are assisted in using improved climate information in decision-making, and supported in the development of climate change adaptation and mitigation policies. Here, we present the first results of the EDgE modelling chain, which is divided into three main processes: 1) pre-processing and downscaling; 2) hydrological modelling; 3) post-processing. Consistent downscaling and bias corrections for historical simulations, seasonal forecasts and climate projections ensure that the results across scales are robust. The daily temporal resolution and 5km spatial resolution ensure locally relevant simulations. With the use of four hydrological models (PCR-GLOBWB, VIC, mHM, Noah-MP), uncertainty between models is properly addressed, while consistency is guaranteed by using identical input data for static land surface parameterizations. The forecast results are communicated to stakeholders via Sectoral Climate Impact Indicators (SCIIs) that have been created in collaboration with the end-user community of the EDgE project. The final product of this project is composed of 15 years of seasonal forecast and 10 climate change projections, all combined with four hydrological models. These unique high-resolution climate information simulations in the EDgE project provide an unprecedented information system for decision-making over Europe.

Operational Applications of Satellite Snowcover Observations

NASA Technical Reports Server (NTRS)

Rango, A. (Editor); Peterson, R. (Editor)

1980-01-01

The history of remote sensing of snow cover is reviewed and the following topics are covered: various techniques for interpreting LANDSAT and NOAA satellite data; the status of future systems for continuing snow hydrology applications; the use of snow cover observations in streamflow forecasts by Applications Systems Verification and Transfer participants and selected foreign investigators; and the benefits of using satellite snow cover data in runoff prediction.

Relative effects of statistical preprocessing and postprocessing on a regional hydrological ensemble prediction system

NASA Astrophysics Data System (ADS)

Sharma, Sanjib; Siddique, Ridwan; Reed, Seann; Ahnert, Peter; Mendoza, Pablo; Mejia, Alfonso

2018-03-01

The relative roles of statistical weather preprocessing and streamflow postprocessing in hydrological ensemble forecasting at short- to medium-range forecast lead times (day 1-7) are investigated. For this purpose, a regional hydrologic ensemble prediction system (RHEPS) is developed and implemented. The RHEPS is comprised of the following components: (i) hydrometeorological observations (multisensor precipitation estimates, gridded surface temperature, and gauged streamflow); (ii) weather ensemble forecasts (precipitation and near-surface temperature) from the National Centers for Environmental Prediction 11-member Global Ensemble Forecast System Reforecast version 2 (GEFSRv2); (iii) NOAA's Hydrology Laboratory-Research Distributed Hydrologic Model (HL-RDHM); (iv) heteroscedastic censored logistic regression (HCLR) as the statistical preprocessor; (v) two statistical postprocessors, an autoregressive model with a single exogenous variable (ARX(1,1)) and quantile regression (QR); and (vi) a comprehensive verification strategy. To implement the RHEPS, 1 to 7 days weather forecasts from the GEFSRv2 are used to force HL-RDHM and generate raw ensemble streamflow forecasts. Forecasting experiments are conducted in four nested basins in the US Middle Atlantic region, ranging in size from 381 to 12 362 km2. Results show that the HCLR preprocessed ensemble precipitation forecasts have greater skill than the raw forecasts. These improvements are more noticeable in the warm season at the longer lead times (> 3 days). Both postprocessors, ARX(1,1) and QR, show gains in skill relative to the raw ensemble streamflow forecasts, particularly in the cool season, but QR outperforms ARX(1,1). The scenarios that implement preprocessing and postprocessing separately tend to perform similarly, although the postprocessing-alone scenario is often more effective. The scenario involving both preprocessing and postprocessing consistently outperforms the other scenarios. In some cases, however, the differences between this scenario and the scenario with postprocessing alone are not as significant. We conclude that implementing both preprocessing and postprocessing ensures the most skill improvements, but postprocessing alone can often be a competitive alternative.

Forecasting Global Rainfall for Points Using ECMWF's Global Ensemble and Its Applications in Flood Forecasting

NASA Astrophysics Data System (ADS)

Pillosu, F. M.; Hewson, T.; Mazzetti, C.

2017-12-01

Prediction of local extreme rainfall has historically been the remit of nowcasting and high resolution limited area modelling, which represent only limited areas, may not be spatially accurate, give reasonable results only for limited lead times (<2 days) and become prohibitively expensive at global scale. ECMWF/EFAS/GLOFAS have developed a novel, cost-effective and physically-based statistical post-processing software ("ecPoint-Rainfall, ecPR", operational in 2017) that uses ECMWF Ensemble (ENS) output to deliver global probabilistic rainfall forecasts for points up to day 10. Firstly, ecPR applies a new notion of "remote calibration", which 1) allows us to replicate a multi-centennial training period using only one year of data, and 2) provides forecasts for anywhere in the world. Secondly, the software applies an understanding of how different rainfall generation mechanisms lead to different degrees of sub-grid variability in rainfall totals, and of where biases in the model can be improved upon. A long-term verification has shown that the post-processed rainfall has better reliability and resolution at every lead time if compared with ENS, and for large totals, ecPR outputs have the same skill at day 5 that the raw ENS has at day 1 (ROC area metric). ecPR could be used as input for hydrological models if its probabilistic output is modified accordingly to the inputs requirements for hydrological models. Indeed, ecPR does not provide information on where the highest total is likely to occur inside the gridbox, nor on the spatial distribution of rainfall values nearby. "Scenario forecasts" could be a solution. They are derived from locating the rainfall peak in sensitive positions (e.g. urban areas), and then redistributing the remaining quantities in the gridbox modifying traditional spatial correlation characterization methodologies (e.g. variogram analysis) in order to take account, for instance, of the type of rainfall forecast (stratiform, convective). Such an approach could be a turning point in the field of medium-range global real-time riverine flood forecasts. This presentation will illustrate for ecPR 1) system calibration, 2) operational implementation, 3) long-term verification, 4) future developments, and 5) early ideas for the application of ecPR outputs in hydrological models.

An SVM model with hybrid kernels for hydrological time series

NASA Astrophysics Data System (ADS)

Wang, C.; Wang, H.; Zhao, X.; Xie, Q.

2017-12-01

Support Vector Machine (SVM) models have been widely applied to the forecast of climate/weather and its impact on other environmental variables such as hydrologic response to climate/weather. When using SVM, the choice of the kernel function plays the key role. Conventional SVM models mostly use one single type of kernel function, e.g., radial basis kernel function. Provided that there are several featured kernel functions available, each having its own advantages and drawbacks, a combination of these kernel functions may give more flexibility and robustness to SVM approach, making it suitable for a wide range of application scenarios. This paper presents such a linear combination of radial basis kernel and polynomial kernel for the forecast of monthly flowrate in two gaging stations using SVM approach. The results indicate significant improvement in the accuracy of predicted series compared to the approach with either individual kernel function, thus demonstrating the feasibility and advantages of such hybrid kernel approach for SVM applications.

Streamflow Forecasting Using Nuero-Fuzzy Inference System

NASA Astrophysics Data System (ADS)

Nanduri, U. V.; Swain, P. C.

2005-12-01

The prediction of flow into a reservoir is fundamental in water resources planning and management. The need for timely and accurate streamflow forecasting is widely recognized and emphasized by many in water resources fraternity. Real-time forecasts of natural inflows to reservoirs are of particular interest for operation and scheduling. The physical system of the river basin that takes the rainfall as an input and produces the runoff is highly nonlinear, complicated and very difficult to fully comprehend. The system is influenced by large number of factors and variables. The large spatial extent of the systems forces the uncertainty into the hydrologic information. A variety of methods have been proposed for forecasting reservoir inflows including conceptual (physical) and empirical (statistical) models (WMO 1994), but none of them can be considered as unique superior model (Shamseldin 1997). Owing to difficulties of formulating reasonable non-linear watershed models, recent attempts have resorted to Neural Network (NN) approach for complex hydrologic modeling. In recent years the use of soft computing in the field of hydrological forecasting is gaining ground. The relatively new soft computing technique of Adaptive Neuro-Fuzzy Inference System (ANFIS), developed by Jang (1993) is able to take care of the non-linearity, uncertainty, and vagueness embedded in the system. It is a judicious combination of the Neural Networks and fuzzy systems. It can learn and generalize highly nonlinear and uncertain phenomena due to the embedded neural network (NN). NN is efficient in learning and generalization, and the fuzzy system mimics the cognitive capability of human brain. Hence, ANFIS can learn the complicated processes involved in the basin and correlate the precipitation to the corresponding discharge. In the present study, one step ahead forecasts are made for ten-daily flows, which are mostly required for short term operational planning of multipurpose reservoirs. A Neuro-Fuzzy model is developed to forecast ten-daily flows into the Hirakud reservoir on River Mahanadi in the state of Orissa in India. Correlation analysis is carried out to find out the most influential variables on the ten daily flow at Hirakud. Based on this analysis, four variables, namely, flow during the previous time period, ql1, rainfall during the previous two time periods, rl1 and rl2, and flow during the same period in previous year, qpy, are identified as the most influential variables to forecast the ten daily flow. Performance measures such as Root Mean Square Error (RMSE), Correlation Coefficient (CORR) and coefficient of efficiency R2 are computed for training and testing phases of the model to evaluate its performance. The results indicate that the ten-daily forecasting model is efficient in predicting the high and medium flows with reasonable accuracy. The forecast of low flows is associated with less efficiency. REFERENCES Jang, J.S.R. (1993). "ANFIS: Adaptive - network- based fuzzy inference system." IEEE Trans. on Systems, Man and Cybernetics, 23 (3), 665-685. Shamseldin, A.Y. (1997). "Application of a neural network technique to rainfall-runoff modeling." Journal of Hydrology, 199, 272-294. World Meteorological Organization (1975). Intercomparison of conceptual models used in operational hydrological forecasting. World Meteorological Organization, Technical Report No.429, Geneva, Switzerland.

Application of bayesian networks to real-time flood risk estimation

NASA Astrophysics Data System (ADS)

Garrote, L.; Molina, M.; Blasco, G.

2003-04-01

This paper presents the application of a computational paradigm taken from the field of artificial intelligence - the bayesian network - to model the behaviour of hydrologic basins during floods. The final goal of this research is to develop representation techniques for hydrologic simulation models in order to define, develop and validate a mechanism, supported by a software environment, oriented to build decision models for the prediction and management of river floods in real time. The emphasis is placed on providing decision makers with tools to incorporate their knowledge of basin behaviour, usually formulated in terms of rainfall-runoff models, in the process of real-time decision making during floods. A rainfall-runoff model is only a step in the process of decision making. If a reliable rainfall forecast is available and the rainfall-runoff model is well calibrated, decisions can be based mainly on model results. However, in most practical situations, uncertainties in rainfall forecasts or model performance have to be incorporated in the decision process. The computation paradigm adopted for the simulation of hydrologic processes is the bayesian network. A bayesian network is a directed acyclic graph that represents causal influences between linked variables. Under this representation, uncertain qualitative variables are related through causal relations quantified with conditional probabilities. The solution algorithm allows the computation of the expected probability distribution of unknown variables conditioned to the observations. An approach to represent hydrologic processes by bayesian networks with temporal and spatial extensions is presented in this paper, together with a methodology for the development of bayesian models using results produced by deterministic hydrologic simulation models

Valuing hydrological forecasts for a pumped storage assisted hydro facility

NASA Astrophysics Data System (ADS)

Zhao, Guangzhi; Davison, Matt

2009-07-01

SummaryThis paper estimates the value of a perfectly accurate short-term hydrological forecast to the operator of a hydro electricity generating facility which can sell its power at time varying but predictable prices. The expected value of a less accurate forecast will be smaller. We assume a simple random model for water inflows and that the costs of operating the facility, including water charges, will be the same whether or not its operator has inflow forecasts. Thus, the improvement in value from better hydrological prediction results from the increased ability of the forecast using facility to sell its power at high prices. The value of the forecast is therefore the difference between the sales of a facility operated over some time horizon with a perfect forecast, and the sales of a similar facility operated over the same time horizon with similar water inflows which, though governed by the same random model, cannot be forecast. This paper shows that the value of the forecast is an increasing function of the inflow process variance and quantifies how much the value of this perfect forecast increases with the variance of the water inflow process. Because the lifetime of hydroelectric facilities is long, the small increase observed here can lead to an increase in the profitability of hydropower investments.

Comparison of Conventional and ANN Models for River Flow Forecasting

NASA Astrophysics Data System (ADS)

Jain, A.; Ganti, R.

2011-12-01

Hydrological models are useful in many water resources applications such as flood control, irrigation and drainage, hydro power generation, water supply, erosion and sediment control, etc. Estimates of runoff are needed in many water resources planning, design development, operation and maintenance activities. River flow is generally estimated using time series or rainfall-runoff models. Recently, soft artificial intelligence tools such as Artificial Neural Networks (ANNs) have become popular for research purposes but have not been extensively adopted in operational hydrological forecasts. There is a strong need to develop ANN models based on real catchment data and compare them with the conventional models. In this paper, a comparative study has been carried out for river flow forecasting using the conventional and ANN models. Among the conventional models, multiple linear, and non linear regression, and time series models of auto regressive (AR) type have been developed. Feed forward neural network model structure trained using the back propagation algorithm, a gradient search method, was adopted. The daily river flow data derived from Godavari Basin @ Polavaram, Andhra Pradesh, India have been employed to develop all the models included here. Two inputs, flows at two past time steps, (Q(t-1) and Q(t-2)) were selected using partial auto correlation analysis for forecasting flow at time t, Q(t). A wide range of error statistics have been used to evaluate the performance of all the models developed in this study. It has been found that the regression and AR models performed comparably, and the ANN model performed the best amongst all the models investigated in this study. It is concluded that ANN model should be adopted in real catchments for hydrological modeling and forecasting.

Integrating Fluvial and Oceanic Drivers in Operational Flooding Forecasts for San Francisco Bay

NASA Astrophysics Data System (ADS)

Herdman, Liv; Erikson, Li; Barnard, Patrick; Kim, Jungho; Cifelli, Rob; Johnson, Lynn

2016-04-01

The nine counties that make up the San Francisco Bay area are home to 7.5 million people and these communties are susceptible to flooding along the bay shoreline and inland creeks that drain to the bay. A forecast model that integrates fluvial and oceanic drivers is necessary for predicting flooding in this complex urban environment. The U.S. Geological Survey ( USGS) and National Weather Service (NWS) are developing a state-of-the-art flooding forecast model for the San Francisco Bay area that will predict watershed and ocean-based flooding up to 72 hours in advance of an approaching storm. The model framework for flood forecasts is based on the USGS-developed Coastal Storm Modeling System (CoSMoS) that was applied to San Francisco Bay under the Our Coast Our Future project. For this application, we utilize Delft3D-FM, a hydrodynamic model based on a flexible mesh grid, to calculate water levels that account for tidal forcing, seasonal water level anomalies, surge and in-Bay generated wind waves from the wind and pressure fields of a NWS forecast model, and tributary discharges from the Research Distributed Hydrologic Model (RDHM), developed by the NWS Office of Hydrologic Development. The flooding extent is determined by overlaying the resulting water levels onto a recently completed 2-m digital elevation model of the study area which best resolves the extensive levee and tidal marsh systems in the region. Here we present initial pilot results of hindcast winter storms in January 2010 and December 2012, where the flooding is driven by oceanic and fluvial factors respectively. We also demonstrate the feasibility of predicting flooding on an operational time scale that incorporates both atmospheric and hydrologic forcings.

Implementation of remote sensing data for flood forecasting

NASA Astrophysics Data System (ADS)

Grimaldi, S.; Li, Y.; Pauwels, V. R. N.; Walker, J. P.; Wright, A. J.

2016-12-01

Flooding is one of the most frequent and destructive natural disasters. A timely, accurate and reliable flood forecast can provide vital information for flood preparedness, warning delivery, and emergency response. An operational flood forecasting system typically consists of a hydrologic model, which simulates runoff generation and concentration, and a hydraulic model, which models riverine flood wave routing and floodplain inundation. However, these two types of models suffer from various sources of uncertainties, e.g., forcing data initial conditions, model structure and parameters. To reduce those uncertainties, current forecasting systems are typically calibrated and/or updated using streamflow measurements, and such applications are limited in well-gauged areas. The recent increasing availability of spatially distributed Remote Sensing (RS) data offers new opportunities for flood events investigation and forecast. Based on an Australian case study, this presentation will discuss the use 1) of RS soil moisture data to constrain a hydrologic model, and 2) of RS-derived flood extent and level to constrain a hydraulic model. The hydrological model is based on a semi-distributed system coupled with a two-soil-layer rainfall-runoff model GRKAL and a linear Muskingum routing model. Model calibration was performed using either 1) streamflow data only or 2) both streamflow and RS soil moisture data. The model was then further constrained through the integration of real-time soil moisture data. The hydraulic model is based on LISFLOOD-FP which solves the 2D inertial approximation of the Shallow Water Equations. Streamflow data and RS-derived flood extent and levels were used to apply a multi-objective calibration protocol. The effectiveness with which each data source or combination of data sources constrained the parameter space was quantified and discussed.

Operational hydrological forecasting in Bavaria. Part I: Forecast uncertainty

NASA Astrophysics Data System (ADS)

Ehret, U.; Vogelbacher, A.; Moritz, K.; Laurent, S.; Meyer, I.; Haag, I.

2009-04-01

In Bavaria, operational flood forecasting has been established since the disastrous flood of 1999. Nowadays, forecasts based on rainfall information from about 700 raingauges and 600 rivergauges are calculated and issued for nearly 100 rivergauges. With the added experience of the 2002 and 2005 floods, awareness grew that the standard deterministic forecast, neglecting the uncertainty associated with each forecast is misleading, creating a false feeling of unambiguousness. As a consequence, a system to identify, quantify and communicate the sources and magnitude of forecast uncertainty has been developed, which will be presented in part I of this study. In this system, the use of ensemble meteorological forecasts plays a key role which will be presented in part II. Developing the system, several constraints stemming from the range of hydrological regimes and operational requirements had to be met: Firstly, operational time constraints obviate the variation of all components of the modeling chain as would be done in a full Monte Carlo simulation. Therefore, an approach was chosen where only the most relevant sources of uncertainty were dynamically considered while the others were jointly accounted for by static error distributions from offline analysis. Secondly, the dominant sources of uncertainty vary over the wide range of forecasted catchments: In alpine headwater catchments, typically of a few hundred square kilometers in size, rainfall forecast uncertainty is the key factor for forecast uncertainty, with a magnitude dynamically changing with the prevailing predictability of the atmosphere. In lowland catchments encompassing several thousands of square kilometers, forecast uncertainty in the desired range (usually up to two days) is mainly dependent on upstream gauge observation quality, routing and unpredictable human impact such as reservoir operation. The determination of forecast uncertainty comprised the following steps: a) From comparison of gauge observations and several years of archived forecasts, overall empirical error distributions termed 'overall error' were for each gauge derived for a range of relevant forecast lead times. b) The error distributions vary strongly with the hydrometeorological situation, therefore a subdivision into the hydrological cases 'low flow, 'rising flood', 'flood', flood recession' was introduced. c) For the sake of numerical compression, theoretical distributions were fitted to the empirical distributions using the method of moments. Here, the normal distribution was generally best suited. d) Further data compression was achieved by representing the distribution parameters as a function (second-order polynome) of lead time. In general, the 'overall error' obtained from the above procedure is most useful in regions where large human impact occurs and where the influence of the meteorological forecast is limited. In upstream regions however, forecast uncertainty is strongly dependent on the current predictability of the atmosphere, which is contained in the spread of an ensemble forecast. Including this dynamically in the hydrological forecast uncertainty estimation requires prior elimination of the contribution of the weather forecast to the 'overall error'. This was achieved by calculating long series of hydrometeorological forecast tests, where rainfall observations were used instead of forecasts. The resulting error distribution is termed 'model error' and can be applied on hydrological ensemble forecasts, where ensemble rainfall forecasts are used as forcing. The concept will be illustrated by examples (good and bad ones) covering a wide range of catchment sizes, hydrometeorological regimes and quality of hydrological model calibration. The methodology to combine the static and dynamic shares of uncertainty will be presented in part II of this study.

The skill of ECMWF long range Forecasting System to drive impact models for health and hydrology in Africa

NASA Astrophysics Data System (ADS)

Di Giuseppe, F.; Tompkins, A. M.; Lowe, R.; Dutra, E.; Wetterhall, F.

2012-04-01

As the quality of numerical weather prediction over the monthly to seasonal leadtimes steadily improves there is an increasing motivation to apply these fruitfully to the impacts sectors of health, water, energy and agriculture. Despite these improvements, the accuracy of fields such as temperature and precipitation that are required to drive sectoral models can still be poor. This is true globally, but particularly so in Africa, the region of focus in the present study. In the last year ECMWF has been particularly active through EU research founded projects in demonstrating the capability of its longer range forecasting system to drive impact modeling systems in this region. A first assessment on the consequences of the documented errors in ECMWF forecasting system is therefore presented here looking at two different application fields which we found particularly critical for Africa - vector-born diseases prevention and hydrological monitoring. A new malaria community model (VECTRI) has been developed at ICTP and tested for the 3 target regions participating in the QWECI project. The impacts on the mean malaria climate is assessed using the newly realized seasonal forecasting system (Sys4) with the dismissed system 3 (Sys3) which had the same model cycle of the up-to-date ECMWF re-analysis product (ERA-Interim). The predictive skill of Sys4 to be employed for malaria monitoring and forecast are also evaluated by aggregating the fields to country level. As a part of the DEWFORA projects, ECMWF is also developing a system for drought monitoring and forecasting over Africa whose main meteorological input is precipitation. Similarly to what is done for the VECTRI model, the skill of seasonal forecasts of precipitation is, in this application, translated into the capability of predicting drought while ERA-Interim is used in monitoring. On a monitoring level, the near real-time update of ERA-Interim could compensate the lack of observations in the regions. However, ERA-Interim suffers from biases and drifts that limit its application for drought monitoring purposes in some regions.

Effects of temperature on flood forecasting: analysis of an operative case study in Alpine basins

NASA Astrophysics Data System (ADS)

Ceppi, A.; Ravazzani, G.; Salandin, A.; Rabuffetti, D.; Montani, A.; Borgonovo, E.; Mancini, M.

2013-04-01

In recent years the interest in the forecast and prevention of natural hazards related to hydro-meteorological events has increased the challenge for numerical weather modelling, in particular for limited area models, to improve the quantitative precipitation forecasts (QPF) for hydrological purposes. After the encouraging results obtained in the MAP D-PHASE Project, we decided to devote further analyses to show recent improvements in the operational use of hydro-meteorological chains, and above all to better investigate the key role played by temperature during snowy precipitation. In this study we present a reanalysis simulation of one meteorological event, which occurred in November 2008 in the Piedmont Region. The attention is focused on the key role of air temperature, which is a crucial feature in determining the partitioning of precipitation in solid and liquid phase, influencing the quantitative discharge forecast (QDF) into the Alpine region. This is linked to the basin ipsographic curve and therefore by the total contributing area related to the snow line of the event. In order to assess hydrological predictions affected by meteorological forcing, a sensitivity analysis of the model output was carried out to evaluate different simulation scenarios, considering the forecast effects which can radically modify the discharge forecast. Results show how in real-time systems hydrological forecasters have to consider also the temperature uncertainty in forecasts in order to better understand the snow dynamics and its effect on runoff during a meteorological warning with a crucial snow line over the basin. The hydrological ensemble forecasts are based on the 16 members of the meteorological ensemble system COSMO-LEPS (developed by ARPA-SIMC) based on the non-hydrostatic model COSMO, while the hydrological model used to generate the runoff simulations is the rainfall-runoff distributed FEST-WB model, developed at Politecnico di Milano.

Impact of Initial Condition Errors and Precipitation Forecast Bias on Drought Simulation and Prediction in the Huaihe River Basin

NASA Astrophysics Data System (ADS)

Xu, H.; Luo, L.; Wu, Z.

2016-12-01

Drought, regarded as one of the major disasters all over the world, is not always easy to detect and forecast. Hydrological models coupled with Numerical Weather Prediction (NWP) has become a relatively effective method for drought monitoring and prediction. The accuracy of hydrological initial condition (IC) and the skill of NWP precipitation forecast can both heavily affect the quality and skill of hydrological forecast. In the study, the Variable Infiltration Capacity (VIC) model and Global Environmental Multi-scale (GEM) model were used to investigate the roles of IC and NWP forecast accuracy on hydrological predictions. A rev-ESP type experiment was conducted for a number of drought events in the Huaihe river basin. The experiment suggests that errors in ICs indeed affect the drought simulations by VIC and thus the drought monitoring. Although errors introduced in the ICs diminish gradually, the influence sometimes can last beyond 12 months. Using the soil moisture anomaly percentage index (SMAPI) as the metric to measure drought severity for the study region, we are able to quantify that time scale of influence from IC ranges. The analysis shows that the time scale is directly related to the magnitude of the introduced IC range and the average precipitation intensity. In order to explore how systematic bias correction in GEM forecasted precipitation can affect precipitation and hydrological forecast, we then both used station and gridded observations to eliminate biases of forecasted data. Meanwhile, different precipitation inputs with corrected data during drought process were conducted by VIC to investigate the changes of drought simulations, thus demonstrated short-term rolling drought prediction using a better performed corrected precipitation forecast. There is a word limit on the length of the abstract. So make sure your abstract fits the requirement. If this version is too long, try to shorten it as much as you can.

Real-time monitoring and short-term forecasting of drought in Norway

NASA Astrophysics Data System (ADS)

Kwok Wong, Wai; Hisdal, Hege

2013-04-01

Drought is considered to be one of the most costly natural disasters. Drought monitoring and forecasting are thus important for sound water management. In this study hydrological drought characteristics applicable for real-time monitoring and short-term forecasting of drought in Norway were developed. A spatially distributed hydrological model (HBV) implemented in a Web-based GIS framework provides a platform for drought analyses and visualizations. A number of national drought maps can be produced, which is a simple and effective way to communicate drought conditions to decision makers and the public. The HBV model is driven by precipitation and air temperature data. On a daily time step it calculates the water balance for 1 x 1 km2 grid cells characterized by their elevation and land use. Drought duration and areal drought coverage for runoff and subsurface storage (sum of soil moisture and groundwater) were derived. The threshold level method was used to specify drought conditions on a grid cell basis. The daily 10th percentile thresholds were derived from seven-day windows centered on that calendar day from the reference period 1981-2010 (threshold not exceeded 10% of the time). Each individual grid cell was examined to determine if it was below its respective threshold level. Daily drought-stricken areas can then be easily identified when visualized on a map. The drought duration can also be tracked and calculated by a retrospective analysis. Real-time observations from synoptic stations interpolated to a regular grid of 1 km resolution constituted the forcing data for the current situation. 9-day meteorological forecasts were used as input to the HBV model to obtain short-term hydrological drought forecasts. Downscaled precipitation and temperature fields from two different atmospheric models were applied. The first two days of the forecast period adopted the forecasts from Unified Model (UM4) while the following seven days were based on the 9-day forecasts from ECMWF. The approach has been tested and is now available on the Web for operational water management.

Practical implementation of a particle filter data assimilation approach to estimate initial hydrologic conditions and initialize medium-range streamflow forecasts

NASA Astrophysics Data System (ADS)

Clark, E.; Wood, A.; Nijssen, B.; Newman, A. J.; Mendoza, P. A.

2016-12-01

The System for Hydrometeorological Applications, Research and Prediction (SHARP), developed at the National Center for Atmospheric Research (NCAR), University of Washington, U.S. Army Corps of Engineers, and U.S. Bureau of Reclamation, is a fully automated ensemble prediction system for short-term to seasonal applications. It incorporates uncertainty in initial hydrologic conditions (IHCs) and in hydrometeorological predictions. In this implementation, IHC uncertainty is estimated by propagating an ensemble of 100 plausible temperature and precipitation time series through the Sacramento/Snow-17 model. The forcing ensemble explicitly accounts for measurement and interpolation uncertainties in the development of gridded meteorological forcing time series. The resulting ensemble of derived IHCs exhibits a broad range of possible soil moisture and snow water equivalent (SWE) states. To select the IHCs that are most consistent with the observations, we employ a particle filter (PF) that weights IHC ensemble members based on observations of streamflow and SWE. These particles are then used to initialize ensemble precipitation and temperature forecasts downscaled from the Global Ensemble Forecast System (GEFS), generating a streamflow forecast ensemble. We test this method in two basins in the Pacific Northwest that are important for water resources management: 1) the Green River upstream of Howard Hanson Dam, and 2) the South Fork Flathead River upstream of Hungry Horse Dam. The first of these is characterized by mixed snow and rain, while the second is snow-dominated. The PF-based forecasts are compared to forecasts based on a single IHC (corresponding to median streamflow) paired with the full GEFS ensemble, and 2) the full IHC ensemble, without filtering, paired with the full GEFS ensemble. In addition to assessing improvements in the spread of IHCs, we perform a hindcast experiment to evaluate the utility of PF-based data assimilation on streamflow forecasts at 1- to 7-day lead times.

Operational hydrological forecasting in Bavaria. Part II: Ensemble forecasting

NASA Astrophysics Data System (ADS)

Ehret, U.; Vogelbacher, A.; Moritz, K.; Laurent, S.; Meyer, I.; Haag, I.

2009-04-01

In part I of this study, the operational flood forecasting system in Bavaria and an approach to identify and quantify forecast uncertainty was introduced. The approach is split into the calculation of an empirical 'overall error' from archived forecasts and the calculation of an empirical 'model error' based on hydrometeorological forecast tests, where rainfall observations were used instead of forecasts. The 'model error' can especially in upstream catchments where forecast uncertainty is strongly dependent on the current predictability of the atrmosphere be superimposed on the spread of a hydrometeorological ensemble forecast. In Bavaria, two meteorological ensemble prediction systems are currently tested for operational use: the 16-member COSMO-LEPS forecast and a poor man's ensemble composed of DWD GME, DWD Cosmo-EU, NCEP GFS, Aladin-Austria, MeteoSwiss Cosmo-7. The determination of the overall forecast uncertainty is dependent on the catchment characteristics: 1. Upstream catchment with high influence of weather forecast a) A hydrological ensemble forecast is calculated using each of the meteorological forecast members as forcing. b) Corresponding to the characteristics of the meteorological ensemble forecast, each resulting forecast hydrograph can be regarded as equally likely. c) The 'model error' distribution, with parameters dependent on hydrological case and lead time, is added to each forecast timestep of each ensemble member d) For each forecast timestep, the overall (i.e. over all 'model error' distribution of each ensemble member) error distribution is calculated e) From this distribution, the uncertainty range on a desired level (here: the 10% and 90% percentile) is extracted and drawn as forecast envelope. f) As the mean or median of an ensemble forecast does not necessarily exhibit meteorologically sound temporal evolution, a single hydrological forecast termed 'lead forecast' is chosen and shown in addition to the uncertainty bounds. This can be either an intermediate forecast between the extremes of the ensemble spread or a manually selected forecast based on a meteorologists advice. 2. Downstream catchments with low influence of weather forecast In downstream catchments with strong human impact on discharge (e.g. by reservoir operation) and large influence of upstream gauge observation quality on forecast quality, the 'overall error' may in most cases be larger than the combination of the 'model error' and an ensemble spread. Therefore, the overall forecast uncertainty bounds are calculated differently: a) A hydrological ensemble forecast is calculated using each of the meteorological forecast members as forcing. Here, additionally the corresponding inflow hydrograph from all upstream catchments must be used. b) As for an upstream catchment, the uncertainty range is determined by combination of 'model error' and the ensemble member forecasts c) In addition, the 'overall error' is superimposed on the 'lead forecast'. For reasons of consistency, the lead forecast must be based on the same meteorological forecast in the downstream and all upstream catchments. d) From the resulting two uncertainty ranges (one from the ensemble forecast and 'model error', one from the 'lead forecast' and 'overall error'), the envelope is taken as the most prudent uncertainty range. In sum, the uncertainty associated with each forecast run is calculated and communicated to the public in the form of 10% and 90% percentiles. As in part I of this study, the methodology as well as the useful- or uselessness of the resulting uncertainty ranges will be presented and discussed by typical examples.

On the Dominant Factor Controlling Seasonal Hydrological Forecast Skill in China

DOE PAGES

Zhang, Xuejun; Tang, Qiuhong; Leng, Guoyong; ...

2017-11-20

Initial conditions (ICs) and climate forecasts (CFs) are the two primary sources of seasonal hydrological forecast skill. However, their relative contribution to predictive skill remains unclear in China. In this study, we investigate the relative roles of ICs and CFs in cumulative runoff (CR) and soil moisture (SM) forecasts using 31-year (1980–2010) ensemble streamflow prediction (ESP) and reverse-ESP (revESP) simulations with the Variable Capacity Infiltration (VIC) hydrologic model. The results show that the relative importance of ICs and CFs largely depends on climate regimes. The influence of ICs is stronger in a dry or wet-to-dry climate regime that covers themore » northern and western interior regions during the late fall to early summer. In particular, ICs may dominate the forecast skill for up to three months or even six months during late fall and winter months, probably due to the low precipitation value and variability in the dry period. In contrast, CFs become more important for most of southern China or during summer months. The impact of ICs on SM forecasts tends to cover larger domains than on CR forecasts. These findings will greatly benefit future work that will target efforts towards improving current forecast levels for the particular regions and forecast periods.« less

On the Dominant Factor Controlling Seasonal Hydrological Forecast Skill in China

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, Xuejun; Tang, Qiuhong; Leng, Guoyong

Initial conditions (ICs) and climate forecasts (CFs) are the two primary sources of seasonal hydrological forecast skill. However, their relative contribution to predictive skill remains unclear in China. In this study, we investigate the relative roles of ICs and CFs in cumulative runoff (CR) and soil moisture (SM) forecasts using 31-year (1980–2010) ensemble streamflow prediction (ESP) and reverse-ESP (revESP) simulations with the Variable Capacity Infiltration (VIC) hydrologic model. The results show that the relative importance of ICs and CFs largely depends on climate regimes. The influence of ICs is stronger in a dry or wet-to-dry climate regime that covers themore » northern and western interior regions during the late fall to early summer. In particular, ICs may dominate the forecast skill for up to three months or even six months during late fall and winter months, probably due to the low precipitation value and variability in the dry period. In contrast, CFs become more important for most of southern China or during summer months. The impact of ICs on SM forecasts tends to cover larger domains than on CR forecasts. These findings will greatly benefit future work that will target efforts towards improving current forecast levels for the particular regions and forecast periods.« less

Global system for hydrological monitoring and forecasting in real time at high resolution

NASA Astrophysics Data System (ADS)

Ortiz, Enrique; De Michele, Carlo; Todini, Ezio; Cifres, Enrique

2016-04-01

This project presented at the EGU 2016 born of solidarity and the need to dignify the most disadvantaged people living in the poorest countries (Africa, South America and Asia, which are continually exposed to changes in the hydrologic cycle suffering events of large floods and/or long periods of droughts. It is also a special year this 2016, Year of Mercy, in which we must engage with the most disadvantaged of our Planet (Gaia) making available to them what we do professionally and scientifically. The project called "Global system for hydrological monitoring and forecasting in real time at high resolution" is Non-Profit and aims to provide at global high resolution (1km2) hydrological monitoring and forecasting in real time and continuously coupling Weather Forecast of Global Circulation Models, such us GFS-0.25° (Deterministic and Ensembles Run) forcing a physically based distributed hydrological model computationally efficient, such as the latest version extended of TOPKAPI model, named TOPKAPI-eXtended. Finally using the MCP approach for the proper use of ensembles for Predictive Uncertainty assessment essentially based on a multiple regression in the Normal space, can be easily extended to use ensembles to represent the local (in time) smaller or larger conditional predictive uncertainty, as a function of the ensemble spread. In this way, each prediction in time accounts for both the predictive uncertainty of the ensemble mean and that of the ensemble spread. To perform a continuous hydrological modeling with TOPKAPI-X model and have hot start of hydrological status of watersheds, the system assimilated products of rainfall and temperature derived from remote sensing, such as product 3B42RT of TRMM NASA and others.The system will be integrated into a Decision Support System (DSS) platform, based on geographical data. The DSS is a web application (For Pc, Tablet/Mobile phone): It does not need installation (all you need is a web browser and an internet connection) and not need update (all upgrade are deployed on the remote server)and DSS is a classical client-server application. The client side will be an HTML 5-CSS 3 application, it runs in one of the most common browser. The server side consist in: A web server (Apache web server); a map server (Geoserver); a Geographical q3456Relational Database Management Sytem (Postgresql+Postgis); Tools based on GDAL Lybraries. A customized web page will be implemented to publish all hydrometeorological information and forecast runs (free) for all users in the world. In this first presentation of the project are invited to attend all those scientific / technical people, Universities, Research Centers (public or private) who want to collaborate in it, opening a brainstorming to improve the System. References: • Liu Z. and Todini E., (2002). Towards a comprehensive physically based rainfall-runoff model. Hydrology and Earth System Sciences (HESS), 6(5):859-881, 2002. • Thielen, J., Bartholmes, J., Ramos, M.-H., and de Roo, A., (2009): The European Flood Alert System - Part 1: Concept and development, Hydrol. Earth Syst. Sci., 13, 125-140, 2009. • Coccia C., Mazzetti C., Ortiz E., Todini E., (2010) - A different soil conceptualization for the TOPKAPI model application within the DMIP 2. American Geophysical Union. Fall Meeting, San Francisco H21H-07, 2010. • Pappenberger, F., Cloke, H. L., Balsamo, G., Ngo-Duc, T., and Oki,T., (2010) Global runoff routing with the hydrological component of the ECMWF NWP system, Int. J. Climatol., 30, 2155-2174, 2010. • Coccia, G. and Todini, E., (2011). Recent developments in predictive uncertainty assessment based on the Model Conditional Processor approach. Hydrology and Earth System Sciences, 15, 3253-3274, 2011. • Wu, H., Adler, R. F., Hong, Y., Tian, Y., and Policelli, F.,(2012): Evaluation of Global Flood Detection Using Satellite-Based Rainfall and a Hydrologic Model, J. Hydrometeorol., 13, 1268-1284, 2012. • Simth M. et al., (2013). The Distributed Model Intercomparison Project - Phase 2: Experiment Design and Summary Results of the Western Basin Experiments, Journal of Hydrology 507, 300-329, 2013. • Pontificiae Academiae Scientiarvm (2014). Proceedings of the Joint Workshop on 2-6 May 2014: Sustainable Humanity Sustainable Nature Our Responsibility. Pontificiae Academiae Scientiarvm Extra Series 41. Vatican City. 2014 • Encyclical letter CARITAS IN VERITATE of the supreme pontiff Benedict XVI to the bishops, priests and deacons, men and women religious the lay faithful and all people of good will on integral human development in charity and truth. Vatican City . 2009. • Encyclical letter LAUDATO SI' of the holy father Francis on care for our common home. Vatican City. 2015

Client-Friendly Forecasting: Seasonal Runoff Predictions Using Out-of-the-Box Indices

NASA Astrophysics Data System (ADS)

Weil, P.

2013-12-01

For more than a century, statistical relationships have been recognized between atmospheric conditions at locations separated by thousands of miles, referred to as teleconnections. Some of the recognized teleconnections provide useful information about expected hydrologic conditions, so certain records of atmospheric conditions are quantified and published as hydroclimate indices. Certain hydroclimate indices can serve as strong leading indicators of climate patterns over North America and can be used to make skillful forecasts of seasonal runoff. The methodology described here creates a simple-to-use model that utilizes easily accessed data to make forecasts of April through September runoff months before the runoff season begins. For this project, forecasting models were developed for two snowmelt-driven river systems in Colorado and Wyoming. In addition to the global hydroclimate indices, the methodology uses several local hydrologic variables including the previous year's drought severity, headwater snow water equivalent and the reservoir contents for the major reservoirs in each basin. To improve the skill of the forecasts, logistic regression is used to develop a model that provides the likelihood that a year will fall into the upper, middle or lower tercile of historical flows. Categorical forecasting has two major advantages over modeling of specific flow amounts: (1) with less prediction outcomes models tend to have better predictive skill and (2) categorical models are very useful to clients and agencies with specific flow thresholds that dictate major changes in water resources management. The resulting methodology and functional forecasting model product is highly portable, applicable to many major river systems and easily explained to a non-technical audience.

Operational applications of a process-based runoff generation module on the Swiss Plateau and Prealps

NASA Astrophysics Data System (ADS)

Horat, Christoph; Antonetti, Manuel; Wernli, Heini; Zappa, Massimiliano

2017-04-01

Flash floods evolve rapidly during and after heavy precipitation events and represent a risk for society, especially in mountainous areas. Knowledge on meteorological variables and their temporal development is often not sufficient to predict their occurrence. Therefore, information about the state of the hydrological system derived from hydrological models is used. These models rely however on strong simplifying assumptions and need therefore to be calibrated. This prevents their application on catchments, where no runoff data is available. Here we present a flash-flood forecasting chain including: (i) a nowcasting product which combines radar and rain gauge rainfall data (CombiPrecip), (ii) meteorological data from numerical weather prediction models at currently finest available resolution (COSMO-1, COSMO-E), (iii) operationally available soil moisture estimations from the PREVAH hydrological model, and (iv) a process-based runoff generation module with no need for calibration (RGM-PRO). This last component uses information on the spatial distribution of dominant runoff processes (DRPs) which can be derived with different mapping approaches, and is parameterised a priori based on expert knowledge. First, we compared the performance of RGM-PRO with the one of a traditional conceptual runoff generation module for several events on Swiss Emme catchment, as well as on their nested catchments. Different DRP-maps are furthermore tested to evaluate the sensitivity of the forecasting chain to the mapping approaches. Then, we benchmarked the new forecasting chain with the traditional chain used on the Swiss Verzasca catchment. The results show that RGM-PRO performs similarly or even better than the traditional calibrated conceptual module on the investigated catchments. The use of strongly simplified DRP mapping approaches still leads to satisfying results, due mainly to the fact that the largest uncertainty source is represented by the meteorological input data. On the Verzasca catchment, RGM-PRO outperformed the traditional forecast chain in terms of mean absolute error, independently from the lead time and threshold quantile, whereas the Brier Skill Score did not show any clear preference. Probabilistic input data led generally to better results compared with those obtained with deterministic forecasts.

Operational hydrological forecasting during the 2 IPHEx-IOP campaign – meet the challenge

USDA-ARS?s Scientific Manuscript database

An operational streamflow forecasting testbed was implemented during the Intense Observing Period (IOP) of the Integrated Precipitation and Hydrology Experiment (IPHEx-IOP) in May-June 2014 to characterize flood predictability skill in complex terrain and to investigate the propagation of uncertaint...

Improving the effectiveness of real-time flood forecasting through Predictive Uncertainty estimation: the multi-temporal approach

NASA Astrophysics Data System (ADS)

Barbetta, Silvia; Coccia, Gabriele; Moramarco, Tommaso; Todini, Ezio

2015-04-01

The negative effects of severe flood events are usually contrasted through structural measures that, however, do not fully eliminate flood risk. Non-structural measures, such as real-time flood forecasting and warning, are also required. Accurate stage/discharge future predictions with appropriate forecast lead-time are sought by decision-makers for implementing strategies to mitigate the adverse effects of floods. Traditionally, flood forecasting has been approached by using rainfall-runoff and/or flood routing modelling. Indeed, both types of forecasts, cannot be considered perfectly representing future outcomes because of lacking of a complete knowledge of involved processes (Todini, 2004). Nonetheless, although aware that model forecasts are not perfectly representing future outcomes, decision makers are de facto implicitly assuming the forecast of water level/discharge/volume, etc. as "deterministic" and coinciding with what is going to occur. Recently the concept of Predictive Uncertainty (PU) was introduced in hydrology (Krzysztofowicz, 1999), and several uncertainty processors were developed (Todini, 2008). PU is defined as the probability of occurrence of the future realization of a predictand (water level/discharge/volume) conditional on: i) prior observations and knowledge, ii) the available information obtained on the future value, typically provided by one or more forecast models. Unfortunately, PU has been frequently interpreted as a measure of lack of accuracy rather than the appropriate tool allowing to take the most appropriate decisions, given a model or several models' forecasts. With the aim to shed light on the benefits for appropriately using PU, a multi-temporal approach based on the MCP approach (Todini, 2008; Coccia and Todini, 2011) is here applied to stage forecasts at sites along the Upper Tiber River. Specifically, the STAge Forecasting-Rating Curve Model Muskingum-based (STAFOM-RCM) (Barbetta et al., 2014) along with the Rating-Curve Model in Real Time (RCM-RT) (Barbetta and Moramarco, 2014) are used to this end. Both models without considering rainfall information explicitly considers, at each time of forecast, the estimate of lateral contribution along the river reach for which the stage forecast is performed at downstream end. The analysis is performed for several reaches using different lead times according to the channel length. Barbetta, S., Moramarco, T., Brocca, L., Franchini, M. and Melone, F. 2014. Confidence interval of real-time forecast stages provided by the STAFOM-RCM model: the case study of the Tiber River (Italy). Hydrological Processes, 28(3),729-743. Barbetta, S. and Moramarco, T. 2014. Real-time flood forecasting by relating local stage and remote discharge. Hydrological Sciences Journal, 59(9 ), 1656-1674. Coccia, G. and Todini, E. 2011. Recent developments in predictive uncertainty assessment based on the Model Conditional Processor approach. Hydrology and Earth System Sciences, 15, 3253-3274. doi:10.5194/hess-15-3253-2011. Krzysztofowicz, R. 1999. Bayesian theory of probabilistic forecasting via deterministic hydrologic model, Water Resour. Res., 35, 2739-2750. Todini, E. 2004. Role and treatment of uncertainty in real-time flood forecasting. Hydrological Processes 18(14), 2743_2746. Todini, E. 2008. A model conditional processor to assess predictive uncertainty in flood forecasting. Intl. J. River Basin Management, 6(2): 123-137.

Impacts of calibration strategies and ensemble methods on ensemble flood forecasting over Lanjiang basin, Southeast China

NASA Astrophysics Data System (ADS)

Liu, Li; Xu, Yue-Ping

2017-04-01

Ensemble flood forecasting driven by numerical weather prediction products is becoming more commonly used in operational flood forecasting applications.In this study, a hydrological ensemble flood forecasting system based on Variable Infiltration Capacity (VIC) model and quantitative precipitation forecasts from TIGGE dataset is constructed for Lanjiang Basin, Southeast China. The impacts of calibration strategies and ensemble methods on the performance of the system are then evaluated.The hydrological model is optimized by parallel programmed ɛ-NSGAII multi-objective algorithm and two respectively parameterized models are determined to simulate daily flows and peak flows coupled with a modular approach.The results indicatethat the ɛ-NSGAII algorithm permits more efficient optimization and rational determination on parameter setting.It is demonstrated that the multimodel ensemble streamflow mean have better skills than the best singlemodel ensemble mean (ECMWF) and the multimodel ensembles weighted on members and skill scores outperform other multimodel ensembles. For typical flood event, it is proved that the flood can be predicted 3-4 days in advance, but the flows in rising limb can be captured with only 1-2 days ahead due to the flash feature. With respect to peak flows selected by Peaks Over Threshold approach, the ensemble means from either singlemodel or multimodels are generally underestimated as the extreme values are smoothed out by ensemble process.

Flood Warning and Forecasting System in Slovakia

NASA Astrophysics Data System (ADS)

Leskova, Danica

2016-04-01

In 2015, it finished project Flood Warning and Forecasting System (POVAPSYS) as part of the flood protection in Slovakia till 2010. The aim was to build POVAPSYS integrated computerized flood forecasting and warning system. It took a qualitatively higher level of output meteorological and hydrological services in case of floods affecting large territorial units, as well as local flood events. It is further unfolding demands on performance and coordination of meteorological and hydrological services, troubleshooting observation, evaluation of data, fast communication, modeling and forecasting of meteorological and hydrological processes. Integration of all information entering and exiting to and from the project POVAPSYS provides Hydrological Flood Forecasting System (HYPOS). The system provides information on the current hydrometeorological situation and its evolution with the generation of alerts and notifications in case of exceeding predefined thresholds. HYPOS's functioning of the system requires flawless operability in critical situations while minimizing the loss of its key parts. HYPOS is a core part of the project POVAPSYS, it is a comprehensive software solutions based on a modular principle, providing data and processed information including alarms, in real time. In order to achieve full functionality of the system, in proposal, we have put emphasis on reliability, robustness, availability and security.

Effect of initial conditions of a catchment on seasonal streamflow prediction using ensemble streamflow prediction (ESP) technique for the Rangitata and Waitaki River basins on the South Island of New Zealand

NASA Astrophysics Data System (ADS)

Singh, Shailesh Kumar; Zammit, Christian; Hreinsson, Einar; Woods, Ross; Clark, Martyn; Hamlet, Alan

2013-04-01

Increased access to water is a key pillar of the New Zealand government plan for economic growths. Variable climatic conditions coupled with market drivers and increased demand on water resource result in critical decision made by water managers based on climate and streamflow forecast. Because many of these decisions have serious economic implications, accurate forecast of climate and streamflow are of paramount importance (eg irrigated agriculture and electricity generation). New Zealand currently does not have a centralized, comprehensive, and state-of-the-art system in place for providing operational seasonal to interannual streamflow forecasts to guide water resources management decisions. As a pilot effort, we implement and evaluate an experimental ensemble streamflow forecasting system for the Waitaki and Rangitata River basins on New Zealand's South Island using a hydrologic simulation model (TopNet) and the familiar ensemble streamflow prediction (ESP) paradigm for estimating forecast uncertainty. To provide a comprehensive database for evaluation of the forecasting system, first a set of retrospective model states simulated by the hydrologic model on the first day of each month were archived from 1972-2009. Then, using the hydrologic simulation model, each of these historical model states was paired with the retrospective temperature and precipitation time series from each historical water year to create a database of retrospective hindcasts. Using the resulting database, the relative importance of initial state variables (such as soil moisture and snowpack) as fundamental drivers of uncertainties in forecasts were evaluated for different seasons and lead times. The analysis indicate that the sensitivity of flow forecast to initial condition uncertainty is depend on the hydrological regime and season of forecast. However initial conditions do not have a large impact on seasonal flow uncertainties for snow dominated catchments. Further analysis indicates that this result is valid when the hindcast database is conditioned by ENSO classification. As a result hydrological forecasts based on ESP technique, where present initial conditions with histological forcing data are used may be plausible for New Zealand catchments.

An operational hydrological ensemble prediction system for the city of Zurich (Switzerland): skill, case studies and scenarios

NASA Astrophysics Data System (ADS)

Addor, N.; Jaun, S.; Zappa, M.

2011-01-01

The Sihl River flows through Zurich, Switzerland's most populated city, for which it represents the largest flood threat. To anticipate extreme discharge events and provide decision support in case of flood risk, a hydrometeorological ensemble prediction system (HEPS) was launched operationally in 2008. This models chain relies on limited-area atmospheric forecasts provided by the deterministic model COSMO-7 and the probabilistic model COSMO-LEPS. These atmospheric forecasts are used to force a semi-distributed hydrological model (PREVAH), coupled to a hydraulic model (FLORIS). The resulting hydrological forecasts are eventually communicated to the stakeholders involved in the Sihl discharge management. This fully operational setting provides a real framework to compare the potential of deterministic and probabilistic discharge forecasts for flood mitigation. To study the suitability of HEPS for small-scale basins and to quantify the added-value conveyed by the probability information, a reforecast was made for the period June 2007 to December 2009 for the Sihl catchment (336 km2). Several metrics support the conclusion that the performance gain can be of up to 2 days lead time for the catchment considered. Brier skill scores show that COSMO-LEPS-based hydrological forecasts overall outperform their COSMO-7 based counterparts for all the lead times and event intensities considered. The small size of the Sihl catchment does not prevent skillful discharge forecasts, but makes them particularly dependent on correct precipitation forecasts, as shown by comparisons with a reference run driven by observed meteorological parameters. Our evaluation stresses that the capacity of the model to provide confident and reliable mid-term probability forecasts for high discharges is limited. The two most intense events of the study period are investigated utilising a novel graphical representation of probability forecasts and used to generate high discharge scenarios. They highlight challenges for making decisions on the basis of hydrological predictions, and indicate the need for a tool to be used in addition to forecasts to compare the different mitigation actions possible in the Sihl catchment.

Applications systems verification and transfer project. Volume 4: Operational applications of satellite snow cover observations. Colorado Field Test Center

NASA Technical Reports Server (NTRS)

Shafer, B. A.; Leaf, C. F.; Danielson, J. A.; Moravec, G. F.

1981-01-01

The study was conducted on six watersheds ranging in size from 277 km to 3460 km in the Rio Grande and Arkansas River basins of southwestern Colorado. Six years of satellite data in the period 1973-78 were analyzed and snowcover maps prepared for all available image dates. Seven snowmapping techniques were explored; the photointerpretative method was selected as the most accurate. Three schemes to forecast snowmelt runoff employing satellite snowcover observations were investigated. They included a conceptual hydrologic model, a statistical model, and a graphical method. A reduction of 10% in the current average forecast error is estimated when snowcover data in snowmelt runoff forecasting is shown to be extremely promising. Inability to obtain repetitive coverage due to the 18 day cycle of LANDSAT, the occurrence of cloud cover and slow image delivery are obstacles to the immediate implementation of satellite derived snowcover in operational streamflow forecasting programs.

Toward Seasonal Forecasting of Global Droughts: Evaluation over USA and Africa

NASA Astrophysics Data System (ADS)

Wood, Eric; Yuan, Xing; Roundy, Joshua; Sheffield, Justin; Pan, Ming

2013-04-01

Extreme hydrologic events in the form of droughts are significant sources of social and economic damage. In the United States according to the National Climatic Data Center, the losses from drought exceed US210 billion during 1980-2011, and account for about 24% of all losses from major weather disasters. Internationally, especially for the developing world, drought has had devastating impacts on local populations through food insecurity and famine. Providing reliable drought forecasts with sufficient early warning will help the governments to move from the management of drought crises to the management of drought risk. After working on drought monitoring and forecasting over the USA for over 10 years, the Princeton land surface hydrology group is now developing a global drought monitoring and forecasting system using a dynamical seasonal climate-hydrologic LSM-model (CHM) approach. Currently there is an active debate on the merits of the CHM-based seasonal hydrologic forecasts as compared to Ensemble Streamflow Prediction (ESP). We use NCEP's operational forecast system, the Climate Forecast System version 2 (CFSv2) and its previous version CFSv1, to investigate the value of seasonal climate model forecasts by conducting a set of 27-year seasonal hydrologic hindcasts over the USA. Through Bayesian downscaling, climate models have higher squared correlation (R2) and smaller error than ESP for monthly precipitation averaged over major river basins across the USA, and the forecasts conditional on ENSO show further improvements (out to four months) over river basins in the southern USA. All three approaches have plausible predictions of soil moisture drought frequency over central USA out to six months because of strong soil moisture memory, and seasonal climate models provide better results over central and eastern USA. The R2 of drought extent is higher for arid basins and for the forecasts initiated during dry seasons, but significant improvements from CFSv2 occur in different seasons for different basins. The R2 of drought severity accumulated over USA is higher during winter, and climate models present added value especially at long leads. For countries with sparse networks and weak reporting systems, remote sensing observations can provide the realtime data for the monitoring of drought. More importantly, these datasets are now available for at least a decade, which allows for estimating a climatology against which current conditions can be compared. Based on our established experimental African Drought Monitor (ADM) (see http://hydrology.princeton.edu/~nchaney/ADM_ML), we use the downscaled CFSv2 climate forcings to drive the re-calibrated VIC model and produce 6-month, 20-member ensemble hydrologic forecasts over Africa starting on the 1st of each calendar month during 1982-2007. Our CHM-based seasonal hydrologic forecasts are now being analyzed for its skill in predicting short-term soil moisture droughts over Africa. Besides relying on a single seasonal climate model or a single drought index, preliminary forecast results will be presented using multiple seasonal climate models based on the NOAA-supported National Multi-Model Ensemble (NMME) project, and with multiple drought indices. Results will be presented for the USA NIDIS test beds such as Southeast US and Colorado NIDIS (National Integrated Drought Information System) test beds, and potentially for other regions of the globe.

Obtaining high-resolution stage forecasts by coupling large-scale hydrologic models with sensor data

NASA Astrophysics Data System (ADS)

Fries, K. J.; Kerkez, B.

2017-12-01

We investigate how "big" quantities of distributed sensor data can be coupled with a large-scale hydrologic model, in particular the National Water Model (NWM), to obtain hyper-resolution forecasts. The recent launch of the NWM provides a great example of how growing computational capacity is enabling a new generation of massive hydrologic models. While the NWM spans an unprecedented spatial extent, there remain many questions about how to improve forecast at the street-level, the resolution at which many stakeholders make critical decisions. Further, the NWM runs on supercomputers, so water managers who may have access to their own high-resolution measurements may not readily be able to assimilate them into the model. To that end, we ask the question: how can the advances of the large-scale NWM be coupled with new local observations to enable hyper-resolution hydrologic forecasts? A methodology is proposed whereby the flow forecasts of the NWM are directly mapped to high-resolution stream levels using Dynamical System Identification. We apply the methodology across a sensor network of 182 gages in Iowa. Of these sites, approximately one third have shown to perform well in high-resolution flood forecasting when coupled with the outputs of the NWM. The quality of these forecasts is characterized using Principal Component Analysis and Random Forests to identify where the NWM may benefit from new sources of local observations. We also discuss how this approach can help municipalities identify where they should place low-cost sensors to most benefit from flood forecasts of the NWM.

Performance assessment of a Bayesian Forecasting System (BFS) for real-time flood forecasting

NASA Astrophysics Data System (ADS)

Biondi, D.; De Luca, D. L.

2013-02-01

SummaryThe paper evaluates, for a number of flood events, the performance of a Bayesian Forecasting System (BFS), with the aim of evaluating total uncertainty in real-time flood forecasting. The predictive uncertainty of future streamflow is estimated through the Bayesian integration of two separate processors. The former evaluates the propagation of input uncertainty on simulated river discharge, the latter computes the hydrological uncertainty of actual river discharge associated with all other possible sources of error. A stochastic model and a distributed rainfall-runoff model were assumed, respectively, for rainfall and hydrological response simulations. A case study was carried out for a small basin in the Calabria region (southern Italy). The performance assessment of the BFS was performed with adequate verification tools suited for probabilistic forecasts of continuous variables such as streamflow. Graphical tools and scalar metrics were used to evaluate several attributes of the forecast quality of the entire time-varying predictive distributions: calibration, sharpness, accuracy, and continuous ranked probability score (CRPS). Besides the overall system, which incorporates both sources of uncertainty, other hypotheses resulting from the BFS properties were examined, corresponding to (i) a perfect hydrological model; (ii) a non-informative rainfall forecast for predicting streamflow; and (iii) a perfect input forecast. The results emphasize the importance of using different diagnostic approaches to perform comprehensive analyses of predictive distributions, to arrive at a multifaceted view of the attributes of the prediction. For the case study, the selected criteria revealed the interaction of the different sources of error, in particular the crucial role of the hydrological uncertainty processor when compensating, at the cost of wider forecast intervals, for the unreliable and biased predictive distribution resulting from the Precipitation Uncertainty Processor.

A "total parameter estimation" method in the varification of distributed hydrological models

NASA Astrophysics Data System (ADS)

Wang, M.; Qin, D.; Wang, H.

2011-12-01

Conventionally hydrological models are used for runoff or flood forecasting, hence the determination of model parameters are common estimated based on discharge measurements at the catchment outlets. With the advancement in hydrological sciences and computer technology, distributed hydrological models based on the physical mechanism such as SWAT, MIKESHE, and WEP, have gradually become the mainstream models in hydrology sciences. However, the assessments of distributed hydrological models and model parameter determination still rely on runoff and occasionally, groundwater level measurements. It is essential in many countries, including China, to understand the local and regional water cycle: not only do we need to simulate the runoff generation process and for flood forecasting in wet areas, we also need to grasp the water cycle pathways and consumption process of transformation in arid and semi-arid regions for the conservation and integrated water resources management. As distributed hydrological model can simulate physical processes within a catchment, we can get a more realistic representation of the actual water cycle within the simulation model. Runoff is the combined result of various hydrological processes, using runoff for parameter estimation alone is inherits problematic and difficult to assess the accuracy. In particular, in the arid areas, such as the Haihe River Basin in China, runoff accounted for only 17% of the rainfall, and very concentrated during the rainy season from June to August each year. During other months, many of the perennial rivers within the river basin dry up. Thus using single runoff simulation does not fully utilize the distributed hydrological model in arid and semi-arid regions. This paper proposed a "total parameter estimation" method to verify the distributed hydrological models within various water cycle processes, including runoff, evapotranspiration, groundwater, and soil water; and apply it to the Haihe river basin in China. The application results demonstrate that this comprehensive testing method is very useful in the development of a distributed hydrological model and it provides a new way of thinking in hydrological sciences.

Snow cover, snowmelt and runoff in the Himalayan River basins

NASA Technical Reports Server (NTRS)

Dey, B.; Sharma, V. K.; Goswami, D. C.; Rao, P. Subba

1988-01-01

Not withstanding the seasonal vagaries of both rainfall amount and snowcover extent, the Himalayan rivers retain their basic perennial character. However, it is the component of snowmelt yield that accounts for some 60 to 70 percent of the total annual flow volumes from Hamilayan watersheds. On this large hydropotential predominantly depends the temporal performance of hydropower generation and major irrigation projects. The large scale effects of Himalayan snowcover on the hydrologic responses of a few selected catchments in western Himalayas was studied. The antecedent effects of snowcover area on long and short term meltwater yields can best be analyzed by developing appropriate hydrologic models forecasting the pattern of snowmelt as a function of variations in snowcover area. It is hoped that these models would be of practical value in the management of water resources. The predictability of meltwater for the entire snowmelt season was studied, as was the concurrent flow variation in adjacent watersheds, and their hydrologic significance. And the applicability of the Snowmelt-Runoff Model for real time forecast of daily discharges during the major part of the snowmelt season is examined.

Statistical control in hydrologic forecasting.

Treesearch

H.G. Wilm

1950-01-01

With rapidly growing development and uses of water, a correspondingly great demand has developed for advance estimates of the volumes or rates of flow which are supplied by streams. Therefore much attention is being devoted to hydrologic forecasting, and numerous methods have been tested in efforts to make increasingly reliable estimates of future supplies.

Monitoring and forecasting the 2009-2010 severe drought in Southwest China

NASA Astrophysics Data System (ADS)

Zhang, X.; Tang, Q.; Liu, X.; Leng, G.; Li, Z.; Cui, H.

2015-12-01

From the fall of 2009 to the spring of 2010, an unprecedented drought swept across southwest China (SW) and led to a severe shortage in drinking water and a huge loss to regional economy. Monitoring and predicting the severe drought with several months in advance is of critical importance for such hydrological disaster assessment, preparation and mitigation. In this study, we attempted to carry out a model-based hydrological monitoring and seasonal forecasting framework, and assessed its skill in capturing the evolution of the SW drought in 2009-2010. Using the satellite-based meteorological forcings and the Variable Infiltration Capacity (VIC) hydrologic model, the drought conditions were assessed in a near-real-time manner based on a 62-year (1952-2013) retrospective simulation, wherein the satellite data was adjusted by a gauge-based forcing to remove systematic biases. Bias-corrected seasonal forecasting outputs from the National Centers for Environmental Prediction (NCEP) Climate Forecast System Version 2 (CFSv2) was tentatively applied for a seasonal hydrologic prediction and its predictive skill was overall evaluated relative to a traditional Ensemble Streamflow Prediction (ESP) method with lead time varying from 1 to 6 months. The results show that the climate model-driven hydrologic predictability is generally limited to 1-month lead time and exhibits negligible skill improvement relative to ESP during this drought event, suggesting the initial hydrologic conditions (IHCs) play a dominant role in forecasting performance. The research highlights the value of the framework in providing accurate IHCs in a real-time manner which will greatly benefit drought early-warning.

Error discrimination of an operational hydrological forecasting system at a national scale

NASA Astrophysics Data System (ADS)

Jordan, F.; Brauchli, T.

2010-09-01

The use of operational hydrological forecasting systems is recommended for hydropower production as well as flood management. However, the forecast uncertainties can be important and lead to bad decisions such as false alarms and inappropriate reservoir management of hydropower plants. In order to improve the forecasting systems, it is important to discriminate the different sources of uncertainties. To achieve this task, reanalysis of past predictions can be realized and provide information about the structure of the global uncertainty. In order to discriminate between uncertainty due to the weather numerical model and uncertainty due to the rainfall-runoff model, simulations assuming perfect weather forecast must be realized. This contribution presents the spatial analysis of the weather uncertainties and their influence on the river discharge prediction of a few different river basins where an operational forecasting system exists. The forecast is based on the RS 3.0 system [1], [2], which is also running the open Internet platform www.swissrivers.ch [3]. The uncertainty related to the hydrological model is compared to the uncertainty related to the weather prediction. A comparison between numerous weather prediction models [4] at different lead times is also presented. The results highlight an important improving potential of both forecasting components: the hydrological rainfall-runoff model and the numerical weather prediction models. The hydrological processes must be accurately represented during the model calibration procedure, while weather prediction models suffer from a systematic spatial bias. REFERENCES [1] Garcia, J., Jordan, F., Dubois, J. & Boillat, J.-L. 2007. "Routing System II, Modélisation d'écoulements dans des systèmes hydrauliques", Communication LCH n° 32, Ed. Prof. A. Schleiss, Lausanne [2] Jordan, F. 2007. Modèle de prévision et de gestion des crues - optimisation des opérations des aménagements hydroélectriques à accumulation pour la réduction des débits de crue, thèse de doctorat n° 3711, Ecole Polytechnique Fédérale, Lausanne [3] Keller, R. 2009. "Le débit des rivières au peigne fin", Revue Technique Suisse, N°7/8 2009, Swiss engineering RTS, UTS SA, Lausanne, p. 11 [4] Kaufmann, P., Schubiger, F. & Binder, P. 2003. Precipitation forecasting by a mesoscale numerical weather prediction (NWP) model : eight years of experience, Hydrology and Earth System

Assimilating uncertain, dynamic and intermittent streamflow observations in hydrological models

NASA Astrophysics Data System (ADS)

Mazzoleni, Maurizio; Alfonso, Leonardo; Chacon-Hurtado, Juan; Solomatine, Dimitri

2015-09-01

Catastrophic floods cause significant socio-economical losses. Non-structural measures, such as real-time flood forecasting, can potentially reduce flood risk. To this end, data assimilation methods have been used to improve flood forecasts by integrating static ground observations, and in some cases also remote sensing observations, within water models. Current hydrologic and hydraulic research works consider assimilation of observations coming from traditional, static sensors. At the same time, low-cost, mobile sensors and mobile communication devices are becoming also increasingly available. The main goal and innovation of this study is to demonstrate the usefulness of assimilating uncertain streamflow observations that are dynamic in space and intermittent in time in the context of two different semi-distributed hydrological model structures. The developed method is applied to the Brue basin, where the dynamic observations are imitated by the synthetic observations of discharge. The results of this study show how model structures and sensors locations affect in different ways the assimilation of streamflow observations. In addition, it proves how assimilation of such uncertain observations from dynamic sensors can provide model improvements similar to those of streamflow observations coming from a non-optimal network of static physical sensors. This can be a potential application of recent efforts to build citizen observatories of water, which can make the citizens an active part in information capturing, evaluation and communication, helping simultaneously to improvement of model-based flood forecasting.

A probabilistic drought forecasting framework: A combined dynamical and statistical approach

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yan, Hongxiang; Moradkhani, Hamid; Zarekarizi, Mahkameh

In order to improve drought forecasting skill, this study develops a probabilistic drought forecasting framework comprised of dynamical and statistical modeling components. The novelty of this study is to seek the use of data assimilation to quantify initial condition uncertainty with the Monte Carlo ensemble members, rather than relying entirely on the hydrologic model or land surface model to generate a single deterministic initial condition, as currently implemented in the operational drought forecasting systems. Next, the initial condition uncertainty is quantified through data assimilation and coupled with a newly developed probabilistic drought forecasting model using a copula function. The initialmore » condition at each forecast start date are sampled from the data assimilation ensembles for forecast initialization. Finally, seasonal drought forecasting products are generated with the updated initial conditions. This study introduces the theory behind the proposed drought forecasting system, with an application in Columbia River Basin, Pacific Northwest, United States. Results from both synthetic and real case studies suggest that the proposed drought forecasting system significantly improves the seasonal drought forecasting skills and can facilitate the state drought preparation and declaration, at least three months before the official state drought declaration.« less

Hydrological information system based on on-line monitoring--from strategy to implementation in the Brantas River Basin, East Java, Indonesia.

PubMed

Marini, G W; Wellguni, H

2003-01-01

The worsening environmental situation of the Brantas River, East Java, is addressed by a comprehensive basin management strategy which relies on accurate water quantity and quality data retrieved from a newly installed online monitoring network. Integrated into a Hydrological Information System, the continuously measured indicative parameters allow early warning, control and polluter identification. Additionally, long-term analyses have been initiated for improving modelling applications like flood forecasting, water resource management and pollutant propagation. Preliminary results illustrate the efficiency of the installed system.

The Effect of Model Grid Resolution on the Distributed Hydrologic Simulations for Forecasting Stream Flows and Reservoir Storage

NASA Astrophysics Data System (ADS)

Turnbull, S. J.

2017-12-01

Within the US Army Corps of Engineers (USACE), reservoirs are typically operated according to a rule curve that specifies target water levels based on the time of year. The rule curve is intended to maximize flood protection by specifying releases of water before the dominant rainfall period for a region. While some operating allowances are permissible, generally the rule curve elevations must be maintained. While this operational approach provides for the required flood control purpose, it may not result in optimal reservoir operations for multi-use impoundments. In the Russian River Valley of California a multi-agency research effort called Forecast-Informed Reservoir Operations (FIRO) is assessing the application of forecast weather and streamflow predictions to potentially enhance the operation of reservoirs in the watershed. The focus of the study has been on Lake Mendocino, a USACE project important for flood control, water supply, power generation and ecological flows. As part of this effort the Engineer Research and Development Center is assessing the ability of utilizing the physics based, distributed watershed model Gridded Surface Subsurface Hydrologic Analysis (GSSHA) model to simulate stream flows, reservoir stages, and discharges while being driven by weather forecast products. A key question in this application is the effect of watershed model resolution on forecasted stream flows. To help resolve this question, GSSHA models of multiple grid resolutions, 30, 50, and 270m, were developed for the upper Russian River, which includes Lake Mendocino. The models were derived from common inputs: DEM, soils, land use, stream network, reservoir characteristics, and specified inflows and discharges. All the models were calibrated in both event and continuous simulation mode using measured precipitation gages and then driven with the West-WRF atmospheric model in prediction mode to assess the ability of the model to function in short term, less than one week, forecasting mode. In this presentation we will discuss the effect the grid resolution has model development, parameter assignment, streamflow prediction and forecasting capability utilizing the West-WRF forecast hydro-meteorology.

An operational hydrological ensemble prediction system for the city of Zurich (Switzerland): skill, case studies and scenarios

NASA Astrophysics Data System (ADS)

Addor, N.; Jaun, S.; Fundel, F.; Zappa, M.

2011-07-01

The Sihl River flows through Zurich, Switzerland's most populated city, for which it represents the largest flood threat. To anticipate extreme discharge events and provide decision support in case of flood risk, a hydrometeorological ensemble prediction system (HEPS) was launched operationally in 2008. This model chain relies on limited-area atmospheric forecasts provided by the deterministic model COSMO-7 and the probabilistic model COSMO-LEPS. These atmospheric forecasts are used to force a semi-distributed hydrological model (PREVAH), coupled to a hydraulic model (FLORIS). The resulting hydrological forecasts are eventually communicated to the stakeholders involved in the Sihl discharge management. This fully operational setting provides a real framework with which to compare the potential of deterministic and probabilistic discharge forecasts for flood mitigation. To study the suitability of HEPS for small-scale basins and to quantify the added-value conveyed by the probability information, a reforecast was made for the period June 2007 to December 2009 for the Sihl catchment (336 km2). Several metrics support the conclusion that the performance gain can be of up to 2 days lead time for the catchment considered. Brier skill scores show that overall COSMO-LEPS-based hydrological forecasts outperforms their COSMO-7-based counterparts for all the lead times and event intensities considered. The small size of the Sihl catchment does not prevent skillful discharge forecasts, but makes them particularly dependent on correct precipitation forecasts, as shown by comparisons with a reference run driven by observed meteorological parameters. Our evaluation stresses that the capacity of the model to provide confident and reliable mid-term probability forecasts for high discharges is limited. The two most intense events of the study period are investigated utilising a novel graphical representation of probability forecasts, and are used to generate high discharge scenarios. They highlight challenges for making decisions on the basis of hydrological predictions, and indicate the need for a tool to be used in addition to forecasts to compare the different mitigation actions possible in the Sihl catchment. No definitive conclusion on the model chain capacity to forecast flooding events endangering the city of Zurich could be drawn because of the under-sampling of extreme events. Further research on the form of the reforecasts needed to infer on floods associated to return periods of several decades, centuries, is encouraged.

Major Risks, Uncertain Outcomes: Making Ensemble Forecasts Work for Multiple Audiences

NASA Astrophysics Data System (ADS)

Semmens, K. A.; Montz, B.; Carr, R. H.; Maxfield, K.; Ahnert, P.; Shedd, R.; Elliott, J.

2017-12-01

When extreme river levels are possible in a community, effective communication of weather and hydrologic forecasts is critical to protect life and property. Residents, emergency personnel, and water resource managers need to make timely decisions about how and when to prepare. Uncertainty in forecasting is a critical component of this decision-making, but often poses a confounding factor for public and professional understanding of forecast products. In 2016 and 2017, building on previous research about the use of uncertainty forecast products, and with funding from NOAA's CSTAR program, East Carolina University and Nurture Nature Center (a non-profit organization with a focus on flooding issues, based in Easton, PA) conducted a research project to understand how various audiences use and interpret ensemble forecasts showing a range of hydrologic forecast possibilities. These audiences include community residents, emergency managers and water resource managers. The research team held focus groups in Jefferson County, WV and Frederick County, MD, to test a new suite of products from the National Weather Service's Hydrologic Ensemble Forecast System (HEFS). HEFS is an ensemble system that provides short and long-range forecasts, ranging from 6 hours to 1 year, showing uncertainty in hydrologic forecasts. The goal of the study was to assess the utility of the HEFS products, identify the barriers to proper understanding of the products, and suggest modifications to product design that could improve the understandability and accessibility for residential, emergency managers, and water resource managers. The research team worked with the Sterling, VA Weather Forecast Office and the Middle Atlantic River Forecast center to develop a weather scenario as the basis of the focus group discussions, which also included pre and post session surveys. This presentation shares the findings from those focus group discussions and surveys, including recommendations for revisions to HEFS products to improve accessibility of the forecast tools for various audiences. The presentation will provide a broad perspective on the range of graphic design considerations that affected how the public responded to products and will provide an overview of lessons learned about how product design can influence decision-making by users.

Assessing skill of a global bimonthly streamflow ensemble prediction system

NASA Astrophysics Data System (ADS)

van Dijk, A. I.; Peña-Arancibia, J.; Sheffield, J.; Wood, E. F.

2011-12-01

Ideally, a seasonal streamflow forecasting system might be conceived of as a system that ingests skillful climate forecasts from general circulation models and propagates these through thoroughly calibrated hydrological models that are initialised using hydrometric observations. In practice, there are practical problems with each of these aspects. Instead, we analysed whether a comparatively simple hydrological model-based Ensemble Prediction System (EPS) can provide global bimonthly streamflow forecasts with some skill and if so, under what circumstances the greatest skill may be expected. The system tested produces ensemble forecasts for each of six annual bimonthly periods based on the previous 30 years of global daily gridded 1° resolution climate variables and an initialised global hydrological model. To incorporate some of the skill derived from ocean conditions, a post-EPS analog method was used to sample from the ensemble based on El Niño Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), North Atlantic Oscillation (NAO) and Pacific Decadal Oscillation (PDO) index values observed prior to the forecast. Forecasts skill was assessed through a hind-casting experiment for the period 1979-2008. Potential skill was calculated with reference to a model run with the actual forcing for the forecast period (the 'perfect' model) and was compared to actual forecast skill calculated for each of the six forecast times for an average 411 Australian and 51 pan-tropical catchments. Significant potential skill in bimonthly forecasts was largely limited to northern regions during the snow melt period, seasonally wet tropical regions at the transition of wet to dry season, and the Indonesian region where rainfall is well correlated to ENSO. The actual skill was approximately 34-50% of the potential skill. We attribute this primarily to limitations in the model structure, parameterisation and global forcing data. Use of better climate forecasts and remote sensing observations of initial catchment conditions should help to increase actual skill in future. Future work also could address the potential skill gain from using weather and climate forecasts and from a calibrated and/or alternative hydrological model or model ensemble. The approach and data might be useful as a benchmark for joint seasonal forecasting experiments planned under GEWEX.

The Value of Humans in the Operational River Forecasting Enterprise

NASA Astrophysics Data System (ADS)

Pagano, T. C.

2012-04-01

The extent of human control over operational river forecasts, such as by adjusting model inputs and outputs, varies from nearly completely automated systems to those where forecasts are generated after discussion among a group of experts. Historical and realtime data availability, the complexity of hydrologic processes, forecast user needs, and forecasting institution support/resource availability (e.g. computing power, money for model maintenance) influence the character and effectiveness of operational forecasting systems. Automated data quality algorithms, if used at all, are typically very basic (e.g. checks for impossible values); substantial human effort is devoted to cleaning up forcing data using subjective methods. Similarly, although it is an active research topic, nearly all operational forecasting systems struggle to make quantitative use of Numerical Weather Prediction model-based precipitation forecasts, instead relying on the assessment of meteorologists. Conversely, while there is a strong tradition in meteorology of making raw model outputs available to forecast users via the Internet, this is rarely done in hydrology; Operational river forecasters express concerns about exposing users to raw guidance, due to the potential for misinterpretation and misuse. However, this limits the ability of users to build their confidence in operational products through their own value-added analyses. Forecasting agencies also struggle with provenance (i.e. documenting the production process and archiving the pieces that went into creating a forecast) although this is necessary for quantifying the benefits of human involvement in forecasting and diagnosing weak links in the forecasting chain. In hydrology, the space between model outputs and final operational products is nearly unstudied by the academic community, although some studies exist in other fields such as meteorology.

The Global Precipitation Measurement (GPM) Mission contributions to hydrology and societal applications

NASA Astrophysics Data System (ADS)

Kirschbaum, D.; Huffman, G. J.; Skofronick Jackson, G.

2016-12-01

Too much or too little rain can serve as a tipping point for triggering catastrophic flooding and landslides or widespread drought. Knowing when, where and how much rain is falling globally is vital to understanding how vulnerable areas may be more or less impacted by these disasters. The Global Precipitation Measurement (GPM) mission provides near real-time precipitation data worldwide that is used by a broad range of end users, from tropical cyclone forecasters to agricultural modelers to researchers evaluating the spread of diseases. The GPM constellation provides merged, multi-satellite data products at three latencies that are critical for research and societal applications around the world. This presentation will outline current capabilities in using accurate and timely information of precipitation to directly benefit society, including examples of end user applications within the tropical cyclone forecasting, disasters response, agricultural forecasting, and disease tracking communities, among others. The presentation will also introduce some of the new visualization and access tools developed by the GPM team.

Ensemble Streamflow Forecast Improvements in NYC's Operations Support Tool

NASA Astrophysics Data System (ADS)

Wang, L.; Weiss, W. J.; Porter, J.; Schaake, J. C.; Day, G. N.; Sheer, D. P.

2013-12-01

Like most other water supply utilities, New York City's Department of Environmental Protection (DEP) has operational challenges associated with drought and wet weather events. During drought conditions, DEP must maintain water supply reliability to 9 million customers as well as meet environmental release requirements downstream of its reservoirs. During and after wet weather events, DEP must maintain turbidity compliance in its unfiltered Catskill and Delaware reservoir systems and minimize spills to mitigate downstream flooding. Proactive reservoir management - such as release restrictions to prepare for a drought or preventative drawdown in advance of a large storm - can alleviate negative impacts associated with extreme events. It is important for water managers to understand the risks associated with proactive operations so unintended consequences such as endangering water supply reliability with excessive drawdown prior to a storm event are minimized. Probabilistic hydrologic forecasts are a critical tool in quantifying these risks and allow water managers to make more informed operational decisions. DEP has recently completed development of an Operations Support Tool (OST) that integrates ensemble streamflow forecasts, real-time observations, and a reservoir system operations model into a user-friendly graphical interface that allows its water managers to take robust and defensible proactive measures in the face of challenging system conditions. Since initial development of OST was first presented at the 2011 AGU Fall Meeting, significant improvements have been made to the forecast system. First, the monthly AR1 forecasts ('Hirsch method') were upgraded with a generalized linear model (GLM) utilizing historical daily correlations ('Extended Hirsch method' or 'eHirsch'). The development of eHirsch forecasts improved predictive skill over the Hirsch method in the first week to a month from the forecast date and produced more realistic hydrographs on the tail end of high flow periods. These improvements allowed DEP to more effectively manage water quality control and spill mitigation operations immediately after storm events. Later on, post-processed hydrologic forecasts from the National Weather Service (NWS) including the Advanced Hydrologic Prediction Service (AHPS) and the Hydrologic Ensemble Forecast Service (HEFS) were implemented into OST. These forecasts further increased the predictive skill over the initial statistical models as current basin conditions (e.g. soil moisture, snowpack) and meteorological forecasts (with HEFS) are now explicitly represented. With the post-processed HEFS forecasts, DEP may now truly quantify impacts associated with wet weather events on the horizon, rather than relying on statistical representations of current hydrologic trends. This presentation will highlight the benefits of the improved forecasts using examples from actual system operations.

Does a more skilful meteorological input lead to a more skilful flood forecast at seasonal timescales?

NASA Astrophysics Data System (ADS)

Neumann, Jessica; Arnal, Louise; Magnusson, Linus; Cloke, Hannah

2017-04-01

Seasonal river flow forecasts are important for many aspects of the water sector including flood forecasting, water supply, hydropower generation and navigation. In addition to short term predictions, seasonal forecasts have the potential to realise higher benefits through more optimal and consistent decisions. Their operational use however, remains a challenge due to uncertainties posed by the initial hydrologic conditions (e.g. soil moisture, groundwater levels) and seasonal climate forcings (mainly forecasts of precipitation and temperature), leading to a decrease in skill with increasing lead times. Here we present a stakeholder-led case study for the Thames catchment (UK), currently being undertaken as part of the H2020 IMPREX project. The winter of 2013-14 was the wettest on record in the UK; driven by 12 major Atlantic depressions, the Thames catchment was subject to compound (concurrent) flooding from fluvial and groundwater sources. Focusing on the 2013-14 floods, this study aims to see whether increased skill in meteorological input translates through to more accurate forecasting of compound flood events at seasonal timescales in the Thames catchment. An earlier analysis of the ECMWF System 4 (S4) seasonal meteorological forecasts revealed that it did not skilfully forecast the extreme event of winter 2013-14. This motivated the implementation of an atmospheric experiment by the ECMWF to force the S4 to more accurately represent the low-pressure weather conditions prevailing in winter 2013-14 [1]. Here, we used both the standard and the "improved" S4 seasonal meteorological forecasts to force the EFAS (European Flood Awareness System) LISFLOOD hydrological model. Both hydrological forecasts were started on the 1st of November 2013 and run for 4 months of lead time to capture the peak of the 2013-14 flood event. Comparing the seasonal hydrological forecasts produced with both meteorological forcing data will enable us to assess how the improved meteorology translates into skill in the hydrological forecast for this extreme compound event. As primary stakeholders involved in the study, the Environment Agency and Flood Forecasting Centre are responsible for managing flood risk in the UK. For them, the detection of a potential flood signal weeks or months in advance could be of great value in terms of operational practice, decision-making and early warning. [1] Rodwell, M.J., Ferranti, L., Magnusson, L., Weisheimer, A., Rabier, F. & Richardson, D. (2015) Diagnosis of northern hemispheric regime behaviour during winter 2013/14. ECMWF Technical Memoranda 769.

L-band microwave remote sensing and land data assimilation improve the representation of pre-storm soil moisture conditions for hydrologic forecasting

USDA-ARS?s Scientific Manuscript database

Recent advances in remote sensing and land data assimilation purport to improve the quality of antecedent soil moisture information available for operational hydrologic forecasting. We objectively validate this claim by calculating the strength of the relationship between storm-scale runoff ratio (i...

Benchmarking Ensemble Streamflow Prediction Skill in the UK

NASA Astrophysics Data System (ADS)

Harrigan, Shaun; Smith, Katie; Parry, Simon; Tanguy, Maliko; Prudhomme, Christel

2017-04-01

Skilful hydrological forecasts at weekly to seasonal lead times would be extremely beneficial for decision-making in operational water management, especially during drought conditions. Hydro-meteorological ensemble forecasting systems are an attractive approach as they use two sources of streamflow predictability: (i) initial hydrologic conditions (IHCs), where soil moisture, groundwater and snow storage states can provide an estimate of future streamflow situations, and (ii) atmospheric predictability, where skilful forecasts of weather and climate variables can be used to force hydrological models. In the UK, prediction of rainfall at long lead times and for summer months in particular is notoriously difficult given the large degree of natural climate variability in ocean influenced mid-latitude regions, but recent research has uncovered exciting prospects for improved rainfall skill at seasonal lead times due to improved prediction of the North Atlantic Oscillation. However, before we fully understand what this improved atmospheric predictability might mean in terms of improved hydrological forecasts, we must first evaluate how much skill can be gained from IHCs alone. Ensemble Streamflow Prediction (ESP) is a well-established method for generating an ensemble of streamflow forecasts in the absence of skilful future meteorological predictions. The aim of this study is therefore to benchmark when (lead time/forecast initialisation month) and where (spatial pattern/catchment characteristics) ESP is skilful across a diverse set of catchments in the UK. Forecast skill was evaluated seamlessly from lead times of 1-day to 12-months and forecasts were initialised at the first of each month over the 1965-2015 hindcast period. This ESP output also provides a robust benchmark against which to assess how much improvement in skill can be achieved when meteorological forecasts are incorporated (next steps). To provide a 'tough to beat' benchmark, several variants of ESP with increasing complexity were produced, including better model representation of hydrological processes and sub-sampling of historic climate sequences (e.g. NAO+/NAO- years). This work is part of the Improving Predictions of Drought for User Decision Making (IMPETUS) project and provides insight to where advancements in atmospheric predictability is most needed in the UK in the context of water management.

Present and future hydropower scheduling in Statkraft

NASA Astrophysics Data System (ADS)

Bruland, O.

2012-12-01

Statkraft produces close to 40 TWH in an average year and is one of the largest hydropower producers in Europe. For hydropower producers the scheduling of electricity generation is the key to success and this depend on optimal use of the water resources. The hydrologist and his forecasts both on short and on long terms are crucial to this success. The hydrological forecasts in Statkraft and most hydropower companies in Scandinavia are based on lumped models and the HBV concept. But before the hydrological model there is a complex system for collecting, controlling and correcting data applied in the models and the production scheduling and, equally important, routines for surveillance of the processes and manual intervention. Prior to the forecasting the states in the hydrological models are updated based on observations. When snow is present in the catchments snow surveys are an important source for model updating. The meteorological forecast is another premise provider to the hydrological forecast and to get as precise meteorological forecast as possible Statkraft hires resources from the governmental forecasting center. Their task is to interpret the meteorological situation, describe the uncertainties and if necessary use their knowledge and experience to manually correct the forecast in the hydropower production regions. This is one of several forecast applied further in the scheduling process. Both to be able to compare and evaluate different forecast providers and to ensure that we get the best available forecast, forecasts from different sources are applied. Some of these forecasts have undergone statistical corrections to reduce biases. The uncertainties related to the meteorological forecast have for a long time been approached and described by ensemble forecasts. But also the observations used for updating the model have a related uncertainty. Both to the observations itself and to how well they represent the catchment. Though well known, these uncertainties have thus far been handled superficially. Statkraft has initiated a program called ENKI to approach these issues. A part of this program is to apply distributed models for hydrological forecasting. Developing methodologies to handle uncertainties in the observations, the meteorological forecasts, the model itself and how to update the model with this information are other parts of the program. Together with energy price expectations and information about the state of the energy production system the hydrological forecast is input to the next step in the production scheduling both on short and long term. The long term schedule for reservoir filling is premise provider to the short term optimizing of water. The long term schedule is based on the actual reservoir levels, snow storages and a long history of meteorological observations and gives an overall schedule at a regional level. Within the regions a more detailed tool is used for short term optimizing of the hydropower production Each reservoir is scheduled taking into account restrictions in the water courses and cost of start and stop of aggregates. The value of the water is calculated for each reservoir and reflects the risk of water spillage. This compared to the energy price determines whether an aggregate will run or not. In a gradually more complex energy system with relatively lower regulated capacity this is an increasingly more challenging task.

Neural Networks for Hydrological Modeling Tool for Operational Purposes

NASA Astrophysics Data System (ADS)

Bhatt, Divya; Jain, Ashu

2010-05-01

Hydrological models are useful in many water resources applications such as flood control, irrigation and drainage, hydro power generation, water supply, erosion and sediment control, etc. Estimates of runoff are needed in many water resources planning, design development, operation and maintenance activities. Runoff is generally computed using rainfall-runoff models. Computer based hydrologic models have become popular for obtaining hydrological forecasts and for managing water systems. Rainfall-runoff library (RRL) is computer software developed by Cooperative Research Centre for Catchment Hydrology (CRCCH), Australia consisting of five different conceptual rainfall-runoff models, and has been in operation in many water resources applications in Australia. Recently, soft artificial intelligence tools such as Artificial Neural Networks (ANNs) have become popular for research purposes but have not been adopted in operational hydrological forecasts. There is a strong need to develop ANN models based on real catchment data and compare them with the conceptual models actually in use in real catchments. In this paper, the results from an investigation on the use of RRL and ANNs are presented. Out of the five conceptual models in the RRL toolkit, SimHyd model has been used. Genetic Algorithm has been used as an optimizer in the RRL to calibrate the SimHyd model. Trial and error procedures were employed to arrive at the best values of various parameters involved in the GA optimizer to develop the SimHyd model. The results obtained from the best configuration of the SimHyd model are presented here. Feed-forward neural network model structure trained by back-propagation training algorithm has been adopted here to develop the ANN models. The daily rainfall and runoff data derived from Bird Creek Basin, Oklahoma, USA have been employed to develop all the models included here. A wide range of error statistics have been used to evaluate the performance of all the models developed in this study. The ANN models developed consistently outperformed the conceptual model developed in this study. The results obtained in this study indicate that the ANNs can be extremely useful tools for modeling the complex rainfall-runoff process in real catchments. The ANNs should be adopted in real catchments for hydrological modeling and forecasting. It is hoped that more research will be carried out to compare the performance of ANN model with the conceptual models actually in use at catchment scales. It is hoped that such efforts may go a long way in making the ANNs more acceptable by the policy makers, water resources decision makers, and traditional hydrologists.

SMAP Impact Analysis of Early Adopter Research-Two Case studies on the scientific and societal benefits of SMAP data

NASA Astrophysics Data System (ADS)

Escobar, V. M.; Wu, H. T.; Moran, S.; O'Neill, P. E.

2016-12-01

To document and evaluate the use of SMAP science products in applications, the SMAP Phase E Applications Plan proposes to "conduct case studies to address a basic question: How are SMAP science products used in decision support systems and how does the new data stream affect the system performance?" The objective is to determine the value of SMAP data to the six categories of applications based on Early Adopters' experiences, where value is defined as the scientific and/or societal benefit. Since SMAP is the first mission with a pre-launch Early Adopter Program, the post-launch case study is also unprecedented. In this talk, we will show some results of the SMAP Early Adopters, with focus on the two case studies in the applications of agriculture and weather forecasting, respectively. For agriculture, we will show the work of USDA/NASS (National Agriculture Statistics Service) scientists (Zhengwei Yang and Rick Mueller). Using SMAP soil moisture products, they have been working on the establishment of a visualization, analytics, and dissemination tool to support and improve US national crop condition monitoring. Scientifically, this study will improve our understanding on the impact of crop canopy on the SMAP SM retrieval and on the mapping relation between SMAP SM and NASS soil moisture survey results. Socio-economically, the use of SMAP data and web-based tool will improve the consistency, reliability, objectivity, and efficiency of cropland soil moisture monitoring and assessment, which will benefit the current end users of the NASS weekly report including farmers, insurance companies, and financial institutes. For weather, we will show the work of NOAA scientists (Xiwu Zhan, Weizhong Zheng, and Mike Ek) on the transition of NASA SMAP research products to NOAA operational numerical weather and seasonal climate predictions and research hydrological forecasts. Results of initial analyses and validation of the assimilation of SMAP soil moisture in NOAA's Global Forecast System are promising. The implementation of SMAP data into NOAA's operational forecasting systems is expected to increase the skill and confidence level of our weather, seasonal climate, and hydrological forecasts, which has huge socio-economic benefit.

Snowfall Rate Retrieval Using Passive Microwave Measurements and Its Applications in Weather Forecast and Hydrology

NASA Technical Reports Server (NTRS)

Meng, Huan; Ferraro, Ralph; Kongoli, Cezar; Yan, Banghua; Zavodsky, Bradley; Zhao, Limin; Dong, Jun; Wang, Nai-Yu

2015-01-01

(AMSU), Microwave Humidity Sounder (MHS) and Advance Technology Microwave Sounder (ATMS). ATMS is the follow-on sensor to AMSU and MHS. Currently, an AMSU and MHS based land snowfall rate (SFR) product is running operationally at NOAA/NESDIS. Based on the AMSU/MHS SFR, an ATMS SFR algorithm has also been developed. The algorithm performs retrieval in three steps: snowfall detection, retrieval of cloud properties, and estimation of snow particle terminal velocity and snowfall rate. The snowfall detection component utilizes principal component analysis and a logistic regression model. It employs a combination of temperature and water vapor sounding channels to detect the scattering signal from falling snow and derives the probability of snowfall. Cloud properties are retrieved using an inversion method with an iteration algorithm and a two-stream radiative transfer model. A method adopted to calculate snow particle terminal velocity. Finally, snowfall rate is computed by numerically solving a complex integral. The SFR products are being used mainly in two communities: hydrology and weather forecast. Global blended precipitation products traditionally do not include snowfall derived from satellites because such products were not available operationally in the past. The ATMS and AMSU/MHS SFR now provide the winter precipitation information for these blended precipitation products. Weather forecasters mainly rely on radar and station observations for snowfall forecast. The SFR products can fill in gaps where no conventional snowfall data are available to forecasters. The products can also be used to confirm radar and gauge snowfall data and increase forecasters' confidence in their prediction.

Probabilistic flood warning using grand ensemble weather forecasts

NASA Astrophysics Data System (ADS)

He, Y.; Wetterhall, F.; Cloke, H.; Pappenberger, F.; Wilson, M.; Freer, J.; McGregor, G.

2009-04-01

As the severity of floods increases, possibly due to climate and landuse change, there is urgent need for more effective and reliable warning systems. The incorporation of numerical weather predictions (NWP) into a flood warning system can increase forecast lead times from a few hours to a few days. A single NWP forecast from a single forecast centre, however, is insufficient as it involves considerable non-predictable uncertainties and can lead to a high number of false or missed warnings. An ensemble of weather forecasts from one Ensemble Prediction System (EPS), when used on catchment hydrology, can provide improved early flood warning as some of the uncertainties can be quantified. EPS forecasts from a single weather centre only account for part of the uncertainties originating from initial conditions and stochastic physics. Other sources of uncertainties, including numerical implementations and/or data assimilation, can only be assessed if a grand ensemble of EPSs from different weather centres is used. When various models that produce EPS from different weather centres are aggregated, the probabilistic nature of the ensemble precipitation forecasts can be better retained and accounted for. The availability of twelve global EPSs through the 'THORPEX Interactive Grand Global Ensemble' (TIGGE) offers a new opportunity for the design of an improved probabilistic flood forecasting framework. This work presents a case study using the TIGGE database for flood warning on a meso-scale catchment. The upper reach of the River Severn catchment located in the Midlands Region of England is selected due to its abundant data for investigation and its relatively small size (4062 km2) (compared to the resolution of the NWPs). This choice was deliberate as we hypothesize that the uncertainty in the forcing of smaller catchments cannot be represented by a single EPS with a very limited number of ensemble members, but only through the variance given by a large number ensembles and ensemble system. A coupled atmospheric-hydrologic-hydraulic cascade system driven by the TIGGE ensemble forecasts is set up to study the potential benefits of using the TIGGE database in early flood warning. Physically based and fully distributed LISFLOOD suite of models is selected to simulate discharge and flood inundation consecutively. The results show the TIGGE database is a promising tool to produce forecasts of discharge and flood inundation comparable with the observed discharge and simulated inundation driven by the observed discharge. The spread of discharge forecasts varies from centre to centre, but it is generally large, implying a significant level of uncertainties. Precipitation input uncertainties dominate and propagate through the cascade chain. The current NWPs fall short of representing the spatial variability of precipitation on a comparatively small catchment. This perhaps indicates the need to improve NWPs resolution and/or disaggregation techniques to narrow down the spatial gap between meteorology and hydrology. It is not necessarily true that early flood warning becomes more reliable when more ensemble forecasts are employed. It is difficult to identify the best forecast centre(s), but in general the chance of detecting floods is increased by using the TIGGE database. Only one flood event was studied because most of the TIGGE data became available after October 2007. It is necessary to test the TIGGE ensemble forecasts with other flood events in other catchments with different hydrological and climatic regimes before general conclusions can be made on its robustness and applicability.

Documentation of a restart option for the U.S. Geological Survey coupled Groundwater and Surface-Water Flow (GSFLOW) model

USGS Publications Warehouse

Regan, R. Steve; Niswonger, Richard G.; Markstrom, Steven L.; Barlow, Paul M.

2015-10-02

The spin-up simulation should be run for a sufficient length of time necessary to establish antecedent conditions throughout a model domain. Each GSFLOW application can require different lengths of time to account for the hydrologic stresses to propagate through a coupled groundwater and surface-water system. Typically, groundwater hydrologic processes require many years to come into equilibrium with dynamic climate and other forcing (or stress) data, such as precipitation and well pumping, whereas runoff-dominated surface-water processes respond relatively quickly. Use of a spin-up simulation can substantially reduce execution-time requirements for applications where the time period of interest is small compared to the time for hydrologic memory; thus, use of the restart option can be an efficient strategy for forecast and calibration simulations that require multiple simulations starting from the same day.

Should we use seasonnal meteorological ensemble forecasts for hydrological forecasting? A case study for nordic watersheds in Canada.

NASA Astrophysics Data System (ADS)

Bazile, Rachel; Boucher, Marie-Amélie; Perreault, Luc; Leconte, Robert; Guay, Catherine

2017-04-01

Hydro-electricity is a major source of energy for many countries throughout the world, including Canada. Long lead-time streamflow forecasts are all the more valuable as they help decision making and dam management. Different techniques exist for long-term hydrological forecasting. Perhaps the most well-known is 'Extended Streamflow Prediction' (ESP), which considers past meteorological scenarios as possible, often equiprobable, future scenarios. In the ESP framework, those past-observed meteorological scenarios (climatology) are used in turn as the inputs of a chosen hydrological model to produce ensemble forecasts (one member corresponding to each year in the available database). Many hydropower companies, including Hydro-Québec (province of Quebec, Canada) use variants of the above described ESP system operationally for long-term operation planning. The ESP system accounts for the hydrological initial conditions and for the natural variability of the meteorological variables. However, it cannot consider the current initial state of the atmosphere. Climate models can help remedy this drawback. In the context of a changing climate, dynamical forecasts issued from climate models seem to be an interesting avenue to improve upon the ESP method and could help hydropower companies to adapt their management practices to an evolving climate. Long-range forecasts from climate models can also be helpful for water management at locations where records of past meteorological conditions are short or nonexistent. In this study, we compare 7-month hydrological forecasts obtained from climate model outputs to an ESP system. The ESP system mimics the one used operationally at Hydro-Québec. The dynamical climate forecasts are produced by the European Center for Medium range Weather Forecasts (ECMWF) System4. Forecasts quality is assessed using numerical scores such as the Continuous Ranked Probability Score (CRPS) and the Ignorance score and also graphical tools such as the reliability diagram. This study covers 10 nordic watersheds. We show that forecast performance according to the CRPS varies with lead-time but also with the period of the year. The raw forecasts from the ECMWF System4 display important biases for both temperature and precipitation, which need to be corrected. The linear scaling method is used for this purpose and is found effective. Bias correction improves forecasts performance, especially during the summer when the precipitations are over-estimated. According to the CRPS, bias corrected forecasts from System4 show performances comparable to those of the ESP system. However, the Ignorance score, which penalizes the lack of calibration (under-dispersive forecasts in this case) more severely than the CRPS, provides a different outlook for the comparison of the two systems. In fact, according to the Ignorance score, the ESP system outperforms forecasts based on System4 in most cases. This illustrates that the joint use of several metrics is crucial to assess the quality of a forecasts system thoroughly. Globally, ESP provide reliable forecasts which can be over-dispersed whereas bias corrected ECMWF System4 forecasts are sharper but at the risk of missing events.

Towards a National Hydrological Forecasting system for Canada : Lessons Learned from the Great Lakes and St. Lawrence Prediction System

NASA Astrophysics Data System (ADS)

Fortin, V.; Durnford, D.; Gaborit, E.; Davison, B.; Dimitrijevic, M.; Matte, P.

2016-12-01

Environment and Climate Change Canada has recently deployed a water cycle prediction system for the Great Lakes and St. Lawrence River. The model domain includes both the Canadian and US portions of the watershed. It provides 84-h forecasts of weather elements, lake level, lake ice cover and surface currents based on two-way coupling of the GEM numerical weather prediction (NWP) model with the NEMO ocean model. Streamflow of all the major tributaries of the Great Lakes and St. Lawrence River are estimated by the WATROUTE routing model, which routes the surface runoff forecasted by GEM's land-surface scheme and assimilates streamflow observations where available. Streamflow forecasts are updated twice daily and are disseminated through an OGC compliant web map service (WMS) and a web feature service (WFS). In this presentation, in addition to describing the system and documenting its forecast skill, we show how it is being used by clients for various environmental prediction applications. We then discuss the importance of two-way coupling, land-surface and hillslope modelling and the impact of horizontal resolution on hydrological prediction skill. In the second portion of the talk, we discuss plans for implementing a similar system at the national scale, using what we have learned in the Great Lakes and St. Lawrence watershed. Early results obtained for the headwaters of the Saskatchewan River as well as for the whole Nelson-Churchill watershed are presented.

Improving Assimilated Global Data Sets using TMI Rainfall and Columnar Moisture Observations

NASA Technical Reports Server (NTRS)

Hou, Arthur Y.; Zhang, Sara Q.; daSilva, Arlindo M.; Olson, William S.

1999-01-01

A global analysis that optimally combine observations from diverse sources with physical models of atmospheric and land processes can provide a comprehensive description of the climate systems. Currently, such data products contain significant errors in primary hydrological fields such as precipitation and evaporation, especially in the tropics. In this study, we show that assimilating precipitation and total precipitable water (TPW) retrievals derived from the TRMM Microwave Imager (TMI) improves not only the hydrological cycle but also key climate parameters such as clouds, radiation, and the large-scale circulation produced by the Goddard Earth Observing System (GEOS) data assimilation system (DAS). In particular, assimilating TMI rain improves clouds and radiation in areas of active convection, as well as the latent heating distribution and the large-scale motion field in the tropics, while assimilating TMI TPW heating distribution and the large-scale motion field in the tropics, while assimilating TMI TPW retrievals leads to reduced moisture biases and improved radiative fluxes in clear-sky regions. The improved analysis also improves short-range forecasts in the tropics. Ensemble forecasts initialized with the GEOS analysis incorporating TMI rain rates and TPW yield smaller biases in tropical precipitation forecasts beyond 1 day and better 500 hPa geopotential height forecasts up to 5 days. Results of this study demonstrate the potential of using high-quality space-borne rainfall and moisture observations to improve the quality of assimilated global data for climate analysis and weather forecasting applications

Development of Hydro-Informatic Modelling System and its Application

NASA Astrophysics Data System (ADS)

Wang, Z.; Liu, C.; Zheng, H.; Zhang, L.; Wu, X.

2009-12-01

The understanding of hydrological cycle is the core of hydrology and the scientific base of water resources management. Meanwhile, simulation of hydrological cycle has long been regarded as an important tool for the assessment, utilization and protection of water resources. In this paper, a new tool named Hydro-Informatic Modelling System (HIMS) has been developed and introduced with case studies in the Yellow River Basin in China and 331 catchments in Australia. The case studies showed that HIMS can be employed as an integrated platform for hydrological simulation in different regions. HIMS is a modular based framework of hydrological model designed for different utilization such as flood forecasting, water resources planning and evaluating hydrological impacts of climate change and human activities. The unique of HIMS is its flexibility in providing alternative modules in the simulation of hydrological cycle, which successfully overcome the difficulties in the availability of input data, the uncertainty of parameters, and the difference of rainfall-runoff processes. The modular based structure of HIMS makes it possible for developing new hydrological models by the users.

eWaterCycle visualisation. combining the strength of NetCDF and Web Map Service: ncWMS

NASA Astrophysics Data System (ADS)

Hut, R.; van Meersbergen, M.; Drost, N.; Van De Giesen, N.

2016-12-01

As a result of the eWatercycle global hydrological forecast we have created Cesium-ncWMS, a web application based on ncWMS and Cesium. ncWMS is a server side application capable of reading any NetCDF file written using the Climate and Forecasting (CF) conventions, and making the data available as a Web Map Service(WMS). ncWMS automatically determines available variables in a file, and creates maps colored according to map data and a user selected color scale. Cesium is a Javascript 3D virtual Globe library. It uses WebGL for rendering, which makes it very fast, and it is capable of displaying a wide variety of data types such as vectors, 3D models, and 2D maps. The forecast results are automatically uploaded to our web server running ncWMS. In turn, the web application can be used to change the settings for color maps and displayed data. The server uses the settings provided by the web application, together with the data in NetCDF to provide WMS image tiles, time series data and legend graphics to the Cesium-NcWMS web application. The user can simultaneously zoom in to the very high resolution forecast results anywhere on the world, and get time series data for any point on the globe. The Cesium-ncWMS visualisation combines a global overview with local relevant information in any browser. See the visualisation live at forecast.ewatercycle.org

Terrestrial Hydrological Data from NASA's Hydrology Data and Information Services Center (HDISC): Products, Services, and Applications

NASA Technical Reports Server (NTRS)

Fang, Hongliang; Beaudoing, Hiroko K.; Mocko, David M.; Rodell, Matthew; Teng, Bill; Vollmer, Bruce

2010-01-01

Terrestrial hydrological variables are important in global hydrology, climate, and carbon cycle studies. The North American and Global Land Data Assimilation Systems (NLDAS and GLDAS, respectively) have been generating a series of land surface states (soil moisture, snow, and temperature) and fluxes (evapotranspiration, radiation, and heat flux) variables. These data, hosted at and available from NASA s Hydrology Data and Information Services Center (HDISC), include the NLDAS hourly 1/8 degree products and the GLDAS 3-hourly 0.25 and 1.0 degree products. HDISC provides easy access and visualization and analysis capabilities for these products, thus reducing the time and resources spent by scientists on data management and facilitating hydrological research. Users can perform spatial and parameter subsetting, data format transformation, and data analysis operations without needing to first download the data. HDISC is continually being developed as a data and services portal that supports weather and climate forecasts, and water and energy cycle research.

On the difficulty to optimally implement the Ensemble Kalman filter: An experiment based on many hydrological models and catchments

NASA Astrophysics Data System (ADS)

Thiboult, A.; Anctil, F.

2015-10-01

Forecast reliability and accuracy is a prerequisite for successful hydrological applications. This aim may be attained by using data assimilation techniques such as the popular Ensemble Kalman filter (EnKF). Despite its recognized capacity to enhance forecasting by creating a new set of initial conditions, implementation tests have been mostly carried out with a single model and few catchments leading to case specific conclusions. This paper performs an extensive testing to assess ensemble bias and reliability on 20 conceptual lumped models and 38 catchments in the Province of Québec with perfect meteorological forecast forcing. The study confirms that EnKF is a powerful tool for short range forecasting but also that it requires a more subtle setting than it is frequently recommended. The success of the updating procedure depends to a great extent on the specification of the hyper-parameters. In the implementation of the EnKF, the identification of the hyper-parameters is very unintuitive if the model error is not explicitly accounted for and best estimates of forcing and observation error lead to overconfident forecasts. It is shown that performance are also related to the choice of updated state variables and that all states variables should not systematically be updated. Additionally, the improvement over the open loop scheme depends on the watershed and hydrological model structure, as some models exhibit a poor compatibility with EnKF updating. Thus, it is not possible to conclude in detail on a single ideal manner to identify an optimal implementation; conclusions drawn from a unique event, catchment, or model are likely to be misleading since transferring hyper-parameters from a case to another may be hazardous. Finally, achieving reliability and bias jointly is a daunting challenge as the optimization of one score is done at the cost of the other.

A simple snowmelt lysimeter

Treesearch

Harold F. Haupt

1969-01-01

A simple gage on the lysimeter principle has been developed to provide continuous readings of the volume of water flowing from the base of a snowpack in the form of surface melt alone or rain percolate and surface melt combined. The data obtained show promise, after two seasons of being applicable in river flood forecasting, as well as in studies of snow hydrology....

Evaluation of ensemble precipitation forecasts generated through post-processing in a Canadian catchment

NASA Astrophysics Data System (ADS)

Jha, Sanjeev K.; Shrestha, Durga L.; Stadnyk, Tricia A.; Coulibaly, Paulin

2018-03-01

Flooding in Canada is often caused by heavy rainfall during the snowmelt period. Hydrologic forecast centers rely on precipitation forecasts obtained from numerical weather prediction (NWP) models to enforce hydrological models for streamflow forecasting. The uncertainties in raw quantitative precipitation forecasts (QPFs) are enhanced by physiography and orography effects over a diverse landscape, particularly in the western catchments of Canada. A Bayesian post-processing approach called rainfall post-processing (RPP), developed in Australia (Robertson et al., 2013; Shrestha et al., 2015), has been applied to assess its forecast performance in a Canadian catchment. Raw QPFs obtained from two sources, Global Ensemble Forecasting System (GEFS) Reforecast 2 project, from the National Centers for Environmental Prediction, and Global Deterministic Forecast System (GDPS), from Environment and Climate Change Canada, are used in this study. The study period from January 2013 to December 2015 covered a major flood event in Calgary, Alberta, Canada. Post-processed results show that the RPP is able to remove the bias and reduce the errors of both GEFS and GDPS forecasts. Ensembles generated from the RPP reliably quantify the forecast uncertainty.

Interactive Forecasting with the National Weather Service River Forecast System

NASA Technical Reports Server (NTRS)

Smith, George F.; Page, Donna

1993-01-01

The National Weather Service River Forecast System (NWSRFS) consists of several major hydrometeorologic subcomponents to model the physics of the flow of water through the hydrologic cycle. The entire NWSRFS currently runs in both mainframe and minicomputer environments, using command oriented text input to control the system computations. As computationally powerful and graphically sophisticated scientific workstations became available, the National Weather Service (NWS) recognized that a graphically based, interactive environment would enhance the accuracy and timeliness of NWS river and flood forecasts. Consequently, the operational forecasting portion of the NWSRFS has been ported to run under a UNIX operating system, with X windows as the display environment on a system of networked scientific workstations. In addition, the NWSRFS Interactive Forecast Program was developed to provide a graphical user interface to allow the forecaster to control NWSRFS program flow and to make adjustments to forecasts as necessary. The potential market for water resources forecasting is immense and largely untapped. Any private company able to market the river forecasting technologies currently developed by the NWS Office of Hydrology could provide benefits to many information users and profit from providing these services.

An Overview of the National Weather Service National Water Model

NASA Astrophysics Data System (ADS)

Cosgrove, B.; Gochis, D.; Clark, E. P.; Cui, Z.; Dugger, A. L.; Feng, X.; Karsten, L. R.; Khan, S.; Kitzmiller, D.; Lee, H. S.; Liu, Y.; McCreight, J. L.; Newman, A. J.; Oubeidillah, A.; Pan, L.; Pham, C.; Salas, F.; Sampson, K. M.; Sood, G.; Wood, A.; Yates, D. N.; Yu, W.

2016-12-01

The National Weather Service (NWS) Office of Water Prediction (OWP), in conjunction with the National Center for Atmospheric Research (NCAR) and the NWS National Centers for Environmental Prediction (NCEP) recently implemented version 1.0 of the National Water Model (NWM) into operations. This model is an hourly cycling uncoupled analysis and forecast system that provides streamflow for 2.7 million river reaches and other hydrologic information on 1km and 250m grids. It will provide complementary hydrologic guidance at current NWS river forecast locations and significantly expand guidance coverage and type in underserved locations. The core of this system is the NCAR-supported community Weather Research and Forecasting (WRF)-Hydro hydrologic model. It ingests forcing from a variety of sources including Multi-Sensor Multi-Radar (MRMS) radar-gauge observed precipitation data and High Resolution Rapid Refresh (HRRR), Rapid Refresh (RAP), Global Forecast System (GFS) and Climate Forecast System (CFS) forecast data. WRF-Hydro is configured to use the Noah-Multi Parameterization (Noah-MP) Land Surface Model (LSM) to simulate land surface processes. Separate water routing modules perform diffusive wave surface routing and saturated subsurface flow routing on a 250m grid, and Muskingum-Cunge channel routing down National Hydrogaphy Dataset Plus V2 (NHDPlusV2) stream reaches. River analyses and forecasts are provided across a domain encompassing the Continental United States (CONUS) and hydrologically contributing areas, while land surface output is available on a larger domain that extends beyond the CONUS into Canada and Mexico (roughly from latitude 19N to 58N). The system includes an analysis and assimilation configuration along with three forecast configurations. These include a short-range 15 hour deterministic forecast, a medium-Range 10 day deterministic forecast and a long-range 30 day 16-member ensemble forecast. United Sates Geologic Survey (USGS) streamflow observations are assimilated into the analysis and assimilation configuration, and all four configurations benefit from the inclusion of 1,260 reservoirs. An overview of the National Water Model will be given, along with information on ongoing evaluation activities and plans for future NWM enhancements.

Alternative configurations of Quantile Regression for estimating predictive uncertainty in water level forecasts for the Upper Severn River: a comparison

NASA Astrophysics Data System (ADS)

Lopez, Patricia; Verkade, Jan; Weerts, Albrecht; Solomatine, Dimitri

2014-05-01

Hydrological forecasting is subject to many sources of uncertainty, including those originating in initial state, boundary conditions, model structure and model parameters. Although uncertainty can be reduced, it can never be fully eliminated. Statistical post-processing techniques constitute an often used approach to estimate the hydrological predictive uncertainty, where a model of forecast error is built using a historical record of past forecasts and observations. The present study focuses on the use of the Quantile Regression (QR) technique as a hydrological post-processor. It estimates the predictive distribution of water levels using deterministic water level forecasts as predictors. This work aims to thoroughly verify uncertainty estimates using the implementation of QR that was applied in an operational setting in the UK National Flood Forecasting System, and to inter-compare forecast quality and skill in various, differing configurations of QR. These configurations are (i) 'classical' QR, (ii) QR constrained by a requirement that quantiles do not cross, (iii) QR derived on time series that have been transformed into the Normal domain (Normal Quantile Transformation - NQT), and (iv) a piecewise linear derivation of QR models. The QR configurations are applied to fourteen hydrological stations on the Upper Severn River with different catchments characteristics. Results of each QR configuration are conditionally verified for progressively higher flood levels, in terms of commonly used verification metrics and skill scores. These include Brier's probability score (BS), the continuous ranked probability score (CRPS) and corresponding skill scores as well as the Relative Operating Characteristic score (ROCS). Reliability diagrams are also presented and analysed. The results indicate that none of the four Quantile Regression configurations clearly outperforms the others.

User manuals for the Delaware River Basin Water Availability Tool for Environmental Resources (DRB–WATER) and associated WATER application utilities

USGS Publications Warehouse

Williamson, Tanja N.; Lant, Jeremiah G.

2015-11-18

The Water Availability Tool for Environmental Resources (WATER) is a decision support system (DSS) for the nontidal part of the Delaware River Basin (DRB) that provides a consistent and objective method of simulating streamflow under historical, forecasted, and managed conditions. WATER integrates geospatial sampling of landscape characteristics, including topographic and soil properties, with a regionally calibrated hillslope-hydrology model, an impervious-surface model, and hydroclimatic models that have been parameterized using three hydrologic response units—forested, agricultural, and developed land cover. It is this integration that enables the regional hydrologic-modeling approach used in WATER without requiring site-specific optimization or those stationary conditions inferred when using a statistical model. The DSS provides a “historical” database, ideal for simulating streamflow for 2001–11, in addition to land-cover forecasts that focus on 2030 and 2060. The WATER Application Utilities are provided with the DSS and apply change factors for precipitation, temperature, and potential evapotranspiration to a 1981–2011 climatic record provided with the DSS. These change factors were derived from a suite of general circulation models (GCMs) and representative concentration pathway (RCP) emission scenarios. These change factors are based on 25-year monthly averages (normals) that are centere on 2030 and 2060. The WATER Application Utilities also can be used to apply a 2010 snapshot of water use for the DRB; a factorial approach enables scenario testing of increased or decreased water use for each simulation. Finally, the WATER Application Utilities can be used to reformat streamflow time series for input to statistical or reservoir management software. 

Precipitation and floodiness

NASA Astrophysics Data System (ADS)

Stephens, E.; Day, J. J.; Pappenberger, F.; Cloke, H.

2015-12-01

There are a number of factors that lead to nonlinearity between precipitation anomalies and flood hazard; this nonlinearity is a pertinent issue for applications that use a precipitation forecast as a proxy for imminent flood hazard. We assessed the degree of this nonlinearity for the first time using a recently developed global-scale hydrological model driven by the ERA-Interim/Land precipitation reanalysis (1980-2010). We introduced new indices to assess large-scale flood hazard, or floodiness, and quantified the link between monthly precipitation, river discharge, and floodiness anomalies at the global and regional scales. The results show that monthly floodiness is not well correlated with precipitation, therefore demonstrating the value of hydrometeorological systems for providing floodiness forecasts for decision-makers. A method is described for forecasting floodiness using the Global Flood Awareness System, building a climatology of regional floodiness from which to forecast floodiness anomalies out to 2 weeks.

Real time hydro-metereological hazards monitoring system for the Ravenna municipality

NASA Astrophysics Data System (ADS)

Bertoni, W.; Cattarossi, A.; Gonella, M.

2003-04-01

The Ravenna municipality (Italy, Emilia Romagna region), through a cooperative agreement with ENI S.p.A’s., AGIP division, is carrying out a research study for the development of a real time monitoring system of hydro-meteorological conditions. The system aims to support the city Crisis Response Unit to provide more efficient support all over the municipal territory that is the largest in Italy with more than 700 km2. The support unit, a GIS computer based application, directly links to a broad range of sources, gathering real time information from a Local Area Model (meteorological data), a Wave Model (sea hydrodynamic circulation), monitoring stations, located partially on the Adriatic sea (AGIP offshore platform, SIMN) and partially over the Ravenna inland (SPDS, SIN). In the first phase, now completed and undergoing testing, this vast and diversified collection of data feeds a number of statistical models with up to 72 hours of forecast capabilities. The GIS application displays actual and forecast sea conditions offshore of Ravenna littorals in addition to actual and forecast flood conditions along the Ravenna Province inland. Model generated data are used for the forecast, which is then calibrated using the measured data. When the predefined warning limits are exceeded, end users are alerted via prerecorded phone messages, SMS, or visually through the direct or remote interaction with the GIS system (remotely accessible via portable computers). In the second stage, the statistical approach will be substituted by a more deterministic approach. A coupled hydrologic-hydraulic model will be used to forecast water stages along rivers and runoff volume along major watersheds. Moreover, already functioning capabilities allows direct control of remote monitoring points (stream and rain gages, etc.) The entire Real Time Monitoring System was developed on a GIS platform. The GEOdatabase, a relational database based on MSDE technology, is the core of the application which revolves around the conceptualization of a Hydro Data Model, a standardized way to store hydraulic based data such as watershed delineation, hydrologic network, monitoring points and time series data. Recent advancement in GIS software technologies and ready to use hydro-meteorological data offer an unprecedented opportunity to customize the GIS application and provide a powerful application to prevent and defeat flood hazards.

Seasonal scale water deficit forecasting in Africa and the Middle East using NASA's Land Information System (LIS)

NASA Astrophysics Data System (ADS)

Peters-Lidard, C. D.; Arsenault, K. R.; Shukla, S.; Getirana, A.; McNally, A.; Koster, R. D.; Zaitchik, B. F.; Badr, H. S.; Roningen, J. M.; Kumar, S.; Funk, C. C.

2017-12-01

A seamless and effective water deficit monitoring and early warning system is critical for assessing food security in Africa and the Middle East. In this presentation, we report on the ongoing development and validation of a seasonal scale water deficit forecasting system based on NASA's Land Information System (LIS) and seasonal climate forecasts. First, our presentation will focus on the implementation and validation of drought and water availability monitoring products in the region. Next, it will focus on evaluating drought and water availability forecasts. Finally, details will be provided of our ongoing collaboration with end-user partners in the region (e.g., USAID's Famine Early Warning Systems Network, FEWS NET), on formulating meaningful early warning indicators, effective communication and seamless dissemination of the products through NASA's web-services. The water deficit forecasting system thus far incorporates NASA GMAO's Catchment and the Noah Multi-Physics (MP) LSMs. In addition, the LSMs' surface and subsurface runoff are routed through the Hydrological Modeling and Analysis Platform (HyMAP) to simulate surface water dynamics. To establish a climatology from 1981-2015, the two LSMs are driven by NASA/GMAO's Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2), and the USGS and UCSB Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) daily rainfall dataset. Comparison of the models' energy and hydrological budgets with independent observations suggests that major droughts are well-reflected in the climatology. The system uses seasonal climate forecasts from NASA's GEOS-5 (the Goddard Earth Observing System Model-5) and NCEP's Climate Forecast System-2, and it produces forecasts of soil moisture, ET and streamflow out to 6 months in the future. Forecasts of those variables are formulated in terms of indicators to provide forecasts of drought and water availability in the region. Current work suggests that for the Blue Nile basin, (1) the combination of GEOS-5 and CFSv2 is equivalent in skill to the full North American Multimodel Ensemble (NMME); and (2) the seasonal water deficit forecasting system skill for both soil moisture and streamflow anomalies is greater than the standard Ensemble Streamflow Prediction (ESP) approach.

GloFAS-Seasonal: Operational Seasonal Ensemble River Flow Forecasts at the Global Scale

NASA Astrophysics Data System (ADS)

Emerton, Rebecca; Zsoter, Ervin; Smith, Paul; Salamon, Peter

2017-04-01

Seasonal hydrological forecasting has potential benefits for many sectors, including agriculture, water resources management and humanitarian aid. At present, no global scale seasonal hydrological forecasting system exists operationally; although smaller scale systems have begun to emerge around the globe over the past decade, a system providing consistent global scale seasonal forecasts would be of great benefit in regions where no other forecasting system exists, and to organisations operating at the global scale, such as disaster relief. We present here a new operational global ensemble seasonal hydrological forecast, currently under development at ECMWF as part of the Global Flood Awareness System (GloFAS). The proposed system, which builds upon the current version of GloFAS, takes the long-range forecasts from the ECMWF System4 ensemble seasonal forecast system (which incorporates the HTESSEL land surface scheme) and uses this runoff as input to the Lisflood routing model, producing a seasonal river flow forecast out to 4 months lead time, for the global river network. The seasonal forecasts will be evaluated using the global river discharge reanalysis, and observations where available, to determine the potential value of the forecasts across the globe. The seasonal forecasts will be presented as a new layer in the GloFAS interface, which will provide a global map of river catchments, indicating whether the catchment-averaged discharge forecast is showing abnormally high or low flows during the 4-month lead time. Each catchment will display the corresponding forecast as an ensemble hydrograph of the weekly-averaged discharge forecast out to 4 months, with percentile thresholds shown for comparison with the discharge climatology. The forecast visualisation is based on a combination of the current medium-range GloFAS forecasts and the operational EFAS (European Flood Awareness System) seasonal outlook, and aims to effectively communicate the nature of a seasonal outlook while providing useful information to users and partners. We demonstrate the first version of an operational GloFAS seasonal outlook, outlining the model set-up and presenting a first look at the seasonal forecasts that will be displayed in the GloFAS interface, and discuss the initial results of the forecast evaluation.

Cloud Computing Applications in Support of Earth Science Activities at Marshall Space Flight Center

NASA Technical Reports Server (NTRS)

Molthan, Andrew L.; Limaye, Ashutosh S.; Srikishen, Jayanthi

2011-01-01

Currently, the NASA Nebula Cloud Computing Platform is available to Agency personnel in a pre-release status as the system undergoes a formal operational readiness review. Over the past year, two projects within the Earth Science Office at NASA Marshall Space Flight Center have been investigating the performance and value of Nebula s "Infrastructure as a Service", or "IaaS" concept and applying cloud computing concepts to advance their respective mission goals. The Short-term Prediction Research and Transition (SPoRT) Center focuses on the transition of unique NASA satellite observations and weather forecasting capabilities for use within the operational forecasting community through partnerships with NOAA s National Weather Service (NWS). SPoRT has evaluated the performance of the Weather Research and Forecasting (WRF) model on virtual machines deployed within Nebula and used Nebula instances to simulate local forecasts in support of regional forecast studies of interest to select NWS forecast offices. In addition to weather forecasting applications, rapidly deployable Nebula virtual machines have supported the processing of high resolution NASA satellite imagery to support disaster assessment following the historic severe weather and tornado outbreak of April 27, 2011. Other modeling and satellite analysis activities are underway in support of NASA s SERVIR program, which integrates satellite observations, ground-based data and forecast models to monitor environmental change and improve disaster response in Central America, the Caribbean, Africa, and the Himalayas. Leveraging SPoRT s experience, SERVIR is working to establish a real-time weather forecasting model for Central America. Other modeling efforts include hydrologic forecasts for Kenya, driven by NASA satellite observations and reanalysis data sets provided by the broader meteorological community. Forecast modeling efforts are supplemented by short-term forecasts of convective initiation, determined by geostationary satellite observations processed on virtual machines powered by Nebula.

Research on classified real-time flood forecasting framework based on K-means cluster and rough set.

PubMed

Xu, Wei; Peng, Yong

2015-01-01

This research presents a new classified real-time flood forecasting framework. In this framework, historical floods are classified by a K-means cluster according to the spatial and temporal distribution of precipitation, the time variance of precipitation intensity and other hydrological factors. Based on the classified results, a rough set is used to extract the identification rules for real-time flood forecasting. Then, the parameters of different categories within the conceptual hydrological model are calibrated using a genetic algorithm. In real-time forecasting, the corresponding category of parameters is selected for flood forecasting according to the obtained flood information. This research tests the new classified framework on Guanyinge Reservoir and compares the framework with the traditional flood forecasting method. It finds that the performance of the new classified framework is significantly better in terms of accuracy. Furthermore, the framework can be considered in a catchment with fewer historical floods.

Towards a better knowledge of flash flood forecasting at the Three Gorges Region: Progress over the past decade and challenges ahead

NASA Astrophysics Data System (ADS)

Li, Zhe; Yang, Dawen; Yang, Hanbo; Wu, Tianjiao; Xu, Jijun; Gao, Bing; Xu, Tao

2015-04-01

The study area, the Three Gorges Region (TGR), plays a critical role in predicting the floods drained into the Three Gorges Reservoir, as reported local floods often exceed 10000m3/s during rainstorm events and trigger fast as well as significant impacts on the Three Gorges Reservoir's regulation. Meanwhile, it is one of typical mountainous areas in China, which is located in the transition zone between two monsoon systems: the East Asian monsoon and the South Asian (Indian) monsoon. This climatic feature, combined with local irregular terrains, has shaped complicated rainfall-runoff regimes in this focal region. However, due to the lack of high-resolution hydrometeorological data and physically-based hydrologic modeling framework, there was little knowledge about rainfall variability and flood pattern in this historically ungauged region, which posed great uncertainties to flash flood forecasting in the past. The present study summarize latest progresses of regional flash floods monitoring and prediction, including installation of a ground-based Hydrometeorological Observation Network (TGR-HMON), application of a regional geomorphology-based hydrological model (TGR-GBHM), development of an integrated forecasting and modeling system (TGR-INFORMS), and evaluation of quantitative precipitation estimations (QPE) and quantitative precipitation forecasting (QPF) products in TGR flash flood forecasting. With these continuing efforts to improve the forecasting performance of flash floods in TGR, we have addressed several critical issues: (1) Current observation network is still insufficient to capture localized rainstorms, and weather radar provides valuable information to forecast flash floods induced by localized rainstorms, although current radar QPE products can be improved substantially in future; (2) Long-term evaluation shows that the geomorphology-based distributed hydrologic model (GBHM) is able to simulate flash flooding processes reasonably, while model performance will decline at hourly scale with larger uncertainties. However, model comparison suggests that this physically-based distributed model (GBHM), compared with a traditional lumped model (Xin'anjiang model), shows more robust performance and larger transferability for prediction in those ungauged basins in TGR; (3) Operational test of our integrated forecasting system (TRG-INFORMS) shows that it works reasonably to simulate the flood routing in Three Gorges reservoir, indicating the accuracy of simulation of total floods generated at region scale; (4) Current operational QPF is too coarse to provide valuable information even for flood forecasting of whole TGR, thus, downscaling and high-resolution QPF are necessary to unravel the potentials of weather forecasting. Finally, according to these results, we also discuss about some possible solutions with high priority for future advanced forecasting scheme of local flash floods in TGR.

Continental scale data assimilation of discharge and its effect on flow predictions

NASA Astrophysics Data System (ADS)

Weerts, Albrecht; Schellekens, Jaap; van Dijk, Albert

2017-04-01

Floods are the most frequent of natural disasters, affecting millions of people across the globe every year. The anticipation and forecasting of floods at the global scale is crucial to preparing for severe events and providing early awareness where local flood models and warning services may not exist (Emmerton et al., 2016). Current global flood forecasting system heavily rely on forecast forcing (precipitation, temperature, reference potential evaporation) to derive initial state estimates of the hydrological model for the next forecast (e.g. by glueing the first day of subsequent forecast as proxy for the historical observed forcing). It is clear that this approach is not perfect and that data assimilation can help to overcome some of the weaknesses of this approach. So far most hydrologic da studies have focused mostly on catchment scale. Here we conduct a da experiment by assimilating multiple streamflow observations across the contiguous united states (CONUS) and Europe into a global hydrological model (W3RA) and run with and without localization method using OpenDA in the global flood forecasting information system (GLOFFIS). It is shown that assimilation of streamflow holds considerable potential for improving global scale flood forecasting (improving NSE scores from 0 to 0.7 and beyond). Weakness in the model (e.g. structural problems and missing processes) and forcing that influence the performance will be highlighted.

Continental scale data assimilation of discharge and its effect on flow predictions across the contiguous US (CONUS)

NASA Astrophysics Data System (ADS)

Weerts, A.; Schellekens, J.; van Dijk, A.; Molenaar, R.

2016-12-01

Floods are the most frequent of natural disasters, affecting millions of people across the globe every year. The anticipation and forecasting of floods at the global scale is crucial to preparing for severe events and providing early awareness where local flood models and warning services may not exist (Emmerton et al., 2016). Current global flood forecasting system heavily rely on forecast forcing (precipitation, temperature, reference potential evaporation) to derive initial state estimates of the hydrological model for the next forecast (e.g. by glueing the first day of subsequent forecast as proxy for the historical observed forcing). It is clear that this approach is not perfect and that data assimilation can help to overcome some of the weaknesses of this approach. So far most hydrologic da studies have focused mostly on catchment scale. Here we conduct a da experiment by assimilating multiple streamflow observations across the contiguous united states (CONUS) into a global hydrological model (W3RA) and run with and without localization method using OpenDA in the global flood forecasting information system (GLOFFIS). It is shown that assimilation of streamflow holds considerable potential for improving global scale flood forecasting (improving NSE scores from 0 to 0.7 and beyond). Weakness in the model (e.g. structural problems and missing processes) and forcing that influence the performance will be highlighted.

Assimilation of water temperature and discharge data for ensemble water temperature forecasting

NASA Astrophysics Data System (ADS)

Ouellet-Proulx, Sébastien; Chimi Chiadjeu, Olivier; Boucher, Marie-Amélie; St-Hilaire, André

2017-11-01

Recent work demonstrated the value of water temperature forecasts to improve water resources allocation and highlighted the importance of quantifying their uncertainty adequately. In this study, we perform a multisite cascading ensemble assimilation of discharge and water temperature on the Nechako River (Canada) using particle filters. Hydrological and thermal initial conditions were provided to a rainfall-runoff model, coupled to a thermal module, using ensemble meteorological forecasts as inputs to produce 5 day ensemble thermal forecasts. Results show good performances of the particle filters with improvements of the accuracy of initial conditions by more than 65% compared to simulations without data assimilation for both the hydrological and the thermal component. All thermal forecasts returned continuous ranked probability scores under 0.8 °C when using a set of 40 initial conditions and meteorological forecasts comprising 20 members. A greater contribution of the initial conditions to the total uncertainty of the system for 1-dayforecasts is observed (mean ensemble spread = 1.1 °C) compared to meteorological forcings (mean ensemble spread = 0.6 °C). The inclusion of meteorological uncertainty is critical to maintain reliable forecasts and proper ensemble spread for lead times of 2 days and more. This work demonstrates the ability of the particle filters to properly update the initial conditions of a coupled hydrological and thermal model and offers insights regarding the contribution of two major sources of uncertainty to the overall uncertainty in thermal forecasts.

Forecasting wetting and drying of post-wildfire soils in response to precipitation: A time series optimization approach

NASA Astrophysics Data System (ADS)

Basak, A.; Kulkarni, C.; Schmidt, K. M.; Mengshoel, O. J.

2015-12-01

Volumetric water content (VWC) in soils is critical for forecasting thresholds for runoff-driven erosion caused by rainfall. Even though theoretical relations (e.g., Richards equation) have been developed to quantify VWC in unsaturated granular soils, site-specific field conditions and hysteresis of suction and VWC in soil preclude their direct use. Although attempts have previously been made to forecast VWC using various time-series models (e.g., autoregressive integrated moving average or ARIMA), these approaches lack hydrologic foundations and perform poorly when used to forecast VWC over time periods longer than 24 hours. In this work, we extend an existing Antecedent Water Index (AWI) based model to express VWC as a function of time and rainfall. AWI models typically overfit data and cannot be used for forecast VWC over long time periods. We developed a new model to overcome this limitation, which accumulates rainfall over a time window and fits a diverse range of wetting and drying curves. Hydraulic redistribution parameters in this model bear resemblance to hydrologic processes driven by gravity and suction. This model reasonably forecasts VWC using only initial VWC values and rainfall forecasts. Experimental VWC data were collected from steep gradient post-wildfire sites in southern California. Rapid landscape change was observed in response to small to moderate rain storms. We formulated a mean-squared error minimization problem over the model parameters and optimized using genetic algorithms. We found that our model fits VWC data for 3 distinct soil textures, each occurring at 3 different depths below the ground surface (5 cm, 15 cm, and 30 cm). Our model successfully forecasts VWC trends, such as drying and wetting rate. To a certain extent, our model achieves spatial and seasonal generalizability. Our accumulative rainfall model is also applicable to continuous predictions, where VWC values are repeatedly used to predict future ones within a 12-hr time frame.

A seamless global hydrological monitoring and forecasting system for water resources assessment and hydrological hazard early warning

NASA Astrophysics Data System (ADS)

Sheffield, Justin; He, Xiaogang; Wood, Eric; Pan, Ming; Wanders, Niko; Zhan, Wang; Peng, Liqing

2017-04-01

Sustainable management of water resources and mitigation of the impacts of hydrological hazards are becoming ever more important at large scales because of inter-basin, inter-country and inter-continental connections in water dependent sectors. These include water resources management, food production, and energy production, whose needs must be weighed against the water needs of ecosystems and preservation of water resources for future generations. The strains on these connections are likely to increase with climate change and increasing demand from burgeoning populations and rapid development, with potential for conflict over water. At the same time, network connections may provide opportunities to alleviate pressures on water availability through more efficient use of resources such as trade in water dependent goods. A key constraint on understanding, monitoring and identifying solutions to increasing competition for water resources and hazard risk is the availability of hydrological data for monitoring and forecasting water resources and hazards. We present a global online system that provides continuous and consistent water products across time scales, from the historic instrumental period, to real-time monitoring, short-term and seasonal forecasts, and climate change projections. The system is intended to provide data and tools for analysis of historic hydrological variability and trends, water resources assessment, monitoring of evolving hazards and forecasts for early warning, and climate change scale projections of changes in water availability and extreme events. The system is particular useful for scientists and stakeholders interested in regions with less available in-situ data, and where forecasts have the potential to help decision making. The system is built on a database of high-resolution climate data from 1950 to present that merges available observational records with bias-corrected reanalysis and satellite data, which then drives a coupled land surface model-flood inundation model to produce hydrological variables and indices at daily, 0.25-degree resolution, globally. The system is updated in near real-time (< 2 days) using satellite precipitation and weather model data, and produces forecasts at short-term (out to 7 days) based on the Global Forecast System (GFS) and seasonal (up to 6 months) based on U.S. National Multi-Model Ensemble (NMME) seasonal forecasts. Climate change projections are based on bias-corrected and downscaled CMIP5 climate data that is used to force the hydrological model. Example products from the system include real-time and forecast drought indices for precipitation, soil moisture, and streamflow, and flood magnitude and extent indices. The model outputs are complemented by satellite based products and indices based on satellite data for vegetation health (MODIS NDVI) and soil moisture (SMAP). We show examples of the validation of the system at regional scales, including how local information can significantly improve predictions, and examples of how the system can be used to understand large-scale water resource issues, and in real-world contexts for early warning, decision making and planning.

Error reduction and representation in stages (ERRIS) in hydrological modelling for ensemble streamflow forecasting

NASA Astrophysics Data System (ADS)

Li, Ming; Wang, Q. J.; Bennett, James C.; Robertson, David E.

2016-09-01

This study develops a new error modelling method for ensemble short-term and real-time streamflow forecasting, called error reduction and representation in stages (ERRIS). The novelty of ERRIS is that it does not rely on a single complex error model but runs a sequence of simple error models through four stages. At each stage, an error model attempts to incrementally improve over the previous stage. Stage 1 establishes parameters of a hydrological model and parameters of a transformation function for data normalization, Stage 2 applies a bias correction, Stage 3 applies autoregressive (AR) updating, and Stage 4 applies a Gaussian mixture distribution to represent model residuals. In a case study, we apply ERRIS for one-step-ahead forecasting at a range of catchments. The forecasts at the end of Stage 4 are shown to be much more accurate than at Stage 1 and to be highly reliable in representing forecast uncertainty. Specifically, the forecasts become more accurate by applying the AR updating at Stage 3, and more reliable in uncertainty spread by using a mixture of two Gaussian distributions to represent the residuals at Stage 4. ERRIS can be applied to any existing calibrated hydrological models, including those calibrated to deterministic (e.g. least-squares) objectives.

On the incidence of meteorological and hydrological processors: Effect of resolution, sharpness and reliability of hydrological ensemble forecasts

NASA Astrophysics Data System (ADS)

Abaza, Mabrouk; Anctil, François; Fortin, Vincent; Perreault, Luc

2017-12-01

Meteorological and hydrological ensemble prediction systems are imperfect. Their outputs could often be improved through the use of a statistical processor, opening up the question of the necessity of using both processors (meteorological and hydrological), only one of them, or none. This experiment compares the predictive distributions from four hydrological ensemble prediction systems (H-EPS) utilising the Ensemble Kalman filter (EnKF) probabilistic sequential data assimilation scheme. They differ in the inclusion or not of the Distribution Based Scaling (DBS) method for post-processing meteorological forecasts and the ensemble Bayesian Model Averaging (ensemble BMA) method for hydrological forecast post-processing. The experiment is implemented on three large watersheds and relies on the combination of two meteorological reforecast products: the 4-member Canadian reforecasts from the Canadian Centre for Meteorological and Environmental Prediction (CCMEP) and the 10-member American reforecasts from the National Oceanic and Atmospheric Administration (NOAA), leading to 14 members at each time step. Results show that all four tested H-EPS lead to resolution and sharpness values that are quite similar, with an advantage to DBS + EnKF. The ensemble BMA is unable to compensate for any bias left in the precipitation ensemble forecasts. On the other hand, it succeeds in calibrating ensemble members that are otherwise under-dispersed. If reliability is preferred over resolution and sharpness, DBS + EnKF + ensemble BMA performs best, making use of both processors in the H-EPS system. Conversely, for enhanced resolution and sharpness, DBS is the preferred method.

Hydro-economic assessment of hydrological forecasting systems

NASA Astrophysics Data System (ADS)

Boucher, M.-A.; Tremblay, D.; Delorme, L.; Perreault, L.; Anctil, F.

2012-01-01

SummaryAn increasing number of publications show that ensemble hydrological forecasts exhibit good performance when compared to observed streamflow. Many studies also conclude that ensemble forecasts lead to a better performance than deterministic ones. This investigation takes one step further by not only comparing ensemble and deterministic forecasts to observed values, but by employing the forecasts in a stochastic decision-making assistance tool for hydroelectricity production, during a flood event on the Gatineau River in Canada. This allows the comparison between different types of forecasts according to their value in terms of energy, spillage and storage in a reservoir. The motivation for this is to adopt the point of view of an end-user, here a hydroelectricity production society. We show that ensemble forecasts exhibit excellent performances when compared to observations and are also satisfying when involved in operation management for electricity production. Further improvement in terms of productivity can be reached through the use of a simple post-processing method.

Forecast Based Financing for Managing Weather and Climate Risks to Reduce Potential Disaster Impacts

NASA Astrophysics Data System (ADS)

Arrighi, J.

2017-12-01

There is a critical window of time to reduce potential impacts of a disaster after a forecast for heightened risk is issued and before an extreme event occurs. The concept of Forecast-based Financing focuses on this window of opportunity. Through advanced preparation during system set-up, tailored methodologies are used to 1) analyze a range of potential extreme event forecasts, 2) identify emergency preparedness measures that can be taken when factoring in forecast lead time and inherent uncertainty and 3) develop standard operating procedures that are agreed on and tied to guaranteed funding sources to facilitate emergency measures led by the Red Cross or government actors when preparedness measures are triggered. This presentation will focus on a broad overview of the current state of theory and approaches used in developing a forecast-based financing systems - with a specific focus on hydrologic events, case studies of success and challenges in various contexts where this approach is being piloted, as well as what is on the horizon to be further explored and developed from a research perspective as the application of this approach continues to expand.

Forecasting skills of the ensemble hydro-meteorological system for the Po river floods

NASA Astrophysics Data System (ADS)

Ricciardi, Giuseppe; Montani, Andrea; Paccagnella, Tiziana; Pecora, Silvano; Tonelli, Fabrizio

2013-04-01

The Po basin is the largest and most economically important river-basin in Italy. Extreme hydrological events, including floods, flash floods and droughts, are expected to become more severe in the next future due to climate change, and related ground effects are linked both with environmental and social resilience. A Warning Operational Center (WOC) for hydrological event management was created in Emilia Romagna region. In the last years, the WOC faced challenges in legislation, organization, technology and economics, achieving improvements in forecasting skill and information dissemination. Since 2005, an operational forecasting and modelling system for flood modelling and forecasting has been implemented, aimed at supporting and coordinating flood control and emergency management on the whole Po basin. This system, referred to as FEWSPo, has also taken care of environmental aspects of flood forecast. The FEWSPo system has reached a very high level of complexity, due to the combination of three different hydrological-hydraulic chains (HEC-HMS/RAS - MIKE11 NAM/HD, Topkapi/Sobek), with several meteorological inputs (forecasted - COSMOI2, COSMOI7, COSMO-LEPS among others - and observed). In this hydrological and meteorological ensemble the management of the relative predictive uncertainties, which have to be established and communicated to decision makers, is a debated scientific and social challenge. Real time activities face professional, modelling and technological aspects but are also strongly interrelated with organization and human aspects. The authors will report a case study using the operational flood forecast hydro-meteorological ensemble, provided by the MIKE11 chain fed by COSMO_LEPS EQPF. The basic aim of the proposed approach is to analyse limits and opportunities of the long term forecast (with a lead time ranging from 3 to 5 days), for the implementation of low cost actions, also looking for a well informed decision making and the improvement of flood preparedness and crisis management for basins greater than 1.000 km2.

Water regime of Playa Lakes from southern Spain: conditioning factors and hydrological modeling.

PubMed

Moral, Francisco; Rodriguez-Rodriguez, Miguel; Beltrán, Manuel; Benavente, José; Cifuentes, Victor Juan

2013-07-01

Andalusia's lowland countryside has a network of small geographically isolated playa lakes scattered across an area of 9000 km2 whose watersheds are mostly occupied by clayey rocks. The hydrological model proposed by the authors seeks to find equilibrium among usefulness, simplicity, and applicability to isolated playas in a semiarid context elsewhere. Based in such model, the authors have used monthly climatic data, water stage measurements, and the basin morphometry of a particular case (Los Jarales playa lake) to calibrate the soil water budget in the catchment and the water inputs from the watershed (runoff plus groundwater flow) at different scales, from monthly to daily. After the hydrologic model was calibrated, the authors implemented simulations with the goal of reproducing the past hydrological dynamics and forecasting water regime changes that would be caused by a modification of the wetland morphometry.

Precipitation forecasts for rainfall runoff predictions. A case study in poorly gauged Ribb and Gumara catchments, upper Blue Nile, Ethiopia

NASA Astrophysics Data System (ADS)

Seyoum, Mesgana; van Andel, Schalk Jan; Xuan, Yunqing; Amare, Kibreab

Flow forecasting in poorly gauged, flood-prone Ribb and Gumara sub-catchments of the Blue Nile was studied with the aim of testing the performance of Quantitative Precipitation Forecasts (QPFs). Four types of QPFs namely MM5 forecasts with a spatial resolution of 2 km; the Maximum, Mean and Minimum members (MaxEPS, MeanEPS and MinEPS where EPS stands for Ensemble Prediction System) of the fixed, low resolution (2.5 by 2.5 degrees) National Oceanic and Atmospheric Administration Global Forecast System (NOAA GFS) ensemble forecasts were used. Both the MM5 and the EPS were not calibrated (bias correction, downscaling (for EPS), etc.). In addition, zero forecasts assuming no rainfall in the coming days, and monthly average forecasts assuming average monthly rainfall in the coming days, were used. These rainfall forecasts were then used to drive the Hydrologic Engineering Center’s-Hydrologic Modeling System, HEC-HMS, hydrologic model for flow predictions. The results show that flow predictions using MaxEPS and MM5 precipitation forecasts over-predicted the peak flow for most of the seven events analyzed, whereas under-predicted peak flow was found using zero- and monthly average rainfall. The comparison of observed and predicted flow hydrographs shows that MM5, MaxEPS and MeanEPS precipitation forecasts were able to capture the rainfall signal that caused peak flows. Flow predictions based on MaxEPS and MeanEPS gave results that were quantitatively close to the observed flow for most events, whereas flow predictions based on MM5 resulted in large overestimations for some events. In follow-up research for this particular case study, calibration of the MM5 model will be performed. The overall analysis shows that freely available atmospheric forecasting products can provide additional information on upcoming rainfall and peak flow events in areas where only base-line forecasts such as no-rainfall or climatology are available.

ESA's Soil Moisture dnd Ocean Salinity Mission - Contributing to Water Resource Management

NASA Astrophysics Data System (ADS)

Mecklenburg, S.; Kerr, Y. H.

2015-12-01

The Soil Moisture and Ocean Salinity (SMOS) mission, launched in November 2009, is the European Space Agency's (ESA) second Earth Explorer Opportunity mission. The scientific objectives of the SMOS mission directly respond to the need for global observations of soil moisture and ocean salinity, two key variables used in predictive hydrological, oceanographic and atmospheric models. SMOS observations also provide information on the characterisation of ice and snow covered surfaces and the sea ice effect on ocean-atmosphere heat fluxes and dynamics, which affects large-scale processes of the Earth's climate system. The focus of this paper will be on SMOS's contribution to support water resource management: SMOS surface soil moisture provides the input to derive root-zone soil moisture, which in turn provides the input for the drought index, an important monitoring prediction tool for plant available water. In addition to surface soil moisture, SMOS also provides observations on vegetation optical depth. Both parameters aid agricultural applications such as crop growth, yield forecasting and drought monitoring, and provide input for carbon and land surface modelling. SMOS data products are used in data assimilation and forecasting systems. Over land, assimilating SMOS derived information has shown to have a positive impact on applications such as NWP, stream flow forecasting and the analysis of net ecosystem exchange. Over ocean, both sea surface salinity and severe wind speed have the potential to increase the predictive skill on the seasonal and short- to medium-range forecast range. Operational users in particular in Numerical Weather Prediction and operational hydrology have put forward a requirement for soil moisture data to be available in near-real time (NRT). This has been addressed by developing a fast retrieval for a NRT level 2 soil moisture product based on Neural Networks, which will be available by autumn 2015. This paper will focus on presenting the above applications and used SMOS data products.

Hydrologic Modeling at the National Water Center: Operational Implementation of the WRF-Hydro Model to support National Weather Service Hydrology

NASA Astrophysics Data System (ADS)

Cosgrove, B.; Gochis, D.; Clark, E. P.; Cui, Z.; Dugger, A. L.; Fall, G. M.; Feng, X.; Fresch, M. A.; Gourley, J. J.; Khan, S.; Kitzmiller, D.; Lee, H. S.; Liu, Y.; McCreight, J. L.; Newman, A. J.; Oubeidillah, A.; Pan, L.; Pham, C.; Salas, F.; Sampson, K. M.; Smith, M.; Sood, G.; Wood, A.; Yates, D. N.; Yu, W.; Zhang, Y.

2015-12-01

The National Weather Service (NWS) National Water Center(NWC) is collaborating with the NWS National Centers for Environmental Prediction (NCEP) and the National Center for Atmospheric Research (NCAR) to implement a first-of-its-kind operational instance of the Weather Research and Forecasting (WRF)-Hydro model over the Continental United States (CONUS) and contributing drainage areas on the NWS Weather and Climate Operational Supercomputing System (WCOSS) supercomputer. The system will provide seamless, high-resolution, continuously cycling forecasts of streamflow and other hydrologic outputs of value from both deterministic- and ensemble-type runs. WRF-Hydro will form the core of the NWC national water modeling strategy, supporting NWS hydrologic forecast operations along with emergency response and water management efforts of partner agencies. Input and output from the system will be comprehensively verified via the NWC Water Resource Evaluation Service. Hydrologic events occur on a wide range of temporal scales, from fast acting flash floods, to long-term flow events impacting water supply. In order to capture this range of events, the initial operational WRF-Hydro configuration will feature 1) hourly analysis runs, 2) short-and medium-range deterministic forecasts out to two day and ten day horizons and 3) long-range ensemble forecasts out to 30 days. All three of these configurations are underpinned by a 1km execution of the NoahMP land surface model, with channel routing taking place on 2.67 million NHDPlusV2 catchments covering the CONUS and contributing areas. Additionally, the short- and medium-range forecasts runs will feature surface and sub-surface routing on a 250m grid, while the hourly analyses will feature this same 250m routing in addition to nudging-based assimilation of US Geological Survey (USGS) streamflow observations. A limited number of major reservoirs will be configured within the model to begin to represent the first-order impacts of streamflow regulation.

Multivariate postprocessing techniques for probabilistic hydrological forecasting

NASA Astrophysics Data System (ADS)

Hemri, Stephan; Lisniak, Dmytro; Klein, Bastian

2016-04-01

Hydrologic ensemble forecasts driven by atmospheric ensemble prediction systems need statistical postprocessing in order to account for systematic errors in terms of both mean and spread. Runoff is an inherently multivariate process with typical events lasting from hours in case of floods to weeks or even months in case of droughts. This calls for multivariate postprocessing techniques that yield well calibrated forecasts in univariate terms and ensure a realistic temporal dependence structure at the same time. To this end, the univariate ensemble model output statistics (EMOS; Gneiting et al., 2005) postprocessing method is combined with two different copula approaches that ensure multivariate calibration throughout the entire forecast horizon. These approaches comprise ensemble copula coupling (ECC; Schefzik et al., 2013), which preserves the dependence structure of the raw ensemble, and a Gaussian copula approach (GCA; Pinson and Girard, 2012), which estimates the temporal correlations from training observations. Both methods are tested in a case study covering three subcatchments of the river Rhine that represent different sizes and hydrological regimes: the Upper Rhine up to the gauge Maxau, the river Moselle up to the gauge Trier, and the river Lahn up to the gauge Kalkofen. The results indicate that both ECC and GCA are suitable for modelling the temporal dependences of probabilistic hydrologic forecasts (Hemri et al., 2015). References Gneiting, T., A. E. Raftery, A. H. Westveld, and T. Goldman (2005), Calibrated probabilistic forecasting using ensemble model output statistics and minimum CRPS estimation, Monthly Weather Review, 133(5), 1098-1118, DOI: 10.1175/MWR2904.1. Hemri, S., D. Lisniak, and B. Klein, Multivariate postprocessing techniques for probabilistic hydrological forecasting, Water Resources Research, 51(9), 7436-7451, DOI: 10.1002/2014WR016473. Pinson, P., and R. Girard (2012), Evaluating the quality of scenarios of short-term wind power generation, Applied Energy, 96, 12-20, DOI: 10.1016/j.apenergy.2011.11.004. Schefzik, R., T. L. Thorarinsdottir, and T. Gneiting (2013), Uncertainty quantification in complex simulation models using ensemble copula coupling, Statistical Science, 28, 616-640, DOI: 10.1214/13-STS443.

A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series

NASA Astrophysics Data System (ADS)

Wang, Wen-Chuan; Chau, Kwok-Wing; Cheng, Chun-Tian; Qiu, Lin

2009-08-01

SummaryDeveloping a hydrological forecasting model based on past records is crucial to effective hydropower reservoir management and scheduling. Traditionally, time series analysis and modeling is used for building mathematical models to generate hydrologic records in hydrology and water resources. Artificial intelligence (AI), as a branch of computer science, is capable of analyzing long-series and large-scale hydrological data. In recent years, it is one of front issues to apply AI technology to the hydrological forecasting modeling. In this paper, autoregressive moving-average (ARMA) models, artificial neural networks (ANNs) approaches, adaptive neural-based fuzzy inference system (ANFIS) techniques, genetic programming (GP) models and support vector machine (SVM) method are examined using the long-term observations of monthly river flow discharges. The four quantitative standard statistical performance evaluation measures, the coefficient of correlation ( R), Nash-Sutcliffe efficiency coefficient ( E), root mean squared error (RMSE), mean absolute percentage error (MAPE), are employed to evaluate the performances of various models developed. Two case study river sites are also provided to illustrate their respective performances. The results indicate that the best performance can be obtained by ANFIS, GP and SVM, in terms of different evaluation criteria during the training and validation phases.

First Assessment of Itaipu Dam Ensemble Inflow Forecasting System

NASA Astrophysics Data System (ADS)

Mainardi Fan, Fernando; Machado Vieira Lisboa, Auder; Gomes Villa Trinidad, Giovanni; Rógenes Monteiro Pontes, Paulo; Collischonn, Walter; Tucci, Carlos; Costa Buarque, Diogo

2017-04-01

Inflow forecasting for Hydropower Plants (HPP) Dams is one of the prominent uses for hydrological forecasts. A very important HPP in terms of energy generation for South America is the Itaipu Dam, located in the Paraná River, between Brazil and Paraguay countries, with a drainage area of 820.000km2. In this work, we present the development of an ensemble forecasting system for Itaipu, operational since November 2015. The system is based in the MGB-IPH hydrological model, includes hydrodynamics simulations of the main river, and is run every day morning forced by seven different rainfall forecasts: (i) CPTEC-ETA 15km; (ii) CPTEC-BRAMS 5km; (iii) SIMEPAR WRF Ferrier; (iv) SIMEPAR WRF Lin; (v) SIMEPAR WRF Morrison; (vi) SIMEPAR WRF WDM6; (vii) SIMEPAR MEDIAN. The last one (vii) corresponds to the median value of SIMEPAR WRF model versions (iii to vi) rainfall forecasts. Besides the developed system, the "traditional" method for inflow forecasting generation for the Itaipu Dam is also run every day. This traditional method consists in the approximation of the future inflow based on the discharge tendency of upstream telemetric gauges. Nowadays, after all the forecasts are run, the hydrology team of Itaipu develop a consensus forecast, based on all obtained results, which is the one used for the Itaipu HPP Dam operation. After one year of operation a first evaluation of the Ensemble Forecasting System was conducted. Results show that the system performs satisfactory for rising flows up to five days lead time. However, some false alarms were also issued by most ensemble members in some cases. And not in all cases the system performed better than the traditional method, especially during hydrograph recessions. In terms of meteorological forecasts, some members usage are being discontinued. In terms of the hydrodynamics representation, it seems that a better information of rivers cross section could improve hydrographs recession curves forecasts. Those opportunities for improvements are currently being addressed in the system next update.

Verification of Ensemble Forecasts for the New York City Operations Support Tool

NASA Astrophysics Data System (ADS)

Day, G.; Schaake, J. C.; Thiemann, M.; Draijer, S.; Wang, L.

2012-12-01

The New York City water supply system operated by the Department of Environmental Protection (DEP) serves nine million people. It covers 2,000 square miles of portions of the Catskill, Delaware, and Croton watersheds, and it includes nineteen reservoirs and three controlled lakes. DEP is developing an Operations Support Tool (OST) to support its water supply operations and planning activities. OST includes historical and real-time data, a model of the water supply system complete with operating rules, and lake water quality models developed to evaluate alternatives for managing turbidity in the New York City Catskill reservoirs. OST will enable DEP to manage turbidity in its unfiltered system while satisfying its primary objective of meeting the City's water supply needs, in addition to considering secondary objectives of maintaining ecological flows, supporting fishery and recreation releases, and mitigating downstream flood peaks. The current version of OST relies on statistical forecasts of flows in the system based on recent observed flows. To improve short-term decision making, plans are being made to transition to National Weather Service (NWS) ensemble forecasts based on hydrologic models that account for short-term weather forecast skill, longer-term climate information, as well as the hydrologic state of the watersheds and recent observed flows. To ensure that the ensemble forecasts are unbiased and that the ensemble spread reflects the actual uncertainty of the forecasts, a statistical model has been developed to post-process the NWS ensemble forecasts to account for hydrologic model error as well as any inherent bias and uncertainty in initial model states, meteorological data and forecasts. The post-processor is designed to produce adjusted ensemble forecasts that are consistent with the DEP historical flow sequences that were used to develop the system operating rules. A set of historical hindcasts that is representative of the real-time ensemble forecasts is needed to verify that the post-processed forecasts are unbiased, statistically reliable, and preserve the skill inherent in the "raw" NWS ensemble forecasts. A verification procedure and set of metrics will be presented that provide an objective assessment of ensemble forecasts. The procedure will be applied to both raw ensemble hindcasts and to post-processed ensemble hindcasts. The verification metrics will be used to validate proper functioning of the post-processor and to provide a benchmark for comparison of different types of forecasts. For example, current NWS ensemble forecasts are based on climatology, using each historical year to generate a forecast trace. The NWS Hydrologic Ensemble Forecast System (HEFS) under development will utilize output from both the National Oceanic Atmospheric Administration (NOAA) Global Ensemble Forecast System (GEFS) and the Climate Forecast System (CFS). Incorporating short-term meteorological forecasts and longer-term climate forecast information should provide sharper, more accurate forecasts. Hindcasts from HEFS will enable New York City to generate verification results to validate the new forecasts and further fine-tune system operating rules. Project verification results will be presented for different watersheds across a range of seasons, lead times, and flow levels to assess the quality of the current ensemble forecasts.

A back-fitting algorithm to improve real-time flood forecasting

NASA Astrophysics Data System (ADS)

Zhang, Xiaojing; Liu, Pan; Cheng, Lei; Liu, Zhangjun; Zhao, Yan

2018-07-01

Real-time flood forecasting is important for decision-making with regards to flood control and disaster reduction. The conventional approach involves a postprocessor calibration strategy that first calibrates the hydrological model and then estimates errors. This procedure can simulate streamflow consistent with observations, but obtained parameters are not optimal. Joint calibration strategies address this issue by refining hydrological model parameters jointly with the autoregressive (AR) model. In this study, five alternative schemes are used to forecast floods. Scheme I uses only the hydrological model, while scheme II includes an AR model for error correction. In scheme III, differencing is used to remove non-stationarity in the error series. A joint inference strategy employed in scheme IV calibrates the hydrological and AR models simultaneously. The back-fitting algorithm, a basic approach for training an additive model, is adopted in scheme V to alternately recalibrate hydrological and AR model parameters. The performance of the five schemes is compared with a case study of 15 recorded flood events from China's Baiyunshan reservoir basin. Our results show that (1) schemes IV and V outperform scheme III during the calibration and validation periods and (2) scheme V is inferior to scheme IV in the calibration period, but provides better results in the validation period. Joint calibration strategies can therefore improve the accuracy of flood forecasting. Additionally, the back-fitting recalibration strategy produces weaker overcorrection and a more robust performance compared with the joint inference strategy.

Development of a flood early warning system and communication with end-users: the Vipava/Vipacco case study in the KULTURisk FP7 project

NASA Astrophysics Data System (ADS)

Grossi, Giovanna; Caronna, Paolo; Ranzi, Roberto

2014-05-01

Within the framework of risk communication, the goal of an early warning system is to support the interaction between technicians and authorities (and subsequently population) as a prevention measure. The methodology proposed in the KULTURisk FP7 project aimed to build a closer collaboration between these actors, in the perspective of promoting pro-active actions to mitigate the effects of flood hazards. The transnational (Slovenia/ Italy) Soča/Isonzo case study focused on this concept of cooperation between stakeholders and hydrological forecasters. The DIMOSHONG_VIP hydrological model was calibrated for the Vipava/Vipacco River (650 km2), a tributary of the Soča/Isonzo River, on the basis of flood events occurred between 1998 and 2012. The European Centre for Medium-Range Weather Forecasts (ECMWF) provided the past meteorological forecasts, both deterministic (1 forecast) and probabilistic (51 ensemble members). The resolution of the ECMWF grid is currently about 15 km (Deterministic-DET) and 30 km (Ensemble Prediction System-EPS). A verification was conducted to validate the flood-forecast outputs of the DIMOSHONG_VIP+ECMWF early warning system. Basic descriptive statistics, like event probability, probability of a forecast occurrence and frequency bias were determined. Some performance measures were calculated, such as hit rate (probability of detection) and false alarm rate (probability of false detection). Relative Opening Characteristic (ROC) curves were generated both for deterministic and probabilistic forecasts. These analysis showed a good performance of the early warning system, in respect of the small size of the sample. A particular attention was spent to the design of flood-forecasting output charts, involving and inquiring stakeholders (Alto Adriatico River Basin Authority), hydrology specialists in the field, and common people. Graph types for both forecasted precipitation and discharge were set. Three different risk thresholds were identified ("attention", "pre-alarm" or "alert", "alarm"), with an "icon-style" representation, suitable for communication to civil protection stakeholders or the public. Aiming at showing probabilistic representations in a "user-friendly" way, we opted for the visualization of the single deterministic forecasted hydrograph together with the 5%, 25%, 50%, 75% and 95% percentiles bands of the Hydrological Ensemble Prediction System (HEPS). HEPS is generally used for 3-5 days hydrological forecasts, while the error due to incorrect initial data is comparable to the error due to the lower resolution with respect to the deterministic forecast. In the short term forecasting (12-48 hours) the HEPS-members show obviously a similar tendency; in this case, considering its higher resolution, the deterministic forecast is expected to be more effective. The plot of different forecasts in the same chart allows the use of model outputs from 4/5 days to few hours before a potential flood event. This framework was built to help a stakeholder, like a mayor, a civil protection authority, etc, in the flood control and management operations, and was designed to be included in a wider decision support system.

Using subseasonal-to-seasonal (S2S) extreme rainfall forecasts for extended-range flood prediction in Australia

NASA Astrophysics Data System (ADS)

White, C. J.; Franks, S. W.; McEvoy, D.

2015-06-01

Meteorological and hydrological centres around the world are looking at ways to improve their capacity to be able to produce and deliver skilful and reliable forecasts of high-impact extreme rainfall and flooding events on a range of prediction timescales (e.g. sub-daily, daily, multi-week, seasonal). Making improvements to extended-range rainfall and flood forecast models, assessing forecast skill and uncertainty, and exploring how to apply flood forecasts and communicate their benefits to decision-makers are significant challenges facing the forecasting and water resources management communities. This paper presents some of the latest science and initiatives from Australia on the development, application and communication of extreme rainfall and flood forecasts on the extended-range "subseasonal-to-seasonal" (S2S) forecasting timescale, with a focus on risk-based decision-making, increasing flood risk awareness and preparedness, capturing uncertainty, understanding human responses to flood forecasts and warnings, and the growing adoption of "climate services". The paper also demonstrates how forecasts of flood events across a range of prediction timescales could be beneficial to a range of sectors and society, most notably for disaster risk reduction (DRR) activities, emergency management and response, and strengthening community resilience. Extended-range S2S extreme flood forecasts, if presented as easily accessible, timely and relevant information are a valuable resource to help society better prepare for, and subsequently cope with, extreme flood events.

Multi-parametric variational data assimilation for hydrological forecasting

NASA Astrophysics Data System (ADS)

Alvarado-Montero, R.; Schwanenberg, D.; Krahe, P.; Helmke, P.; Klein, B.

2017-12-01

Ensemble forecasting is increasingly applied in flow forecasting systems to provide users with a better understanding of forecast uncertainty and consequently to take better-informed decisions. A common practice in probabilistic streamflow forecasting is to force deterministic hydrological model with an ensemble of numerical weather predictions. This approach aims at the representation of meteorological uncertainty but neglects uncertainty of the hydrological model as well as its initial conditions. Complementary approaches use probabilistic data assimilation techniques to receive a variety of initial states or represent model uncertainty by model pools instead of single deterministic models. This paper introduces a novel approach that extends a variational data assimilation based on Moving Horizon Estimation to enable the assimilation of observations into multi-parametric model pools. It results in a probabilistic estimate of initial model states that takes into account the parametric model uncertainty in the data assimilation. The assimilation technique is applied to the uppermost area of River Main in Germany. We use different parametric pools, each of them with five parameter sets, to assimilate streamflow data, as well as remotely sensed data from the H-SAF project. We assess the impact of the assimilation in the lead time performance of perfect forecasts (i.e. observed data as forcing variables) as well as deterministic and probabilistic forecasts from ECMWF. The multi-parametric assimilation shows an improvement of up to 23% for CRPS performance and approximately 20% in Brier Skill Scores with respect to the deterministic approach. It also improves the skill of the forecast in terms of rank histogram and produces a narrower ensemble spread.

Global analysis of seasonal streamflow predictability using an ensemble prediction system and observations from 6192 small catchments worldwide

NASA Astrophysics Data System (ADS)

van Dijk, Albert I. J. M.; Peña-Arancibia, Jorge L.; Wood, Eric F.; Sheffield, Justin; Beck, Hylke E.

2013-05-01

Ideally, a seasonal streamflow forecasting system would ingest skilful climate forecasts and propagate these through calibrated hydrological models initialized with observed catchment conditions. At global scale, practical problems exist in each of these aspects. For the first time, we analyzed theoretical and actual skill in bimonthly streamflow forecasts from a global ensemble streamflow prediction (ESP) system. Forecasts were generated six times per year for 1979-2008 by an initialized hydrological model and an ensemble of 1° resolution daily climate estimates for the preceding 30 years. A post-ESP conditional sampling method was applied to 2.6% of forecasts, based on predictive relationships between precipitation and 1 of 21 climate indices prior to the forecast date. Theoretical skill was assessed against a reference run with historic forcing. Actual skill was assessed against streamflow records for 6192 small (<10,000 km2) catchments worldwide. The results show that initial catchment conditions provide the main source of skill. Post-ESP sampling enhanced skill in equatorial South America and Southeast Asia, particularly in terms of tercile probability skill, due to the persistence and influence of the El Niño Southern Oscillation. Actual skill was on average 54% of theoretical skill but considerably more for selected regions and times of year. The realized fraction of the theoretical skill probably depended primarily on the quality of precipitation estimates. Forecast skill could be predicted as the product of theoretical skill and historic model performance. Increases in seasonal forecast skill are likely to require improvement in the observation of precipitation and initial hydrological conditions.

Real-time drought forecasting system for irrigation managment

NASA Astrophysics Data System (ADS)

Ceppi, Alessandro; Ravazzani, Giovanni; Corbari, Chiara; Masseroni, Daniele; Meucci, Stefania; Pala, Francesca; Salerno, Raffaele; Meazza, Giuseppe; Chiesa, Marco; Mancini, Marco

2013-04-01

In recent years frequent periods of water scarcity have enhanced the need to use water more carefully, even in in European areas traditionally rich of water such as the Po Valley. In dry periods, the problem of water shortage can be enhanced by conflictual use of water such as irrigation, industrial and power production (hydroelectric and thermoelectric). Further, over the last decade the social perspective on this issue is increasing due to climate change and global warming scenarios which come out from the last IPCC Report. The increased frequency of dry periods has stimulated the improvement of irrigation and water management. In this study we show the development and implementation of the real-time drought forecasting system Pre.G.I., an Italian acronym that stands for "Hydro-Meteorological forecast for irrigation management". The system is based on ensemble prediction at long range (30 days) with hydrological simulation of water balance to forecast the soil water content in every parcel over the Consorzio Muzza basin. The studied area covers 74,000 ha in the middle of the Po Valley, near the city of Lodi. The hydrological ensemble forecasts are based on 20 meteorological members of the non-hydrostatic WRF model with 30 days as lead-time, provided by Epson Meteo Centre, while the hydrological model used to generate the soil moisture and water table simulations is the rainfall-runoff distributed FEST-WB model, developed at Politecnico di Milano. The hydrological model was validated against measurements of latent heat flux and soil moisture acquired by an eddy-covariance station. Reliability of the forecasting system and its benefits was assessed on some cases-study occurred in the recent years.

Improving wind energy forecasts using an Ensemble Kalman Filter data assimilation technique in a fully coupled hydrologic and atmospheric model

NASA Astrophysics Data System (ADS)

Williams, J. L.; Maxwell, R. M.; Delle Monache, L.

2012-12-01

Wind power is rapidly gaining prominence as a major source of renewable energy. Harnessing this promising energy source is challenging because of the chaotic nature of wind and its propensity to change speed and direction over short time scales. Accurate forecasting tools are critical to support the integration of wind energy into power grids and to maximize its impact on renewable energy portfolios. Numerous studies have shown that soil moisture distribution and land surface vegetative processes profoundly influence atmospheric boundary layer development and weather processes on local and regional scales. Using the PF.WRF model, a fully-coupled hydrologic and atmospheric model employing the ParFlow hydrologic model with the Weather Research and Forecasting model coupled via mass and energy fluxes across the land surface, we have explored the connections between the land surface and the atmosphere in terms of land surface energy flux partitioning and coupled variable fields including hydraulic conductivity, soil moisture and wind speed, and demonstrated that reductions in uncertainty in these coupled fields propagate through the hydrologic and atmospheric system. We have adapted the Data Assimilation Research Testbed (DART), an implementation of the robust Ensemble Kalman Filter data assimilation algorithm, to expand our capability to nudge forecasts produced with the PF.WRF model using observational data. Using a semi-idealized simulation domain, we examine the effects of assimilating observations of variables such as wind speed and temperature collected in the atmosphere, and land surface and subsurface observations such as soil moisture on the quality of forecast outputs. The sensitivities we find in this study will enable further studies to optimize observation collection to maximize the utility of the PF.WRF-DART forecasting system.

The HYPE Open Source Community

NASA Astrophysics Data System (ADS)

Strömbäck, L.; Pers, C.; Isberg, K.; Nyström, K.; Arheimer, B.

2013-12-01

The Hydrological Predictions for the Environment (HYPE) model is a dynamic, semi-distributed, process-based, integrated catchment model. It uses well-known hydrological and nutrient transport concepts and can be applied for both small and large scale assessments of water resources and status. In the model, the landscape is divided into classes according to soil type, vegetation and altitude. The soil representation is stratified and can be divided in up to three layers. Water and substances are routed through the same flow paths and storages (snow, soil, groundwater, streams, rivers, lakes) considering turn-over and transformation on the way towards the sea. HYPE has been successfully used in many hydrological applications at SMHI. For Europe, we currently have three different models; The S-HYPE model for Sweden; The BALT-HYPE model for the Baltic Sea; and the E-HYPE model for the whole Europe. These models simulate hydrological conditions and nutrients for their respective areas and are used for characterization, forecasts, and scenario analyses. Model data can be downloaded from hypeweb.smhi.se. In addition, we provide models for the Arctic region, the Arab (Middle East and Northern Africa) region, India, the Niger River basin, the La Plata Basin. This demonstrates the applicability of the HYPE model for large scale modeling in different regions of the world. An important goal with our work is to make our data and tools available as open data and services. For this aim we created the HYPE Open Source Community (OSC) that makes the source code of HYPE available for anyone interested in further development of HYPE. The HYPE OSC (hype.sourceforge.net) is an open source initiative under the Lesser GNU Public License taken by SMHI to strengthen international collaboration in hydrological modeling and hydrological data production. The hypothesis is that more brains and more testing will result in better models and better code. The code is transparent and can be changed and learnt from. New versions of the main code are delivered frequently. HYPE OSC is open to everyone interested in hydrology, hydrological modeling and code development - e.g. scientists, authorities, and consultancies. By joining the HYPE OSC you get access a state-of-the-art operational hydrological model. The HYPE source code is designed to efficiently handle large scale modeling for forecast, hindcast and climate applications. The code is under constant development to improve the hydrological processes, efficiency and readability. In the beginning of 2013 we released a version with new and better modularization based on hydrological processes. This will make the code easier to understand and further develop for a new user. An important challenge in this process is to produce code that is easy for anyone to understand and work with, but still maintain the properties that make the code efficient enough for large scale applications. Input from the HYPE Open Source Community is an important source for future improvements of the HYPE model. Therefore, by joining the community you become an active part of the development, get access to the latest features and can influence future versions of the model.

Evaluation of streamflow forecast for the National Water Model of U.S. National Weather Service

NASA Astrophysics Data System (ADS)

Rafieeinasab, A.; McCreight, J. L.; Dugger, A. L.; Gochis, D.; Karsten, L. R.; Zhang, Y.; Cosgrove, B.; Liu, Y.

2016-12-01

The National Water Model (NWM), an implementation of the community WRF-Hydro modeling system, is an operational hydrologic forecasting model for the contiguous United States. The model forecasts distributed hydrologic states and fluxes, including soil moisture, snowpack, ET, and ponded water. In particular, the NWM provides streamflow forecasts at more than 2.7 million river reaches for three forecast ranges: short (15 hr), medium (10 days), and long (30 days). In this study, we verify short and medium range streamflow forecasts in the context of the verification of their respective quantitative precipitation forecasts/forcing (QPF), the High Resolution Rapid Refresh (HRRR) and the Global Forecast System (GFS). The streamflow evaluation is performed for summer of 2016 at more than 6,000 USGS gauges. Both individual forecasts and forecast lead times are examined. Selected case studies of extreme events aim to provide insight into the quality of the NWM streamflow forecasts. A goal of this comparison is to address how much streamflow bias originates from precipitation forcing bias. To this end, precipitation verification is performed over the contributing areas above (and between assimilated) USGS gauge locations. Precipitation verification is based on the aggregated, blended StageIV/StageII data as the "reference truth". We summarize the skill of the streamflow forecasts, their skill relative to the QPF, and make recommendations for improving NWM forecast skill.

Monthly streamflow forecasting using continuous wavelet and multi-gene genetic programming combination

NASA Astrophysics Data System (ADS)

Hadi, Sinan Jasim; Tombul, Mustafa

2018-06-01

Streamflow is an essential component of the hydrologic cycle in the regional and global scale and the main source of fresh water supply. It is highly associated with natural disasters, such as droughts and floods. Therefore, accurate streamflow forecasting is essential. Forecasting streamflow in general and monthly streamflow in particular is a complex process that cannot be handled by data-driven models (DDMs) only and requires pre-processing. Wavelet transformation is a pre-processing technique; however, application of continuous wavelet transformation (CWT) produces many scales that cause deterioration in the performance of any DDM because of the high number of redundant variables. This study proposes multigene genetic programming (MGGP) as a selection tool. After the CWT analysis, it selects important scales to be imposed into the artificial neural network (ANN). A basin located in the southeast of Turkey is selected as case study to prove the forecasting ability of the proposed model. One month ahead downstream flow is used as output, and downstream flow, upstream, rainfall, temperature, and potential evapotranspiration with associated lags are used as inputs. Before modeling, wavelet coherence transformation (WCT) analysis was conducted to analyze the relationship between variables in the time-frequency domain. Several combinations were developed to investigate the effect of the variables on streamflow forecasting. The results indicated a high localized correlation between the streamflow and other variables, especially the upstream. In the models of the standalone layout where the data were entered to ANN and MGGP without CWT, the performance is found poor. In the best-scale layout, where the best scale of the CWT identified as the highest correlated scale is chosen and enters to ANN and MGGP, the performance increased slightly. Using the proposed model, the performance improved dramatically particularly in forecasting the peak values because of the inclusion of several scales in which seasonality and irregularity can be captured. Using hydrological and meteorological variables also improved the ability to forecast the streamflow.

A RETROSPECTIVE ANALYSIS OF MODEL UNCERTAINTY FOR FORECASTING HYDROLOGIC CHANGE

EPA Science Inventory

GIS-based hydrologic modeling offers a convenient means of assessing the impacts associated with land-cover/use change for environmental planning efforts. Alternative future scenarios can be used as input to hydrologic models and compared with existing conditions to evaluate pot...

A non-parametric postprocessor for bias-correcting multi-model ensemble forecasts of hydrometeorological and hydrologic variables

NASA Astrophysics Data System (ADS)

Brown, James; Seo, Dong-Jun

2010-05-01

Operational forecasts of hydrometeorological and hydrologic variables often contain large uncertainties, for which ensemble techniques are increasingly used. However, the utility of ensemble forecasts depends on the unbiasedness of the forecast probabilities. We describe a technique for quantifying and removing biases from ensemble forecasts of hydrometeorological and hydrologic variables, intended for use in operational forecasting. The technique makes no a priori assumptions about the distributional form of the variables, which is often unknown or difficult to model parametrically. The aim is to estimate the conditional cumulative distribution function (ccdf) of the observed variable given a (possibly biased) real-time ensemble forecast from one or several forecasting systems (multi-model ensembles). The technique is based on Bayesian optimal linear estimation of indicator variables, and is analogous to indicator cokriging (ICK) in geostatistics. By developing linear estimators for the conditional expectation of the observed variable at many thresholds, ICK provides a discrete approximation of the full ccdf. Since ICK minimizes the conditional error variance of the indicator expectation at each threshold, it effectively minimizes the Continuous Ranked Probability Score (CRPS) when infinitely many thresholds are employed. However, the ensemble members used as predictors in ICK, and other bias-correction techniques, are often highly cross-correlated, both within and between models. Thus, we propose an orthogonal transform of the predictors used in ICK, which is analogous to using their principal components in the linear system of equations. This leads to a well-posed problem in which a minimum number of predictors are used to provide maximum information content in terms of the total variance explained. The technique is used to bias-correct precipitation ensemble forecasts from the NCEP Global Ensemble Forecast System (GEFS), for which independent validation results are presented. Extension to multimodel ensembles from the NCEP GFS and Short Range Ensemble Forecast (SREF) systems is also proposed.

Extending medium-range predictability of extreme hydrological events in Europe

PubMed Central

Lavers, David A.; Pappenberger, Florian; Zsoter, Ervin

2014-01-01

Widespread flooding occurred across northwest Europe during the winter of 2013/14, resulting in large socioeconomic damages. In the historical record, extreme hydrological events have been connected with intense water vapour transport. Here we show that water vapour transport has higher medium-range predictability compared with precipitation in the winter 2013/14 forecasts from the European Centre for Medium-Range Weather Forecasts. Applying the concept of potential predictability, the transport is found to extend the forecast horizon by 3 days in some European regions. Our results suggest that the breakdown in precipitation predictability is due to uncertainty in the horizontal mass convergence location, an essential mechanism for precipitation generation. Furthermore, the predictability increases with larger spatial averages. Given the strong association between precipitation and water vapour transport, especially for extreme events, we conclude that the higher transport predictability could be used as a model diagnostic to increase preparedness for extreme hydrological events. PMID:25387309

Flash flood forecasting using simplified hydrological models, radar rainfall forecasts and data assimilation

NASA Astrophysics Data System (ADS)

Smith, P. J.; Beven, K.; Panziera, L.

2012-04-01

The issuing of timely flood alerts may be dependant upon the ability to predict future values of water level or discharge at locations where observations are available. Catchments at risk of flash flooding often have a rapid natural response time, typically less then the forecast lead time desired for issuing alerts. This work focuses on the provision of short-range (up to 6 hours lead time) predictions of discharge in small catchments based on utilising radar forecasts to drive a hydrological model. An example analysis based upon the Verzasca catchment (Ticino, Switzerland) is presented. Parsimonious time series models with a mechanistic interpretation (so called Data-Based Mechanistic model) have been shown to provide reliable accurate forecasts in many hydrological situations. In this study such a model is developed to predict the discharge at an observed location from observed precipitation data. The model is shown to capture the snow melt response at this site. Observed discharge data is assimilated to improve the forecasts, of up to two hours lead time, that can be generated from observed precipitation. To generate forecasts with greater lead time ensemble precipitation forecasts are utilised. In this study the Nowcasting ORographic precipitation in the Alps (NORA) product outlined in more detail elsewhere (Panziera et al. Q. J. R. Meteorol. Soc. 2011; DOI:10.1002/qj.878) is utilised. NORA precipitation forecasts are derived from historical analogues based on the radar field and upper atmospheric conditions. As such, they avoid the need to explicitly model the evolution of the rainfall field through for example Lagrangian diffusion. The uncertainty in the forecasts is represented by characterisation of the joint distribution of the observed discharge, the discharge forecast using the (in operational conditions unknown) future observed precipitation and that forecast utilising the NORA ensembles. Constructing the joint distribution in this way allows the full historic record of data at the site to inform the predictive distribution. It is shown that, in part due to the limited availability of forecasts, the uncertainty in the relationship between the NORA based forecasts and other variates dominated the resulting predictive uncertainty.

An operational ensemble prediction system for catchment rainfall over eastern Africa spanning multiple temporal and spatial scales

NASA Astrophysics Data System (ADS)

Riddle, E. E.; Hopson, T. M.; Gebremichael, M.; Boehnert, J.; Broman, D.; Sampson, K. M.; Rostkier-Edelstein, D.; Collins, D. C.; Harshadeep, N. R.; Burke, E.; Havens, K.

2017-12-01

While it is not yet certain how precipitation patterns will change over Africa in the future, it is clear that effectively managing the available water resources is going to be crucial in order to mitigate the effects of water shortages and floods that are likely to occur in a changing climate. One component of effective water management is the availability of state-of-the-art and easy to use rainfall forecasts across multiple spatial and temporal scales. We present a web-based system for displaying and disseminating ensemble forecast and observed precipitation data over central and eastern Africa. The system provides multi-model rainfall forecasts integrated to relevant hydrological catchments for timescales ranging from one day to three months. A zoom-in features is available to access high resolution forecasts for small-scale catchments. Time series plots and data downloads with forecasts, recent rainfall observations and climatological data are available by clicking on individual catchments. The forecasts are calibrated using a quantile regression technique and an optimal multi-model forecast is provided at each timescale. The forecast skill at the various spatial and temporal scales will discussed, as will current applications of this tool for managing water resources in Sudan and optimizing hydropower operations in Ethiopia and Tanzania.

Application of satellite-based rainfall and medium range meteorological forecast in real-time flood forecasting in the Mahanadi River basin

NASA Astrophysics Data System (ADS)

Nanda, Trushnamayee; Beria, Harsh; Sahoo, Bhabagrahi; Chatterjee, Chandranath

2016-04-01

Increasing frequency of hydrologic extremes in a warming climate call for the development of reliable flood forecasting systems. The unavailability of meteorological parameters in real-time, especially in the developing parts of the world, makes it a challenging task to accurately predict flood, even at short lead times. The satellite-based Tropical Rainfall Measuring Mission (TRMM) provides an alternative to the real-time precipitation data scarcity. Moreover, rainfall forecasts by the numerical weather prediction models such as the medium term forecasts issued by the European Center for Medium range Weather Forecasts (ECMWF) are promising for multistep-ahead flow forecasts. We systematically evaluate these rainfall products over a large catchment in Eastern India (Mahanadi River basin). We found spatially coherent trends, with both the real-time TRMM rainfall and ECMWF rainfall forecast products overestimating low rainfall events and underestimating high rainfall events. However, no significant bias was found for the medium rainfall events. Another key finding was that these rainfall products captured the phase of the storms pretty well, but suffered from consistent under-prediction. The utility of the real-time TRMM and ECMWF forecast products are evaluated by rainfall-runoff modeling using different artificial neural network (ANN)-based models up to 3-days ahead. Keywords: TRMM; ECMWF; forecast; ANN; rainfall-runoff modeling

Monitoring Areal Snow Cover Using NASA Satellite Imagery

NASA Technical Reports Server (NTRS)

Harshburger, Brian J.; Blandford, Troy; Moore, Brandon

2011-01-01

The objective of this project is to develop products and tools to assist in the hydrologic modeling process, including tools to help prepare inputs for hydrologic models and improved methods for the visualization of streamflow forecasts. In addition, this project will facilitate the use of NASA satellite imagery (primarily snow cover imagery) by other federal and state agencies with operational streamflow forecasting responsibilities. A GIS software toolkit for monitoring areal snow cover extent and producing streamflow forecasts is being developed. This toolkit will be packaged as multiple extensions for ArcGIS 9.x and an opensource GIS software package. The toolkit will provide users with a means for ingesting NASA EOS satellite imagery (snow cover analysis), preparing hydrologic model inputs, and visualizing streamflow forecasts. Primary products include a software tool for predicting the presence of snow under clouds in satellite images; a software tool for producing gridded temperature and precipitation forecasts; and a suite of tools for visualizing hydrologic model forecasting results. The toolkit will be an expert system designed for operational users that need to generate accurate streamflow forecasts in a timely manner. The Remote Sensing of Snow Cover Toolbar will ingest snow cover imagery from multiple sources, including the MODIS Operational Snowcover Data and convert them to gridded datasets that can be readily used. Statistical techniques will then be applied to the gridded snow cover data to predict the presence of snow under cloud cover. The toolbar has the ability to ingest both binary and fractional snow cover data. Binary mapping techniques use a set of thresholds to determine whether a pixel contains snow or no snow. Fractional mapping techniques provide information regarding the percentage of each pixel that is covered with snow. After the imagery has been ingested, physiographic data is attached to each cell in the snow cover image. This data can be obtained from a digital elevation model (DEM) for the area of interest.

The Hydrologic Ensemble Prediction Experiment (HEPEX)

NASA Astrophysics Data System (ADS)

Wood, A. W.; Thielen, J.; Pappenberger, F.; Schaake, J. C.; Hartman, R. K.

2012-12-01

The Hydrologic Ensemble Prediction Experiment was established in March, 2004, at a workshop hosted by the European Center for Medium Range Weather Forecasting (ECMWF). With support from the US National Weather Service (NWS) and the European Commission (EC), the HEPEX goal was to bring the international hydrological and meteorological communities together to advance the understanding and adoption of hydrological ensemble forecasts for decision support in emergency management and water resources sectors. The strategy to meet this goal includes meetings that connect the user, forecast producer and research communities to exchange ideas, data and methods; the coordination of experiments to address specific challenges; and the formation of testbeds to facilitate shared experimentation. HEPEX has organized about a dozen international workshops, as well as sessions at scientific meetings (including AMS, AGU and EGU) and special issues of scientific journals where workshop results have been published. Today, the HEPEX mission is to demonstrate the added value of hydrological ensemble prediction systems (HEPS) for emergency management and water resources sectors to make decisions that have important consequences for economy, public health, safety, and the environment. HEPEX is now organised around six major themes that represent core elements of a hydrologic ensemble prediction enterprise: input and pre-processing, ensemble techniques, data assimilation, post-processing, verification, and communication and use in decision making. This poster presents an overview of recent and planned HEPEX activities, highlighting case studies that exemplify the focus and objectives of HEPEX.

Hydro-meteorological evaluation of downscaled global ensemble rainfall forecasts

NASA Astrophysics Data System (ADS)

Gaborit, Étienne; Anctil, François; Fortin, Vincent; Pelletier, Geneviève

2013-04-01

Ensemble rainfall forecasts are of high interest for decision making, as they provide an explicit and dynamic assessment of the uncertainty in the forecast (Ruiz et al. 2009). However, for hydrological forecasting, their low resolution currently limits their use to large watersheds (Maraun et al. 2010). In order to bridge this gap, various implementations of the statistic-stochastic multi-fractal downscaling technique presented by Perica and Foufoula-Georgiou (1996) were compared, bringing Environment Canada's global ensemble rainfall forecasts from a 100 by 70-km resolution down to 6 by 4-km, while increasing each pixel's rainfall variance and preserving its original mean. For comparison purposes, simpler methods were also implemented such as the bi-linear interpolation, which disaggregates global forecasts without modifying their variance. The downscaled meteorological products were evaluated using different scores and diagrams, from both a meteorological and a hydrological view points. The meteorological evaluation was conducted comparing the forecasted rainfall depths against nine days of observed values taken from Québec City rain gauge database. These 9 days present strong precipitation events occurring during the summer of 2009. For the hydrologic evaluation, the hydrological models SWMM5 and (a modified version of) GR4J were implemented on a small 6 km2 urban catchment located in the Québec City region. Ensemble hydrologic forecasts with a time step of 3 hours were then performed over a 3-months period of the summer of 2010 using the original and downscaled ensemble rainfall forecasts. The most important conclusions of this work are that the overall quality of the forecasts was preserved during the disaggregation procedure and that the disaggregated products using this variance-enhancing method were of similar quality than bi-linear interpolation products. However, variance and dispersion of the different members were, of course, much improved for the variance-enhanced products, compared to the bi-linear interpolation, which is a decisive advantage. The disaggregation technique of Perica and Foufoula-Georgiou (1996) hence represents an interesting way of bridging the gap between the meteorological models' resolution and the high degree of spatial precision sometimes required by hydrological models in their precipitation representation. References Maraun, D., Wetterhall, F., Ireson, A. M., Chandler, R. E., Kendon, E. J., Widmann, M., Brienen, S., Rust, H. W., Sauter, T., Themeßl, M., Venema, V. K. C., Chun, K. P., Goodess, C. M., Jones, R. G., Onof, C., Vrac, M., and Thiele-Eich, I. 2010. Precipitation downscaling under climate change: recent developments to bridge the gap between dynamical models and the end user. Reviews of Geophysics, 48 (3): RG3003, [np]. Doi: 10.1029/2009RG000314. Perica, S., and Foufoula-Georgiou, E. 1996. Model for multiscale disaggregation of spatial rainfall based on coupling meteorological and scaling descriptions. Journal Of Geophysical Research, 101(D21): 26347-26361. Ruiz, J., Saulo, C. and Kalnay, E. 2009. Comparison of Methods Used to Generate Probabilistic Quantitative Precipitation Forecasts over South America. Weather and forecasting, 24: 319-336. DOI: 10.1175/2008WAF2007098.1 This work is distributed under the Creative Commons Attribution 3.0 Unported License together with an author copyright. This license does not conflict with the regulations of the Crown Copyright.

Verifying and Postprocesing the Ensemble Spread-Error Relationship

NASA Astrophysics Data System (ADS)

Hopson, Tom; Knievel, Jason; Liu, Yubao; Roux, Gregory; Wu, Wanli

2013-04-01

With the increased utilization of ensemble forecasts in weather and hydrologic applications, there is a need to verify their benefit over less expensive deterministic forecasts. One such potential benefit of ensemble systems is their capacity to forecast their own forecast error through the ensemble spread-error relationship. The paper begins by revisiting the limitations of the Pearson correlation alone in assessing this relationship. Next, we introduce two new metrics to consider in assessing the utility an ensemble's varying dispersion. We argue there are two aspects of an ensemble's dispersion that should be assessed. First, and perhaps more fundamentally: is there enough variability in the ensembles dispersion to justify the maintenance of an expensive ensemble prediction system (EPS), irrespective of whether the EPS is well-calibrated or not? To diagnose this, the factor that controls the theoretical upper limit of the spread-error correlation can be useful. Secondly, does the variable dispersion of an ensemble relate to variable expectation of forecast error? Representing the spread-error correlation in relation to its theoretical limit can provide a simple diagnostic of this attribute. A context for these concepts is provided by assessing two operational ensembles: 30-member Western US temperature forecasts for the U.S. Army Test and Evaluation Command and 51-member Brahmaputra River flow forecasts of the Climate Forecast and Applications Project for Bangladesh. Both of these systems utilize a postprocessing technique based on quantile regression (QR) under a step-wise forward selection framework leading to ensemble forecasts with both good reliability and sharpness. In addition, the methodology utilizes the ensemble's ability to self-diagnose forecast instability to produce calibrated forecasts with informative skill-spread relationships. We will describe both ensemble systems briefly, review the steps used to calibrate the ensemble forecast, and present verification statistics using error-spread metrics, along with figures from operational ensemble forecasts before and after calibration.

Post-processing Seasonal Precipitation Forecasts via Integrating Climate Indices and the Analog Approach

NASA Astrophysics Data System (ADS)

Liu, Y.; Zhang, Y.; Wood, A.; Lee, H. S.; Wu, L.; Schaake, J. C.

2016-12-01

Seasonal precipitation forecasts are a primary driver for seasonal streamflow prediction that is critical for a range of water resources applications, such as reservoir operations and drought management. However, it is well known that seasonal precipitation forecasts from climate models are often biased and also too coarse in spatial resolution for hydrologic applications. Therefore, post-processing procedures such as downscaling and bias correction are often needed. In this presentation, we discuss results from a recent study that applies a two-step methodology to downscale and correct the ensemble mean precipitation forecasts from the Climate Forecast System (CFS). First, CFS forecasts are downscaled and bias corrected using monthly reforecast analogs: we identify past precipitation forecasts that are similar to the current forecast, and then use the finer-scale observational analysis fields from the corresponding dates to represent the post-processed ensemble forecasts. Second, we construct the posterior distribution of forecast precipitation from the post-processed ensemble by integrating climate indices: a correlation analysis is performed to identify dominant climate indices for the study region, which are then used to weight the analysis analogs selected in the first step using a Bayesian approach. The methodology is applied to the California Nevada River Forecast Center (CNRFC) and the Middle Atlantic River Forecast Center (MARFC) regions for 1982-2015, using the North American Land Data Assimilation System (NLDAS-2) precipitation as the analysis. The results from cross validation show that the post-processed CFS precipitation forecast are considerably more skillful than the raw CFS with the analog approach only. Integrating climate indices can further improve the skill if the number of ensemble members considered is large enough; however, the improvement is generally limited to the first couple of months when compared against climatology. Impacts of various factors such as ensemble size, lead time, and choice of climate indices will also be discussed.

Adequacy of satellite derived rainfall data for stream flow modeling

USGS Publications Warehouse

Artan, G.; Gadain, Hussein; Smith, Jodie; Asante, Kwasi; Bandaragoda, C.J.; Verdin, J.P.

2007-01-01

Floods are the most common and widespread climate-related hazard on Earth. Flood forecasting can reduce the death toll associated with floods. Satellites offer effective and economical means for calculating areal rainfall estimates in sparsely gauged regions. However, satellite-based rainfall estimates have had limited use in flood forecasting and hydrologic stream flow modeling because the rainfall estimates were considered to be unreliable. In this study we present the calibration and validation results from a spatially distributed hydrologic model driven by daily satellite-based estimates of rainfall for sub-basins of the Nile and Mekong Rivers. The results demonstrate the usefulness of remotely sensed precipitation data for hydrologic modeling when the hydrologic model is calibrated with such data. However, the remotely sensed rainfall estimates cannot be used confidently with hydrologic models that are calibrated with rain gauge measured rainfall, unless the model is recalibrated. ?? Springer Science+Business Media, Inc. 2007.

Can we use Earth Observations to improve monthly water level forecasts?

NASA Astrophysics Data System (ADS)

Slater, L. J.; Villarini, G.

2017-12-01

Dynamical-statistical hydrologic forecasting approaches benefit from different strengths in comparison with traditional hydrologic forecasting systems: they are computationally efficient, can integrate and `learn' from a broad selection of input data (e.g., General Circulation Model (GCM) forecasts, Earth Observation time series, teleconnection patterns), and can take advantage of recent progress in machine learning (e.g. multi-model blending, post-processing and ensembling techniques). Recent efforts to develop a dynamical-statistical ensemble approach for forecasting seasonal streamflow using both GCM forecasts and changing land cover have shown promising results over the U.S. Midwest. Here, we use climate forecasts from several GCMs of the North American Multi Model Ensemble (NMME) alongside 15-minute stage time series from the National River Flow Archive (NRFA) and land cover classes extracted from the European Space Agency's Climate Change Initiative 300 m annual Global Land Cover time series. With these data, we conduct systematic long-range probabilistic forecasting of monthly water levels in UK catchments over timescales ranging from one to twelve months ahead. We evaluate the improvement in model fit and model forecasting skill that comes from using land cover classes as predictors in the models. This work opens up new possibilities for combining Earth Observation time series with GCM forecasts to predict a variety of hazards from space using data science techniques.

Evaluations of Extended-Range tropical Cyclone Forecasts in the Western North Pacific by using the Ensemble Reforecasts: Preliminary Results

NASA Astrophysics Data System (ADS)

Tsai, Hsiao-Chung; Chen, Pang-Cheng; Elsberry, Russell L.

2017-04-01

The objective of this study is to evaluate the predictability of the extended-range forecasts of tropical cyclone (TC) in the western North Pacific using reforecasts from National Centers for Environmental Prediction (NCEP) Global Ensemble Forecast System (GEFS) during 1996-2015, and from the Climate Forecast System (CFS) during 1999-2010. Tsai and Elsberry have demonstrated that an opportunity exists to support hydrological operations by using the extended-range TC formation and track forecasts in the western North Pacific from the ECMWF 32-day ensemble. To demonstrate this potential for the decision-making processes regarding water resource management and hydrological operation in Taiwan reservoir watershed areas, special attention is given to the skill of the NCEP GEFS and CFS models in predicting the TCs affecting the Taiwan area. The first objective of this study is to analyze the skill of NCEP GEFS and CFS TC forecasts and quantify the forecast uncertainties via verifications of categorical binary forecasts and probabilistic forecasts. The second objective is to investigate the relationships among the large-scale environmental factors [e.g., El Niño Southern Oscillation (ENSO), Madden-Julian Oscillation (MJO), etc.] and the model forecast errors by using the reforecasts. Preliminary results are indicating that the skill of the TC activity forecasts based on the raw forecasts can be further improved if the model biases are minimized by utilizing these reforecasts.

Cloud Computing Applications in Support of Earth Science Activities at Marshall Space Flight Center

NASA Astrophysics Data System (ADS)

Molthan, A.; Limaye, A. S.

2011-12-01

Currently, the NASA Nebula Cloud Computing Platform is available to Agency personnel in a pre-release status as the system undergoes a formal operational readiness review. Over the past year, two projects within the Earth Science Office at NASA Marshall Space Flight Center have been investigating the performance and value of Nebula's "Infrastructure as a Service", or "IaaS" concept and applying cloud computing concepts to advance their respective mission goals. The Short-term Prediction Research and Transition (SPoRT) Center focuses on the transition of unique NASA satellite observations and weather forecasting capabilities for use within the operational forecasting community through partnerships with NOAA's National Weather Service (NWS). SPoRT has evaluated the performance of the Weather Research and Forecasting (WRF) model on virtual machines deployed within Nebula and used Nebula instances to simulate local forecasts in support of regional forecast studies of interest to select NWS forecast offices. In addition to weather forecasting applications, rapidly deployable Nebula virtual machines have supported the processing of high resolution NASA satellite imagery to support disaster assessment following the historic severe weather and tornado outbreak of April 27, 2011. Other modeling and satellite analysis activities are underway in support of NASA's SERVIR program, which integrates satellite observations, ground-based data and forecast models to monitor environmental change and improve disaster response in Central America, the Caribbean, Africa, and the Himalayas. Leveraging SPoRT's experience, SERVIR is working to establish a real-time weather forecasting model for Central America. Other modeling efforts include hydrologic forecasts for Kenya, driven by NASA satellite observations and reanalysis data sets provided by the broader meteorological community. Forecast modeling efforts are supplemented by short-term forecasts of convective initiation, determined by geostationary satellite observations processed on virtual machines powered by Nebula. This presentation will provide an overview of these activities from a scientific and cloud computing applications perspective, identifying the strengths and weaknesses for deploying each project within an IaaS environment, and ways to collaborate with the Nebula or other cloud-user communities to collaborate on projects as they go forward.

Timetable of an operational flood forecasting system

NASA Astrophysics Data System (ADS)

Liechti, Katharina; Jaun, Simon; Zappa, Massimiliano

2010-05-01

At present a new underground part of Zurich main station is under construction. For this purpose the runoff capacity of river Sihl, which is passing beneath the main station, is reduced by 40%. If a flood is to occur the construction site is evacuated and gates can be opened for full runoff capacity to prevent bigger damages. However, flooding the construction site, even if it is controlled, is coupled with costs and retardation. The evacuation of the construction site at Zurich main station takes about 2 to 4 hours and opening the gates takes another 1 to 2 hours each. In the upper part of the 336 km2 Sihl catchment the Sihl lake, a reservoir lake, is situated. It belongs and is used by the Swiss Railway Company for hydropower production. This lake can act as a retention basin for about 46% of the Sihl catchment. Lowering the lake level to gain retention capacity, and therewith safety, is coupled with direct loss for the Railway Company. To calculate the needed retention volume and the water to be released facing unfavourable weather conditions, forecasts with a minimum lead time of 2 to 3 days are needed. Since the catchment is rather small, this can only be realised by the use of meteorological forecast data. Thus the management of the construction site depends on accurate forecasts to base their decisions on. Therefore an operational hydrological ensemble prediction system (HEPS) was introduced in September 2008 by the Swiss Federal Institute for Forest, Snow and Landscape Research (WSL). It delivers daily discharge forecasts with a time horizon of 5 days. The meteorological forecasts are provided by MeteoSwiss and stem from the operational limited-area COSMO-LEPS which downscales the ECMWF ensemble prediction system to a spatial resolution of 7 km. Additional meteorological data for model calibration and initialisation (air temperature, precipitation, water vapour pressure, global radiation, wind speed and sunshine duration) and radar data are also provided by MeteoSwiss. Additional meteorological and hydrological observations are provided by a hydropower company, the Canton of Zurich and the Federal Office for the Environment (FOEN). The hydrological forecasting is calculated by the semi-distributed hydrological model PREVAH (Precipitation-Runoff-EVapotranspiration-HRU-related Model) and is further processed by the hydraulic model FLORIS. Finally the forecasts and alerts along with additional meteorological and hydrological observations and forecasts from collaborating institution are sent to a webserver accessible for decision makers. We will document the setup of our operational flood forecasting system, evaluate its performance and show how the collaboration and communication between science and practice, including all the different interests, works for this particular example.

Forecasting seasonal hydrologic response in major river basins

NASA Astrophysics Data System (ADS)

Bhuiyan, A. M.

2014-05-01

Seasonal precipitation variation due to natural climate variation influences stream flow and the apparent frequency and severity of extreme hydrological conditions such as flood and drought. To study hydrologic response and understand the occurrence of extreme hydrological events, the relevant forcing variables must be identified. This study attempts to assess and quantify the historical occurrence and context of extreme hydrologic flow events and quantify the relation between relevant climate variables. Once identified, the flow data and climate variables are evaluated to identify the primary relationship indicators of hydrologic extreme event occurrence. Existing studies focus on developing basin-scale forecasting techniques based on climate anomalies in El Nino/La Nina episodes linked to global climate. Building on earlier work, the goal of this research is to quantify variations in historical river flows at seasonal temporal-scale, and regional to continental spatial-scale. The work identifies and quantifies runoff variability of major river basins and correlates flow with environmental forcing variables such as El Nino, La Nina, sunspot cycle. These variables are expected to be the primary external natural indicators of inter-annual and inter-seasonal patterns of regional precipitation and river flow. Relations between continental-scale hydrologic flows and external climate variables are evaluated through direct correlations in a seasonal context with environmental phenomenon such as sun spot numbers (SSN), Southern Oscillation Index (SOI), and Pacific Decadal Oscillation (PDO). Methods including stochastic time series analysis and artificial neural networks are developed to represent the seasonal variability evident in the historical records of river flows. River flows are categorized into low, average and high flow levels to evaluate and simulate flow variations under associated climate variable variations. Results demonstrated not any particular method is suited to represent scenarios leading to extreme flow conditions. For selected flow scenarios, the persistence model performance may be comparable to more complex multivariate approaches, and complex methods did not always improve flow estimation. Overall model performance indicates inclusion of river flows and forcing variables on average improve model extreme event forecasting skills. As a means to further refine the flow estimation, an ensemble forecast method is implemented to provide a likelihood-based indication of expected river flow magnitude and variability. Results indicate seasonal flow variations are well-captured in the ensemble range, therefore the ensemble approach can often prove efficient in estimating extreme river flow conditions. The discriminant prediction approach, a probabilistic measure to forecast streamflow, is also adopted to derive model performance. Results show the efficiency of the method in terms of representing uncertainties in the forecasts.

OHD/HL: Presentations

Science.gov Websites

enter or select the go button to submit request City, St Go Science Research and Collaboration Hydrology River Forecasts, January 2002 AMS Short Course on Quantitative Precipitation Estimation and Forecasting

A hybrid least squares support vector machines and GMDH approach for river flow forecasting

NASA Astrophysics Data System (ADS)

Samsudin, R.; Saad, P.; Shabri, A.

2010-06-01

This paper proposes a novel hybrid forecasting model, which combines the group method of data handling (GMDH) and the least squares support vector machine (LSSVM), known as GLSSVM. The GMDH is used to determine the useful input variables for LSSVM model and the LSSVM model which works as time series forecasting. In this study the application of GLSSVM for monthly river flow forecasting of Selangor and Bernam River are investigated. The results of the proposed GLSSVM approach are compared with the conventional artificial neural network (ANN) models, Autoregressive Integrated Moving Average (ARIMA) model, GMDH and LSSVM models using the long term observations of monthly river flow discharge. The standard statistical, the root mean square error (RMSE) and coefficient of correlation (R) are employed to evaluate the performance of various models developed. Experiment result indicates that the hybrid model was powerful tools to model discharge time series and can be applied successfully in complex hydrological modeling.

Exploratory studies into seasonal flow forecasting potential for large lakes

NASA Astrophysics Data System (ADS)

Sene, Kevin; Tych, Wlodek; Beven, Keith

2018-01-01

In seasonal flow forecasting applications, one factor which can help predictability is a significant hydrological response time between rainfall and flows. On account of storage influences, large lakes therefore provide a useful test case although, due to the spatial scales involved, there are a number of modelling challenges related to data availability and understanding the individual components in the water balance. Here some possible model structures are investigated using a range of stochastic regression and transfer function techniques with additional insights gained from simple analytical approximations. The methods were evaluated using records for two of the largest lakes in the world - Lake Malawi and Lake Victoria - with forecast skill demonstrated several months ahead using water balance models formulated in terms of net inflows. In both cases slight improvements were obtained for lead times up to 4-5 months from including climate indices in the data assimilation component. The paper concludes with a discussion of the relevance of the results to operational flow forecasting systems for other large lakes.

Development and verification of a new wind speed forecasting system using an ensemble Kalman filter data assimilation technique in a fully coupled hydrologic and atmospheric model

NASA Astrophysics Data System (ADS)

Williams, John L.; Maxwell, Reed M.; Monache, Luca Delle

2013-12-01

Wind power is rapidly gaining prominence as a major source of renewable energy. Harnessing this promising energy source is challenging because of the chaotic nature of wind and its inherently intermittent nature. Accurate forecasting tools are critical to support the integration of wind energy into power grids and to maximize its impact on renewable energy portfolios. We have adapted the Data Assimilation Research Testbed (DART), a community software facility which includes the ensemble Kalman filter (EnKF) algorithm, to expand our capability to use observational data to improve forecasts produced with a fully coupled hydrologic and atmospheric modeling system, the ParFlow (PF) hydrologic model and the Weather Research and Forecasting (WRF) mesoscale atmospheric model, coupled via mass and energy fluxes across the land surface, and resulting in the PF.WRF model. Numerous studies have shown that soil moisture distribution and land surface vegetative processes profoundly influence atmospheric boundary layer development and weather processes on local and regional scales. We have used the PF.WRF model to explore the connections between the land surface and the atmosphere in terms of land surface energy flux partitioning and coupled variable fields including hydraulic conductivity, soil moisture, and wind speed and demonstrated that reductions in uncertainty in these coupled fields realized through assimilation of soil moisture observations propagate through the hydrologic and atmospheric system. The sensitivities found in this study will enable further studies to optimize observation strategies to maximize the utility of the PF.WRF-DART forecasting system.

Corps Water Management System (CWMS) Decision Support Modeling and Integration Use in the June 2007 Texas Floods

NASA Astrophysics Data System (ADS)

Charley, W. J.; Luna, M.

2007-12-01

The U.S. Army Corps of Engineers Corps Water Management System (CWMS) is a comprehensive data acquisition and hydrologic modeling system for short-term decision support of water control operations in real time. It encompasses data collection, validation and transformation, data storage, visualization, real time model simulation for decision-making support, and data dissemination. CWMS uses an Oracle database and Sun Solaris workstations for data processes, storage and the execution of models, with a client application (the Control and Visualization Interface, or CAVI) that can run on a Windows PC. CWMS was used by the Lower Colorado River Authority (LCRA) to make hydrologic forecasts of flows on the Lower Colorado River and operate reservoirs during the June 2007 event in Texas. The LCRA receives real-time observed gridded spatial rainfall data from OneRain, Inc. that which is a result of adjusting NexRad rainfall data with precipitation gages. This data is used, along with future precipitation estimates, for hydrologic forecasting by the rainfall-runoff modeling program HEC-HMS. Forecasted flows from HEC-HMS and combined with observed flows and reservoir information to simulate LCRA's reservoir operations and help engineers make release decisions based on the results. The river hydraulics program, HEC-RAS, computes river stages and water surface profiles for the computed flow. An inundation boundary and depth map of water in the flood plain can be calculated from the HEC-RAS results using ArcInfo. By varying future precipitation and releases, engineers can evaluate different "What if?" scenarios. What was described as an "extraordinary cluster of thunderstorms" that stalled over Burnet and Llano counties in Texas on June 27, 2007, dropped 17 to 19 inches of rainfall over a 6-hour period. The storm was classified over a 500-year event and the resulting flow over some of the smaller tributaries as a 100-year or better. CWMS was used by LCRA for flood forecasting and reservoir operations. The models accurately forecasting the flows and allowed engineers to determine that only four floodgates needed to be opened for Mansfield dam, in the Chain of Highland lakes. CWMS also forecasted the peak of the flood well before it happened. Smaller rain storms continued for a period of weeks and CWMS was used throughout the event calculating lake levels, closing of gates along with a hydro-generation schedule.

Improving seasonal forecasts of hydroclimatic variables through the state of multiple large-scale climate signals

NASA Astrophysics Data System (ADS)

Castelletti, A.; Giuliani, M.; Block, P. J.

2017-12-01

Increasingly uncertain hydrologic regimes combined with more frequent and intense extreme events are challenging water systems management worldwide, emphasizing the need of accurate medium- to long-term predictions to timely prompt anticipatory operations. Despite modern forecasts are skillful over short lead time (from hours to days), predictability generally tends to decrease on longer lead times. Global climate teleconnection, such as El Niño Southern Oscillation (ENSO), may contribute in extending forecast lead times. However, ENSO teleconnection is well defined in some locations, such as Western USA and Australia, while there is no consensus on how it can be detected and used in other regions, particularly in Europe, Africa, and Asia. In this work, we generalize the Niño Index Phase Analysis (NIPA) framework by contributing the Multi Variate Niño Index Phase Analysis (MV-NIPA), which allows capturing the state of multiple large-scale climate signals (i.e. ENSO, North Atlantic Oscillation, Pacific Decadal Oscillation, Atlantic Multi-decadal Oscillation, Indian Ocean Dipole) to forecast hydroclimatic variables on a seasonal time scale. Specifically, our approach distinguishes the different phases of the considered climate signals and, for each phase, identifies relevant anomalies in Sea Surface Temperature (SST) that influence the local hydrologic conditions. The potential of the MV-NIPA framework is demonstrated through an application to the Lake Como system, a regulated lake in northern Italy which is mainly operated for flood control and irrigation supply. Numerical results show high correlations between seasonal SST values and one season-ahead precipitation in the Lake Como basin. The skill of the resulting MV-NIPA forecast outperforms the one of ECMWF products. This information represents a valuable contribution to partially anticipate the summer water availability, especially during drought events, ultimately supporting the improvement of the Lake Como operations.

SWIFT2: Software for continuous ensemble short-term streamflow forecasting for use in research and operations

NASA Astrophysics Data System (ADS)

Perraud, Jean-Michel; Bennett, James C.; Bridgart, Robert; Robertson, David E.

2016-04-01

Research undertaken through the Water Information Research and Development Alliance (WIRADA) has laid the foundations for continuous deterministic and ensemble short-term forecasting services. One output of this research is the software Short-term Water Information Forecasting Tools version 2 (SWIFT2). SWIFT2 is developed for use in research on short term streamflow forecasting techniques as well as operational forecasting services at the Australian Bureau of Meteorology. The variety of uses in research and operations requires a modular software system whose components can be arranged in applications that are fit for each particular purpose, without unnecessary software duplication. SWIFT2 modelling structures consist of sub-areas of hydrologic models, nodes and links with in-stream routing and reservoirs. While this modelling structure is customary, SWIFT2 is built from the ground up for computational and data intensive applications such as ensemble forecasts necessary for the estimation of the uncertainty in forecasts. Support for parallel computation on multiple processors or on a compute cluster is a primary use case. A convention is defined to store large multi-dimensional forecasting data and its metadata using the netCDF library. SWIFT2 is written in modern C++ with state of the art software engineering techniques and practices. A salient technical feature is a well-defined application programming interface (API) to facilitate access from different applications and technologies. SWIFT2 is already seamlessly accessible on Windows and Linux via packages in R, Python, Matlab and .NET languages such as C# and F#. Command line or graphical front-end applications are also feasible. This poster gives an overview of the technology stack, and illustrates the resulting features of SWIFT2 for users. Research and operational uses share the same common core C++ modelling shell for consistency, but augmented by different software modules suitable for each context. The accessibility via interactive modelling languages is particularly amenable to using SWIFT2 in exploratory research, with a dynamic and versatile experimental modelling workflow. This does not come at the expense of the stability and reliability required for use in operations, where only mature and stable components are used.

The Hydrologic Ensemble Prediction Experiment (HEPEX)

NASA Astrophysics Data System (ADS)

Wood, Andy; Wetterhall, Fredrik; Ramos, Maria-Helena

2015-04-01

The Hydrologic Ensemble Prediction Experiment was established in March, 2004, at a workshop hosted by the European Center for Medium Range Weather Forecasting (ECMWF), and co-sponsored by the US National Weather Service (NWS) and the European Commission (EC). The HEPEX goal was to bring the international hydrological and meteorological communities together to advance the understanding and adoption of hydrological ensemble forecasts for decision support. HEPEX pursues this goal through research efforts and practical implementations involving six core elements of a hydrologic ensemble prediction enterprise: input and pre-processing, ensemble techniques, data assimilation, post-processing, verification, and communication and use in decision making. HEPEX has grown through meetings that connect the user, forecast producer and research communities to exchange ideas, data and methods; the coordination of experiments to address specific challenges; and the formation of testbeds to facilitate shared experimentation. In the last decade, HEPEX has organized over a dozen international workshops, as well as sessions at scientific meetings (including AMS, AGU and EGU) and special issues of scientific journals where workshop results have been published. Through these interactions and an active online blog (www.hepex.org), HEPEX has built a strong and active community of nearly 400 researchers & practitioners around the world. This poster presents an overview of recent and planned HEPEX activities, highlighting case studies that exemplify the focus and objectives of HEPEX.

ERM model analysis for adaptation to hydrological model errors

NASA Astrophysics Data System (ADS)

Baymani-Nezhad, M.; Han, D.

2018-05-01

Hydrological conditions are changed continuously and these phenomenons generate errors on flood forecasting models and will lead to get unrealistic results. Therefore, to overcome these difficulties, a concept called model updating is proposed in hydrological studies. Real-time model updating is one of the challenging processes in hydrological sciences and has not been entirely solved due to lack of knowledge about the future state of the catchment under study. Basically, in terms of flood forecasting process, errors propagated from the rainfall-runoff model are enumerated as the main source of uncertainty in the forecasting model. Hence, to dominate the exciting errors, several methods have been proposed by researchers to update the rainfall-runoff models such as parameter updating, model state updating, and correction on input data. The current study focuses on investigations about the ability of rainfall-runoff model parameters to cope with three types of existing errors, timing, shape and volume as the common errors in hydrological modelling. The new lumped model, the ERM model, has been selected for this study to evaluate its parameters for its use in model updating to cope with the stated errors. Investigation about ten events proves that the ERM model parameters can be updated to cope with the errors without the need to recalibrate the model.

Integrated modeling of storm drain and natural channel networks for real-time flash flood forecasting in large urban areas

NASA Astrophysics Data System (ADS)

Habibi, H.; Norouzi, A.; Habib, A.; Seo, D. J.

2016-12-01

To produce accurate predictions of flooding in urban areas, it is necessary to model both natural channel and storm drain networks. While there exist many urban hydraulic models of varying sophistication, most of them are not practical for real-time application for large urban areas. On the other hand, most distributed hydrologic models developed for real-time applications lack the ability to explicitly simulate storm drains. In this work, we develop a storm drain model that can be coupled with distributed hydrologic models such as the National Weather Service Hydrology Laboratory's Distributed Hydrologic Model, for real-time flash flood prediction in large urban areas to improve prediction and to advance the understanding of integrated response of natural channels and storm drains to rainfall events of varying magnitude and spatiotemporal extent in urban catchments of varying sizes. The initial study area is the Johnson Creek Catchment (40.1 km2) in the City of Arlington, TX. For observed rainfall, the high-resolution (500 m, 1 min) precipitation data from the Dallas-Fort Worth Demonstration Network of the Collaborative Adaptive Sensing of the Atmosphere radars is used.

A study for systematic errors of the GLA forecast model in tropical regions

NASA Technical Reports Server (NTRS)

Chen, Tsing-Chang; Baker, Wayman E.; Pfaendtner, James; Corrigan, Martin

1988-01-01

From the sensitivity studies performed with the Goddard Laboratory for Atmospheres (GLA) analysis/forecast system, it was revealed that the forecast errors in the tropics affect the ability to forecast midlatitude weather in some cases. Apparently, the forecast errors occurring in the tropics can propagate to midlatitudes. Therefore, the systematic error analysis of the GLA forecast system becomes a necessary step in improving the model's forecast performance. The major effort of this study is to examine the possible impact of the hydrological-cycle forecast error on dynamical fields in the GLA forecast system.

Value versus Accuracy: application of seasonal forecasts to a hydro-economic optimization model for the Sudanese Blue Nile

NASA Astrophysics Data System (ADS)

Satti, S.; Zaitchik, B. F.; Siddiqui, S.; Badr, H. S.; Shukla, S.; Peters-Lidard, C. D.

2015-12-01

The unpredictable nature of precipitation within the East African (EA) region makes it one of the most vulnerable, food insecure regions in the world. There is a vital need for forecasts to inform decision makers, both local and regional, and to help formulate the region's climate change adaptation strategies. Here, we present a suite of different seasonal forecast models, both statistical and dynamical, for the EA region. Objective regionalization is performed for EA on the basis of interannual variability in precipitation in both observations and models. This regionalization is applied as the basis for calculating a number of standard skill scores to evaluate each model's forecast accuracy. A dynamically linked Land Surface Model (LSM) is then applied to determine forecasted flows, which drive the Sudanese Hydroeconomic Optimization Model (SHOM). SHOM combines hydrologic, agronomic and economic inputs to determine the optimal decisions that maximize economic benefits along the Sudanese Blue Nile. This modeling sequence is designed to derive the potential added value of information of each forecasting model to agriculture and hydropower management. A rank of each model's forecasting skill score along with its added value of information is analyzed in order compare the performance of each forecast. This research aims to improve understanding of how characteristics of accuracy, lead time, and uncertainty of seasonal forecasts influence their utility to water resources decision makers who utilize them.

Streamflow forecasts from WRF precipitation for flood early warning in mountain tropical areas

NASA Astrophysics Data System (ADS)

Rogelis, María Carolina; Werner, Micha

2018-02-01

Numerical weather prediction (NWP) models are fundamental to extend forecast lead times beyond the concentration time of a watershed. Particularly for flash flood forecasting in tropical mountainous watersheds, forecast precipitation is required to provide timely warnings. This paper aims to assess the potential of NWP for flood early warning purposes, and the possible improvement that bias correction can provide, in a tropical mountainous area. The paper focuses on the comparison of streamflows obtained from the post-processed precipitation forecasts, particularly the comparison of ensemble forecasts and their potential in providing skilful flood forecasts. The Weather Research and Forecasting (WRF) model is used to produce precipitation forecasts that are post-processed and used to drive a hydrologic model. Discharge forecasts obtained from the hydrological model are used to assess the skill of the WRF model. The results show that post-processed WRF precipitation adds value to the flood early warning system when compared to zero-precipitation forecasts, although the precipitation forecast used in this analysis showed little added value when compared to climatology. However, the reduction of biases obtained from the post-processed ensembles show the potential of this method and model to provide usable precipitation forecasts in tropical mountainous watersheds. The need for more detailed evaluation of the WRF model in the study area is highlighted, particularly the identification of the most suitable parameterisation, due to the inability of the model to adequately represent the convective precipitation found in the study area.

Application of a distributed hydrological model to the design of a road inundation warning system for flash flood prone areas

NASA Astrophysics Data System (ADS)

Versini, P.-A.; Gaume, E.; Andrieu, H.

2010-04-01

This paper presents an initial prototype of a distributed hydrological model used to map possible road inundations in a region frequently exposed to severe flash floods: the Gard region (South of France). The prototype has been tested in a pseudo real-time mode on five recent flash flood events for which actual road inundations have been inventoried. The results are promising: close to 100% probability of detection of actual inundations, inundations detected before they were reported by the road management field teams with a false alarm ratios not exceeding 30%. This specific case study differs from the standard applications of rainfall-runoff models to produce flood forecasts, focussed on a single or a limited number of gauged river cross sections. It illustrates that, despite their lack of accuracy, hydro-meteorological forecasts based on rainfall-runoff models, especially distributed models, contain valuable information for flood event management. The possible consequences of landslides, debris flows and local erosion processes, sometimes associated with flash floods, were not considered at this stage of development of the prototype. They are limited in the Gard region but should be taken into account in future developments of the approach to implement it efficiently in other areas more exposed to these phenomena such as the Alpine area.

The POLIMI forecasting chain for real time flood and drought predictions

NASA Astrophysics Data System (ADS)

Ceppi, Alessandro; Ravazzani, Giovanni; Corbari, Chiara; Mancini, Marco

2016-04-01

Nowadays coupling meteorological and hydrological models is recognized by scientific community as a necessary way to forecast extreme hydrological phenomena, in order to activate useful mitigation measurements and alert systems in advance. The development and implementation of a real-time forecasting chain with a hydro-meteorological operational alert procedure for flood and drought events is presented in this study. Different weather models are used to build the POLIMI operative chain: the probabilistic COSMO-LEPS model with 16 ensembles developed by ARPA-Emilia Romagna, the deterministic Bolam and Moloch models, developed by the Italian ISAC-CNR, and nine further simulations obtained by different runs of the WRF-ARW (3), WRF-NMM (2), ETA2012 (1) and the GFS (3), provided by the private Epson Meteo Center and Terraria companies. All the meteorological runs are then implemented with the rainfall-runoff physically-based distributed FEST-WB model, developed at Politecnico di Milano to obtain a multi-model approach system with hydrological ensemble forecasts in different areas of study over the Italian country. As far as concerning drought predictions, three test-beds are monitored: two in maize fields, one in the Puglia region (South of Italy), and another in the Po Valley area, (northern Italy), and one in a golf course in Milan city. The hydrological model was here calibrated and validated against measurements of latent heat flux and soil moisture acquired by an eddy-covariance station, TDR probes and remote sensing images. Regarding flood forecasts, two test-sites are chosen: the first one is the urban area northern Milan where three catchments (the Seveso, Olona, and Lambro River basins) are used to show how early warning systems are an effective complement to structural measures for flood control in Milan city which flooded frequently in the last 25 years, while the second test-site is the Idro Lake, located between the Lombardy and Trentino region where the POLIMI hydro-meteorological chain is performed to forecast the hydrometric lake level for a better management of the upstream and downstream basin. The same hydrological model has been here calibrated and validated with observed data coming from local bodies: ARPA Lombardy, Meteonetwork and Meteo Trentino. Reliability of the forecasting system and its benefits are assessed with skill scores on some cases-study occurred in the recent years and through the real-time visualization of the implemented dashboards.

PRESTIGRIS: an operational system for water resources and droughts management on Tuscany, Central Italy

NASA Astrophysics Data System (ADS)

Campo, Lorenzo; Caparrini, Francesca; Castelli, Fabio

2013-04-01

In the last years the problems of water management faced by local administration due to the growing demand of the territory and to the changes in terms of availability became more and more important. Also in view of problems issued by the Climate Change, it is necessary to have the availability of information about the present and the future state of the water resources on the territory, both in terms of stress of the water bodies and of trends in the near-future. In this respect, an adequate management and planning of the water resources can make use of meteorological seasonal forecasts (one-three month) for the assessment of the primary sources of fresh water in a given region. The PRESTIGRIS project (PREvisioni STagionali Idrologiche per la Gestione della Risorsa Idrica e della Siccità - hydrologic seasonal forecasts for water resources and droughts management), implemented at the University of Florence in collaboration with Eumechanos Environmental Engineering and LaMMa (Laboratorio di Monitoraggio e Modellistica ambientale, Laboratory for Environmental Monitoring and Modeling), is aimed to provide hydrological seasonal forecasts on the territory of the Tuscany Region, Central Italy, basing on the seasonal meteorological forecasts available at different Weather Services (NOAA, IRI, etc.). The PRESTIGRIS system is based on a stochastic disaggregation of the monthly seasonal forecasts of minimum and maximum air temperature at the ground and of the total rainfall height. Through an analysis based on Principal Component Analysis (PCA) techniques, the forecasts are disaggregated in daily maps at a spatial resolution (500 m) compatible with a complete hydrological balance simulation, performed on the entire Tuscany region (about 22000 km2) by the distributed hydrological model MOBIDIC (MOdello di BIlancio Distribuito e Continuo), developed at the Department of Civil and Environmental Engineering of the University of Florence. Given a single seasonal forecast, the system performs an ensemble of 50 hydrological simulations. Basing on the results of the simulations, significant quantiles of the main variables of interest (soil saturation, discharge flows in the stream network, evapotranspiration) are mapped on the territory. The results of the simulations for the year 2003, in particular during the severe drought occurred during the summer, are shown as an example of the capabilities of the system.

Global operational hydrological forecasts through eWaterCycle

NASA Astrophysics Data System (ADS)

van de Giesen, Nick; Bierkens, Marc; Donchyts, Gennadii; Drost, Niels; Hut, Rolf; Sutanudjaja, Edwin

2015-04-01

Central goal of the eWaterCycle project (www.ewatercycle.org) is the development of an operational hyper-resolution hydrological global model. This model is able to produce 14 day ensemble forecasts based on a hydrological model and operational weather data (presently NOAA's Global Ensemble Forecast System). Special attention is paid to prediction of situations in which water related issues are relevant, such as floods, droughts, navigation, hydropower generation, and irrigation stress. Near-real time satellite data will be assimilated in the hydrological simulations, which is a feature that will be presented for the first time at EGU 2015. First, we address challenges that are mainly computer science oriented but have direct practical hydrological implications. An important feature in this is the use of existing standards and open-source software to the maximum extent possible. For example, we use the Community Surface Dynamics Modeling System (CSDMS) approach to coupling models (Basic Model Interface (BMI)). The hydrological model underlying the project is PCR-GLOBWB, built by Utrecht University. This is the motor behind the predictions and state estimations. Parts of PCR-GLOBWB have been re-engineered to facilitate running it in a High Performance Computing (HPC) environment, run parallel on multiple nodes, as well as to use BMI. Hydrological models are not very CPU intensive compared to, say, atmospheric models. They are, however, memory hungry due to the localized processes and associated effective parameters. To accommodate this memory need, especially in an ensemble setting, a variation on the traditional Ensemble Kalman Filter was developed that needs much less on-chip memory. Due to the operational nature, the coupling of the hydrological model with hydraulic models is very important. The idea is not to run detailed hydraulic routing schemes over the complete globe but to have on-demand simulation prepared off-line with respect to topography and parameterizations. This allows for very detailed simulations at hectare to meter scales, where and when this is needed. At EGU 2015, the operational global eWaterCycle model will be presented for the first time, including forecasts at high resolution, the innovative data assimilation approach, and on-demand coupling with hydraulic models.

Uncertainty estimation of long-range ensemble forecasts of snowmelt flood characteristics

NASA Astrophysics Data System (ADS)

Kuchment, L.

2012-04-01

Long-range forecasts of snowmelt flood characteristics with the lead time of 2-3 months have important significance for regulation of flood runoff and mitigation of flood damages at almost all large Russian rivers At the same time, the application of current forecasting techniques based on regression relationships between the runoff volume and the indexes of river basin conditions can lead to serious errors in forecasting resulted in large economic losses caused by wrong flood regulation. The forecast errors can be caused by complicated processes of soil freezing and soil moisture redistribution, too high rate of snow melt, large liquid precipitation before snow melt. or by large difference of meteorological conditions during the lead-time periods from climatologic ones. Analysis of economic losses had shown that the largest damages could, to a significant extent, be avoided if the decision makers had an opportunity to take into account predictive uncertainty and could use more cautious strategies in runoff regulation. Development of methodology of long-range ensemble forecasting of spring/summer floods which is based on distributed physically-based runoff generation models has created, in principle, a new basis for improving hydrological predictions as well as for estimating their uncertainty. This approach is illustrated by forecasting of the spring-summer floods at the Vyatka River and the Seim River basins. The application of the physically - based models of snowmelt runoff generation give a essential improving of statistical estimates of the deterministic forecasts of the flood volume in comparison with the forecasts obtained from the regression relationships. These models had been used also for the probabilistic forecasts assigning meteorological inputs during lead time periods from the available historical daily series, and from the series simulated by using a weather generator and the Monte Carlo procedure. The weather generator consists of the stochastic models of daily temperature and precipitation. The performance of the probabilistic forecasts were estimated by the ranked probability skill scores. The application of Monte Carlo simulations using weather generator has given better results then using the historical meteorological series.

Evaluation of flash-flood discharge forecasts in complex terrain using precipitation

USGS Publications Warehouse

Yates, D.; Warner, T.T.; Brandes, E.A.; Leavesley, G.H.; Sun, Jielun; Mueller, C.K.

2001-01-01

Operational prediction of flash floods produced by thunderstorm (convective) precipitation in mountainous areas requires accurate estimates or predictions of the precipitation distribution in space and time. The details of the spatial distribution are especially critical in complex terrain because the watersheds are generally small in size, and small position errors in the forecast or observed placement of the precipitation can distribute the rain over the wrong watershed. In addition to the need for good precipitation estimates and predictions, accurate flood prediction requires a surface-hydrologic model that is capable of predicting stream or river discharge based on the precipitation-rate input data. Different techniques for the estimation and prediction of convective precipitation will be applied to the Buffalo Creek, Colorado flash flood of July 1996, where over 75 mm of rain from a thunderstorm fell on the watershed in less than 1 h. The hydrologic impact of the precipitation was exacerbated by the fact that a significant fraction of the watershed experienced a wildfire approximately two months prior to the rain event. Precipitation estimates from the National Weather Service's operational Weather Surveillance Radar-Doppler 1988 and the National Center for Atmospheric Research S-band, research, dual-polarization radar, colocated to the east of Denver, are compared. In addition, very short range forecasts from a convection-resolving dynamic model, which is initialized variationally using the radar reflectivity and Doppler winds, are compared with forecasts from an automated-algorithmic forecast system that also employs the radar data. The radar estimates of rain rate, and the two forecasting systems that employ the radar data, have degraded accuracy by virtue of the fact that they are applied in complex terrain. Nevertheless, the radar data and forecasts from the dynamic model and the automated algorithm could be operationally useful for input to surface-hydrologic models employed for flood warning. Precipitation data provided by these various techniques at short time scales and at fine spatial resolutions are employed as detailed input to a distributed-parameter hydrologic model for flash-flood prediction and analysis. With the radar-based precipitation estimates employed as input, the simulated flood discharge was similar to that observed. The dynamic-model precipitation forecast showed the most promise in providing a significant discharge-forecast lead time. The algorithmic system's precipitation forecast did not demonstrate as much skill, but the associated discharge forecast would still have been sufficient to have provided an alert of impending flood danger.

JPSS Operational and Research Applications: The Pathway from Observations to Applications to Information.

NASA Astrophysics Data System (ADS)

Goldberg, M.; Sjoberg, W.; Layns, A. L.

2017-12-01

Applications of satellite data are paramount to transform science and technology to product and services which are used in critical decision making. For the satellite community, good representations of technology are the satellite sensors, while science provides the instrument calibration and derived geophysical parameters. Weather forecasting is an application of the science and technology provided by remote sensing satellites. The Joint Polar Satellite System, which includes the Suomi National Polar-orbiting Partnership (S-NPP) provides formidable science and technology to support many applications and includes support to 1) weather forecasting - data from the JPSS Cross-track Infrared Sounder (CrIS) and the Advanced Technology Microwave Sounder (ATMS) are used to forecast weather events out to 7 days - nearly 85% of all data used in weather forecasting are from polar orbiting satellites; 2) environmental monitoring -data from the JPSS Visible Infrared Imager Radiometer Suite (VIIRS) are used to monitor the environment including the health of coastal ecosystems, drought conditions, fire, smoke, dust, snow and ice, and the state of oceans, including sea surface temperature and ocean color; and 3) climate monitoring - data from JPSS instruments, including OMPS and CERES will provide continuity to climate data records established using NOAA POES and NASA Earth Observing System (EOS) satellite observations. To bridge the gap between products and applications, the JPSS Program has established the Proving Ground and Risk Reduction (PGRR) Program to identify opportunities to maximize the operational application of current JPSS capabilities. The PGRR Program also helps identify and evaluate the use of JPSS capabilities for new operational missions. New PGRR initiatives focus on hydrological, Arctic, data assimilation, atmospheric chemistry, ocean ecosystem applications. At the conference, the benefits of JPSS data on societal benefits will be presented along with results from the PGRR initiatives.

Hydrologic Futures: Using Scenario Analysis to Evaluate Impacts of Forecasted Land Use Change on Hydrologic Services

EPA Science Inventory

Land cover and land use changes can substantially alter hydrologic ecosystem services. Water availability and quality can change with modifications to the type or amount of surface vegetation, the permeability of soil and other surfaces, and the introduction of contaminants throu...

Extending data worth methods to select multiple observations targeting specific hydrological predictions of interest

NASA Astrophysics Data System (ADS)

Vilhelmsen, Troels N.; Ferré, Ty P. A.

2016-04-01

Hydrological models are often developed to forecasting future behavior in response due to natural or human induced changes in stresses affecting hydrologic systems. Commonly, these models are conceptualized and calibrated based on existing data/information about the hydrological conditions. However, most hydrologic systems lack sufficient data to constrain models with adequate certainty to support robust decision making. Therefore, a key element of a hydrologic study is the selection of additional data to improve model performance. Given the nature of hydrologic investigations, it is not practical to select data sequentially, i.e. to choose the next observation, collect it, refine the model, and then repeat the process. Rather, for timing and financial reasons, measurement campaigns include multiple wells or sampling points. There is a growing body of literature aimed at defining the expected data worth based on existing models. However, these are almost all limited to identifying single additional observations. In this study, we present a methodology for simultaneously selecting multiple potential new observations based on their expected ability to reduce the uncertainty of the forecasts of interest. This methodology is based on linear estimates of the predictive uncertainty, and it can be used to determine the optimal combinations of measurements (location and number) established to reduce the uncertainty of multiple predictions. The outcome of the analysis is an estimate of the optimal sampling locations; the optimal number of samples; as well as a probability map showing the locations within the investigated area that are most likely to provide useful information about the forecasting of interest.

User's guide for MAGIC-Meteorologic and hydrologic genscn (generate scenarios) input converter

USGS Publications Warehouse

Ortel, Terry W.; Martin, Angel

2010-01-01

Meteorologic and hydrologic data used in watershed modeling studies are collected by various agencies and organizations, and stored in various formats. Data may be in a raw, un-processed format with little or no quality control, or may be checked for validity before being made available. Flood-simulation systems require data in near real-time so that adequate flood warnings can be made. Additionally, forecasted data are needed to operate flood-control structures to potentially mitigate flood damages. Because real-time data are of a provisional nature, missing data may need to be estimated for use in floodsimulation systems. The Meteorologic and Hydrologic GenScn (Generate Scenarios) Input Converter (MAGIC) can be used to convert data from selected formats into the Hydrologic Simulation System-Fortran hourly-observations format for input to a Watershed Data Management database, for use in hydrologic modeling studies. MAGIC also can reformat the data to the Full Equations model time-series format, for use in hydraulic modeling studies. Examples of the application of MAGIC for use in the flood-simulation system for Salt Creek in northeastern Illinois are presented in this report.

Integrated Forecast-Decision Systems For River Basin Planning and Management

NASA Astrophysics Data System (ADS)

Georgakakos, A. P.

2005-12-01

A central application of climatology, meteorology, and hydrology is the generation of reliable forecasts for water resources management. In principle, effective use of forecasts could improve water resources management by providing extra protection against floods, mitigating the adverse effects of droughts, generating more hydropower, facilitating recreational activities, and minimizing the impacts of extreme events on the environment and the ecosystems. In practice, however, realization of these benefits depends on three requisite elements. First is the skill and reliability of forecasts. Second is the existence of decision support methods/systems with the ability to properly utilize forecast information. And third is the capacity of the institutional infrastructure to incorporate the information provided by the decision support systems into the decision making processes. This presentation discusses several decision support systems (DSS) using ensemble forecasting that have been developed by the Georgia Water Resources Institute for river basin management. These DSS are currently operational in Africa, Europe, and the US and address integrated water resources and energy planning and management in river basins with multiple water uses, multiple relevant temporal and spatial scales, and multiple decision makers. The article discusses the methods used and advocates that the design, development, and implementation of effective forecast-decision support systems must bring together disciplines, people, and institutions necessary to address today's complex water resources challenges.

Time Series Forecasting of Daily Reference Evapotranspiration by Neural Network Ensemble Learning for Irrigation System

NASA Astrophysics Data System (ADS)

Manikumari, N.; Murugappan, A.; Vinodhini, G.

2017-07-01

Time series forecasting has gained remarkable interest of researchers in the last few decades. Neural networks based time series forecasting have been employed in various application areas. Reference Evapotranspiration (ETO) is one of the most important components of the hydrologic cycle and its precise assessment is vital in water balance and crop yield estimation, water resources system design and management. This work aimed at achieving accurate time series forecast of ETO using a combination of neural network approaches. This work was carried out using data collected in the command area of VEERANAM Tank during the period 2004 - 2014 in India. In this work, the Neural Network (NN) models were combined by ensemble learning in order to improve the accuracy for forecasting Daily ETO (for the year 2015). Bagged Neural Network (Bagged-NN) and Boosted Neural Network (Boosted-NN) ensemble learning were employed. It has been proved that Bagged-NN and Boosted-NN ensemble models are better than individual NN models in terms of accuracy. Among the ensemble models, Boosted-NN reduces the forecasting errors compared to Bagged-NN and individual NNs. Regression co-efficient, Mean Absolute Deviation, Mean Absolute Percentage error and Root Mean Square Error also ascertain that Boosted-NN lead to improved ETO forecasting performance.

Hourly runoff forecasting for flood risk management: Application of various computational intelligence models

NASA Astrophysics Data System (ADS)

Badrzadeh, Honey; Sarukkalige, Ranjan; Jayawardena, A. W.

2015-10-01

Reliable river flow forecasts play a key role in flood risk mitigation. Among different approaches of river flow forecasting, data driven approaches have become increasingly popular in recent years due to their minimum information requirements and ability to simulate nonlinear and non-stationary characteristics of hydrological processes. In this study, attempts are made to apply four different types of data driven approaches, namely traditional artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFIS), wavelet neural networks (WNN), and, hybrid ANFIS with multi resolution analysis using wavelets (WNF). Developed models applied for real time flood forecasting at Casino station on Richmond River, Australia which is highly prone to flooding. Hourly rainfall and runoff data were used to drive the models which have been used for forecasting with 1, 6, 12, 24, 36 and 48 h lead-time. The performance of models further improved by adding an upstream river flow data (Wiangaree station), as another effective input. All models perform satisfactorily up to 12 h lead-time. However, the hybrid wavelet-based models significantly outperforming the ANFIS and ANN models in the longer lead-time forecasting. The results confirm the robustness of the proposed structure of the hybrid models for real time runoff forecasting in the study area.

Advanced inflow forecasting for a hydropower plant in an Alpine hydropower regulated catchment - coupling of operational and hydrological forecasts

NASA Astrophysics Data System (ADS)

Tilg, Anna-Maria; Schöber, Johannes; Huttenlau, Matthias; Messner, Jakob; Achleitner, Stefan

2017-04-01

Hydropower is a renewable energy source which can help to stabilize fluctuations in the volatile energy market. Especially pumped-storage infrastructures in the European Alps play an important role within the European energy grid system. Today, the runoff of rivers in the Alps is often influenced by cascades of hydropower infrastructures where the operational procedures are triggered by energy market demands, water deliveries and flood control aspects rather than by hydro-meteorological variables. An example for such a highly hydropower regulated river is the catchment of the river Inn in the Eastern European Alps, originating in the Engadin (Switzerland). A new hydropower plant is going to be built as transboundary project at the boarder of Switzerland and Austria using the water of the Inn River. For the operation, a runoff forecast to the plant is required. The challenge in this case is that a high proportion of runoff is turbine water from an upstream situated hydropower cascade. The newly developed physically based hydrological forecasting system is mainly capable to cover natural hydrological runoff processes caused by storms and snow melt but can model only a small degree of human impact. These discontinuous parts of the runoff downstream of the pumped storage are described by means of an additional statistical model which has been developed. The main goal of the statistical model is to forecast the turbine water up to five days in advance. The lead time of the data driven model exceeds the lead time of the used energy production forecast. Additionally, the amount of turbine water is linked to the need of electricity production and the electricity price. It has been shown that especially the parameters day-ahead prognosis of the energy production and turbine inflow of the previous week are good predictors and are therefore used as input parameters for the model. As the data is restricted due to technical conditions, so-called Tobit models have been used to develop a linear regression for the runoff forecast. Although the day-ahead prognosis cannot always be kept, the regression model delivers, especially during office hours, very reasonable results. In the remaining hours the error between measurement and the forecast increases. Overall, the inflow forecast can be substantially improved by the implementation of the developed regression in the hydrological modelling system.

Military Hydrology. Report 8. Feasibility of Utilizing Satellite and Radar Data in Hydrologic Forecasting.

DTIC Science & Technology

1985-09-01

Extratropical Storm ," Draft Report. Atlas, David. 1964. "Advances in Radar Meteorology," Advances in Geophysics, Vol 10, Academic Press, N.Y., pp 318-478. Barnes...forecasting purposes, data on storm morphology, direction of movement, and rate of movement are re- quired in addition to the data cited above. 7...or storm duration. He also showed that, for a given sampling error, the gage density needed for warm season storms was two to three times greater than

Daily river flow prediction based on Two-Phase Constructive Fuzzy Systems Modeling: A case of hydrological - meteorological measurements asymmetry

NASA Astrophysics Data System (ADS)

Bou-Fakhreddine, Bassam; Mougharbel, Imad; Faye, Alain; Abou Chakra, Sara; Pollet, Yann

2018-03-01

Accurate daily river flow forecast is essential in many applications of water resources such as hydropower operation, agricultural planning and flood control. This paper presents a forecasting approach to deal with a newly addressed situation where hydrological data exist for a period longer than that of meteorological data (measurements asymmetry). In fact, one of the potential solutions to resolve measurements asymmetry issue is data re-sampling. It is a matter of either considering only the hydrological data or the balanced part of the hydro-meteorological data set during the forecasting process. However, the main disadvantage is that we may lose potentially relevant information from the left-out data. In this research, the key output is a Two-Phase Constructive Fuzzy inference hybrid model that is implemented over the non re-sampled data. The introduced modeling approach must be capable of exploiting the available data efficiently with higher prediction efficiency relative to Constructive Fuzzy model trained over re-sampled data set. The study was applied to Litani River in the Bekaa Valley - Lebanon by using 4 years of rainfall and 24 years of river flow daily measurements. A Constructive Fuzzy System Model (C-FSM) and a Two-Phase Constructive Fuzzy System Model (TPC-FSM) are trained. Upon validating, the second model has shown a primarily competitive performance and accuracy with the ability to preserve a higher day-to-day variability for 1, 3 and 6 days ahead. In fact, for the longest lead period, the C-FSM and TPC-FSM were able of explaining respectively 84.6% and 86.5% of the actual river flow variation. Overall, the results indicate that TPC-FSM model has provided a better tool to capture extreme flows in the process of streamflow prediction.

Efficient multi-scenario Model Predictive Control for water resources management with ensemble streamflow forecasts

NASA Astrophysics Data System (ADS)

Tian, Xin; Negenborn, Rudy R.; van Overloop, Peter-Jules; María Maestre, José; Sadowska, Anna; van de Giesen, Nick

2017-11-01

Model Predictive Control (MPC) is one of the most advanced real-time control techniques that has been widely applied to Water Resources Management (WRM). MPC can manage the water system in a holistic manner and has a flexible structure to incorporate specific elements, such as setpoints and constraints. Therefore, MPC has shown its versatile performance in many branches of WRM. Nonetheless, with the in-depth understanding of stochastic hydrology in recent studies, MPC also faces the challenge of how to cope with hydrological uncertainty in its decision-making process. A possible way to embed the uncertainty is to generate an Ensemble Forecast (EF) of hydrological variables, rather than a deterministic one. The combination of MPC and EF results in a more comprehensive approach: Multi-scenario MPC (MS-MPC). In this study, we will first assess the model performance of MS-MPC, considering an ensemble streamflow forecast. Noticeably, the computational inefficiency may be a critical obstacle that hinders applicability of MS-MPC. In fact, with more scenarios taken into account, the computational burden of solving an optimization problem in MS-MPC accordingly increases. To deal with this challenge, we propose the Adaptive Control Resolution (ACR) approach as a computationally efficient scheme to practically reduce the number of control variables in MS-MPC. In brief, the ACR approach uses a mixed-resolution control time step from the near future to the distant future. The ACR-MPC approach is tested on a real-world case study: an integrated flood control and navigation problem in the North Sea Canal of the Netherlands. Such an approach reduces the computation time by 18% and up in our case study. At the same time, the model performance of ACR-MPC remains close to that of conventional MPC.

Seasonal drought ensemble predictions based on multiple climate models in the upper Han River Basin, China

NASA Astrophysics Data System (ADS)

Ma, Feng; Ye, Aizhong; Duan, Qingyun

2017-03-01

An experimental seasonal drought forecasting system is developed based on 29-year (1982-2010) seasonal meteorological hindcasts generated by the climate models from the North American Multi-Model Ensemble (NMME) project. This system made use of a bias correction and spatial downscaling method, and a distributed time-variant gain model (DTVGM) hydrologic model. DTVGM was calibrated using observed daily hydrological data and its streamflow simulations achieved Nash-Sutcliffe efficiency values of 0.727 and 0.724 during calibration (1978-1995) and validation (1996-2005) periods, respectively, at the Danjiangkou reservoir station. The experimental seasonal drought forecasting system (known as NMME-DTVGM) is used to generate seasonal drought forecasts. The forecasts were evaluated against the reference forecasts (i.e., persistence forecast and climatological forecast). The NMME-DTVGM drought forecasts have higher detectability and accuracy and lower false alarm rate than the reference forecasts at different lead times (from 1 to 4 months) during the cold-dry season. No apparent advantage is shown in drought predictions during spring and summer seasons because of a long memory of the initial conditions in spring and a lower predictive skill for precipitation in summer. Overall, the NMME-based seasonal drought forecasting system has meaningful skill in predicting drought several months in advance, which can provide critical information for drought preparedness and response planning as well as the sustainable practice of water resource conservation over the basin.

The potential of radar-based ensemble forecasts for flash-flood early warning in the southern Swiss Alps

NASA Astrophysics Data System (ADS)

Liechti, K.; Panziera, L.; Germann, U.; Zappa, M.

2013-10-01

This study explores the limits of radar-based forecasting for hydrological runoff prediction. Two novel radar-based ensemble forecasting chains for flash-flood early warning are investigated in three catchments in the southern Swiss Alps and set in relation to deterministic discharge forecasts for the same catchments. The first radar-based ensemble forecasting chain is driven by NORA (Nowcasting of Orographic Rainfall by means of Analogues), an analogue-based heuristic nowcasting system to predict orographic rainfall for the following eight hours. The second ensemble forecasting system evaluated is REAL-C2, where the numerical weather prediction COSMO-2 is initialised with 25 different initial conditions derived from a four-day nowcast with the radar ensemble REAL. Additionally, three deterministic forecasting chains were analysed. The performance of these five flash-flood forecasting systems was analysed for 1389 h between June 2007 and December 2010 for which NORA forecasts were issued, due to the presence of orographic forcing. A clear preference was found for the ensemble approach. Discharge forecasts perform better when forced by NORA and REAL-C2 rather then by deterministic weather radar data. Moreover, it was observed that using an ensemble of initial conditions at the forecast initialisation, as in REAL-C2, significantly improved the forecast skill. These forecasts also perform better then forecasts forced by ensemble rainfall forecasts (NORA) initialised form a single initial condition of the hydrological model. Thus the best results were obtained with the REAL-C2 forecasting chain. However, for regions where REAL cannot be produced, NORA might be an option for forecasting events triggered by orographic precipitation.

Effect of Streamflow Forecast Uncertainty on Real-Time Reservoir Operation

NASA Astrophysics Data System (ADS)

Zhao, T.; Cai, X.; Yang, D.

2010-12-01

Various hydrological forecast products have been applied to real-time reservoir operation, including deterministic streamflow forecast (DSF), DSF-based probabilistic streamflow forecast (DPSF), and ensemble streamflow forecast (ESF), which represent forecast uncertainty in the form of deterministic forecast error, deterministic forecast error-based uncertainty distribution, and ensemble forecast errors, respectively. Compared to previous studies that treat these forecast products as ad hoc inputs for reservoir operation models, this paper attempts to model the uncertainties involved in the various forecast products and explores their effect on real-time reservoir operation decisions. In hydrology, there are various indices reflecting the magnitude of streamflow forecast uncertainty; meanwhile, few models illustrate the forecast uncertainty evolution process. This research introduces Martingale Model of Forecast Evolution (MMFE) from supply chain management and justifies its assumptions for quantifying the evolution of uncertainty in streamflow forecast as time progresses. Based on MMFE, this research simulates the evolution of forecast uncertainty in DSF, DPSF, and ESF, and applies the reservoir operation models (dynamic programming, DP; stochastic dynamic programming, SDP; and standard operation policy, SOP) to assess the effect of different forms of forecast uncertainty on real-time reservoir operation. Through a hypothetical single-objective real-time reservoir operation model, the results illustrate that forecast uncertainty exerts significant effects. Reservoir operation efficiency, as measured by a utility function, decreases as the forecast uncertainty increases. Meanwhile, these effects also depend on the type of forecast product being used. In general, the utility of reservoir operation with ESF is nearly as high as the utility obtained with a perfect forecast; the utilities of DSF and DPSF are similar to each other but not as efficient as ESF. Moreover, streamflow variability and reservoir capacity can change the magnitude of the effects of forecast uncertainty, but not the relative merit of DSF, DPSF, and ESF. Schematic diagram of the increase in forecast uncertainty with forecast lead-time and the dynamic updating property of real-time streamflow forecast

Evaluating Satellite Rainfall Estimates for Agro-hydrological Applications in Africa

NASA Astrophysics Data System (ADS)

Senay, G. B.; Verdin, J. P.; Korecha, D.; Asfaw, A.

2004-12-01

Regional water balance techniques are used to monitor and forecast crop performance and flooding potentials around the world. In the last few years, satellite rainfall estimates (RFE) have become available at continental scales, which made it possible to develop operational regional water balance models for the monitoring of crops performance and flooding potentials in Africa and other regions of the world as part of an environmental early warning system . The accuracy of RFE in absolute terms and importantly as it relates to agricultural and hydrological applications have not been evaluated systematically. This study evaluated a subset of the Africa-wide RFE product by comparing station-rainfall data and RFE from 1996 to 2002 using over 100 rain-gauge stations from Ethiopia at a dekadal (~10-day) time step. The results showed a general under-estimation of RFE compared to station rainfall values. The correlation between station rainfall data and RFE varied highly from place to place and between seasons. On the other hand, the correlation improved significantly when comparison was made between RFE-derived crop water satisfaction index (WRSI) and station-rainfall-derived WRSI, indicating the usefulness of the RFE for agro-hydrological applications.

Reduction of forecast uncertainty in the context of hydropower production: a case study for two catchment in Lac-St-Jean, Canada

NASA Astrophysics Data System (ADS)

Brisson, Cathy; Boucher, Marie-Amélie; Latraverse, Marco

2014-05-01

This research focuses on the improvement of streamflow forecasts for two subcatchments in the Lac-St-Jean area, a northern part of the province of Quebec in Canada. Those two subcatchments, named Manouane and Passes-Dangereuses, are part of a bigger system, which comprises many reservoirs and six hydropower plants. This system is managed by Rio Tinto Alcan, an aluminium producer who needs this energy for its processes. Optimal management of the hydropower plants highly depends on the reliability of the inflow forecasts to the reservoirs and also on the reliability of observed streamflow. The latter are not directly measured, but rather deduced from the computation of a water balance. This water balance includes streamflow computation based on rating curves for river sections and upstream reservoirs and a modelling process using CEQUEAU hydrological model (Morin et al., 1981). In addition, mostly during the winter, the model has to account for a transfer of water from Lac Manouane reservoir to Passes-Dangereuses through Bonnard channel. Winter flow though Bonnard channel is controlled by a spillway, and represented in CEQUEAU by a transfer function and a fixed time delay (2 days). However, it is suspected that the evacuation function, as it is currently computed, is inaccurate. The main objective of this work is to reduce predictive uncertainty for Lac Manouane and Passes-Dangereuses catchment, for the one-day ahead horizon. This objective is twofold. First, the uncertainty related to the parameterization of the hydrological model had never been evaluated. It was to be investigated whether it is better to spatialize the calibration of the hydrological model. In its actual form, the calibration of the hydrological model CEQUEAU (Morin et al., 1981) is based exclusively on the downstream outflow. There is, however, intermediate streamflow measurements data available for an intermediate location. Our study shows that calibrating the model using streamflows for both locations (intermediate location and downstream) leads to improved forecasts, as measured by the Nash-Sutcliffe efficiency criterion. The parameter sets thus determined best represent the phenomena of exchange and runoff in the watershed. Second, this study aims at reducing the uncertainty associated to the evacuation function for the Bonnard channel as well as the time delay related to this transfer. Instead of using a fixed 2-day time delay for the transfer, it was attempted to represent the channel in the hydrological model CEQUEAU and compute the time delay from this model. The results show that hydrological modelling does not improve the results and that the 2-day time delay is adequate, especially for first days of opening and few days after closure of the gate. In addition, this research shows that the evacuation function of Bonnard spillway is inexact for large streamflows. It is considered the main source of uncertainty for the prediction of inflows to the reservoirs. We also show that the evacuated streamflows can be successfully corrected by hydrological modelling. This case study shows that a careful revision of the inflow forecasting process for those important watersheds can help reduce predictive uncertainty. Although the application is specific to the Lac-St-Jean area, we believe that our experience could serve other users and water managers with similar issues regarding inflow uncertainty. Reference Morin, G., J.-P. Fortin, J.-P. Lardeau, W. Sochanska and S. Paquette. 1981. Modèle CEQUEAU : Manuel d'utilisation. Rapport de recherche no R-93, INRS-Eau, Sainte-Foy

Opportunities and challenges for extended-range predictions of tropical cyclone impacts on hydrological predictions

NASA Astrophysics Data System (ADS)

Tsai, Hsiao-Chung; Elsberry, Russell L.

2013-12-01

SummaryAn opportunity exists to extend support to the decision-making processes of water resource management and hydrological operations by providing extended-range tropical cyclone (TC) formation and track forecasts in the western North Pacific from the 51-member ECMWF 32-day ensemble. A new objective verification technique demonstrates that the ECMWF ensemble can predict most of the formations and tracks of the TCs during July 2009 to December 2010, even for most of the tropical depressions. Due to the relatively large number of false-alarm TCs in the ECMWF ensemble forecasts that would cause problems for support of hydrological operations, characteristics of these false alarms are discussed. Special attention is given to the ability of the ECMWF ensemble to predict periods of no-TCs in the Taiwan area, since water resource management decisions also depend on the absence of typhoon-related rainfall. A three-tier approach is proposed to provide support for hydrological operations via extended-range forecasts twice weekly on the 30-day timescale, twice-daily on the 15-day timescale, and up to four times a day with a consensus of high-resolution deterministic models.

Extending flood forecasting lead time in large basin by coupling bias-corrected WRF QPF with distributed hydrological model

NASA Astrophysics Data System (ADS)

LI, J.; Chen, Y.; Wang, H. Y.

2016-12-01

In large basin flood forecasting, the forecasting lead time is very important. Advances in numerical weather forecasting in the past decades provides new input to extend flood forecasting lead time in large rivers. Challenges for fulfilling this goal currently is that the uncertainty of QPF with these kinds of NWP models are still high, so controlling the uncertainty of QPF is an emerging technique requirement.The Weather Research and Forecasting (WRF) model is one of these NWPs, and how to control the QPF uncertainty of WRF is the research topic of many researchers among the meteorological community. In this study, the QPF products in the Liujiang river basin, a big river with a drainage area of 56,000 km2, was compared with the ground observation precipitation from a rain gauge networks firstly, and the results show that the uncertainty of the WRF QPF is relatively high. So a post-processed algorithm by correlating the QPF with the observed precipitation is proposed to remove the systematical bias in QPF. With this algorithm, the post-processed WRF QPF is close to the ground observed precipitation in area-averaged precipitation. Then the precipitation is coupled with the Liuxihe model, a physically based distributed hydrological model that is widely used in small watershed flash flood forecasting. The Liuxihe Model has the advantage with gridded precipitation from NWP and could optimize model parameters when there are some observed hydrological data even there is only a few, it also has very high model resolution to improve model performance, and runs on high performance supercomputer with parallel algorithm if executed in large rivers. Two flood events in the Liujiang River were collected, one was used to optimize the model parameters and another is used to validate the model. The results show that the river flow simulation has been improved largely, and could be used for real-time flood forecasting trail in extending flood forecasting leading time.

Real time soil moisture forecasts for irrigation management: the Pre.G.I. project

NASA Astrophysics Data System (ADS)

Ceppi, A.; Ravazzani, G.; Mancini, M.; Salerno, R.

2012-04-01

In recent years frequent periods of water scarcity have enhanced the need to use water more carefully. Future climate change scenarios, combined with limited water resources require better irrigation management and planning for farmers' water cooperatives. This has occurred also in areas traditionally rich of water as Lombardy Region, in the North of Italy. In this study we show the development and implementation of a real-time drought forecasting system with a soil moisture hydrological alert, in particular we describe preliminary results of the Pre.G.I. Project, an Italian acronym that stands for "Hydro-Meteorological forecast for irrigation management", funded by Lombardy Region. The project develops a support decision system based on an ensemble weather prediction in the medium-long range (up to 30 days) with hydrological simulation of water balance to forecast the soil water content in every parcel over the Consorzio Muzza basin, in order to use the irrigation water in a wiser and thriftier way. The studied area covers 74,000 ha in the middle of the Po Valley, near Lodi city. The hydrological ensemble forecasts are based on 20 meteorological members of a modified version of the non-hydrostatic WRF model, with multiple nesting to scale to the region of interest. Different physical schemes are also used to take into account a larger variability; these data are provided by Epson Meteo Centre. The hydrological model used to generate the soil moisture and water table simulations is the rainfall-runoff distributed FEST-WB model, developed at Politecnico di Milano. The analysis shows the system reliability based on most significant case-studies occurred in the recent years.

Assimilation of ASCAT near-surface soil moisture into the French SIM hydrological model

NASA Astrophysics Data System (ADS)

Draper, C.; Mahfouf, J.-F.; Calvet, J.-C.; Martin, E.; Wagner, W.

2011-06-01

The impact of assimilating near-surface soil moisture into the SAFRAN-ISBA-MODCOU (SIM) hydrological model over France is examined. Specifically, the root-zone soil moisture in the ISBA land surface model is constrained over three and a half years, by assimilating the ASCAT-derived surface degree of saturation product, using a Simplified Extended Kalman Filter. In this experiment ISBA is forced with the near-real time SAFRAN analysis, which analyses the variables required to force ISBA from relevant observations available before the real time data cut-off. The assimilation results are tested against ISBA forecasts generated with a higher quality delayed cut-off SAFRAN analysis. Ideally, assimilating the ASCAT data will constrain the ISBA surface state to correct for errors in the near-real time SAFRAN forcing, the most significant of which was a substantial dry bias caused by a dry precipitation bias. The assimilation successfully reduced the mean root-zone soil moisture bias, relative to the delayed cut-off forecasts, by close to 50 % of the open-loop value. The improved soil moisture in the model then led to significant improvements in the forecast hydrological cycle, reducing the drainage, runoff, and evapotranspiration biases (by 17 %, 11 %, and 70 %, respectively). When coupled to the MODCOU hydrogeological model, the ASCAT assimilation also led to improved streamflow forecasts, increasing the mean discharge ratio, relative to the delayed cut off forecasts, from 0.68 to 0.76. These results demonstrate that assimilating near-surface soil moisture observations can effectively constrain the SIM model hydrology, while also confirming the accuracy of the ASCAT surface degree of saturation product. This latter point highlights how assimilation experiments can contribute towards the difficult issue of validating remotely sensed land surface observations over large spatial scales.

Reliable estimates of predictive uncertainty for an Alpine catchment using a non-parametric methodology

NASA Astrophysics Data System (ADS)

Matos, José P.; Schaefli, Bettina; Schleiss, Anton J.

2017-04-01

Uncertainty affects hydrological modelling efforts from the very measurements (or forecasts) that serve as inputs to the more or less inaccurate predictions that are produced. Uncertainty is truly inescapable in hydrology and yet, due to the theoretical and technical hurdles associated with its quantification, it is at times still neglected or estimated only qualitatively. In recent years the scientific community has made a significant effort towards quantifying this hydrologic prediction uncertainty. Despite this, most of the developed methodologies can be computationally demanding, are complex from a theoretical point of view, require substantial expertise to be employed, and are constrained by a number of assumptions about the model error distribution. These assumptions limit the reliability of many methods in case of errors that show particular cases of non-normality, heteroscedasticity, or autocorrelation. The present contribution builds on a non-parametric data-driven approach that was developed for uncertainty quantification in operational (real-time) forecasting settings. The approach is based on the concept of Pareto optimality and can be used as a standalone forecasting tool or as a postprocessor. By virtue of its non-parametric nature and a general operating principle, it can be applied directly and with ease to predictions of streamflow, water stage, or even accumulated runoff. Also, it is a methodology capable of coping with high heteroscedasticity and seasonal hydrological regimes (e.g. snowmelt and rainfall driven events in the same catchment). Finally, the training and operation of the model are very fast, making it a tool particularly adapted to operational use. To illustrate its practical use, the uncertainty quantification method is coupled with a process-based hydrological model to produce statistically reliable forecasts for an Alpine catchment located in Switzerland. Results are presented and discussed in terms of their reliability and resolution.

PAI-OFF: A new proposal for online flood forecasting in flash flood prone catchments

NASA Astrophysics Data System (ADS)

Schmitz, G. H.; Cullmann, J.

2008-10-01

SummaryThe Process Modelling and Artificial Intelligence for Online Flood Forecasting (PAI-OFF) methodology combines the reliability of physically based, hydrologic/hydraulic modelling with the operational advantages of artificial intelligence. These operational advantages are extremely low computation times and straightforward operation. The basic principle of the methodology is to portray process models by means of ANN. We propose to train ANN flood forecasting models with synthetic data that reflects the possible range of storm events. To this end, establishing PAI-OFF requires first setting up a physically based hydrologic model of the considered catchment and - optionally, if backwater effects have a significant impact on the flow regime - a hydrodynamic flood routing model of the river reach in question. Both models are subsequently used for simulating all meaningful and flood relevant storm scenarios which are obtained from a catchment specific meteorological data analysis. This provides a database of corresponding input/output vectors which is then completed by generally available hydrological and meteorological data for characterizing the catchment state prior to each storm event. This database subsequently serves for training both a polynomial neural network (PoNN) - portraying the rainfall-runoff process - and a multilayer neural network (MLFN), which mirrors the hydrodynamic flood wave propagation in the river. These two ANN models replace the hydrological and hydrodynamic model in the operational mode. After presenting the theory, we apply PAI-OFF - essentially consisting of the coupled "hydrologic" PoNN and "hydrodynamic" MLFN - to the Freiberger Mulde catchment in the Erzgebirge (Ore-mountains) in East Germany (3000 km 2). Both the demonstrated computational efficiency and the prediction reliability underline the potential of the new PAI-OFF methodology for online flood forecasting.

Propagation of radar rainfall uncertainty in urban flood simulations

NASA Astrophysics Data System (ADS)

Liguori, Sara; Rico-Ramirez, Miguel

2013-04-01

This work discusses the results of the implementation of a novel probabilistic system designed to improve ensemble sewer flow predictions for the drainage network of a small urban area in the North of England. The probabilistic system has been developed to model the uncertainty associated to radar rainfall estimates and propagate it through radar-based ensemble sewer flow predictions. The assessment of this system aims at outlining the benefits of addressing the uncertainty associated to radar rainfall estimates in a probabilistic framework, to be potentially implemented in the real-time management of the sewer network in the study area. Radar rainfall estimates are affected by uncertainty due to various factors [1-3] and quality control and correction techniques have been developed in order to improve their accuracy. However, the hydrological use of radar rainfall estimates and forecasts remains challenging. A significant effort has been devoted by the international research community to the assessment of the uncertainty propagation through probabilistic hydro-meteorological forecast systems [4-5], and various approaches have been implemented for the purpose of characterizing the uncertainty in radar rainfall estimates and forecasts [6-11]. A radar-based ensemble stochastic approach, similar to the one implemented for use in the Southern-Alps by the REAL system [6], has been developed for the purpose of this work. An ensemble generator has been calibrated on the basis of the spatial-temporal characteristics of the residual error in radar estimates assessed with reference to rainfall records from around 200 rain gauges available for the year 2007, previously post-processed and corrected by the UK Met Office [12-13]. Each ensemble member is determined by summing a perturbation field to the unperturbed radar rainfall field. The perturbations are generated by imposing the radar error spatial and temporal correlation structure to purely stochastic fields. A hydrodynamic sewer network model implemented in the Infoworks software was used to model the rainfall-runoff process in the urban area. The software calculates the flow through the sewer conduits of the urban model using rainfall as the primary input. The sewer network is covered by 25 radar pixels with a spatial resolution of 1 km2. The majority of the sewer system is combined, carrying both urban rainfall runoff as well as domestic and trade waste water [11]. The urban model was configured to receive the probabilistic radar rainfall fields. The results showed that the radar rainfall ensembles provide additional information about the uncertainty in the radar rainfall measurements that can be propagated in urban flood modelling. The peaks of the measured flow hydrographs are often bounded within the uncertainty area produced by using the radar rainfall ensembles. This is in fact one of the benefits of using radar rainfall ensembles in urban flood modelling. More work needs to be done in improving the urban models, but this is out of the scope of this research. The rainfall uncertainty cannot explain the whole uncertainty shown in the flow simulations, and additional sources of uncertainty will come from the structure of the urban models as well as the large number of parameters required by these models. Acknowledgements The authors would like to acknowledge the BADC, the UK Met Office and the UK Environment Agency for providing the various data sets. We also thank Yorkshire Water Services Ltd for providing the urban model. The authors acknowledge the support from the Engineering and Physical Sciences Research Council (EPSRC) via grant EP/I012222/1. References [1] Browning KA, 1978. Meteorological applications of radar. Reports on Progress in Physics 41 761 Doi: 10.1088/0034-4885/41/5/003 [2] Rico-Ramirez MA, Cluckie ID, Shepherd G, Pallot A, 2007. A high-resolution radar experiment on the island of Jersey. Meteorological Applications 14: 117-129. [3] Villarini G, Krajewski WF, 2010. Review of the different sources of uncertainty in single polarization radar-based estimates of rainfall. Surveys in Geophysics 31: 107-129. [4] Rossa A, Liechti K, Zappa M, Bruen M, Germann U, Haase G, Keil C, Krahe P, 2011. The COST 731 Action: A review on uncertainty propagation in advanced hydrometeorological forecast systems. Atmospheric Research 100, 150-167. [5] Rossa A, Bruen M, Germann U, Haase G, Keil C, Krahe P, Zappa M, 2010. Overview and Main Results on the interdisciplinary effort in flood forecasting COST 731-Propagation of Uncertainty in Advanced Meteo-Hydrological Forecast Systems. Proceedings of Sixth European Conference on Radar in Meteorology and Hydrology ERAD 2010. [6] Germann U, Berenguer M, Sempere-Torres D, Zappa M, 2009. REAL - ensemble radar precipitation estimation for hydrology in a mountainous region. Quarterly Journal of the Royal Meteorological Society 135: 445-456. [8] Bowler NEH, Pierce CE, Seed AW, 2006. STEPS: a probabilistic precipitation forecasting scheme which merges and extrapolation nowcast with downscaled NWP. Quarterly Journal of the Royal Meteorological Society 132: 2127-2155. [9] Zappa M, Rotach MW, Arpagaus M, Dorninger M, Hegg C, Montani A, Ranzi R, Ament F, Germann U, Grossi G et al., 2008. MAP D-PHASE: real-time demonstration of hydrological ensemble prediction systems. Atmospheric Science Letters 9, 80-87. [10] Liguori S, Rico-Ramirez MA. Quantitative assessment of short-term rainfall forecasts from radar nowcasts and MM5 forecasts. Hydrological Processes, accepted article. DOI: 10.1002/hyp.8415 [11] Liguori S, Rico-Ramirez MA, Schellart ANA, Saul AJ, 2012. Using probabilistic radar rainfall nowcasts and NWP forecasts for flow prediction in urban catchments. Atmospheric Research 103: 80-95. [12] Harrison DL, Driscoll SJ, Kitchen M, 2000. Improving precipitation estimates from weather radar using quality control and correction techniques. Meteorological Applications 7: 135-144. [13] Harrison DL, Scovell RW, Kitchen M, 2009. High-resolution precipitation estimates for hydrological uses. Proceedings of the Institution of Civil Engineers - Water Management 162: 125-135.

Hydrologic Services Course.

ERIC Educational Resources Information Center

National Oceanic and Atmospheric Administration (DOC), Rockville, MD. National Weather Service.

A course to develop an understanding of the scope of water resource activities, of the need for forecasting, of the National Weather Service's role in hydrology, and of the proper procedures to follow in fulfilling this role is presented. The course is one of self-help, guided by correspondence. Nine lessons are included: (1) Hydrology in the…

Community-based early warning systems for flood risk mitigation in Nepal

NASA Astrophysics Data System (ADS)

Smith, Paul J.; Brown, Sarah; Dugar, Sumit

2017-03-01

This paper focuses on the use of community-based early warning systems for flood resilience in Nepal. The first part of the work outlines the evolution and current status of these community-based systems, highlighting the limited lead times currently available for early warning. The second part of the paper focuses on the development of a robust operational flood forecasting methodology for use by the Nepal Department of Hydrology and Meteorology (DHM) to enhance early warning lead times. The methodology uses data-based physically interpretable time series models and data assimilation to generate probabilistic forecasts, which are presented in a simple visual tool. The approach is designed to work in situations of limited data availability with an emphasis on sustainability and appropriate technology. The successful application of the forecast methodology to the flood-prone Karnali River basin in western Nepal is outlined, increasing lead times from 2-3 to 7-8 h. The challenges faced in communicating probabilistic forecasts to the last mile of the existing community-based early warning systems across Nepal is discussed. The paper concludes with an assessment of the applicability of this approach in basins and countries beyond Karnali and Nepal and an overview of key lessons learnt from this initiative.

Repurposing of open data through large scale hydrological modelling - hypeweb.smhi.se

NASA Astrophysics Data System (ADS)

Strömbäck, Lena; Andersson, Jafet; Donnelly, Chantal; Gustafsson, David; Isberg, Kristina; Pechlivanidis, Ilias; Strömqvist, Johan; Arheimer, Berit

2015-04-01

Hydrological modelling demands large amounts of spatial data, such as soil properties, land use, topography, lakes and reservoirs, ice and snow coverage, water management (e.g. irrigation patterns and regulations), meteorological data and observed water discharge in rivers. By using such data, the hydrological model will in turn provide new data that can be used for new purposes (i.e. re-purposing). This presentation will give an example of how readily available open data from public portals have been re-purposed by using the Hydrological Predictions for the Environment (HYPE) model in a number of large-scale model applications covering numerous subbasins and rivers. HYPE is a dynamic, semi-distributed, process-based, and integrated catchment model. The model output is launched as new Open Data at the web site www.hypeweb.smhi.se to be used for (i) Climate change impact assessments on water resources and dynamics; (ii) The European Water Framework Directive (WFD) for characterization and development of measure programs to improve the ecological status of water bodies; (iii) Design variables for infrastructure constructions; (iv) Spatial water-resource mapping; (v) Operational forecasts (1-10 days and seasonal) on floods and droughts; (vi) Input to oceanographic models for operational forecasts and marine status assessments; (vii) Research. The following regional domains have been modelled so far with different resolutions (number of subbasins within brackets): Sweden (37 000), Europe (35 000), Arctic basin (30 000), La Plata River (6 000), Niger River (800), Middle-East North-Africa (31 000), and the Indian subcontinent (6 000). The Hype web site provides several interactive web applications for exploring results from the models. The user can explore an overview of various water variables for historical and future conditions. Moreover the user can explore and download historical time series of discharge for each basin and explore the performance of the model towards observed river flow. The presentation will describe the Open Data sources used, show the functionality of the web site and discuss model performance and experience from this world-wide hydrological modelling of multi-basins using open data.

Short-term ensemble streamflow forecasting using operationally-produced single-valued streamflow forecasts - A Hydrologic Model Output Statistics (HMOS) approach

NASA Astrophysics Data System (ADS)

Regonda, Satish Kumar; Seo, Dong-Jun; Lawrence, Bill; Brown, James D.; Demargne, Julie

2013-08-01

We present a statistical procedure for generating short-term ensemble streamflow forecasts from single-valued, or deterministic, streamflow forecasts produced operationally by the U.S. National Weather Service (NWS) River Forecast Centers (RFCs). The resulting ensemble streamflow forecast provides an estimate of the predictive uncertainty associated with the single-valued forecast to support risk-based decision making by the forecasters and by the users of the forecast products, such as emergency managers. Forced by single-valued quantitative precipitation and temperature forecasts (QPF, QTF), the single-valued streamflow forecasts are produced at a 6-h time step nominally out to 5 days into the future. The single-valued streamflow forecasts reflect various run-time modifications, or "manual data assimilation", applied by the human forecasters in an attempt to reduce error from various sources in the end-to-end forecast process. The proposed procedure generates ensemble traces of streamflow from a parsimonious approximation of the conditional multivariate probability distribution of future streamflow given the single-valued streamflow forecast, QPF, and the most recent streamflow observation. For parameter estimation and evaluation, we used a multiyear archive of the single-valued river stage forecast produced operationally by the NWS Arkansas-Red River Basin River Forecast Center (ABRFC) in Tulsa, Oklahoma. As a by-product of parameter estimation, the procedure provides a categorical assessment of the effective lead time of the operational hydrologic forecasts for different QPF and forecast flow conditions. To evaluate the procedure, we carried out hindcasting experiments in dependent and cross-validation modes. The results indicate that the short-term streamflow ensemble hindcasts generated from the procedure are generally reliable within the effective lead time of the single-valued forecasts and well capture the skill of the single-valued forecasts. For smaller basins, however, the effective lead time is significantly reduced by short basin memory and reduced skill in the single-valued QPF.

OHD/HL - National Weather Hydrology Laboratory

Science.gov Websites

Organization Search NWS All NOAA Go Local forecast by "City, St" Search by city or zip code. Press enter or select the go button to submit request City, St Go Science Research and Collaboration Hydrology

Parameter estimation of an ARMA model for river flow forecasting using goal programming

NASA Astrophysics Data System (ADS)

Mohammadi, Kourosh; Eslami, H. R.; Kahawita, Rene

2006-11-01

SummaryRiver flow forecasting constitutes one of the most important applications in hydrology. Several methods have been developed for this purpose and one of the most famous techniques is the Auto regressive moving average (ARMA) model. In the research reported here, the goal was to minimize the error for a specific season of the year as well as for the complete series. Goal programming (GP) was used to estimate the ARMA model parameters. Shaloo Bridge station on the Karun River with 68 years of observed stream flow data was selected to evaluate the performance of the proposed method. The results when compared with the usual method of maximum likelihood estimation were favorable with respect to the new proposed algorithm.

Using Satellite Data and Land Surface Models to Monitor and Forecast Drought Conditions in Africa and Middle East

NASA Astrophysics Data System (ADS)

Arsenault, K. R.; Shukla, S.; Getirana, A.; Peters-Lidard, C. D.; Kumar, S.; McNally, A.; Zaitchik, B. F.; Badr, H. S.; Funk, C. C.; Koster, R. D.; Narapusetty, B.; Jung, H. C.; Roningen, J. M.

2017-12-01

Drought and water scarcity are among the important issues facing several regions within Africa and the Middle East. In addition, these regions typically have sparse ground-based data networks, where sometimes remotely sensed observations may be the only data available. Long-term satellite records can help with determining historic and current drought conditions. In recent years, several new satellites have come on-line that monitor different hydrological variables, including soil moisture and terrestrial water storage. Though these recent data records may be considered too short for the use in identifying major droughts, they do provide additional information that can better characterize where water deficits may occur. We utilize recent satellite data records of Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage (TWS) and the European Space Agency's Advanced Scatterometer (ASCAT) soil moisture retrievals. Combining these records with land surface models (LSMs), NASA's Catchment and the Noah Multi-Physics (MP), is aimed at improving the land model states and initialization for seasonal drought forecasts. The LSMs' total runoff is routed through the Hydrological Modeling and Analysis Platform (HyMAP) to simulate surface water dynamics, which can provide an additional means of validation against in situ streamflow data. The NASA Land Information System (LIS) software framework drives the LSMs and HyMAP and also supports the capability to assimilate these satellite retrievals, such as soil moisture and TWS. The LSMs are driven for 30+ years with NASA's Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2), and the USGS/UCSB Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) rainfall dataset. The seasonal water deficit forecasts are generated using downscaled and bias-corrected versions of NASA's Goddard Earth Observing System Model (GEOS-5), and NOAA's Climate Forecast System (CFSv2) forecasts. These combined satellite and model records and forecasts are intended for use in different decision support tools, like the Famine Early Warning Systems Network (FEWS NET) and the Middle East-North Africa (MENA) Regional Drought Management System, for aiding and forecasting in water and food insecure regions.

UK Environmental Prediction - integration and evaluation at the convective scale

NASA Astrophysics Data System (ADS)

Fallmann, Joachim; Lewis, Huw; Castillo, Juan Manuel; Pearson, David; Harris, Chris; Saulter, Andy; Bricheno, Lucy; Blyth, Eleanor

2016-04-01

Traditionally, the simulation of regional ocean, wave and atmosphere components of the Earth System have been considered separately, with some information on other components provided by means of boundary or forcing conditions. More recently, the potential value of a more integrated approach, as required for global climate and Earth System prediction, for regional short-term applications has begun to gain increasing research effort. In the UK, this activity is motivated by an understanding that accurate prediction and warning of the impacts of severe weather requires an integrated approach to forecasting. The substantial impacts on individuals, businesses and infrastructure of such events indicate a pressing need to understand better the value that might be delivered through more integrated environmental prediction. To address this need, the Met Office, NERC Centre for Ecology & Hydrology and NERC National Oceanography Centre have begun to develop the foundations of a coupled high resolution probabilistic forecast system for the UK at km-scale. This links together existing model components of the atmosphere, coastal ocean, land surface and hydrology. Our initial focus has been on a 2-year Prototype project to demonstrate the UK coupled prediction concept in research mode. This presentation will provide an update on UK environmental prediction activities. We will present the results from the initial implementation of an atmosphere-land-ocean coupled system, including a new eddy-permitting resolution ocean component, and discuss progress and initial results from further development to integrate wave interactions in this relatively high resolution system. We will discuss future directions and opportunities for collaboration in environmental prediction, and the challenges to realise the potential of integrated regional coupled forecasting for improving predictions and applications.

HYDROSCAPE: A SCAlable and ParallelizablE Rainfall Runoff Model for Hydrological Applications

NASA Astrophysics Data System (ADS)

Piccolroaz, S.; Di Lazzaro, M.; Zarlenga, A.; Majone, B.; Bellin, A.; Fiori, A.

2015-12-01

In this work we present HYDROSCAPE, an innovative streamflow routing method based on the travel time approach, and modeled through a fine-scale geomorphological description of hydrological flow paths. The model is designed aimed at being easily coupled with weather forecast or climate models providing the hydrological forcing, and at the same time preserving the geomorphological dispersion of the river network, which is kept unchanged independently on the grid size of rainfall input. This makes HYDROSCAPE particularly suitable for multi-scale applications, ranging from medium size catchments up to the continental scale, and to investigate the effects of extreme rainfall events that require an accurate description of basin response timing. Key feature of the model is its computational efficiency, which allows performing a large number of simulations for sensitivity/uncertainty analyses in a Monte Carlo framework. Further, the model is highly parsimonious, involving the calibration of only three parameters: one defining the residence time of hillslope response, one for channel velocity, and a multiplicative factor accounting for uncertainties in the identification of the potential maximum soil moisture retention in the SCS-CN method. HYDROSCAPE is designed with a simple and flexible modular structure, which makes it particularly prone to massive parallelization, customization according to the specific user needs and preferences (e.g., rainfall-runoff model), and continuous development and improvement. Finally, the possibility to specify the desired computational time step and evaluate streamflow at any location in the domain, makes HYDROSCAPE an attractive tool for many hydrological applications, and a valuable alternative to more complex and highly parametrized large scale hydrological models. Together with model development and features, we present an application to the Upper Tiber River basin (Italy), providing a practical example of model performance and characteristics.

Visualising probabilistic flood forecast information: expert preferences and perceptions of best practice in uncertainty communication

NASA Astrophysics Data System (ADS)

Pappenberger, F.; Stephens, E. M.; Thielen, J.; Salomon, P.; Demeritt, D.; van Andel, S.; Wetterhall, F.; Alfieri, L.

2011-12-01

The aim of this paper is to understand and to contribute to improved communication of the probabilistic flood forecasts generated by Hydrological Ensemble Prediction Systems (HEPS) with particular focus on the inter expert communication. Different users are likely to require different kinds of information from HEPS and thus different visualizations. The perceptions of this expert group are important both because they are the designers and primary users of existing HEPS. Nevertheless, they have sometimes resisted the release of uncertainty information to the general public because of doubts about whether it can be successfully communicated in ways that would be readily understood to non-experts. In this paper we explore the strengths and weaknesses of existing HEPS visualization methods and thereby formulate some wider recommendations about best practice for HEPS visualization and communication. We suggest that specific training on probabilistic forecasting would foster use of probabilistic forecasts with a wider range of applications. The result of a case study exercise showed that there is no overarching agreement between experts on how to display probabilistic forecasts and what they consider essential information that should accompany plots and diagrams. In this paper we propose a list of minimum properties that, if consistently displayed with probabilistic forecasts, would make the products more easily understandable.

Evaluating Downscaling Methods for Seasonal Climate Forecasts over East Africa

NASA Technical Reports Server (NTRS)

Roberts, J. Brent; Robertson, Franklin R.; Bosilovich, Michael; Lyon, Bradfield; Funk, Chris

2013-01-01

The U.S. National Multi-Model Ensemble seasonal forecasting system is providing hindcast and real-time data streams to be used in assessing and improving seasonal predictive capacity. The NASA / USAID SERVIR project, which leverages satellite and modeling-based resources for environmental decision making in developing nations, is focusing on the evaluation of NMME forecasts specifically for use in impact modeling within hub regions including East Africa, the Hindu Kush-Himalayan (HKH) region and Mesoamerica. One of the participating models in NMME is the NASA Goddard Earth Observing System (GEOS5). This work will present an intercomparison of downscaling methods using the GEOS5 seasonal forecasts of temperature and precipitation over East Africa. The current seasonal forecasting system provides monthly averaged forecast anomalies. These anomalies must be spatially downscaled and temporally disaggregated for use in application modeling (e.g. hydrology, agriculture). There are several available downscaling methodologies that can be implemented to accomplish this goal. Selected methods include both a non-homogenous hidden Markov model and an analogue based approach. A particular emphasis will be placed on quantifying the ability of different methods to capture the intermittency of precipitation within both the short and long rain seasons. Further, the ability to capture spatial covariances will be assessed. Both probabilistic and deterministic skill measures will be evaluated over the hindcast period

Evaluating Downscaling Methods for Seasonal Climate Forecasts over East Africa

NASA Technical Reports Server (NTRS)

Robertson, Franklin R.; Roberts, J. Brent; Bosilovich, Michael; Lyon, Bradfield

2013-01-01

The U.S. National Multi-Model Ensemble seasonal forecasting system is providing hindcast and real-time data streams to be used in assessing and improving seasonal predictive capacity. The NASA / USAID SERVIR project, which leverages satellite and modeling-based resources for environmental decision making in developing nations, is focusing on the evaluation of NMME forecasts specifically for use in impact modeling within hub regions including East Africa, the Hindu Kush-Himalayan (HKH) region and Mesoamerica. One of the participating models in NMME is the NASA Goddard Earth Observing System (GEOS5). This work will present an intercomparison of downscaling methods using the GEOS5 seasonal forecasts of temperature and precipitation over East Africa. The current seasonal forecasting system provides monthly averaged forecast anomalies. These anomalies must be spatially downscaled and temporally disaggregated for use in application modeling (e.g. hydrology, agriculture). There are several available downscaling methodologies that can be implemented to accomplish this goal. Selected methods include both a non-homogenous hidden Markov model and an analogue based approach. A particular emphasis will be placed on quantifying the ability of different methods to capture the intermittency of precipitation within both the short and long rain seasons. Further, the ability to capture spatial covariances will be assessed. Both probabilistic and deterministic skill measures will be evaluated over the hindcast period.

Open hydrological data at hypeweb.smhi.se

NASA Astrophysics Data System (ADS)

Arheimer, Berit; Strömbäck, Lena; Andersson, Jafet; Donnelly, Chantal; Gustafsson, David; Pechlivianidis, Ilias; Strömqvist, Johan

2016-04-01

Following the EU open data strategy the Swedish Meteorological and Hydrological Institute (SMHI) is providing large parts of the databases openly available. These data are ranging from historical observations to climate predictions in various areas such as weather, oceanography and hydrology. For the Water Service called Hypeweb (www.hypeweb.smhi.se), we provide data for water management. So far, the data has been used in: (i) Climate change impact assessments on water resources and dynamics; (ii) The European Water Framework Directive (WFD) for characterization and development of measure programs to improve the ecological status of water bodies; (iii) Design variables for infrastructure constructions; (iv) Spatial water-resource mapping; (v) Operational forecasts (1-10 days and seasonal) on floods and droughts; (vi) Input to oceanographic models for operational forecasts and marine status assessments; and (vii) Research. The data of Hypeweb is based on other open data sources that has been merged and re-purposed by using the Hydrological Predictions for the Environment (HYPE) model in world-wide applications with high resolution. HYPE is a dynamic, semi-distributed, process-based, and integrated catchment model. So far, the following regional domains have been modelled with different resolutions (number of subbasins within brackets): Sweden (37 000), Europe (35 000), Arctic basin (30 000), La Plata River (6 000), Niger River (800), Middle-East North-Africa (31 000), and the Indian subcontinent (6 000). The web site provides several interactive applications for exploring results from the models. The user can explore an overview of various water variables for historical and future conditions. Moreover the user can explore and download historical time series of discharge for each basin and explore the performance of the model towards observed river flow. The presentation will give an overview of the functionality of the web site and the available hydrological datasets. The first version if the site was launched early 2015, and new functionality and updated model data is regularly added. During the first year the site has attracted more than 2000 users from over 90 different countries, and we see an increasing trend in number of visitors. The presentation will describe the Open Data sources used, show the functionality of the web site and discuss model performance and experience from this world-wide hydrological modelling of multi-basins using open data.

Simultaneous calibration of ensemble river flow predictions over an entire range of lead times

NASA Astrophysics Data System (ADS)

Hemri, S.; Fundel, F.; Zappa, M.

2013-10-01

Probabilistic estimates of future water levels and river discharge are usually simulated with hydrologic models using ensemble weather forecasts as main inputs. As hydrologic models are imperfect and the meteorological ensembles tend to be biased and underdispersed, the ensemble forecasts for river runoff typically are biased and underdispersed, too. Thus, in order to achieve both reliable and sharp predictions statistical postprocessing is required. In this work Bayesian model averaging (BMA) is applied to statistically postprocess ensemble runoff raw forecasts for a catchment in Switzerland, at lead times ranging from 1 to 240 h. The raw forecasts have been obtained using deterministic and ensemble forcing meteorological models with different forecast lead time ranges. First, BMA is applied based on mixtures of univariate normal distributions, subject to the assumption of independence between distinct lead times. Then, the independence assumption is relaxed in order to estimate multivariate runoff forecasts over the entire range of lead times simultaneously, based on a BMA version that uses multivariate normal distributions. Since river runoff is a highly skewed variable, Box-Cox transformations are applied in order to achieve approximate normality. Both univariate and multivariate BMA approaches are able to generate well calibrated probabilistic forecasts that are considerably sharper than climatological forecasts. Additionally, multivariate BMA provides a promising approach for incorporating temporal dependencies into the postprocessed forecasts. Its major advantage against univariate BMA is an increase in reliability when the forecast system is changing due to model availability.

Seasonal forecasting for water resource management: the example of CNR Genissiat dam on the Rhone River in France

NASA Astrophysics Data System (ADS)

Dommanget, Etienne; Bellier, Joseph; Ben Daoud, Aurélien; Graff, Benjamin

2014-05-01

Compagnie Nationale du Rhône (CNR) has been granted the concession to operate the Rhone River from the Swiss border to the Mediterranean Sea since 1933 and carries out three interdependent missions: navigation, irrigation and hydropower production. Nowadays, CNR generates one quarter of France's hydropower electricity. The convergence of public and private interests around optimizing the management of water resources throughout the French Rhone valley led CNR to develop hydrological models dedicated to discharge seasonal forecasting. Indeed, seasonal forecasting is a major issue for CNR and water resource management, in order to optimize long-term investments of the produced electricity, plan dam maintenance operations and anticipate low water period. Seasonal forecasting models have been developed on the Genissiat dam. With an installed capacity of 420MW, Genissiat dam is the first of the 19 CNR's hydropower plants. Discharge forecasting at Genissiat dam is strategic since its inflows contributes to 20% of the total Rhone average discharge and consequently to 40% of the total Rhone hydropower production. Forecasts are based on hydrological statistical models. Discharge on the main Rhone River tributaries upstream Genissiat dam are forecasted from 1 to 6 months ahead thanks to multiple linear regressions. Inputs data of these regressions are identified depending on river hydrological regimes and periods of the year. For the melting season, from spring to summer, snow water equivalent (SWE) data are of major importance. SWE data are calculated from Crocus model (Météo France) and SLF's model (Switzerland). CNR hydro-meteorological forecasters assessed meteorological trends regarding precipitations for the next coming months. These trends are used to generate stochastically precipitation scenarios in order to complement regression data set. This probabilistic approach build a decision-making supports for CNR's water resource management team and provides them with seasonal forecasts and their confidence interval. After a presentation of CNR methodology, results for the years 2011 and 2013 will illustrate CNR's seasonal forecasting models ability. These years are of particular interest regarding water resource management seeing that they are, respectively, unusually dry and snowy. Model performances will be assessed in comparison with historical climatology thanks to CRPS skill score.

Replacing climatological potential evapotranspiration estimates with dynamic satellite-based observations in operational hydrologic prediction models

NASA Astrophysics Data System (ADS)

Franz, K. J.; Bowman, A. L.; Hogue, T. S.; Kim, J.; Spies, R.

2011-12-01

In the face of a changing climate, growing populations, and increased human habitation in hydrologically risky locations, both short- and long-range planners increasingly require robust and reliable streamflow forecast information. Current operational forecasting utilizes watershed-scale, conceptual models driven by ground-based (commonly point-scale) observations of precipitation and temperature and climatological potential evapotranspiration (PET) estimates. The PET values are derived from historic pan evaporation observations and remain static from year-to-year. The need for regional dynamic PET values is vital for improved operational forecasting. With the advent of satellite remote sensing and the adoption of a more flexible operational forecast system by the National Weather Service, incorporation of advanced data products is now more feasible than in years past. In this study, we will test a previously developed satellite-derived PET product (UCLA MODIS-PET) in the National Weather Service forecast models and compare the model results to current methods. The UCLA MODIS-PET method is based on the Priestley-Taylor formulation, is driven with MODIS satellite products, and produces a daily, 250m PET estimate. The focus area is eight headwater basins in the upper Midwest U.S. There is a need to develop improved forecasting methods for this region that are able to account for climatic and landscape changes more readily and effectively than current methods. This region is highly flood prone yet sensitive to prolonged dry periods in late summer and early fall, and is characterized by a highly managed landscape, which has drastically altered the natural hydrologic cycle. Our goal is to improve model simulations, and thereby, the initial conditions prior to the start of a forecast through the use of PET values that better reflect actual watershed conditions. The forecast models are being tested in both distributed and lumped mode.

An Integrated Ensemble-Based Operational Framework to Predict Urban Flooding: A Case Study of Hurricane Sandy in the Passaic and Hackensack River Basins

NASA Astrophysics Data System (ADS)

Saleh, F.; Ramaswamy, V.; Georgas, N.; Blumberg, A. F.; Wang, Y.

2016-12-01

Advances in computational resources and modeling techniques are opening the path to effectively integrate existing complex models. In the context of flood prediction, recent extreme events have demonstrated the importance of integrating components of the hydrosystem to better represent the interactions amongst different physical processes and phenomena. As such, there is a pressing need to develop holistic and cross-disciplinary modeling frameworks that effectively integrate existing models and better represent the operative dynamics. This work presents a novel Hydrologic-Hydraulic-Hydrodynamic Ensemble (H3E) flood prediction framework that operationally integrates existing predictive models representing coastal (New York Harbor Observing and Prediction System, NYHOPS), hydrologic (US Army Corps of Engineers Hydrologic Modeling System, HEC-HMS) and hydraulic (2-dimensional River Analysis System, HEC-RAS) components. The state-of-the-art framework is forced with 125 ensemble meteorological inputs from numerical weather prediction models including the Global Ensemble Forecast System, the European Centre for Medium-Range Weather Forecasts (ECMWF), the Canadian Meteorological Centre (CMC), the Short Range Ensemble Forecast (SREF) and the North American Mesoscale Forecast System (NAM). The framework produces, within a 96-hour forecast horizon, on-the-fly Google Earth flood maps that provide critical information for decision makers and emergency preparedness managers. The utility of the framework was demonstrated by retrospectively forecasting an extreme flood event, hurricane Sandy in the Passaic and Hackensack watersheds (New Jersey, USA). Hurricane Sandy caused significant damage to a number of critical facilities in this area including the New Jersey Transit's main storage and maintenance facility. The results of this work demonstrate that ensemble based frameworks provide improved flood predictions and useful information about associated uncertainties, thus improving the assessment of risks as when compared to a deterministic forecast. The work offers perspectives for short-term flood forecasts, flood mitigation strategies and best management practices for climate change scenarios.

Using snow data assimilation to improve ensemble streamflow forecasting for the Upper Colorado River Basin

NASA Astrophysics Data System (ADS)

Micheletty, P. D.; Perrot, D.; Day, G. N.; Lhotak, J.; Quebbeman, J.; Park, G. H.; Carney, S.

2017-12-01

Water supply forecasting in the western United States is inextricably linked to snowmelt processes, as approximately 70-85% of total annual runoff comes from water stored in seasonal mountain snowpacks. Snowmelt-generated streamflow is vital to a variety of downstream uses; the Upper Colorado River Basin (UCRB) alone provides water supply for 25 million people, irrigation water for 3.5 million acres, and drives hydropower generation at Lake Powell. April-July water supply forecasts produced by the National Weather Service (NWS) Colorado Basin River Forecast Center (CBRFC) are critical to basin water management. The primary objective of this project as part of the NASA Water Resources Applied Science Program, is to improve water supply forecasting for the UCRB by assimilating satellite and ground snowpack observations into a distributed hydrologic model at various times during the snow accumulation and melt seasons. To do this, we have built a framework that uses an Ensemble Kalman Filter (EnKF) to update modeled snow water equivalent (SWE) states in the Hydrology Laboratory-Research Distributed Hydrologic Model (HL-RDHM) with spatially interpolated SNOTEL snow water equivalent (SWE) observations and products from the MODIS Snow Covered-Area and Grain size retrieval algorithm (when available). We have generated April-July water supply reforecasts for a 20-year period (1991-2010) for several headwater catchments in the UCRB using HL-RDHM and snow data assimilation in the Ensemble Streamflow Prediction (ESP) framework. The existing CBRFC ESP reforecasts will provide a baseline for comparison to determine whether the data assimilation process adds skill to the water supply forecasts. Preliminary results from one headwater basin show improved skill in water supply forecasting when HL-RDHM is run with the data assimilation step compared to HL-RDHM run without the data assimilation step, particularly in years when MODSCAG data were available (2000-2010). The final forecasting framework developed during this project will be delivered to CBRFC and run operationally for a set of pilot basins.

Application of Hydrometeorological Information for Short-term and Long-term Water Resources Management over Ungauged Basin in Korea

NASA Astrophysics Data System (ADS)

Kim, Ji-in; Ryu, Kyongsik; Suh, Ae-sook

2016-04-01

In 2014, three major governmental organizations that are Korea Meteorological Administration (KMA), K-water, and Korea Rural Community Corporation have been established the Hydrometeorological Cooperation Center (HCC) to accomplish more effective water management for scarcely gauged river basins, where data are uncertain or non-consistent. To manage the optimal drought and flood control over the ungauged river, HCC aims to interconnect between weather observations and forecasting information, and hydrological model over sparse regions with limited observations sites in Korean peninsula. In this study, long-term forecasting ensemble models so called Global Seasonal forecast system version 5 (GloSea5): a high-resolution seasonal forecast system, provided by KMA was used in order to produce drought outlook. Glosea5 ensemble model prediction provides predicted drought information for 1 and 3 months ahead with drought index including Standardized Precipitation Index (SPI3) and Palmer Drought Severity Index (PDSI). Also, Global Precipitation Measurement and Global Climate Observation Measurement - Water1 satellites data products are used to estimate rainfall and soil moisture contents over the ungauged region.

Quantifying The Effects of Initial Soil Moisture On Seasonal Streamflow Forecasts In The Columbia River Basin

NASA Astrophysics Data System (ADS)

Hamlet, A. F.; Wood, A.; Lettenmaier, D. P.

The role of soil moisture storage in the hydrologic cycle is well understood at a funda- mental level. Antecedent conditions are known to have potentially significant effects on streamflow forecasts, especially for short (e.g., flood) lead times. For this reason, the U.S. Geological Survey defines its "water year" as extending from October through September, a time period selected because over most of the U.S., soil moisture is at a seasonal low at summer's end. The effects of carryover soil moisture storage in the Columbia River basin have usually been considered to be minimal when forecasts are made on a water year or seasonal basis. Our study demonstrates that the role of carry- over soil moisture storage can be important. Absent direct observations of ET and soil moisture that would permit a closing of the water balance from observations, we use a physically based hydrologic model to estimate the soil moisture state at the begin- ning of the forecast period (Oct 1). We then evaluate, in a self-consistent manner, the subsequent effects of interannual variations in fall soil moisture on streamflow during the subsequent spring and summer snowmelt season (April-September). We analyze the period from 1950-1999, and the subsequent effects to the seasonal water balance at The Dalles, OR for representative high, medium, and low water years. The effects of initial soil state in fall are remarkably persistent, with significant effects occurring in the summer of the following water year. For a representative low flow year (1992), the simulated variability of the soil moisture state in September produces a range of summer streamflows (April-September mean) equivalent to about 16 percent of the mean summer flows for all initial soil conditions, with analogous, but smaller, relative changes for medium and high flow years. Winter flows are also affected, and the rel- ative intensity of effects in winter and summer is variable, an effect that is probably attributable to the amount of soil recharge that occurs (or does not occur) in early fall in a particular water year. Issues relating to hydrologic model calibration and some applications to experimental long-lead forecasts in the Columbia basin are also dis- cussed.

Global scale predictability of floods

NASA Astrophysics Data System (ADS)

Weerts, Albrecht; Gijsbers, Peter; Sperna Weiland, Frederiek

2016-04-01

Flood (and storm surge) forecasting at the continental and global scale has only become possible in recent years (Emmerton et al., 2016; Verlaan et al., 2015) due to the availability of meteorological forecast, global scale precipitation products and global scale hydrologic and hydrodynamic models. Deltares has setup GLOFFIS a research-oriented multi model operational flood forecasting system based on Delft-FEWS in an open experimental ICT facility called Id-Lab. In GLOFFIS both the W3RA and PCRGLOB-WB model are run in ensemble mode using GEFS and ECMWF-EPS (latency 2 days). GLOFFIS will be used for experiments into predictability of floods (and droughts) and their dependency on initial state estimation, meteorological forcing and the hydrologic model used. Here we present initial results of verification of the ensemble flood forecasts derived with the GLOFFIS system. Emmerton, R., Stephens, L., Pappenberger, F., Pagano, T., Weerts, A., Wood, A. Salamon, P., Brown, J., Hjerdt, N., Donnelly, C., Cloke, H. Continental and Global Scale Flood Forecasting Systems, WIREs Water (accepted), 2016 Verlaan M, De Kleermaeker S, Buckman L. GLOSSIS: Global storm surge forecasting and information system 2015, Australasian Coasts & Ports Conference, 15-18 September 2015,Auckland, New Zealand.

Modelling and Forecasting of Rice Yield in support of Crop Insurance

NASA Astrophysics Data System (ADS)

Weerts, A.; van Verseveld, W.; Trambauer, P.; de Vries, S.; Conijn, S.; van Valkengoed, E.; Hoekman, D.; Hengsdijk, H.; Schrevel, A.

2016-12-01

The Government of Indonesia has embarked on a policy to bring crop insurance to all of Indonesia's farmers. To support the Indonesian government, the G4INDO project (www.g4indo.org) is developing/constructing an integrated platform for judging and handling insurance claims. The platform consists of bringing together remote sensed data (both visible and radar) and hydrologic and crop modelling and forecasting to improve predictions in one forecasting platform (i.e. Delft-FEWS, Werner et al., 2013). The hydrological model and crop model (LINTUL) are coupled on time stepping basis in the OpenStreams framework (see https://github.com/openstreams/wflow) and deployed in a Delft-FEWS forecasting platform to support seasonal forecasting of water availability and crop yield. First we will show the general idea about the project, the integrated platform (including Sentinel 1 & 2 data) followed by first (reforecast) results of the coupled models for predicting water availability and crop yield in the Brantas catchment in Java, Indonesia. Werner, M., Schellekens, J., Gijsbers, P., Van Dijk, M., Van den Akker, O. and Heynert K, 2013. The Delft-FEWS flow forecasting system, Environmental Modelling & Software; 40:65-77. DOI: 10.1016/j.envsoft.2012.07.010 .

Impact of Reservoir Operation to the Inflow Flood - a Case Study of Xinfengjiang Reservoir

NASA Astrophysics Data System (ADS)

Chen, L.

2017-12-01

Building of reservoir shall impact the runoff production and routing characteristics, and changes the flood formation. This impact, called as reservoir flood effect, could be divided into three parts, including routing effect, volume effect and peak flow effect, and must be evaluated in a whole by using hydrological model. After analyzing the reservoir flood formation, the Liuxihe Model for reservoir flood forecasting is proposed. The Xinfengjiang Reservoir is studied as a case. Results show that the routing effect makes peak flow appear 4 to 6 hours in advance, volume effect is bigger for large flood than small one, and when rainfall focus on the reservoir area, this effect also increases peak flow largely, peak flow effect makes peak flow increase 6.63% to 8.95%. Reservoir flood effect is obvious, which have significant impact to reservoir flood. If this effect is not considered in the flood forecasting model, the flood could not be forecasted accurately, particularly the peak flow. Liuxihe Model proposed for Xinfengjiang Reservoir flood forecasting has a good performance, and could be used for real-time flood forecasting of Xinfengjiang Reservoir.Key words: Reservoir flood effect, reservoir flood forecasting, physically based distributed hydrological model, Liuxihe Model, parameter optimization

State updating of a distributed hydrological model with Ensemble Kalman Filtering: effects of updating frequency and observation network density on forecast accuracy

NASA Astrophysics Data System (ADS)

Rakovec, O.; Weerts, A. H.; Hazenberg, P.; Torfs, P. J. J. F.; Uijlenhoet, R.

2012-09-01

This paper presents a study on the optimal setup for discharge assimilation within a spatially distributed hydrological model. The Ensemble Kalman filter (EnKF) is employed to update the grid-based distributed states of such an hourly spatially distributed version of the HBV-96 model. By using a physically based model for the routing, the time delay and attenuation are modelled more realistically. The discharge and states at a given time step are assumed to be dependent on the previous time step only (Markov property). Synthetic and real world experiments are carried out for the Upper Ourthe (1600 km2), a relatively quickly responding catchment in the Belgian Ardennes. We assess the impact on the forecasted discharge of (1) various sets of the spatially distributed discharge gauges and (2) the filtering frequency. The results show that the hydrological forecast at the catchment outlet is improved by assimilating interior gauges. This augmentation of the observation vector improves the forecast more than increasing the updating frequency. In terms of the model states, the EnKF procedure is found to mainly change the pdfs of the two routing model storages, even when the uncertainty in the discharge simulations is smaller than the defined observation uncertainty.

Informing Drought Preparedness and Response with the South Asia Land Data Assimilation System

NASA Astrophysics Data System (ADS)

Zaitchik, B. F.; Ghatak, D.; Matin, M. A.; Qamer, F. M.; Adhikary, B.; Bajracharya, B.; Nelson, J.; Pulla, S. T.; Ellenburg, W. L.

2017-12-01

Decision-relevant drought monitoring in South Asia is a challenge from both a scientific and an institutional perspective. Scientifically, climatic diversity, inconsistent in situ monitoring, complex hydrology, and incomplete knowledge of atmospheric processes mean that monitoring and prediction are fraught with uncertainty. Institutionally, drought monitoring efforts need to align with the information needs and decision-making processes of relevant agencies at national and subnational levels. Here we present first results from an emerging operational drought monitoring and forecast system developed and supported by the NASA SERVIR Hindu-Kush Himalaya hub. The system has been designed in consultation with end users from multiple sectors in South Asian countries to maximize decision-relevant information content in the monitoring and forecast products. Monitoring of meteorological, agricultural, and hydrological drought is accomplished using the South Asia Land Data Assimilation System, a platform that supports multiple land surface models and meteorological forcing datasets to characterize uncertainty, and subseasonal to seasonal hydrological forecasts are produced by driving South Asia LDAS with downscaled meteorological fields drawn from an ensemble of global dynamically-based forecast systems. Results are disseminated to end users through a Tethys online visualization platform and custom communications that provide user oriented, easily accessible, timely, and decision-relevant scientific information.

Medium range flood forecasts at global scale

NASA Astrophysics Data System (ADS)

Voisin, N.; Wood, A. W.; Lettenmaier, D. P.; Wood, E. F.

2006-12-01

While weather and climate forecast methods have advanced greatly over the last two decades, this capability has yet to be evidenced in mitigation of water-related natural hazards (primarily floods and droughts), especially in the developing world. Examples abound of extreme property damage and loss of life due to floods in the underdeveloped world. For instance, more than 4.5 million people were affected by the July 2000 flooding of the Mekong River and its tributaries in Cambodia, Vietnam, Laos and Thailand. The February- March 2000 floods in the Limpopo River of Mozambique caused extreme disruption to that country's fledgling economy. Mitigation of these events through advance warning has typically been modest at best. Despite the above noted improvement in weather and climate forecasts, there is at present no system for forecasting of floods globally, notwithstanding that the potential clearly exists. We describe a methodology that is eventually intended to generate global flood predictions routinely. It draws heavily from the experimental North American Land Data Assimilation System (NLDAS) and the companion Global Land Data Assimilation System (GLDAS) for development of nowcasts, and the University of Washington Experimental Hydrologic Prediction System to develop ensemble hydrologic forecasts based on Numerical Weather Prediction (NWP) models which serve both as nowcasts (and hence reduce the need for in situ precipitation and other observations in parts of the world where surface networks are critically deficient) and provide forecasts for lead times as long as fifteen days. The heart of the hydrologic modeling system is the University of Washington/Princeton University Variable Infiltration Capacity (VIC) macroscale hydrology model. In the prototype (tested using retrospective data), VIC is driven globally up to the time of forecast with daily ERA40 precipitation (rescaled on a monthly basis to a station-based global climatology), ERA40 wind, and ERA40 average surface air temperature (with temperature ranges adjusted to a station-based climatology). In the retrospective forecasting mode, VIC is driven by global NCEP ensemble 15-day reforecasts provided by Tom Hamill (NOAA/ERL), bias corrected with respect to the adjusted ERA40 data and further downscaled spatially using higher spatial resolution Global Precipitation Climatology Project (GPCP) 1dd daily precipitation. Downward solar and longwave radiation, surface relative humidity, and other model forcings are derived from relationships with the daily temperature range during both the retrospective (spinup) and forecast period. The initial system is implemented globally at one-half degree spatial resolution. We evaluate model performance retrospectively for predictions of major floods for the Oder River in 1997, the Mekong River in 2000 and the Limpopo River in 2000.

Extending flood forecasting lead time in a large watershed by coupling WRF QPF with a distributed hydrological model

NASA Astrophysics Data System (ADS)

Li, Ji; Chen, Yangbo; Wang, Huanyu; Qin, Jianming; Li, Jie; Chiao, Sen

2017-03-01

Long lead time flood forecasting is very important for large watershed flood mitigation as it provides more time for flood warning and emergency responses. The latest numerical weather forecast model could provide 1-15-day quantitative precipitation forecasting products in grid format, and by coupling this product with a distributed hydrological model could produce long lead time watershed flood forecasting products. This paper studied the feasibility of coupling the Liuxihe model with the Weather Research and Forecasting quantitative precipitation forecast (WRF QPF) for large watershed flood forecasting in southern China. The QPF of WRF products has three lead times, including 24, 48 and 72 h, with the grid resolution being 20 km  × 20 km. The Liuxihe model is set up with freely downloaded terrain property; the model parameters were previously optimized with rain gauge observed precipitation, and re-optimized with the WRF QPF. Results show that the WRF QPF has bias with the rain gauge precipitation, and a post-processing method is proposed to post-process the WRF QPF products, which improves the flood forecasting capability. With model parameter re-optimization, the model's performance improves also. This suggests that the model parameters be optimized with QPF, not the rain gauge precipitation. With the increasing of lead time, the accuracy of the WRF QPF decreases, as does the flood forecasting capability. Flood forecasting products produced by coupling the Liuxihe model with the WRF QPF provide a good reference for large watershed flood warning due to its long lead time and rational results.

Improving real-time inflow forecasting into hydropower reservoirs through a complementary modelling framework

NASA Astrophysics Data System (ADS)

Gragne, A. S.; Sharma, A.; Mehrotra, R.; Alfredsen, K.

2015-08-01

Accuracy of reservoir inflow forecasts is instrumental for maximizing the value of water resources and benefits gained through hydropower generation. Improving hourly reservoir inflow forecasts over a 24 h lead time is considered within the day-ahead (Elspot) market of the Nordic exchange market. A complementary modelling framework presents an approach for improving real-time forecasting without needing to modify the pre-existing forecasting model, but instead formulating an independent additive or complementary model that captures the structure the existing operational model may be missing. We present here the application of this principle for issuing improved hourly inflow forecasts into hydropower reservoirs over extended lead times, and the parameter estimation procedure reformulated to deal with bias, persistence and heteroscedasticity. The procedure presented comprises an error model added on top of an unalterable constant parameter conceptual model. This procedure is applied in the 207 km2 Krinsvatn catchment in central Norway. The structure of the error model is established based on attributes of the residual time series from the conceptual model. Besides improving forecast skills of operational models, the approach estimates the uncertainty in the complementary model structure and produces probabilistic inflow forecasts that entrain suitable information for reducing uncertainty in the decision-making processes in hydropower systems operation. Deterministic and probabilistic evaluations revealed an overall significant improvement in forecast accuracy for lead times up to 17 h. Evaluation of the percentage of observations bracketed in the forecasted 95 % confidence interval indicated that the degree of success in containing 95 % of the observations varies across seasons and hydrologic years.

Improving regional climate and hydrological forecasting following the record setting flooding across the Lake Ontario - St. Lawrence River system

NASA Astrophysics Data System (ADS)

Gronewold, A.; Seglenieks, F.; Bruxer, J.; Fortin, V.; Noel, J.

2017-12-01

In the spring of 2017, water levels across Lake Ontario and the upper St. Lawrence River exceeded record high levels, leading to widespread flooding, damage to property, and controversy over regional dam operating protocols. Only a few years earlier, water levels on Lakes Superior, Michigan, and Huron (upstream of Lake Ontario) had dropped to record low levels leading to speculation that either anthropogenic controls or climate change were leading to chronic water loss from the Great Lakes. The contrast between low water level conditions across Earth's largest lake system from the late 1990s through 2013, and the rapid rise prior to the flooding in early 2017, underscores the challenges of quantifying and forecasting hydrologic impacts of rising regional air and water temperatures (and associated changes in lake evaporation) and persistent increases in long-term precipitation. Here, we assess the hydrologic conditions leading to the recent record flooding across the Lake Ontario - St. Lawrence River system, with a particular emphasis on understanding the extent to which those conditions were consistent with observed and anticipated changes in historical and future climate, and the extent to which those conditions could have been anticipated through improvements in seasonal climate outlooks and hydrological forecasts.

Long-term flow forecasts based on climate and hydrologic modeling: Uruguay River basin

NASA Astrophysics Data System (ADS)

Tucci, Carlos Eduardo Morelli; Clarke, Robin Thomas; Collischonn, Walter; da Silva Dias, Pedro Leite; de Oliveira, Gilvan Sampaio

2003-07-01

This paper describes a procedure for predicting seasonal flow in the Rio Uruguay drainage basin (area 75,000 km2, lying in Brazilian territory), using sequences of future daily rainfall given by the global climate model (GCM) of the Brazilian agency for climate prediction (Centro de Previsão de Tempo e Clima, or CPTEC). Sequences of future daily rainfall given by this model were used as input to a rainfall-runoff model appropriate for large drainage basins. Forecasts of flow in the Rio Uruguay were made for the period 1995-2001 of the full record, which began in 1940. Analysis showed that GCM forecasts underestimated rainfall over almost all the basin, particularly in winter, although interannual variability in regional rainfall was reproduced relatively well. A statistical procedure was used to correct for the underestimation of rainfall. When the corrected rainfall sequences were transformed to flow by the hydrologic model, forecasts of flow in the Rio Uruguay basin were better than forecasts based on historic mean or median flows by 37% for monthly flows and by 54% for 3-monthly flows.

Use of distributed snow cover information to update snow storages of a lumped rainfall-runoff model operationally

NASA Astrophysics Data System (ADS)

Lisniak, D.; Meissner, D.; Klein, B.; Pinzinger, R.

2013-12-01

The German Federal Institute of Hydrology (BfG) offers navigational water-level forecasting services on the Federal Waterways, like the rivers Rhine and Danube. In cooperation with the Federal States this mandate also includes the forecasting of flood events. For the River Rhine, the most frequented inland waterway in Central Europe, the BfG employs a hydrological model (HBV) coupled to a hydraulic model (SOBEK) by the FEWS-framework to perform daily forecasts of water-levels operationally. Sensitivity studies have shown that the state of soil water storage in the hydrological model is a major factor of uncertainty when performing short- to medium-range forecasts some days ahead. Taking into account the various additional sources of uncertainty associated with hydrological modeling, including measurement uncertainties, it is essential to estimate an optimal initial state of the soil water storage before propagating it in time, forced by meteorological forecasts, and transforming it into discharge. We show, that using the Ensemble Kalman Filter these initial states can be updated straightforward under certain hydrologic conditions. However, this approach is not sufficient if the runoff is mainly generated by snow melt. Since the snow cover evolution is modeled rather poorly by the HBV-model in our operational setting, flood events caused by snow melt are consistently underestimated by the HBV-model, which has long term effects in basins characterized by a nival runoff regime. Thus, it appears beneficial to update the snow storage of the HBV-model with information derived from regionalized snow cover observations. We present a method to incorporate spatially distributed snow cover observations into the lumped HBV-model. We show the plausibility of this approach and asses the benefits of a coupled snow cover and soil water storage updating, which combine a direct insertion with an Ensemble Kalman Filter. The Ensemble Kalman Filter used here takes into account the internal routing mechanism of the HBV-model, which causes a delayed response of the simulated discharge at the catchment outlet to changes in internal states.

Modular GIS Framework for National Scale Hydrologic and Hydraulic Modeling Support

NASA Astrophysics Data System (ADS)

Djokic, D.; Noman, N.; Kopp, S.

2015-12-01

Geographic information systems (GIS) have been extensively used for pre- and post-processing of hydrologic and hydraulic models at multiple scales. An extensible GIS-based framework was developed for characterization of drainage systems (stream networks, catchments, floodplain characteristics) and model integration. The framework is implemented as a set of free, open source, Python tools and builds on core ArcGIS functionality and uses geoprocessing capabilities to ensure extensibility. Utilization of COTS GIS core capabilities allows immediate use of model results in a variety of existing online applications and integration with other data sources and applications.The poster presents the use of this framework to downscale global hydrologic models to local hydraulic scale and post process the hydraulic modeling results and generate floodplains at any local resolution. Flow forecasts from ECMWF or WRF-Hydro are downscaled and combined with other ancillary data for input into the RAPID flood routing model. RAPID model results (stream flow along each reach) are ingested into a GIS-based scale dependent stream network database for efficient flow utilization and visualization over space and time. Once the flows are known at localized reaches, the tools can be used to derive the floodplain depth and extent for each time step in the forecast at any available local resolution. If existing rating curves are available they can be used to relate the flow to the depth of flooding, or synthetic rating curves can be derived using the tools in the toolkit and some ancillary data/assumptions. The results can be published as time-enabled spatial services to be consumed by web applications that use floodplain information as an input. Some of the existing online presentation templates can be easily combined with available online demographic and infrastructure data to present the impact of the potential floods on the local community through simple, end user products. This framework has been successfully used in both the data rich environments as well as in locales with minimum available spatial and hydrographic data.

Establishing a rainfall threshold for flash flood warnings based on the DFFG method in Yunnan province, China

NASA Astrophysics Data System (ADS)

Ma, M.; Wang, H.; Chen, Y.; Tang, G.; Hong, Z.; Zhang, K.; Hong, Y.

2017-12-01

Flash floods, one of the deadliest natural hazards worldwide due to their multidisciplinary nature, rank highly in terms of heavy damage and casualties. Such as in the United States, flash flood is the No.1 cause of death and the No. 2 most deadly weather-related hazard among all storm-related hazards, with approximately 100 lives lost each year. According to China Floods and Droughts Disasters Bullet in 2015 (http://www.mwr.gov.cn/zwzc/hygb/zgshzhgb), about 935 deaths per year on average were caused by flash floods from 2000 to 2015, accounting for 73 % of the fatalities due to floods. Therefore, significant efforts have been made toward understanding flash flood processes as well as modeling and forecasting them, it still remains challenging because of their short response time and limited monitoring capacity. This study advances the use of high-resolution Global Precipitation Measurement forecasts (GPMs), disaster data obtained from the government officials in 2011 and 2016, and the improved Distributed Flash Flood Guidance (DFFG) method combining the Distributed Hydrologic Model and Soil Conservation Service Curve Numbers. The objectives of this paper are (1) to examines changes in flash flood occurrence, (2) to estimate the effect of the rainfall spatial variability ,(2) to improve the lead time in flash floods warning and get the rainfall threshold, (3) to assess the DFFG method applicability in Dongchuan catchments, and (4) to yield the probabilistic information about the forecast hydrologic response that accounts for the locational uncertainties of the GPMs. Results indicate: (1) flash flood occurrence increased in the study region, (2) the occurrence of predicted flash floods show high sensitivity to total infiltration and soil water content, (3) the DFFG method is generally capable of making accurate predictions of flash flood events in terms of their locations and time of occurrence, and (4) the accumulative rainfall over a certain time span is an appropriate threshold for flash flood warnings. Finally, the article highlights the importance of accurately simulating the hydrological processes and high-resolution satellite rainfall data on the accurate forecasting of rainfall triggered flash flood events.

15 CFR 946.4 - Menu of services.

Code of Federal Regulations, 2010 CFR

2010-01-01

... AND ATMOSPHERIC ADMINISTRATION, DEPARTMENT OF COMMERCE REGULATIONS OF THE NATIONAL WEATHER SERVICE MODERNIZATION OF THE NATIONAL WEATHER SERVICE § 946.4 Menu of services. The following are the basic weather...) Marine Forecasts, Statements, and Warnings (g) Hydrologic Forecasts and Warnings (h) Fire Weather...

15 CFR 946.4 - Menu of services.

Code of Federal Regulations, 2013 CFR

2013-01-01

... AND ATMOSPHERIC ADMINISTRATION, DEPARTMENT OF COMMERCE REGULATIONS OF THE NATIONAL WEATHER SERVICE MODERNIZATION OF THE NATIONAL WEATHER SERVICE § 946.4 Menu of services. The following are the basic weather...) Marine Forecasts, Statements, and Warnings (g) Hydrologic Forecasts and Warnings (h) Fire Weather...

Customizing WRF-Hydro for the Laurentian Great Lakes Basin

NASA Astrophysics Data System (ADS)

Gronewold, A.; Pei, L.; Gochis, D.; Mason, L.; Sampson, K. M.; Dugger, A. L.; Read, L.; McCreight, J. L.; Xiao, C.; Lofgren, B. M.; Anderson, E. J.; Chu, P. Y.

2017-12-01

To advance the state of the art in regional hydrological forecasting, and to align with operational deployment of the National Water Model, a team of scientists has been customizing WRF-Hydro (the Weather Research and Forecasting model - Hydrological modeling extension package) to the entirety (including binational land and lake surfaces) of the Laurentian Great Lakes basin. Objectives of this customization project include opererational simulation and forecasting of the Great Lakes water balance and, in the short-term, research-oriented insights into modeling one- and two-way coupled lake-atmosphere and near-shore processes. Initial steps in this project have focused on overcoming inconsistencies in land surface hydrographic datasets between the United States and Canada. Improvements in the model's current representation of lake physics and stream routing are also critical components of this effort. Here, we present an update on the status of this project, including a synthesis of offline tests with WRF-Hydro based on the newly developed Great Lakes hydrographic data, and an assessment of the model's ability to simulate seasonal and multi-decadal hydrological response across the Great Lakes.

Improving the accuracy of flood forecasting with transpositions of ensemble NWP rainfall fields considering orographic effects

NASA Astrophysics Data System (ADS)

Yu, Wansik; Nakakita, Eiichi; Kim, Sunmin; Yamaguchi, Kosei

2016-08-01

The use of meteorological ensembles to produce sets of hydrological predictions increased the capability to issue flood warnings. However, space scale of the hydrological domain is still much finer than meteorological model, and NWP models have challenges with displacement. The main objective of this study to enhance the transposition method proposed in Yu et al. (2014) and to suggest the post-processing ensemble flood forecasting method for the real-time updating and the accuracy improvement of flood forecasts that considers the separation of the orographic rainfall and the correction of misplaced rain distributions using additional ensemble information through the transposition of rain distributions. In the first step of the proposed method, ensemble forecast rainfalls from a numerical weather prediction (NWP) model are separated into orographic and non-orographic rainfall fields using atmospheric variables and the extraction of topographic effect. Then the non-orographic rainfall fields are examined by the transposition scheme to produce additional ensemble information and new ensemble NWP rainfall fields are calculated by recombining the transposition results of non-orographic rain fields with separated orographic rainfall fields for a generation of place-corrected ensemble information. Then, the additional ensemble information is applied into a hydrologic model for post-flood forecasting with a 6-h interval. The newly proposed method has a clear advantage to improve the accuracy of mean value of ensemble flood forecasting. Our study is carried out and verified using the largest flood event by typhoon 'Talas' of 2011 over the two catchments, which are Futatsuno (356.1 km2) and Nanairo (182.1 km2) dam catchments of Shingu river basin (2360 km2), which is located in the Kii peninsula, Japan.

Integration of Remote Sensing Data In Operational Flood Forecast In Southwest Germany

NASA Astrophysics Data System (ADS)

Bach, H.; Appel, F.; Schulz, W.; Merkel, U.; Ludwig, R.; Mauser, W.

Methods to accurately assess and forecast flood discharge are mandatory to minimise the impact of hydrological hazards. However, existing rainfall-runoff models rarely accurately consider the spatial characteristics of the watershed, which is essential for a suitable and physics-based description of processes relevant for runoff formation. Spatial information with low temporal variability like elevation, slopes and land use can be mapped or extracted from remote sensing data. However, land surface param- eters of high temporal variability, like soil moisture and snow properties are hardly available and used in operational forecasts. Remote sensing methods can improve flood forecast by providing information on the actual water retention capacities in the watershed and facilitate the regionalisation of hydrological models. To prove and demonstrate this, the project 'InFerno' (Integration of remote sensing data in opera- tional water balance and flood forecast modelling) has been set up, funded by DLR (50EE0053). Within InFerno remote sensing data (optical and microwave) are thor- oughly processed to deliver spatially distributed parameters of snow properties and soil moisture. Especially during the onset of a flood this information is essential to estimate the initial conditions of the model. At the flood forecast centres of 'Baden- Württemberg' and 'Rheinland-Pfalz' (Southwest Germany) the remote sensing based maps on soil moisture and snow properties will be integrated in the continuously op- erated water balance and flood forecast model LARSIM. The concept is to transfer the developed methodology from the Neckar to the Mosel basin. The major challenges lie on the one hand in the implementation of algorithms developed for a multisensoral synergy and the creation of robust, operationally applicable remote sensing products. On the other hand, the operational flood forecast must be adapted to make full use of the new data sources. In the operational phase of the project ESA's ENVISAT satellite, which will be launched in 2002, will serve as remote sensing data source. Until EN- VISAT data is available, algorithm retrieval, software development and product gener- ation is performed using existing sensors with ENVISAT-like specifications. Based on these data sets test cases and demonstration runs are conducted and will be presented to prove the advantages of the approach.

Operational Hydrological Forecasting During the Iphex-iop Campaign - Meet the Challenge

NASA Technical Reports Server (NTRS)

Tao, Jing; Wu, Di; Gourley, Jonathan; Zhang, Sara Q.; Crow, Wade; Peters-Lidard, Christa D.; Barros, Ana P.

2016-01-01

An operational streamflow forecasting testbed was implemented during the Intense Observing Period (IOP) of the Integrated Precipitation and Hydrology Experiment (IPHEx-IOP) in May-June 2014 to characterize flood predictability in complex terrain. Specifically, hydrological forecasts were issued daily for 12 headwater catchments in the Southern Appalachians using the Duke Coupled surface-groundwater Hydrology Model (DCHM) forced by hourly atmospheric fields and QPFs (Quantitative Precipitation Forecasts) produced by the NASA-Unified Weather Research and Forecasting (NU-WRF) model. Previous day hindcasts forced by radar-based QPEs (Quantitative Precipitation Estimates) were used to provide initial conditions for present day forecasts. This manuscript first describes the operational testbed framework and workflow during the IPHEx-IOP including a synthesis of results. Second, various data assimilation approaches are explored a posteriori (post-IOP) to improve operational (flash) flood forecasting. Although all flood events during the IOP were predicted by the IPHEx operational testbed with lead times of up to 6 h, significant errors of over- and, or under-prediction were identified that could be traced back to the QPFs and subgrid-scale variability of radar QPEs. To improve operational flood prediction, three data-merging strategies were pursued post-IOP: (1) the spatial patterns of QPFs were improved through assimilation of satellite-based microwave radiances into NU-WRF; (2) QPEs were improved by merging raingauge observations with ground-based radar observations using bias-correction methods to produce streamflow hindcasts and associated uncertainty envelope capturing the streamflow observations, and (3) river discharge observations were assimilated into the DCHM to improve streamflow forecasts using the Ensemble Kalman Filter (EnKF), the fixed-lag Ensemble Kalman Smoother (EnKS), and the Asynchronous EnKF (i.e. AEnKF) methods. Both flood hindcasts and forecasts were significantly improved by assimilating discharge observations into the DCHM. Specifically, Nash-Sutcliff Efficiency (NSE) values as high as 0.98, 0.71 and 0.99 at 15-min time-scales were attained for three headwater catchments in the inner mountain region demonstrating that the assimilation of discharge observations at the basins outlet can reduce the errors and uncertainties in soil moisture at very small scales. Success in operational flood forecasting at lead times of 6, 9, 12 and 15 h was also achieved through discharge assimilation with NSEs of 0.87, 0.78, 0.72 and 0.51, respectively. Analysis of experiments using various data assimilation system configurations indicates that the optimal assimilation time window depends both on basin properties and storm-specific space-time-structure of rainfall, and therefore adaptive, context-aware configurations of the data assimilation system are recommended to address the challenges of flood prediction in headwater basins.

Operational hydrological forecasting during the IPHEx-IOP campaign - Meet the challenge

NASA Astrophysics Data System (ADS)

Tao, Jing; Wu, Di; Gourley, Jonathan; Zhang, Sara Q.; Crow, Wade; Peters-Lidard, Christa; Barros, Ana P.

2016-10-01

An operational streamflow forecasting testbed was implemented during the Intense Observing Period (IOP) of the Integrated Precipitation and Hydrology Experiment (IPHEx-IOP) in May-June 2014 to characterize flood predictability in complex terrain. Specifically, hydrological forecasts were issued daily for 12 headwater catchments in the Southern Appalachians using the Duke Coupled surface-groundwater Hydrology Model (DCHM) forced by hourly atmospheric fields and QPFs (Quantitative Precipitation Forecasts) produced by the NASA-Unified Weather Research and Forecasting (NU-WRF) model. Previous day hindcasts forced by radar-based QPEs (Quantitative Precipitation Estimates) were used to provide initial conditions for present day forecasts. This manuscript first describes the operational testbed framework and workflow during the IPHEx-IOP including a synthesis of results. Second, various data assimilation approaches are explored a posteriori (post-IOP) to improve operational (flash) flood forecasting. Although all flood events during the IOP were predicted by the IPHEx operational testbed with lead times of up to 6 h, significant errors of over- and, or under-prediction were identified that could be traced back to the QPFs and subgrid-scale variability of radar QPEs. To improve operational flood prediction, three data-merging strategies were pursued post-IOP: (1) the spatial patterns of QPFs were improved through assimilation of satellite-based microwave radiances into NU-WRF; (2) QPEs were improved by merging raingauge observations with ground-based radar observations using bias-correction methods to produce streamflow hindcasts and associated uncertainty envelope capturing the streamflow observations, and (3) river discharge observations were assimilated into the DCHM to improve streamflow forecasts using the Ensemble Kalman Filter (EnKF), the fixed-lag Ensemble Kalman Smoother (EnKS), and the Asynchronous EnKF (i.e. AEnKF) methods. Both flood hindcasts and forecasts were significantly improved by assimilating discharge observations into the DCHM. Specifically, Nash-Sutcliff Efficiency (NSE) values as high as 0.98, 0.71 and 0.99 at 15-min time-scales were attained for three headwater catchments in the inner mountain region demonstrating that the assimilation of discharge observations at the basin's outlet can reduce the errors and uncertainties in soil moisture at very small scales. Success in operational flood forecasting at lead times of 6, 9, 12 and 15 h was also achieved through discharge assimilation with NSEs of 0.87, 0.78, 0.72 and 0.51, respectively. Analysis of experiments using various data assimilation system configurations indicates that the optimal assimilation time window depends both on basin properties and storm-specific space-time-structure of rainfall, and therefore adaptive, context-aware configurations of the data assimilation system are recommended to address the challenges of flood prediction in headwater basins.

Sequential data assimilation for a distributed hydrologic model considering different time scale of internal processes

NASA Astrophysics Data System (ADS)

Noh, S.; Tachikawa, Y.; Shiiba, M.; Kim, S.

2011-12-01

Applications of the sequential data assimilation methods have been increasing in hydrology to reduce uncertainty in the model prediction. In a distributed hydrologic model, there are many types of state variables and each variable interacts with each other based on different time scales. However, the framework to deal with the delayed response, which originates from different time scale of hydrologic processes, has not been thoroughly addressed in the hydrologic data assimilation. In this study, we propose the lagged filtering scheme to consider the lagged response of internal states in a distributed hydrologic model using two filtering schemes; particle filtering (PF) and ensemble Kalman filtering (EnKF). The EnKF is one of the widely used sub-optimal filters implementing an efficient computation with limited number of ensemble members, however, still based on Gaussian approximation. PF can be an alternative in which the propagation of all uncertainties is carried out by a suitable selection of randomly generated particles without any assumptions about the nature of the distributions involved. In case of PF, advanced particle regularization scheme is implemented together to preserve the diversity of the particle system. In case of EnKF, the ensemble square root filter (EnSRF) are implemented. Each filtering method is parallelized and implemented in the high performance computing system. A distributed hydrologic model, the water and energy transfer processes (WEP) model, is applied for the Katsura River catchment, Japan to demonstrate the applicability of proposed approaches. Forecasted results via PF and EnKF are compared and analyzed in terms of the prediction accuracy and the probabilistic adequacy. Discussions are focused on the prospects and limitations of each data assimilation method.

Providing peak river flow statistics and forecasting in the Niger River basin

NASA Astrophysics Data System (ADS)

Andersson, Jafet C. M.; Ali, Abdou; Arheimer, Berit; Gustafsson, David; Minoungou, Bernard

2017-08-01

Flooding is a growing concern in West Africa. Improved quantification of discharge extremes and associated uncertainties is needed to improve infrastructure design, and operational forecasting is needed to provide timely warnings. In this study, we use discharge observations, a hydrological model (Niger-HYPE) and extreme value analysis to estimate peak river flow statistics (e.g. the discharge magnitude with a 100-year return period) across the Niger River basin. To test the model's capacity of predicting peak flows, we compared 30-year maximum discharge and peak flow statistics derived from the model vs. derived from nine observation stations. The results indicate that the model simulates peak discharge reasonably well (on average + 20%). However, the peak flow statistics have a large uncertainty range, which ought to be considered in infrastructure design. We then applied the methodology to derive basin-wide maps of peak flow statistics and their associated uncertainty. The results indicate that the method is applicable across the hydrologically active part of the river basin, and that the uncertainty varies substantially depending on location. Subsequently, we used the most recent bias-corrected climate projections to analyze potential changes in peak flow statistics in a changed climate. The results are generally ambiguous, with consistent changes only in very few areas. To test the forecasting capacity, we ran Niger-HYPE with a combination of meteorological data sets for the 2008 high-flow season and compared with observations. The results indicate reasonable forecasting capacity (on average 17% deviation), but additional years should also be evaluated. We finish by presenting a strategy and pilot project which will develop an operational flood monitoring and forecasting system based in-situ data, earth observations, modelling, and extreme statistics. In this way we aim to build capacity to ultimately improve resilience toward floods, protecting lives and infrastructure in the region.

Strategies to reduce the complexity of hydrologic data assimilation for high-dimensional models

NASA Astrophysics Data System (ADS)

Hernandez, F.; Liang, X.

2017-12-01

Probabilistic forecasts in the geosciences offer invaluable information by allowing to estimate the uncertainty of predicted conditions (including threats like floods and droughts). However, while forecast systems based on modern data assimilation algorithms are capable of producing multi-variate probability distributions of future conditions, the computational resources required to fully characterize the dependencies between the model's state variables render their applicability impractical for high-resolution cases. This occurs because of the quadratic space complexity of storing the covariance matrices that encode these dependencies and the cubic time complexity of performing inference operations with them. In this work we introduce two complementary strategies to reduce the size of the covariance matrices that are at the heart of Bayesian assimilation methods—like some variants of (ensemble) Kalman filters and of particle filters—and variational methods. The first strategy involves the optimized grouping of state variables by clustering individual cells of the model into "super-cells." A dynamic fuzzy clustering approach is used to take into account the states (e.g., soil moisture) and forcings (e.g., precipitation) of each cell at each time step. The second strategy consists in finding a compressed representation of the covariance matrix that still encodes the most relevant information but that can be more efficiently stored and processed. A learning and a belief-propagation inference algorithm are developed to take advantage of this modified low-rank representation. The two proposed strategies are incorporated into OPTIMISTS, a state-of-the-art hybrid Bayesian/variational data assimilation algorithm, and comparative streamflow forecasting tests are performed using two watersheds modeled with the Distributed Hydrology Soil Vegetation Model (DHSVM). Contrasts are made between the efficiency gains and forecast accuracy losses of each strategy used in isolation, and of those achieved through their coupling. We expect these developments to help catalyze improvements in the predictive accuracy of large-scale forecasting operations by lowering the costs of deploying advanced data assimilation techniques.

Transforming Atmospheric and Remotely-Sensed Information to Hydrologic Predictability in South Asia

NASA Astrophysics Data System (ADS)

Hopson, T. M.; Riddle, E. E.; Broman, D.; Brakenridge, G. R.; Birkett, C. M.; Kettner, A.; Sampson, K. M.; Boehnert, J.; Priya, S.; Collins, D. C.; Rostkier-Edelstein, D.; Young, W.; Singh, D.; Islam, A. S.

2017-12-01

South Asia is a flashpoint for natural disasters with profound societal impacts for the region and globally. Although close to 40% of the world's population depends on the Greater Himalaya's great rivers, $20 Billion of GDP is affected by river floods each year. The frequent occurrence of floods, combined with large and rapidly growing populations with high levels of poverty, make South Asia highly susceptible to humanitarian disasters. The challenges of mitigating such devastating disasters are exacerbated by the limited availability of real-time rain and stream gauge measuring stations and transboundary data sharing, and by constrained institutional commitments to overcome these challenges. To overcome such limitations, India and the World Bank have committed resources to the National Hydrology Project III, with the development objective to improve the extent, quality, and accessibility of water resources information and to strengthen the capacity of targeted water resources management institutions in India. The availability and application of remote sensing products and weather forecasts from ensemble prediction systems (EPS) have transformed river forecasting capability over the last decade, and is of interest to India. In this talk, we review the potential predictability of river flow contributed by some of the freely-available remotely-sensed and weather forecasting products within the framework of the physics of water migration through a watershed. Our specific geographical context is the Ganges, Brahmaputra, and Meghna river basin and a newly-available set of stream gauge measurements located over the region. We focus on satellite rainfall estimation, river height and width estimation, and EPS weather forecasts. For the later, we utilize the THORPEX-TIGGE dataset of global forecasts, and discuss how atmospheric predictability, as measured by an EPS, is transformed into hydrometeorological predictability. We provide an overview of the strengths and weaknesses of each of these data sets to the river flow prediction problem, generalizing their utility across spatial- and temporal-scales, and highlight the benefits of joint utilization and multi-modeling to minimize uncertainty and enhance operational robustness.

Satellite Altimetry based River Forecasting of Transboundary Flow

NASA Astrophysics Data System (ADS)

Hossain, F.; Siddique-E-Akbor, A.; Lee, H.; Shum, C.; Biancamaria, S.

2012-12-01

Forecasting of this transboundary flow in downstream nations however remains notoriously difficult due to the lack of basin-wide in-situ hydrologic measurements or its real-time sharing among nations. In addition, human regulation of upstream flow through diversion projects and dams, make hydrologic models less effective for forecasting on their own. Using the Ganges-Brahmaputra (GB) basin as an example, this study assesses the feasibility of using JASON-2 satellite altimetry for forecasting such transboundary flow at locations further inside the downstream nation of Bangladesh by propagating forecasts derived from upstream (Indian) locations through a hydrodynamic river model. The 5-day forecast of river levels at upstream boundary points inside Bangladesh are used to initialize daily simulation of the hydrodynamic river model and yield the 5-day forecast river level further downstream inside Bangladesh. The forecast river levels are then compared with the 5-day-later "now cast" simulation by the river model based on in-situ river level at the upstream boundary points in Bangladesh. Future directions for satellite-based forecasting of flow are also briefly overviewed.round tracks or virtual stations of JASON-2 (J2) altimeter over the GB basin shown in yellow lines. The locations where the track crosses a river and used for deriving forecasting rating curves is shown with a circle and station number (magenta- Brahmaputra basin; blue - Ganges basin). Circles without a station number represent the broader view of sampling by JASON-2 if all the ground tracks on main stem rivers and neighboring tributaries of Ganges and Brahmaputra are considered.

UK Environmental Prediction - integration and evaluation at the convective scale

NASA Astrophysics Data System (ADS)

Fallmann, Joachim; Lewis, Huw; Castillo, Juan Manuel; Pearson, David; Harris, Chris; Saulter, Andy; Bricheno, Lucy; Blyth, Eleanor

2016-04-01

It has long been understood that accurate prediction and warning of the impacts of severe weather requires an integrated approach to forecasting. For example, high impact weather is typically manifested through various interactions and feedbacks between different components of the Earth System. Damaging high winds can lead to significant damage from the large waves and storm surge along coastlines. The impact of intense rainfall can be translated through saturated soils and land surface processes, high river flows and flooding inland. The substantial impacts on individuals, businesses and infrastructure of such events indicate a pressing need to understand better the value that might be delivered through more integrated environmental prediction. To address this need, the Met Office, NERC Centre for Ecology & Hydrology and NERC National Oceanography Centre have begun to develop the foundations of a coupled high resolution probabilistic forecast system for the UK at km-scale. This links together existing model components of the atmosphere, coastal ocean, land surface and hydrology. Our initial focus has been on a 2-year Prototype project to demonstrate the UK coupled prediction concept in research mode. This presentation will provide an update on UK environmental prediction activities. We will present the results from the initial implementation of an atmosphere-land-ocean coupled system and discuss progress and initial results from further development to integrate wave interactions. We will discuss future directions and opportunities for collaboration in environmental prediction, and the challenges to realise the potential of integrated regional coupled forecasting for improving predictions and applications.

How can we deal with ANN in flood forecasting? As a simulation model or updating kernel!

NASA Astrophysics Data System (ADS)

Hassan Saddagh, Mohammad; Javad Abedini, Mohammad

2010-05-01

Flood forecasting and early warning, as a non-structural measure for flood control, is often considered to be the most effective and suitable alternative to mitigate the damage and human loss caused by flood. Forecast results which are output of hydrologic, hydraulic and/or black box models should secure accuracy of flood values and timing, especially for long lead time. The application of the artificial neural network (ANN) in flood forecasting has received extensive attentions in recent years due to its capability to capture the dynamics inherent in complex processes including flood. However, results obtained from executing plain ANN as simulation model demonstrate dramatic reduction in performance indices as lead time increases. This paper is intended to monitor the performance indices as it relates to flood forecasting and early warning using two different methodologies. While the first method employs a multilayer neural network trained using back-propagation scheme to forecast output hydrograph of a hypothetical river for various forecast lead time up to 6.0 hr, the second method uses 1D hydrodynamic MIKE11 model as forecasting model and multilayer neural network as updating kernel to monitor and assess the performance indices compared to ANN alone in light of increase in lead time. Results presented in both graphical and tabular format indicate superiority of MIKE11 coupled with ANN as updating kernel compared to ANN as simulation model alone. While plain ANN produces more accurate results for short lead time, the errors increase expeditiously for longer lead time. The second methodology provides more accurate and reliable results for longer forecast lead time.

Coupling Radar Rainfall Estimation and Hydrological Modelling For Flash-flood Hazard Mitigation

NASA Astrophysics Data System (ADS)

Borga, M.; Creutin, J. D.

Flood risk mitigation is accomplished through managing either or both the hazard and vulnerability. Flood hazard may be reduced through structural measures which alter the frequency of flood levels in the area. The vulnerability of a community to flood loss can be mitigated through changing or regulating land use and through flood warning and effective emergency response. When dealing with flash-flood hazard, it is gener- ally accepted that the most effective way (and in many instances the only affordable in a sustainable perspective) to mitigate the risk is by reducing the vulnerability of the involved communities, in particular by implementing flood warning systems and community self-help programs. However, both the inherent characteristics of the at- mospheric and hydrologic processes involved in flash-flooding and the changing soci- etal needs provide a tremendous challenge to traditional flood forecasting and warning concepts. In fact, the targets of these systems are traditionally localised like urbanised sectors or hydraulic structures. Given the small spatial scale that characterises flash floods and the development of dispersed urbanisation, transportation, green tourism and water sports, human lives and property are exposed to flash flood risk in a scat- tered manner. This must be taken into consideration in flash flood warning strategies and the investigated region should be considered as a whole and every section of the drainage network as a potential target for hydrological warnings. Radar technology offers the potential to provide information describing rain intensities almost contin- uously in time and space. Recent research results indicate that coupling radar infor- mation to distributed hydrologic modelling can provide hydrologic forecasts at all potentially flooded points of a region. Nevertheless, very few flood warning services use radar data more than on a qualitative basis. After a short review of current under- standing in this area, two issues are examined: advantages and caveats of using radar rainfall estimates in operational flash flood forecasting, methodological problems as- sociated to the use of hydrological models for distributed flash flood forecasting with rainfall input estimated from radar.

The NASA GPM Iowa Flood Studies Experiment

NASA Astrophysics Data System (ADS)

Petersen, W. A.; Krajewski, W. F.; Peters-Lidard, C. D.; Rutledge, S. A.; Wolff, D. B.

2013-12-01

The overarching objective of NASA Global Precipitation Measurement Mission (GPM) integrated hydrologic ground validation (GV) is to provide a better understanding of the strengths and limitations of the satellite products, in the context of hydrologic applications. Accordingly, the NASA GPM GV program recently completed the first of several hydrology-oriented field efforts: the Iowa Flood Studies (IFloodS) experiment. IFloodS was conducted in central Iowa during the months of April-June, 2013. IFloodS science objectives focused on: a) The collection of reference multi-parameter radar, rain gauge, disdrometer, soil moisture, and hydrologic network measurements to quantify the physical character and space/time variability of rain (e.g., rates, drop size distributions, processes), land surface- state and hydrologic response; b) Application of the ground reference measurements to assessment of satellite-based rainfall estimation uncertainties; c) Propagation of both ground and satellite rainfall estimation uncertainties in coupled hydrologic prediction models to assess impacts on predictive skill; and d) Evaluation of rainfall properties such as rate and accumulation relative to basin hydrologic characteristics in modeled flood genesis. IFloodS observational objectives were achieved via deployments of the NASA NPOL S-band and D3R Ka/Ku-band dual-polarimetric radars (operating in coordinated scanning modes), four University of Iowa X-band dual-polarimetric radars, four Micro Rain Radars, a network of 25 paired rain gauge platforms with attendant soil moisture and temperature probes, a network of six 2D Video and 14 Parsivel disdrometers, and 15 USDA-ARS rain gauge and soil-moisture stations (collaboration with the USDA-ARS and NASA Soil Moisture Active-Passive mission). The aforementioned platforms complemented existing operational WSR-88D S-band polarimetric radar, USGS streamflow, and Iowa Flood Center-affiliated stream monitoring and rainfall measurements. Coincident low-earth orbiter microwave, geostationary infrared, and derived satellite-algorithm rainfall products were also archived during the experiment. Twice daily NASA Unified Weather Research and Forecasting model simulations were conducted to provide weather forecast guidance and a coupled atmospheric/land-surface model simulation benchmark. During the experiment the IFloodS observational domain experienced heavy rainfall (> 250-300 mm) and significant flooding. Deployed observational assets, especially the research radars performed well throughout the experiment, sampling a broad range of precipitation system types including multi-day mixtures of rain and snow, warm-season mesoscale convective systems, and supercell thunderstorms. The variety of regimes and large rain accumulations sampled creates a rich source of data for testing both satellite products and coupled atmospheric, land system, and hydrologic models. In this study we will provide an overview of the IFloodS experiment, datasets, and preliminary observational results.

Climate Change Adaptation in the Western U.S.: the Case for Dynamic Rule Curves in Water Resources Management

NASA Astrophysics Data System (ADS)

Lee, S.; Hamlet, A. F.; Burges, S. J.

2008-12-01

Climate change in the Western U.S. will bring systematic hydrologic changes affecting many water resources systems. Successful adaptation to these changes, which will be ongoing through the 21st century, will require the 'rebalancing' of competing system objectives such as water supply, flood control, hydropower production, and environmental services in response to hydrologic (and other) changes. Although fixed operating policies for the operation of reservoirs has been a traditional approach to water management in the 20th century, the rapid pace of projected climate shifts (~0.5 F per decade), and the prohibitive costs of recursive policy intervention to mitigate impacts, suggest that more sophisticated approaches will be needed to cope with climate change on a long term basis. The use of 'dynamic rule curves' is an approach that maintains some of the key characteristics of current water management practice (reservoir rule curves) while avoiding many of the fundamental drawbacks of traditional water resources management strategies in a non-stationary climate. In this approach, water resources systems are optimized for each operational period using ensemble streamflow and/or water demand forecasts. The ensemble of optimized reservoir storage traces are then analyzed to produce a set of unique reservoir rule curves for each operational period reflecting the current state of the system. The potential advantage of this approach is that hydrologic changes associated with climate change (such as systematically warmer temperatures) can be captured explicitly in operational hydrologic forecasts, which would in turn inform the optimized reservoir management solutions, creating water resources systems that are largely 'self tending' as the climate system evolves. Furthermore, as hydrologic forecasting systems improve (e.g. in response to improved ENSO forecasting or other scientific advances), so does the performance of reservoir operations. An example of the approach is given for flood control in the Columbia River basin.

Towards real-time assimilation of crowdsourced observations in hydrological modeling

NASA Astrophysics Data System (ADS)

Mazzoleni, Maurizio; Verlaan, Martin; Alfonso, Leonardo; Norbiato, Daniele; Monego, Martina; Ferri, Michele; Solomatine, Dimitri

2016-04-01

The continued technological advances have stimulated the spread of low-cost sensors that can be used by citizens to provide crowdsourced observations (CO) of different hydrological variables. An example of such low-cost sensors is a staff gauge connected to a QR code on which people can read the water level indication and send the measurement via a mobile phone application. The goal of this study is to assess the combined effect of the assimilation of CO coming from a distributed network of low-cost sensors, and the existing streamflow observations from physical sensors, on the performance of a semi-distributed hydrological model. The methodology is applied to the Bacchiglione catchment, North East of Italy, where an early warning system is used by the Alto Adriatico Water Authority to issue forecasted water level along the river network which cross important cities such as Vicenza and Padua. In this study, forecasted precipitation values are used as input in the hydrological model to estimate the simulated streamflow hydrograph used as boundary condition for the hydraulic model. Observed precipitation values are used to generate realistic synthetic streamflow values with various characteristics of arrival frequency and accuracy, to simulate CO coming at irregular time steps. These observations are assimilated into the semi-distributed model using a Kalman filter based method. The results of this study show that CO, asynchronous in time and with variable accuracy, can still improve flood prediction when integrated in hydrological models. When both physical and low-cost sensors are located at the same places, the assimilation of CO gives the same model improvement than the assimilation of physical observations only for high number of non-intermittent sensors. However, the integration of observations from low-cost sensors and single physical sensors can improve the flood prediction even when small a number of intermittent CO are available. This study is part of the FP7 European Project WeSenseIt Citizen Water Observatory (www.http://wesenseit.eu/).

Towards guided data assimilation for operational hydrologic forecasting in the US Tennessee River basin

NASA Astrophysics Data System (ADS)

Weerts, A.; Wood, A. W.; Clark, M. P.; Carney, S.; Day, G. N.; Lemans, M.; Sumihar, J.; Newman, A. J.

2014-12-01

In the US, the forecasting approach used by the NWS River Forecast Centers and other regional organizations such as the Bonneville Power Administration (BPA) or Tennessee Valley Authority (TVA) has traditionally involved manual model input and state modifications made by forecasters in real-time. This process is time consuming and requires expert knowledge and experience. The benefits of automated data assimilation (DA) as a strategy for avoiding manual modification approaches have been demonstrated in research studies (eg. Seo et al., 2009). This study explores the usage of various ensemble DA algorithms within the operational platform used by TVA. The final goal is to identify a DA algorithm that will guide the manual modification process used by TVA forecasters and realize considerable time gains (without loss of quality or even enhance the quality) within the forecast process. We evaluate the usability of various popular algorithms for DA that have been applied on a limited basis for operational hydrology. To this end, Delft-FEWS was wrapped (via piwebservice) in OpenDA to enable execution of FEWS workflows (and the chained models within these workflows, including SACSMA, UNITHG and LAGK) in a DA framework. Within OpenDA, several filter methods are available. We considered 4 algorithms: particle filter (RRF), Ensemble Kalman Filter and Asynchronous Ensemble Kalman and Particle filter. Retrospective simulation results for one location and algorithm (AEnKF) are illustrated in Figure 1. The initial results are promising. We will present verification results for these methods (and possible more) for a variety of sub basins in the Tennessee River basin. Finally, we will offer recommendations for guided DA based on our results. References Seo, D.-J., L. Cajina, R. Corby and T. Howieson, 2009: Automatic State Updating for Operational Streamflow Forecasting via Variational Data Assimilation, 367, Journal of Hydrology, 255-275. Figure 1. Retrospectively simulated streamflow for the headwater basin above Powell River at Jonesville (red is observed flow, blue is simulated flow without DA, black is simulated flow with DA)

Infrastructure Upgrades to Support Model Longevity and New Applications: The Variable Infiltration Capacity Model Version 5.0 (VIC 5.0)

NASA Astrophysics Data System (ADS)

Nijssen, B.; Hamman, J.; Bohn, T. J.

2015-12-01

The Variable Infiltration Capacity (VIC) model is a macro-scale semi-distributed hydrologic model. VIC development began in the early 1990s and it has been used extensively, applied from basin to global scales. VIC has been applied in a many use cases, including the construction of hydrologic data sets, trend analysis, data evaluation and assimilation, forecasting, coupled climate modeling, and climate change impact analysis. Ongoing applications of the VIC model include the University of Washington's drought monitor and forecast systems, and NASA's land data assimilation systems. The development of VIC version 5.0 focused on reconfiguring the legacy VIC source code to support a wider range of modern modeling applications. The VIC source code has been moved to a public Github repository to encourage participation by the model development community-at-large. The reconfiguration has separated the physical core of the model from the driver, which is responsible for memory allocation, pre- and post-processing and I/O. VIC 5.0 includes four drivers that use the same physical model core: classic, image, CESM, and Python. The classic driver supports legacy VIC configurations and runs in the traditional time-before-space configuration. The image driver includes a space-before-time configuration, netCDF I/O, and uses MPI for parallel processing. This configuration facilitates the direct coupling of streamflow routing, reservoir, and irrigation processes within VIC. The image driver is the foundation of the CESM driver; which couples VIC to CESM's CPL7 and a prognostic atmosphere. Finally, we have added a Python driver that provides access to the functions and datatypes of VIC's physical core from a Python interface. This presentation demonstrates how reconfiguring legacy source code extends the life and applicability of a research model.

National Weather Service Forecast Office Guam Home

Science.gov Websites

National Alerts Text Current Conditions Observations Satellite Hydrology River & Lake AHPS Radar Imagery AAFB (Guam) AAFB (Guam) Dial up CONUS Radar Forecasts Activity Planner Guam Public Marine Aviation ; Weather Topics: Local Alerts, Current Conditions, Radar, Satellite, Climate, W-GUM.Webmaster@noaa.gov

Supporting Crop Loss Insurance Policy of Indonesia through Rice Yield Modelling and Forecasting

NASA Astrophysics Data System (ADS)

van Verseveld, Willem; Weerts, Albrecht; Trambauer, Patricia; de Vries, Sander; Conijn, Sjaak; van Valkengoed, Eric; Hoekman, Dirk; Grondard, Nicolas; Hengsdijk, Huib; Schrevel, Aart; Vlasbloem, Pieter; Klauser, Dominik

2017-04-01

The Government of Indonesia has decided on a crop insurance policy to assist Indonesia's farmers and to boost food security. To support the Indonesian government, the G4INDO project (www.g4indo.org) is developing/constructing an integrated platform implemented in the Delft-FEWS forecasting system (Werner et al., 2013). The integrated platform brings together remote sensed data (both visible and radar) and hydrologic, crop and reservoir modelling and forecasting to improve the modelling and forecasting of rice yield. The hydrological model (wflow_sbm), crop model (wflow_lintul) and reservoir models (RTC-Tools) are coupled on time stepping basis in the OpenStreams framework (see https://github.com/openstreams/wflow) and deployed in the integrated platform to support seasonal forecasting of water availability and crop yield. First we will show the general idea about the G4INDO project, the integrated platform (including Sentinel 1 & 2 data) followed by first (reforecast) results of the coupled models for predicting water availability and crop yield in the Brantas catchment in Java, Indonesia. Werner, M., Schellekens, J., Gijsbers, P., Van Dijk, M., Van den Akker, O. and Heynert K, 2013. The Delft-FEWS flow forecasting system, Environmental Modelling & Software; 40:65-77. DOI: 10.1016/j.envsoft.2012.07.010.

Distributed Application of the Unified Noah LSM with Hydrologic Flow Routing on an Appalachian Headwater Basin

NASA Astrophysics Data System (ADS)

Garcia, M.; Kumar, S.; Gochis, D.; Yates, D.; McHenry, J.; Burnet, T.; Coats, C.; Condrey, J.

2006-05-01

Collaboration between scientists at UMBC-GEST and NASA-GSFC, the NCAR Research Applications Laboratory (RAL), and Baron Advanced Meteorological Services (BAMS), has produced a modeling framework for the application of traditional land surface models (LSMs) in a distributed hydrologic system which can be used for diagnosis and prediction of routed stream discharge hydrographs. This collaboration is oriented on near-term system implementation across Romania for flood and flash-flood analyses and forecasting as part of the World Bank-funded Destructive Waters Abatement (DESWAT) program. Meteorological forcing from surface observations, model analyses and numerical forecasts are employed in the NASA-GSFC Land Information System (LIS) to drive the Unified Noah LSM with Noah-Distributed components, stream network delineation and routing schemes original to this work. The Unified Noah LSM is the outgrowth of a joint modeling effort between several research partners including NCAR, the NOAA National Center for Environmental Prediction (NCEP), and the Air Force Weather Agency (AFWA). At NCAR, hydrologically-oriented extensions to the Noah LSM have been developed for LSM applications in a distributed domain in order to address the lateral redistribution of soil moisture by surface and subsurface flow processes. These advancements have been integrated into the NASA-GSFC Land Information System (LIS) and coupled with an original framework for hydraulic channel network definition and specification, linkages with the Noah-Distributed overland and subsurface flow framework, and distributed cell- to-cell (or link-node) hydraulic routing. This poster presents an overview of the system components and their organization, as well as results of the first U.S. case study performed with this system under various configurations. The case study simulated precipitation events over a headwater basin in the southern Appalachian Mountains in October 2005 following the landfall of Tropical Storm Tammy in South Carolina. These events followed on a long dry period in the region, lending to the demonstration of watershed response to strong precipitation forcing under nearly ideal and easily-specified initial conditions. The results presented here will compare simulated versus observed streamflow conditions at various locations in the test watershed using a selection of routing methods.

Developing reservoir monthly inflow forecasts using artificial intelligence and climate phenomenon information

NASA Astrophysics Data System (ADS)

Yang, Tiantian; Asanjan, Ata Akbari; Welles, Edwin; Gao, Xiaogang; Sorooshian, Soroosh; Liu, Xiaomang

2017-04-01

Reservoirs are fundamental human-built infrastructures that collect, store, and deliver fresh surface water in a timely manner for many purposes. Efficient reservoir operation requires policy makers and operators to understand how reservoir inflows are changing under different hydrological and climatic conditions to enable forecast-informed operations. Over the last decade, the uses of Artificial Intelligence and Data Mining [AI & DM] techniques in assisting reservoir streamflow subseasonal to seasonal forecasts have been increasing. In this study, Random Forest [RF), Artificial Neural Network (ANN), and Support Vector Regression (SVR) are employed and compared with respect to their capabilities for predicting 1 month-ahead reservoir inflows for two headwater reservoirs in USA and China. Both current and lagged hydrological information and 17 known climate phenomenon indices, i.e., PDO and ENSO, etc., are selected as predictors for simulating reservoir inflows. Results show (1) three methods are capable of providing monthly reservoir inflows with satisfactory statistics; (2) the results obtained by Random Forest have the best statistical performances compared with the other two methods; (3) another advantage of Random Forest algorithm is its capability of interpreting raw model inputs; (4) climate phenomenon indices are useful in assisting monthly or seasonal forecasts of reservoir inflow; and (5) different climate conditions are autocorrelated with up to several months, and the climatic information and their lags are cross correlated with local hydrological conditions in our case studies.

Accuracy enhancement for forecasting water levels of reservoirs and river streams using a multiple-input-pattern fuzzification approach.

PubMed

Valizadeh, Nariman; El-Shafie, Ahmed; Mirzaei, Majid; Galavi, Hadi; Mukhlisin, Muhammad; Jaafar, Othman

2014-01-01

Water level forecasting is an essential topic in water management affecting reservoir operations and decision making. Recently, modern methods utilizing artificial intelligence, fuzzy logic, and combinations of these techniques have been used in hydrological applications because of their considerable ability to map an input-output pattern without requiring prior knowledge of the criteria influencing the forecasting procedure. The artificial neurofuzzy interface system (ANFIS) is one of the most accurate models used in water resource management. Because the membership functions (MFs) possess the characteristics of smoothness and mathematical components, each set of input data is able to yield the best result using a certain type of MF in the ANFIS models. The objective of this study is to define the different ANFIS model by applying different types of MFs for each type of input to forecast the water level in two case studies, the Klang Gates Dam and Rantau Panjang station on the Johor river in Malaysia, to compare the traditional ANFIS model with the new introduced one in two different situations, reservoir and stream, showing the new approach outweigh rather than the traditional one in both case studies. This objective is accomplished by evaluating the model fitness and performance in daily forecasting.

WRF model for precipitation simulation and its application in real-time flood forecasting in the Jinshajiang River Basin, China

NASA Astrophysics Data System (ADS)

Zhou, Jianzhong; Zhang, Hairong; Zhang, Jianyun; Zeng, Xiaofan; Ye, Lei; Liu, Yi; Tayyab, Muhammad; Chen, Yufan

2017-07-01

An accurate flood forecasting with long lead time can be of great value for flood prevention and utilization. This paper develops a one-way coupled hydro-meteorological modeling system consisting of the mesoscale numerical weather model Weather Research and Forecasting (WRF) model and the Chinese Xinanjiang hydrological model to extend flood forecasting lead time in the Jinshajiang River Basin, which is the largest hydropower base in China. Focusing on four typical precipitation events includes: first, the combinations and mode structures of parameterization schemes of WRF suitable for simulating precipitation in the Jinshajiang River Basin were investigated. Then, the Xinanjiang model was established after calibration and validation to make up the hydro-meteorological system. It was found that the selection of the cloud microphysics scheme and boundary layer scheme has a great impact on precipitation simulation, and only a proper combination of the two schemes could yield accurate simulation effects in the Jinshajiang River Basin and the hydro-meteorological system can provide instructive flood forecasts with long lead time. On the whole, the one-way coupled hydro-meteorological model could be used for precipitation simulation and flood prediction in the Jinshajiang River Basin because of its relatively high precision and long lead time.

Accuracy Enhancement for Forecasting Water Levels of Reservoirs and River Streams Using a Multiple-Input-Pattern Fuzzification Approach

PubMed Central

Mirzaei, Majid; Jaafar, Othman

2014-01-01

Water level forecasting is an essential topic in water management affecting reservoir operations and decision making. Recently, modern methods utilizing artificial intelligence, fuzzy logic, and combinations of these techniques have been used in hydrological applications because of their considerable ability to map an input-output pattern without requiring prior knowledge of the criteria influencing the forecasting procedure. The artificial neurofuzzy interface system (ANFIS) is one of the most accurate models used in water resource management. Because the membership functions (MFs) possess the characteristics of smoothness and mathematical components, each set of input data is able to yield the best result using a certain type of MF in the ANFIS models. The objective of this study is to define the different ANFIS model by applying different types of MFs for each type of input to forecast the water level in two case studies, the Klang Gates Dam and Rantau Panjang station on the Johor river in Malaysia, to compare the traditional ANFIS model with the new introduced one in two different situations, reservoir and stream, showing the new approach outweigh rather than the traditional one in both case studies. This objective is accomplished by evaluating the model fitness and performance in daily forecasting. PMID:24790567

PREFACE: XXIVth Conference of the Danubian Countries on the Hydrological Forecasting and Hydrological Bases of Water Management

NASA Astrophysics Data System (ADS)

Brilly, Mitja; Bonacci, Ognjen; Nachtnebel, Peter Hans; Szolgay, Ján; Balint, Gabor

2008-10-01

This volume of IOP Conference Series: Earth and Environmental Science presents a selection of papers that were given at the 24th Conference of the Danube Countries. Within the framework of the International Hydrological Program IHP of UNESCO. Since 1961 the Danube countries have successfully co-operated in organizing conferences on Hydrological Forecasting and Hydrological Water Management Issues. The 24th Conference of the Danube Countries took place between 2-4 June 2008 in Bled, Slovenia and was organized by the National Committee of Slovenia for the International Hydrological Program of UNESCO, under the auspices of the President of Republic of Slovenia. It was organized jointly by the Slovenian National Commission for UNESCO and the Environmental Agency of the Republic of Slovenia, under the support of UNESCO, WMO, and IAHS. Support for the attendance of some participants was provided by UNESCO. Additional support for the symposium was provided by the Slovene Commission for UNESCO, Environmental Agency of Slovenia, Karst Research Institute, Hydropower plants on the lower Sava River and Chair of Hydraulics Engineering FGG University of Ljubljana. All participants expressed great interest and enthusiasm in presenting the latest research results and sharing practical experiences in the Hydrology of the Danube River basin. The Editorial Board, who were nominated at the Conference, initially selected 80 full papers for publication from 210 submitted extended abstracts and papers provided by authors from twenty countries. Altogether 51 revised papers were accepted for publishing in this volume. Papers are divided by conference topics: Hydrological forecasting Hydro-meteorological extremes, floods and droughts Global climate change and antropogenic impacts on hydrological processes Water management Floods, morphological processes, erosion, sediment transport and sedimentation Developments in hydrology Mitja Brilly, Ognjen Bonacci, Peter Hans Nachtnebel, Ján Szolgay and Gabor Balint Editorial Board International Scientific Committee: P Hubert: Centre d'Informatique Géologique, France H P Nachtnebel: Universität für Bodenkultur Wien, Austria H Weber: Bavarian Water Management Administration, Germany H Moser: Federal Institute of Hydrology, Germany M Domokos: VITUKI, Hungary P Stanciu: National Institute of Meteorology and Hydrology, Romania O Bonacci: University of Split, Croatia S Prohaska: Institute Jaroslav Černi, Belgrade, Serbia J Szolgay: Faculty of Civil Engineering, Bratislava, Slovak Republic K Tzankov: Institute of Meteorology and Hydrology, Sofia, Bulgaria E Soukalová: Czech Hydrometeorological Institute, Czech Republic B Matičič: National Committee on Irrigation and Drainage, Slovenia M Mikoš: University of Ljubljana, Ljubljana, Slovenia J Rakovec: University of Ljubljana, Ljubljana, Slovenia M Brilly: University of Ljubljana, Ljubljana, Slovenia M Veselič: ARAO, Slovenia

Probabilistic calibration of the distributed hydrological model RIBS applied to real-time flood forecasting: the Harod river basin case study (Israel)

NASA Astrophysics Data System (ADS)

Nesti, Alice; Mediero, Luis; Garrote, Luis; Caporali, Enrica

2010-05-01

An automatic probabilistic calibration method for distributed rainfall-runoff models is presented. The high number of parameters in hydrologic distributed models makes special demands on the optimization procedure to estimate model parameters. With the proposed technique it is possible to reduce the complexity of calibration while maintaining adequate model predictions. The first step of the calibration procedure of the main model parameters is done manually with the aim to identify their variation range. Afterwards a Monte-Carlo technique is applied, which consists on repetitive model simulations with randomly generated parameters. The Monte Carlo Analysis Toolbox (MCAT) includes a number of analysis methods to evaluate the results of these Monte Carlo parameter sampling experiments. The study investigates the use of a global sensitivity analysis as a screening tool to reduce the parametric dimensionality of multi-objective hydrological model calibration problems, while maximizing the information extracted from hydrological response data. The method is applied to the calibration of the RIBS flood forecasting model in the Harod river basin, placed on Israel. The Harod basin has an extension of 180 km2. The catchment has a Mediterranean climate and it is mainly characterized by a desert landscape, with a soil that is able to absorb large quantities of rainfall and at the same time is capable to generate high peaks of discharge. Radar rainfall data with 6 minute temporal resolution are available as input to the model. The aim of the study is the validation of the model for real-time flood forecasting, in order to evaluate the benefits of improved precipitation forecasting within the FLASH European project.

A multi-scale ensemble-based framework for forecasting compound coastal-riverine flooding: The Hackensack-Passaic watershed and Newark Bay

NASA Astrophysics Data System (ADS)

Saleh, F.; Ramaswamy, V.; Wang, Y.; Georgas, N.; Blumberg, A.; Pullen, J.

2017-12-01

Estuarine regions can experience compound impacts from coastal storm surge and riverine flooding. The challenges in forecasting flooding in such areas are multi-faceted due to uncertainties associated with meteorological drivers and interactions between hydrological and coastal processes. The objective of this work is to evaluate how uncertainties from meteorological predictions propagate through an ensemble-based flood prediction framework and translate into uncertainties in simulated inundation extents. A multi-scale framework, consisting of hydrologic, coastal and hydrodynamic models, was used to simulate two extreme flood events at the confluence of the Passaic and Hackensack rivers and Newark Bay. The events were Hurricane Irene (2011), a combination of inland flooding and coastal storm surge, and Hurricane Sandy (2012) where coastal storm surge was the dominant component. The hydrodynamic component of the framework was first forced with measured streamflow and ocean water level data to establish baseline inundation extents with the best available forcing data. The coastal and hydrologic models were then forced with meteorological predictions from 21 ensemble members of the Global Ensemble Forecast System (GEFS) to retrospectively represent potential future conditions up to 96 hours prior to the events. Inundation extents produced by the hydrodynamic model, forced with the 95th percentile of the ensemble-based coastal and hydrologic boundary conditions, were in good agreement with baseline conditions for both events. The USGS reanalysis of Hurricane Sandy inundation extents was encapsulated between the 50th and 95th percentile of the forecasted inundation extents, and that of Hurricane Irene was similar but with caveats associated with data availability and reliability. This work highlights the importance of accounting for meteorological uncertainty to represent a range of possible future inundation extents at high resolution (∼m).

Evaluating hydrological response to forecasted land-use change—scenario testing with the automated geospatial watershed assessment (AGWA) tool

USGS Publications Warehouse

Kepner, William G.; Semmens, Darius J.; Hernandez, Mariano; Goodrich, David C.

2009-01-01

Envisioning and evaluating future scenarios has emerged as a critical component of both science and social decision-making. The ability to assess, report, map, and forecast the life support functions of ecosystems is absolutely critical to our capacity to make informed decisions to maintain the sustainable nature of our ecosystem services now and into the future. During the past two decades, important advances in the integration of remote imagery, computer processing, and spatial-analysis technologies have been used to develop landscape information that can be integrated with hydrologic models to determine long-term change and make predictive inferences about the future. Two diverse case studies in northwest Oregon (Willamette River basin) and southeastern Arizona (San Pedro River) were examined in regard to future land use scenarios relative to their impact on surface water conditions (e.g., sediment yield and surface runoff) using hydrologic models associated with the Automated Geospatial Watershed Assessment (AGWA) tool. The base reference grid for land cover was modified in both study locations to reflect stakeholder preferences 20 to 60 yrs into the future, and the consequences of landscape change were evaluated relative to the selected future scenarios. The two studies provide examples of integrating hydrologic modeling with a scenario analysis framework to evaluate plausible future forecasts and to understand the potential impact of landscape change on ecosystem services.

Use of medium-range numerical weather prediction model output to produce forecasts of streamflow

USGS Publications Warehouse

Clark, M.P.; Hay, L.E.

2004-01-01

This paper examines an archive containing over 40 years of 8-day atmospheric forecasts over the contiguous United States from the NCEP reanalysis project to assess the possibilities for using medium-range numerical weather prediction model output for predictions of streamflow. This analysis shows the biases in the NCEP forecasts to be quite extreme. In many regions, systematic precipitation biases exceed 100% of the mean, with temperature biases exceeding 3??C. In some locations, biases are even higher. The accuracy of NCEP precipitation and 2-m maximum temperature forecasts is computed by interpolating the NCEP model output for each forecast day to the location of each station in the NWS cooperative network and computing the correlation with station observations. Results show that the accuracy of the NCEP forecasts is rather low in many areas of the country. Most apparent is the generally low skill in precipitation forecasts (particularly in July) and low skill in temperature forecasts in the western United States, the eastern seaboard, and the southern tier of states. These results outline a clear need for additional processing of the NCEP Medium-Range Forecast Model (MRF) output before it is used for hydrologic predictions. Techniques of model output statistics (MOS) are used in this paper to downscale the NCEP forecasts to station locations. Forecasted atmospheric variables (e.g., total column precipitable water, 2-m air temperature) are used as predictors in a forward screening multiple linear regression model to improve forecasts of precipitation and temperature for stations in the National Weather Service cooperative network. This procedure effectively removes all systematic biases in the raw NCEP precipitation and temperature forecasts. MOS guidance also results in substantial improvements in the accuracy of maximum and minimum temperature forecasts throughout the country. For precipitation, forecast improvements were less impressive. MOS guidance increases he accuracy of precipitation forecasts over the northeastern United States, but overall, the accuracy of MOS-based precipitation forecasts is slightly lower than the raw NCEP forecasts. Four basins in the United States were chosen as case studies to evaluate the value of MRF output for predictions of streamflow. Streamflow forecasts using MRF output were generated for one rainfall-dominated basin (Alapaha River at Statenville, Georgia) and three snowmelt-dominated basins (Animas River at Durango, Colorado: East Fork of the Carson River near Gardnerville, Nevada: and Cle Elum River near Roslyn, Washington). Hydrologic model output forced with measured-station data were used as "truth" to focus attention on the hydrologic effects of errors in the MRF forecasts. Eight-day streamflow forecasts produced using the MOS-corrected MRF output as input (MOS) were compared with those produced using the climatic Ensemble Streamflow Prediction (ESP) technique. MOS-based streamflow forecasts showed increased skill in the snowmelt-dominated river basins, where daily variations in streamflow are strongly forced by temperature. In contrast, the skill of MOS forecasts in the rainfall-dominated basin (the Alapaha River) were equivalent to the skill of the ESP forecasts. Further improvements in streamflow forecasts require more accurate local-scale forecasts of precipitation and temperature, more accurate specification of basin initial conditions, and more accurate model simulations of streamflow. ?? 2004 American Meteorological Society.

A national framework for flood forecasting model assessment for use in operations and investment planning over England and Wales

NASA Astrophysics Data System (ADS)

Moore, Robert J.; Wells, Steven C.; Cole, Steven J.

2016-04-01

It has been common for flood forecasting systems to be commissioned at a catchment or regional level in response to local priorities and hydrological conditions, leading to variety in system design and model choice. As systems mature and efficiencies of national management are sought, there can be a drive towards system rationalisation, gaining an overview of model performance and consideration of simplification through model-type convergence. Flood forecasting model assessments, whilst overseen at a national level, may be commissioned and managed at a catchment and regional level, take a variety of forms and be large in number. This presents a challenge when an integrated national assessment is required to guide operational use of flood forecasts and plan future investment in flood forecasting models and supporting hydrometric monitoring. This contribution reports on how a nationally consistent framework for flood forecasting model performance has been developed to embrace many past, ongoing and future assessments for local river systems by engineering consultants across England & Wales. The outcome is a Performance Summary for every site model assessed which, on a single page, contains relevant catchment information for context, a selection of overlain forecast and observed hydrographs and a set of performance statistics with associated displays of novel condensed form. One display provides performance comparison with other models that may exist for the site. The performance statistics include skill scores for forecasting events (flow/level threshold crossings) of differing severity/rarity, indicating their probability and likely timing, which have real value in an operational setting. The local models assessed can be of any type and span rainfall-runoff (conceptual and transfer function) and flow routing (hydrological and hydrodynamic) forms. Also accommodated by the framework is the national G2G (Grid-to-Grid) distributed hydrological model, providing area-wide coverage across the fluvial rivers of England and Wales, which can be assessed at gauged sites. Thus the performance of the national G2G model forecasts can be directly compared with that from the local models. The Performance Summary for each site model is complemented by a national spatial analysis of model performance stratified by model-type, geographical region and forecast lead-time. The map displays provide an extensive evidence-base that can be interrogated, through a Flood Forecasting Model Performance web portal, to reveal fresh insights into comparative performance across locations, lead-times and models. This work was commissioned by the Environment Agency in partnership with Natural Resources Wales and the Flood Forecasting Centre for England and Wales.

Integrated Flood Forecast and Virtual Dam Operation System for Water Resources and Flood Risk Management

NASA Astrophysics Data System (ADS)

Shibuo, Yoshihiro; Ikoma, Eiji; Lawford, Peter; Oyanagi, Misa; Kanauchi, Shizu; Koudelova, Petra; Kitsuregawa, Masaru; Koike, Toshio

2014-05-01

While availability of hydrological- and hydrometeorological data shows growing tendency and advanced modeling techniques are emerging, such newly available data and advanced models may not always be applied in the field of decision-making. In this study we present an integrated system of ensemble streamflow forecast (ESP) and virtual dam simulator, which is designed to support river and dam manager's decision making. The system consists of three main functions: real time hydrological model, ESP model, and dam simulator model. In the real time model, the system simulates current condition of river basins, such as soil moisture and river discharges, using LSM coupled distributed hydrological model. The ESP model takes initial condition from the real time model's output and generates ESP, based on numerical weather prediction. The dam simulator model provides virtual dam operation and users can experience impact of dam control on remaining reservoir volume and downstream flood under the anticipated flood forecast. Thus the river and dam managers shall be able to evaluate benefit of priori dam release and flood risk reduction at the same time, on real time basis. Furthermore the system has been developed under the concept of data and models integration, and it is coupled with Data Integration and Analysis System (DIAS) - a Japanese national project for integrating and analyzing massive amount of observational and model data. Therefore it has advantage in direct use of miscellaneous data from point/radar-derived observation, numerical weather prediction output, to satellite imagery stored in data archive. Output of the system is accessible over the web interface, making information available with relative ease, e.g. from ordinary PC to mobile devices. We have been applying the system to the Upper Tone region, located northwest from Tokyo metropolitan area, and we show application example of the system in recent flood events caused by typhoons.

Application of Multi-Satellite Precipitation Analysis to Floods and Landslides

NASA Technical Reports Server (NTRS)

Adler, Robert; Hong, Yang; Huffman, George

2007-01-01

Satellite data acquired and processed in real time now have the potential to provide the spacetime information on rainfall needed to monitor flood and landslide events around the world. This can be achieved by integrating the satellite-derived forcing data with hydrological models and landslide algorithms. Progress in using the TRMM Multi-satellite Precipitation Analysis (TMPA) as input to flood and landslide forecasts is outlined, with a focus on understanding limitations of the rainfall data and impacts of those limitations on flood/landslide analyses. Case studies of both successes and failures will be shown, as well as comparison with ground comparison data sets both in terms of rainfall and in terms of flood/landslide events. In addition to potential uses in real-time, the nearly ten years of TMPA data allow retrospective running of the models to examine variations in extreme events. The flood determination algorithm consists of four major components: 1) multi-satellite precipitation estimation; 2) characterization of land surface including digital elevation from NASA SRTM (Shuttle Radar Terrain Mission), topography-derived hydrologic parameters such as flow direction, flow accumulation, basin, and river network etc.; 3) a hydrological model to infiltrate rainfall and route overland runoff; and 4) an implementation interface to relay the input data to the models and display the flood inundation results to potential users and decision-makers. In terms of landslides, the satellite rainfall information is combined with a global landslide susceptibility map, derived from a combination of global surface characteristics (digital elevation topography, slope, soil types, soil texture, and land cover classification etc.) using a weighted linear combination approach. In those areas identified as "susceptible" (based on the surface characteristics), landslides are forecast where and when a rainfall intensity/duration threshold is exceeded. Results are described indicating general agreement with landslide occurrences. However, difficulties in comparing landslide event information (mostly from news reports) with the satellite-based forecasts are analyzed.

EVALUATING HYDROLOGICAL RESPONSE TO FORECASTED LAND-USE CHANGE: SCENARIO TESTING IN TWO WESTERN U.S. WATERSHEDS

EPA Science Inventory

Envisioning and evaluating future scenarios has emerged as a critical component of both science and social decision-making. The ability to assess, report, map, and forecast the life support functions of ecosystems is absolutely critical to our capacity to make informed decisions ...

Flash flood warnings for ungauged basins based on high-resolution precipitation forecasts

NASA Astrophysics Data System (ADS)

Demargne, Julie; Javelle, Pierre; Organde, Didier; de Saint Aubin, Céline; Janet, Bruno

2016-04-01

Early detection of flash floods, which are typically triggered by severe rainfall events, is still challenging due to large meteorological and hydrologic uncertainties at the spatial and temporal scales of interest. Also the rapid rising of waters necessarily limits the lead time of warnings to alert communities and activate effective emergency procedures. To better anticipate such events and mitigate their impacts, the French national service in charge of flood forecasting (SCHAPI) is implementing a national flash flood warning system for small-to-medium (up to 1000 km²) ungauged basins based on a discharge-threshold flood warning method called AIGA (Javelle et al. 2014). The current deterministic AIGA system has been run in real-time in the South of France since 2005 and has been tested in the RHYTMME project (rhytmme.irstea.fr/). It ingests the operational radar-gauge QPE grids from Météo-France to run a simplified hourly distributed hydrologic model at a 1-km² resolution every 15 minutes. This produces real-time peak discharge estimates along the river network, which are subsequently compared to regionalized flood frequency estimates to provide warnings according to the AIGA-estimated return period of the ongoing event. The calibration and regionalization of the hydrologic model has been recently enhanced for implementing the national flash flood warning system for the entire French territory by 2016. To further extend the effective warning lead time, the flash flood warning system is being enhanced to ingest Météo-France's AROME-NWC high-resolution precipitation nowcasts. The AROME-NWC system combines the most recent available observations with forecasts from the nowcasting version of the AROME convection-permitting model (Auger et al. 2015). AROME-NWC pre-operational deterministic precipitation forecasts, produced every hour at a 2.5-km resolution for a 6-hr forecast horizon, were provided for 3 significant rain events in September and November 2014 and ingested as time-lagged ensembles. The time-lagged approach is a practical choice of accounting for the atmospheric forecast uncertainty when no extensive forecast archive is available for statistical modelling. The evaluation on 185 basins in the South of France showed significant improvements in terms of flash flood event detection and effective warning lead-time, compared to warnings from the current AIGA setup (without any future precipitation). Various verification metrics (e.g., Relative Mean Error, Brier Skill Score) show the skill of ensemble precipitation and flow forecasts compared to single-valued persistency benchmarks. Planned enhancements include integrating additional probabilistic NWP products (e.g., AROME precipitation ensembles on longer forecast horizon), accounting for and reducing hydrologic uncertainties from the model parameters and initial conditions via data assimilation, and developing a comprehensive observational and post-event damage database to determine decision-relevant warning thresholds for flood magnitude and probability. Javelle, P., Demargne, J., Defrance, D., Arnaud, P., 2014. Evaluating flash flood warnings at ungauged locations using post-event surveys: a case study with the AIGA warning system. Hydrological Sciences Journal, doi: 10.1080/02626667.2014.923970 Auger, L., Dupont, O., Hagelin, S., Brousseau, P., Brovelli, P., 2015. AROME-NWC: a new nowcasting tool based on an operational mesoscale forecasting system. Quarterly Journal of the Royal Meteorological Society, 141: 1603-1611, doi: 10.1002/qj.2463

Seasonal scale water deficit forecasting in Africa and the Middle East using NASA's Land Information System (LIS)

NASA Astrophysics Data System (ADS)

Shukla, Shraddhanand; Arsenault, Kristi R.; Getirana, Augusto; Kumar, Sujay V.; Roningen, Jeanne; Zaitchik, Ben; McNally, Amy; Koster, Randal D.; Peters-Lidard, Christa

2017-04-01

Drought and water scarcity are among the important issues facing several regions within Africa and the Middle East. A seamless and effective monitoring and early warning system is needed by regional/national stakeholders. Such system should support a proactive drought management approach and mitigate the socio-economic losses up to the extent possible. In this presentation, we report on the ongoing development and validation of a seasonal scale water deficit forecasting system based on NASA's Land Information System (LIS) and seasonal climate forecasts. First, our presentation will focus on the implementation and validation of the LIS models used for drought and water availability monitoring in the region. The second part will focus on evaluating drought and water availability forecasts. Finally, details will be provided of our ongoing collaboration with end-user partners in the region (e.g., USAID's Famine Early Warning Systems Network, FEWS NET), on formulating meaningful early warning indicators, effective communication and seamless dissemination of the monitoring and forecasting products through NASA's web-services. The water deficit forecasting system thus far incorporates NOAA's Noah land surface model (LSM), version 3.3, the Variable Infiltration Capacity (VIC) model, version 4.12, NASA GMAO's Catchment LSM, and the Noah Multi-Physics (MP) LSM (the latter two incorporate prognostic water table schemes). In addition, the LSMs' surface and subsurface runoff are routed through the Hydrological Modeling and Analysis Platform (HyMAP) to simulate surface water dynamics. The LSMs are driven by NASA/GMAO's Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2), and the USGS and UCSB Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) daily rainfall dataset. The LIS software framework integrates these forcing datasets and drives the four LSMs and HyMAP. The Land Verification Toolkit (LVT) is used for the evaluation of the LSMs, as it provides model ensemble metrics and the ability to compare against a variety of remotely sensed measurements, like different evapotranspiration (ET) and soil moisture products, and other reanalysis datasets that are available for this region. Comparison of the models' energy and hydrological budgets will be shown for this region (and sub-basin level, e.g., Blue Nile River) and time period (1981-2015), along with evaluating ET, streamflow, groundwater storage and soil moisture, using evaluation metrics (e.g., anomaly correlation, RMSE, etc.). The system uses seasonal climate forecasts from NASA's GMAO (the Goddard Earth Observing System Model, version 5) and NCEP's Climate Forecast System, version 2, and it produces forecasts of soil moisture, ET and streamflow out to 6 months in the future. Forecasts of those variables are formulated in terms of indicators to provide forecasts of drought and water availability in the region.

Assessment of an ensemble seasonal streamflow forecasting system for Australia

NASA Astrophysics Data System (ADS)

Bennett, James C.; Wang, Quan J.; Robertson, David E.; Schepen, Andrew; Li, Ming; Michael, Kelvin

2017-11-01

Despite an increasing availability of skilful long-range streamflow forecasts, many water agencies still rely on simple resampled historical inflow sequences (stochastic scenarios) to plan operations over the coming year. We assess a recently developed forecasting system called forecast guided stochastic scenarios (FoGSS) as a skilful alternative to standard stochastic scenarios for the Australian continent. FoGSS uses climate forecasts from a coupled ocean-land-atmosphere prediction system, post-processed with the method of calibration, bridging and merging. Ensemble rainfall forecasts force a monthly rainfall-runoff model, while a staged hydrological error model quantifies and propagates hydrological forecast uncertainty through forecast lead times. FoGSS is able to generate ensemble streamflow forecasts in the form of monthly time series to a 12-month forecast horizon. FoGSS is tested on 63 Australian catchments that cover a wide range of climates, including 21 ephemeral rivers. In all perennial and many ephemeral catchments, FoGSS provides an effective alternative to resampled historical inflow sequences. FoGSS generally produces skilful forecasts at shorter lead times ( < 4 months), and transits to climatology-like forecasts at longer lead times. Forecasts are generally reliable and unbiased. However, FoGSS does not perform well in very dry catchments (catchments that experience zero flows more than half the time in some months), sometimes producing strongly negative forecast skill and poor reliability. We attempt to improve forecasts through the use of (i) ESP rainfall forcings, (ii) different rainfall-runoff models, and (iii) a Bayesian prior to encourage the error model to return climatology forecasts in months when the rainfall-runoff model performs poorly. Of these, the use of the prior offers the clearest benefit in very dry catchments, where it moderates strongly negative forecast skill and reduces bias in some instances. However, the prior does not remedy poor reliability in very dry catchments. Overall, FoGSS is an attractive alternative to historical inflow sequences in all but the driest catchments. We discuss ways in which forecast reliability in very dry catchments could be improved in future work.

Accessibility and Utilization of WSR-88D Radar Precipitation Data for Natural Resource Modeling Applications

NASA Astrophysics Data System (ADS)

Hardegree, S. P.

2001-12-01

The National Weather Service (NWS) operates approximately 160 WSR-88D radar-precipitation stations as part of a Next Generation Radar (NEXRAD) program that began implementation in 1992. Among other products, these radar sites provide spatial rainfall estimates, at approximately 4 km2 resolution (Stage 1, Level 3 data), with nominal coverage of 96% of the coterminous United States. Effective coverage is much less than this in a given radar domain depending upon storm type and topography. As the original intent of this network was to support operational objectives of the Departments of Defense, Transportation and Commerce, the production of these data have been optimized for detection and mitigation of severe weather events that might result in flooding, destruction of property and loss of life. The primary hydrologic application has been river and flood forecast modeling by 13 NWS River Forecast Centers (RFC). As each RFC is responsible for a large river drainage, data processing and quality control of these data are geared toward optimization over a relatively large spatial domain (>100,000 km2). Use of these data for other hydrologic and natural resource applications is hampered by a lack of tools for data access and manipulation. NWRC has modified decoding and geo-referencing programs to facilitate utilization of these data for other research and management applications. Stage 1, Level 3 Digital Precipitation Array (DPA) files were obtained for the Boise, Idaho radar location (CBX) for the period of January 1998 to December 2000. Nine rain-gauge locations in the Reynolds Creek Experimental Watershed and Snake River Birds of Prey National Conservation Area, south of Boise, were georeferenced relative to the CBX Hydrologic Rainfall Analysis Project (HRAP) grid. NEXRAD estimates of total cumulative rainfall at these sites averaged only 20% of that measured by the local gauge network. This underestimate was attributed in the most part to truncation of low intensity rainfall events by the precipitation detection function (pdf) rather than to mis-calibration of the ZR relationship. At this time, these data are unsuitable as inputs for long-term water balance modeling but may be useful in extreme-event or flood-modeling applications. New tools to extract and manipulate specific subsets of Stage 1, Level 2 radar data may improve our ability to use radar reflectance data for a broader number of applications than are currently supported.

Evaluation of uncertainty in capturing the spatial variability and magnitudes of extreme hydrological events for the uMngeni catchment, South Africa

NASA Astrophysics Data System (ADS)

Kusangaya, Samuel; Warburton Toucher, Michele L.; van Garderen, Emma Archer

2018-02-01

Downscaled General Circulation Models (GCMs) output are used to forecast climate change and provide information used as input for hydrological modelling. Given that our understanding of climate change points towards an increasing frequency, timing and intensity of extreme hydrological events, there is therefore the need to assess the ability of downscaled GCMs to capture these extreme hydrological events. Extreme hydrological events play a significant role in regulating the structure and function of rivers and associated ecosystems. In this study, the Indicators of Hydrologic Alteration (IHA) method was adapted to assess the ability of simulated streamflow (using downscaled GCMs (dGCMs)) in capturing extreme river dynamics (high and low flows), as compared to streamflow simulated using historical climate data from 1960 to 2000. The ACRU hydrological model was used for simulating streamflow for the 13 water management units of the uMngeni Catchment, South Africa. Statistically downscaled climate models obtained from the Climate System Analysis Group at the University of Cape Town were used as input for the ACRU Model. Results indicated that, high flows and extreme high flows (one in ten year high flows/large flood events) were poorly represented both in terms of timing, frequency and magnitude. Simulated streamflow using dGCMs data also captures more low flows and extreme low flows (one in ten year lowest flows) than that captured in streamflow simulated using historical climate data. The overall conclusion was that although dGCMs output can reasonably be used to simulate overall streamflow, it performs poorly when simulating extreme high and low flows. Streamflow simulation from dGCMs must thus be used with caution in hydrological applications, particularly for design hydrology, as extreme high and low flows are still poorly represented. This, arguably calls for the further improvement of downscaling techniques in order to generate climate data more relevant and useful for hydrological applications such as in design hydrology. Nevertheless, the availability of downscaled climatic output provide the potential of exploring climate model uncertainties in different hydro climatic regions at local scales where forcing data is often less accessible but more accurate at finer spatial scales and with adequate spatial detail.

Citizen observatory of water as a data engine supporting the people-hydrology nexus: experience of the WeSenseIt project

NASA Astrophysics Data System (ADS)

Ferri, Michele; Baruffi, Francesco; Norbiato, Daniele; Monego, Martina; Tomei, Giovanni; Solomatine, Dimitri; Alfonso, Leonardo; Mazzoleni, Maurizio; Chacon, Juan Carlos; Wehn, Uta; Ciravegna, Fabio

2016-04-01

Citizen observatories (COs) present an interesting case of strong multi-facet feedback between the physical (water) system and humans. CO is a form of crowdsourcing ensuring a data flow from citizens observing environment (e.g. water level in a river) to a central data processing unit which is typically part of a more complex social arrangement (e.g. water authorities responsible for flood forecasting). The EU-funded project WeSenseIt (www.wesenseit.eu) aims at developing technologies and tools supporting creation of such COs [1,2,3,4]. Citizens which form a CO play the role of "social sensors" which however are very specific. The data streams from such sensors have varying temporal and spatial coverage and information value (uncertainty). The crowdsourced data can be of course simply visualized and presented to public, but it is much more interesting to consider cases when such data are assimilated into the existing forecasting systems, e.g. flood early warning systems based on hydrological and hydraulic models. COs may also affect water management and governance [4], and in fact can be seen as data engines supporting the people-hydrology nexus. In the framework of WeSenseIt project several approaches were developed allowing for optimal assimilation of intermittent data streams with varying spatial coverage into distributed hydrological models [1, 2]. The mentioned specific features of CO data required updates of the existing data assimilation algorithms (Ensemble Kalman Filter was used as the basic algorithm). The developed algorithms have been implemented in the operational flood forecasting systems of the Alto Adriatico Water Authority (AAWA), Venice. In this paper we analyse various scenarios of employing citizens data (COs) for flood forecasting. This study is partly supported by the FP7 European Project WeSenseIt Citizen Water Observatory (www.http://wesenseit.eu/). References [1] Mazzoleni, M., Alfonso, L., Chacon-Hurtado, J., Solomatine, D. (2015). Assimilating uncertain, dynamic and intermittent streamflow observations in hydrological models. Advances in Water Res., 83, 323-339 (Online on September 1, 2015). [2] Mazzoleni M., Verlaan M., Alfonso L., Monego M., Norbiato D., Ferri M., and Solomatine D.P. (2015) Can assimilation of crowdsourced streamflow observations in hydrological modelling improve flood prediction?, Hydrology and Earth System Sciences, under review. [3] Mazzoleni M., Alfonso L. and Solomatine D.P. (2015) Effect of spatial distribution and quality of sensors on the assimilation of distributed streamflow observations in hydrological modeling, Hydrological Sciences Journal, under review. [4] Wehn, U., McCarty, S., Lanfranchi, V. and Tapsell, S. (2015) Citizen observatories as facilitators of change in water governance? Experiences from three European cases, Special Issue on ICTs and Water, Journal of Environmental Engineering and Management, 2073-2086.

The evolution of location and data collection systems in the United States.

NASA Technical Reports Server (NTRS)

Morakis, J. C.; Cote, C. E.

1973-01-01

Satellite location and data collection systems development began in the early 1960's in NASA and the French CNES. These systems were initially developed for application to meteorology and oceanography as a means of tracking moving platforms on a global scale. Additional applications such as geology, hydrology, and ecology have since evolved. To date, five successful missions have been completed. With each successive launch, systems improved in accordance with user requirements - particularly reduction in cost and complexity of platform equipment. With planned launches, facilities will be available to the user community through 1980; NASA is currently forecasting needs beyond 1980.

State of the Art in Large-Scale Soil Moisture Monitoring

NASA Technical Reports Server (NTRS)

Ochsner, Tyson E.; Cosh, Michael Harold; Cuenca, Richard H.; Dorigo, Wouter; Draper, Clara S.; Hagimoto, Yutaka; Kerr, Yan H.; Larson, Kristine M.; Njoku, Eni Gerald; Small, Eric E.;

Is the economic value of hydrological forecasts related to their quality? Case study of the hydropower sector.

NASA Astrophysics Data System (ADS)

Cassagnole, Manon; Ramos, Maria-Helena; Thirel, Guillaume; Gailhard, Joël; Garçon, Rémy

2017-04-01

The improvement of a forecasting system and the evaluation of the quality of its forecasts are recurrent steps in operational practice. However, the evaluation of forecast value or forecast usefulness for better decision-making is, to our knowledge, less frequent, even if it might be essential in many sectors such as hydropower and flood warning. In the hydropower sector, forecast value can be quantified by the economic gain obtained with the optimization of operations or reservoir management rules. Several hydropower operational systems use medium-range forecasts (up to 7-10 days ahead) and energy price predictions to optimize hydropower production. Hence, the operation of hydropower systems, including the management of water in reservoirs, is impacted by weather, climate and hydrologic variability as well as extreme events. In order to assess how the quality of hydrometeorological forecasts impact operations, it is essential to first understand if and how operations and management rules are sensitive to input predictions of different quality. This study investigates how 7-day ahead deterministic and ensemble streamflow forecasts of different quality might impact the economic gains of energy production. It is based on a research model developed by Irstea and EDF to investigate issues relevant to the links between quality and value of forecasts in the optimisation of energy production at the short range. Based on streamflow forecasts and pre-defined management constraints, the model defines the best hours (i.e., the hours with high energy prices) to produce electricity. To highlight the link between forecasts quality and their economic value, we built several synthetic ensemble forecasts based on observed streamflow time series. These inputs are generated in a controlled environment in order to obtain forecasts of different quality in terms of accuracy and reliability. These forecasts are used to assess the sensitivity of the decision model to forecast quality. Relationships between forecast quality and economic value are discussed. This work is part of the IMPREX project, a research project supported by the European Commission under the Horizon 2020 Framework programme, with grant No. 641811 (http://www.imprex.eu)

A parametric study of the value of hydrological information for irrigation and hydropower management of the Feather River

NASA Technical Reports Server (NTRS)

Wetzler, E.; Sand, F.; Stevenson, P.; Putnam, M.

1975-01-01

A case study analysis is presented of the relationships between improvements in the accuracy, frequency, and timeliness of information used in making hydrological forecasts and economic benefits in the areas of hydropower and irrigation. The area chosen for the case study is the Oroville Dam and Reservoir. Emphasis is placed on the use of timely and accurate mapping of the aerial extent of snow in the basin by earth resources survey systems such as LANDSAT. The subject of benefits resulting from improved runoff forecasts is treated in a generalized way without specifying the source of the improvements.

Assessment of the water balance over France using regionalized Turc-Pike formula

NASA Astrophysics Data System (ADS)

Le Lay, Matthieu; Garçon, Rémy; Gailhard, Joël; Garavaglia, Federico

2016-04-01

With extensive use of hydrological models over a wide range of hydro-climatic contexts, bias in hydro-climatic data may lead to unreliable models and thus hydrological forecasts and projections. This issue is particularly pregnant when considering mountainous areas with great uncertainties on precipitations, or when considering complex unconservative catchments (e.g. karstic systems). The Turc-Pike water balance formula, analogous to the classical Budyko formula, is a simple and efficient mathematical formulation relating long-term average streamflow to long-term average precipitation and potential evaporation. In this study, we propose to apply this framework to assess and eventually adjust the water-balance before calibrating an operational hydrologic model (MORDOR model). Considering a large set of 350 french catchments, the Turc-Pike formula is regionalized based on ecohydrologic criterions to handle various hydro-climatic contexts. This interannual regional model is then applied to assess the water-balance over numerous catchments and various conditions, such as karstic, snow-driven or glaciarized and even anthropized catchments. Results show that it is possible to obtain pretty realistic corrections of meteorological inputs (precipitations, temperature or potential evaporation) or hydrologic surface (or runoff). These corrections can often be confirmed a posteriori by exogenous information. Positive impacts on hydrologic model's calibration are also demonstrated. This methodology is now operational for hydrologic applications at EDF (Electricité de France, French electric utility company), and therefore applied on hundreds of catchments.

Real-world hydrologic assessment of a fully-distributed hydrological model in a parallel computing environment

NASA Astrophysics Data System (ADS)

Vivoni, Enrique R.; Mascaro, Giuseppe; Mniszewski, Susan; Fasel, Patricia; Springer, Everett P.; Ivanov, Valeriy Y.; Bras, Rafael L.

2011-10-01

SummaryA major challenge in the use of fully-distributed hydrologic models has been the lack of computational capabilities for high-resolution, long-term simulations in large river basins. In this study, we present the parallel model implementation and real-world hydrologic assessment of the Triangulated Irregular Network (TIN)-based Real-time Integrated Basin Simulator (tRIBS). Our parallelization approach is based on the decomposition of a complex watershed using the channel network as a directed graph. The resulting sub-basin partitioning divides effort among processors and handles hydrologic exchanges across boundaries. Through numerical experiments in a set of nested basins, we quantify parallel performance relative to serial runs for a range of processors, simulation complexities and lengths, and sub-basin partitioning methods, while accounting for inter-run variability on a parallel computing system. In contrast to serial simulations, the parallel model speed-up depends on the variability of hydrologic processes. Load balancing significantly improves parallel speed-up with proportionally faster runs as simulation complexity (domain resolution and channel network extent) increases. The best strategy for large river basins is to combine a balanced partitioning with an extended channel network, with potential savings through a lower TIN resolution. Based on these advances, a wider range of applications for fully-distributed hydrologic models are now possible. This is illustrated through a set of ensemble forecasts that account for precipitation uncertainty derived from a statistical downscaling model.

EVALUATING HYDROLOGICAL RESPONSE TO FORECASTED LAND-USE CHANGE: SCENARIO TESTING WITH THE AUTOMATED GEOSPATIAL WATERSHED ASSESSMENT (AGWA) TOOL

EPA Science Inventory

Envisioning and evaluating future scenarios has emerged as a critical component of both science and social decision-making. The ability to assess, report, map, and forecast the life support functions of ecosystems is absolutely critical to our capacity to make informed decisions...

How reliable is the offline linkage of Weather Research & Forecasting Model (WRF) and Variable Infiltration Capacity (VIC) model?

EPA Science Inventory

The aim for this research is to evaluate the ability of the offline linkage of Weather Research & Forecasting Model (WRF) and Variable Infiltration Capacity (VIC) model to produce hydrological, e.g. evaporation (ET), soil moisture (SM), runoff, and baseflow. First, the VIC mo...

On using TRMM data and rainfall forecasts from meteorological models in data-scarce transboundary catchments - an example of Bangladesh

NASA Astrophysics Data System (ADS)

Bhattacharya, Biswa; Tohidul Islam, Md.

2014-05-01

This research focuses on the flood risk of the Haor region in the north-eastern part of Bangladesh. The prediction of the hydrological variables at different spatial and temporal scales in the Haor region is dependent on the influence of several upstream rivers in the Meghalaya catchment in India. Limitation in hydro-meteorological data collection and data sharing issues between the two countries dominate the feasibility of hydrological studies, particularly for near-realtime predictions. One of the possible solutions seems to be in making use of the variety of satellite based and meteorological model products for rainfall. The abundance of a variety of rainfall products provides a good basis of hydrological modelling of a part of the Ganges and Brahmaputra basin. In this research the TRMM data and rainfall forecasts from ECMWF have been compared with the scarce rain gauge data from the upstream Meghalaya catchment. Subsequently, the TRMM data and rainfall forecasts from ECMWF have been used as the meteorological input to a rainfall-runoff model of the Meghalaya catchment. The rainfall-runoff model of Meghalaya has been developed using the DEM data from SRTM. The generated runoff at the outlet of Meghalaya has been used as the upstream boundary condition in the existing rainfall-runoff model of the Haor region. The simulation results have been compared with the existing results based on simulations without any information of the rainfall-runoff in the upstream Meghalaya catchment. The comparison showed that the forecasting lead time has been substantially increased. As per the existing results the forecasting lead time at a number of locations in the catchment was about 6 to 8 hours. With the new results the forecasting lead time has gone up, with different levels of accuracy, to about 24 hours. This additional lead time will be highly beneficial in managing flood risk of the Haor region of Bangladesh. The research shows that satellite based rainfall products and rainfall forecasts from meteorological models can be very useful in flood risk management, particularly for data scarce regions and/or transboundary regions with data sharing issues. Keywords: flood risk management, TRMM, ECMWF, flood forecasting, Haor, Bangladesh. Abbreviations: TRMM: Tropical Rainfall Measuring Mission ECMWF: European Centre for Medium-Range Weather Forecasts DEM: Digital Elevation Model SRTM: Shuttle Radar Topography Mission

Knowledge-based modularization and global optimization of artificial neural network models in hydrological forecasting.

PubMed

Corzo, Gerald; Solomatine, Dimitri

2007-05-01

Natural phenomena are multistationary and are composed of a number of interacting processes, so one single model handling all processes often suffers from inaccuracies. A solution is to partition data in relation to such processes using the available domain knowledge or expert judgment, to train separate models for each of the processes, and to merge them in a modular model (committee). In this paper a problem of water flow forecast in watershed hydrology is considered where the flow process can be presented as consisting of two subprocesses -- base flow and excess flow, so that these two processes can be separated. Several approaches to data separation techniques are studied. Two case studies with different forecast horizons are considered. Parameters of the algorithms responsible for data partitioning are optimized using genetic algorithms and global pattern search. It was found that modularization of ANN models using domain knowledge makes models more accurate, if compared with a global model trained on the whole data set, especially when forecast horizon (and hence the complexity of the modelled processes) is increased.

High-school software development project helps increasing students' awareness of geo-hydrological hazards and their risks

NASA Astrophysics Data System (ADS)

Marchesini, Ivan; Rossi, Mauro; Balducci, Vinicio; Salvati, Paola; Guzzetti, Fausto; Bianchini, Andrea; Grzeleswki, Emanuell; Canonico, Andrea; Coccia, Rita; Fiorucci, Gianni Mario; Gobbi, Francesca; Ciuchetti, Monica

2015-04-01

In Italy, inundation and landslides are widespread phenomena that impact the population and cause significant economic damage to private and public properties. The perception of the risk posed by these natural geo-hydrological hazards varies geographically and in time. The variation in the perception of the risks has negative consequences on risk management, and limits the adoption of effective risk reduction strategies. We maintain that targeted education can foster the understanding of geo-hydrological hazards, improving their perception and the awareness of the associated risk. Collaboration of a research center experienced in geo-hydrological hazards and risks (CNR IRPI, Perugia) and a high school (ITIS Alessandro Volta, Perugia) has resulted in the design and execution of a project aimed at improving the perception of geo-hydrological risks in high school students and teachers through software development. In the two-year project, students, high school teachers and research scientists have jointly developed software broadly related to landslide and flood hazards. User requirements and system specifications were decided to facilitate the distribution and use of the software among students and their peers. This allowed a wider distribution of the project results. We discuss two prototype software developed by the high school students, including an application of augmented reality for improved dissemination of information of landslides and floods with human consequences in Italy, and a crowd science application to allow students (and others, including their families and friends) to collect information on landslide and flood occurrence exploiting modern mobile devices. This information can prove important e.g., for the validation of landslide forecasting models.

A Seamless Framework for Global Water Cycle Monitoring and Prediction

NASA Astrophysics Data System (ADS)

Sheffield, J.; Wood, E. F.; Chaney, N.; Fisher, C. K.; Caylor, K. K.

2013-12-01

The Global Earth Observation System of Systems (GEOSS) Water Strategy ('From Observations to Decisions') recognizes that 'water is essential for ensuring food and energy security, for facilitating poverty reduction and health security, and for the maintenance of ecosystems and biodiversity', and that water cycle data and observations are critical for improved water management and water security - especially in less developed regions. The GEOSS Water Strategy has articulated a number of goals for improved water management, including flood and drought preparedness, that include: (i) facilitating the use of Earth Observations for water cycle observations; (ii) facilitating the acquisition, processing, and distribution of data products needed for effective management; (iii) providing expertise, information systems, and datasets to the global, regional, and national water communities. There are several challenges that must be met to advance our capability to provide near real-time water cycle monitoring, early warning of hydrological hazards (floods and droughts) and risk assessment under climate change, regionally and globally. Current approaches to monitoring and predicting hydrological hazards are limited in many parts of the world, and especially in developing countries where national capacity is limited and monitoring networks are inadequate. This presentation describes the development of a seamless monitoring and prediction framework at all time scales that allows for consistent assessment of water variability from historic to current conditions, and from seasonal and decadal predictions to climate change projections. At the center of the framework is an experimental, global water cycle monitoring and seasonal forecast system that has evolved out of regional and continental systems for the US and Africa. The system is based on land surface hydrological modeling that is driven by satellite remote sensing precipitation to predict current hydrological conditions, flood potential and the state of drought. Seasonal climate model forecasts are downscaled and bias-corrected to drive the land surface model to provide hydrological forecasts and drought products out 6-9 months. The system relies on historic reconstructions of water variability over the 20th century, which forms the background climatology to which current conditions can be assessed. Future changes in water availability and drought risk are quantified based on bias-corrected and downscaled climate model projections that are used to drive the land surface models. For regions with lack of on-the-ground data we are field-testing low-cost environmental sensors and along with new satellite products for terrestrial hydrology and vegetation, integrating these into the system for improved monitoring and prediction. We provide an overview of the system and some examples of real-world applications to flood and drought events, with a focus on Africa.

Predictability and possible earlier awareness of extreme precipitation across Europe

NASA Astrophysics Data System (ADS)

Lavers, David; Pappenberger, Florian; Richardson, David; Zsoter, Ervin

2017-04-01

Extreme hydrological events can cause large socioeconomic damages in Europe. In winter, a large proportion of these flood episodes are associated with atmospheric rivers, a region of intense water vapour transport within the warm sector of extratropical cyclones. When preparing for such extreme events, forecasts of precipitation from numerical weather prediction models or river discharge forecasts from hydrological models are generally used. Given the strong link between water vapour transport (integrated vapour transport IVT) and heavy precipitation, it is possible that IVT could be used to warn of extreme events. Furthermore, as IVT is located in extratropical cyclones, it is hypothesized to be a more predictable variable due to its link with synoptic-scale atmospheric dynamics. In this research, we firstly provide an overview of the predictability of IVT and precipitation forecasts, and secondly introduce and evaluate the ECMWF Extreme Forecast Index (EFI) for IVT. The EFI is a tool that has been developed to evaluate how ensemble forecasts differ from the model climate, thus revealing the extremeness of the forecast. The ability of the IVT EFI to capture extreme precipitation across Europe during winter 2013/14, 2014/15, and 2015/16 is presented. The results show that the IVT EFI is more capable than the precipitation EFI of identifying extreme precipitation in forecast week 2 during forecasts initialized in a positive North Atlantic Oscillation (NAO) phase. However, the precipitation EFI is superior during the negative NAO phase and at shorter lead times. An IVT EFI example is shown for storm Desmond in December 2015 highlighting its potential to identify upcoming hydrometeorological extremes.

Real-time drought forecasting system for irrigation management

NASA Astrophysics Data System (ADS)

Ceppi, A.; Ravazzani, G.; Corbari, C.; Salerno, R.; Meucci, S.; Mancini, M.

2014-09-01

In recent years frequent periods of water scarcity have enhanced the need to use water more carefully, even in European areas which traditionally have an abundant supply of water, such as the Po Valley in northern Italy. In dry periods, water shortage problems can be enhanced by conflicting uses of water, such as irrigation, industry and power production (hydroelectric and thermoelectric). Furthermore, in the last decade the social perspective in relation to this issue has been increasing due to the possible impact of climate change and global warming scenarios which emerge from the IPCC Fifth Assessment Report (IPCC, 2013). Hence, the increased frequency of drought periods has stimulated the improvement of irrigation and water management. In this study we show the development and implementation of the PREGI real-time drought forecasting system; PREGI is an Italian acronym that means "hydro-meteorological forecast for irrigation management". The system, planned as a tool for irrigation optimization, is based on meteorological ensemble forecasts (20 members) at medium range (30 days) coupled with hydrological simulations of water balance to forecast the soil water content on a maize field in the Muzza Bassa Lodigiana (MBL) consortium in northern Italy. The hydrological model was validated against measurements of latent heat flux acquired by an eddy-covariance station, and soil moisture measured by TDR (time domain reflectivity) probes; the reliability of this forecasting system and its benefits were assessed in the 2012 growing season. The results obtained show how the proposed drought forecasting system is able to have a high reliability of forecast at least for 7-10 days ahead of time.

Mid- and long-term runoff predictions by an improved phase-space reconstruction model.

PubMed

Hong, Mei; Wang, Dong; Wang, Yuankun; Zeng, Xiankui; Ge, Shanshan; Yan, Hengqian; Singh, Vijay P

2016-07-01

In recent years, the phase-space reconstruction method has usually been used for mid- and long-term runoff predictions. However, the traditional phase-space reconstruction method is still needs to be improved. Using the genetic algorithm to improve the phase-space reconstruction method, a new nonlinear model of monthly runoff is constructed. The new model does not rely heavily on embedding dimensions. Recognizing that the rainfall-runoff process is complex, affected by a number of factors, more variables (e.g. temperature and rainfall) are incorporated in the model. In order to detect the possible presence of chaos in the runoff dynamics, chaotic characteristics of the model are also analyzed, which shows the model can represent the nonlinear and chaotic characteristics of the runoff. The model is tested for its forecasting performance in four types of experiments using data from six hydrological stations on the Yellow River and the Yangtze River. Results show that the medium-and long-term runoff is satisfactorily forecasted at the hydrological stations. Not only is the forecasting trend accurate, but also the mean absolute percentage error is no more than 15%. Moreover, the forecast results of wet years and dry years are both good, which means that the improved model can overcome the traditional ''wet years and dry years predictability barrier,'' to some extent. The model forecasts for different regions are all good, showing the universality of the approach. Compared with selected conceptual and empirical methods, the model exhibits greater reliability and stability in the long-term runoff prediction. Our study provides a new thinking for research on the association between the monthly runoff and other hydrological factors, and also provides a new method for the prediction of the monthly runoff. Copyright © 2015 Elsevier Inc. All rights reserved.

UK Environmental Prediction - integration and evaluation at the convective scale

NASA Astrophysics Data System (ADS)

Lewis, Huw; Brunet, Gilbert; Harris, Chris; Best, Martin; Saulter, Andrew; Holt, Jason; Bricheno, Lucy; Brerton, Ashley; Reynard, Nick; Blyth, Eleanor; Martinez de la Torre, Alberto

2015-04-01

It has long been understood that accurate prediction and warning of the impacts of severe weather requires an integrated approach to forecasting. This was well demonstrated in the UK throughout winter 2013/14 when an exceptional run of severe winter storms, often with damaging high winds and intense rainfall led to significant damage from the large waves and storm surge along coastlines, and from saturated soils, high river flows and significant flooding inland. The substantial impacts on individuals, businesses and infrastructure indicate a pressing need to understand better the value that might be delivered through more integrated environmental prediction. To address this need, the Met Office, Centre for Ecology & Hydrology and National Oceanography Centre have begun to develop the foundations of a coupled high resolution probabilistic forecast system for the UK at km-scale. This links together existing model components of the atmosphere, coastal ocean, land surface and hydrology. Our initial focus on a 2-year Prototype project will demonstrate the UK coupled prediction concept in research mode, including an analysis of the winter 2013/14 storms and its impacts. By linking science development to operational collaborations such as the UK Natural Hazards Partnership, we can ensure that science priorities are rooted in user requirements. This presentation will provide an overview of UK environmental prediction activities and an update on progress during the first year of the Prototype project. We will present initial results from the coupled model development and discuss the challenges to realise the potential of integrated regional coupled forecasting for improving predictions and applications.

Sub-seasonal predictability of water scarcity at global and local scale

NASA Astrophysics Data System (ADS)

Wanders, N.; Wada, Y.; Wood, E. F.

2016-12-01

Forecasting the water demand and availability for agriculture and energy production has been neglected in previous research, partly due to the fact that most large-scale hydrological models lack the skill to forecast human water demands at sub-seasonal time scale. We study the potential of a sub-seasonal water scarcity forecasting system for improved water management decision making and improved estimates of water demand and availability. We have generated 32 years of global sub-seasonal multi-model water availability, demand and scarcity forecasts. The quality of the forecasts is compared to a reference forecast derived from resampling historic weather observations. The newly developed system has been evaluated for both the global scale and in a real-time local application in the Sacramento valley for the Trinity, Shasta and Oroville reservoirs, where the water demand for agriculture and hydropower is high. On the global scale we find that the reference forecast shows high initial forecast skill (up to 8 months) for water scarcity in the eastern US, Central Asia and Sub-Saharan Africa. Adding dynamical sub-seasonal forecasts results in a clear improvement for most regions in the world, increasing the forecasts' lead time by 2 or more months on average. The strongest improvements are found in the US, Brazil, Central Asia and Australia. For the Sacramento valley we can accurately predict anomalies in the reservoir inflow, hydropower potential and the downstream irrigation water demand 6 months in advance. This allow us to forecast potential water scarcity in the Sacramento valley and adjust the reservoir management to prevent deficits in energy or irrigation water availability. The newly developed forecast system shows that it is possible to reduce the vulnerability to upcoming water scarcity events and allows optimization of the distribution of the available water between the agricultural and energy sector half a year in advance.

Toward an integrated storm surge application: ESA Storm Surge project

NASA Astrophysics Data System (ADS)

Lee, Boram; Donlon, Craig; Arino, Olivier

2010-05-01

Storm surges and their associated coastal inundation are major coastal marine hazards, both in tropical and extra-tropical areas. As sea level rises due to climate change, the impact of storm surges and associated extreme flooding may increase in low-lying countries and harbour cities. Of the 33 world cities predicted to have at least 8 million people by 2015, at least 21 of them are coastal including 8 of the 10 largest. They are highly vulnerable to coastal hazards including storm surges. Coastal inundation forecasting and warning systems depend on the crosscutting cooperation of different scientific disciplines and user communities. An integrated approach to storm surge, wave, sea-level and flood forecasting offers an optimal strategy for building improved operational forecasts and warnings capability for coastal inundation. The Earth Observation (EO) information from satellites has demonstrated high potential to enhanced coastal hazard monitoring, analysis, and forecasting; the GOCE geoid data can help calculating accurate positions of tide gauge stations within the GLOSS network. ASAR images has demonstrated usefulness in analysing hydrological situation in coastal zones with timely manner, when hazardous events occur. Wind speed and direction, which is the key parameters for storm surge forecasting and hindcasting, can be derived by using scatterometer data. The current issue is, although great deal of useful EO information and application tools exist, that sufficient user information on EO data availability is missing and that easy access supported by user applications and documentation is highly required. Clear documentation on the user requirements in support of improved storm surge forecasting and risk assessment is also needed at the present. The paper primarily addresses the requirements for data, models/technologies, and operational skills, based on the results from the recent Scientific and Technical Symposium on Storm Surges (www.surgesymposium.org, organized by the WMO-IOC Joint technical Commission for Oceanography and Marine Meteorology, JCOMM) and following activities, that have been supported by the Intergovernmental Oceanographic Commission (IOC) of UNESCO through JCOMM. The paper also reviews the capabilities of storm surge models, and current status in using Earth Observation (EO) information for advancing storm surge application tools, and further, for improving operational forecasts and warning capability for coastal inundation. In this context, the plans and expected results of the ESA Storm Surge Project (2010-2011) will be introduced.

Real-time short-term forecast of water inflow into Bureyskaya reservoir

NASA Astrophysics Data System (ADS)

Motovilov, Yury

2017-04-01

During several recent years, a methodology for operational optimization in hydrosystems including forecasts of the hydrological situation has been developed on example of Burea reservoir. The forecasts accuracy improvement of the water inflow into the reservoir during planning of water and energy regime was one of the main goals for implemented research. Burea river is the second left largest Amur tributary after Zeya river with its 70.7 thousand square kilometers watershed and 723 km-long river course. A variety of natural conditions - from plains in the southern part to northern mountainous areas determine a significant spatio-temporal variability in runoff generation patterns and river regime. Bureyskaya hydropower plant (HPP) with watershed area 65.2 thousand square kilometers is a key station in the Russian Far Eastern energy system providing its reliable operation. With a spacious reservoir, Bureyskaya HPP makes a significant contribution to the protection of the Amur region from catastrophic floods. A physically-based distributed model of runoff generation based on the ECOMAG (ECOlogical Model for Applied Geophysics) hydrological modeling platform has been developed for the Burea River basin. The model describes processes of interception of rainfall/snowfall by the canopy, snow accumulation and melt, soil freezing and thawing, water infiltration into unfrozen and frozen soil, evapotranspiration, thermal and water regime of soil, overland, subsurface, ground and river flow. The governing model's equations are derived from integration of the basic hydro- and thermodynamics equations of water and heat vertical transfer in snowpack, frozen/unfrozen soil, horizontal water flow under and over catchment slopes, etc. The model setup for Bureya river basin included watershed and river network schematization with GIS module by DEM analysis, meteorological time-series preparation, model calibration and validation against historical observations. The results showed good model performance as compared to observed inflow data into the Bureya reservoir and high diagnostic potential of data-modeling system of the runoff formation. With the use of this system the following flowchart for short-range forecasting inflow into Bureyskoe reservoir and forecast correction technique using continuously updated hydrometeorological data has been developed: 1 - Daily renewal of weather observations and forecasts database via the Internet; 2 - Daily runoff calculation from the beginning of the current year to current date is conducted; 3 - Short-range (up to 7 days) forecast is generated based on weather forecast. The idea underlying the model assimilation of newly obtained hydro meteorological information to adjust short-range hydrological forecasts lies in the assumption of the forecast errors inertia. Then the difference between calculated and observed streamflow at the forecast release date is "scattered" with specific weights to calculated streamflow for the forecast lead time. During 2016 this forecasts method of the inflow into the Bureyskaya reservoir up to 7 days is tested in online mode. Satisfactory evaluated short-range inflow forecast success rate is obtained. Tests of developed method have shown strong sensitivity to the results of short-term precipitation forecasts.

Verification of temperature, precipitation, and streamflow forecasts from the NOAA/NWS Hydrologic Ensemble Forecast Service (HEFS): 1. Experimental design and forcing verification

NASA Astrophysics Data System (ADS)

Brown, James D.; Wu, Limin; He, Minxue; Regonda, Satish; Lee, Haksu; Seo, Dong-Jun

2014-11-01

Retrospective forecasts of precipitation, temperature, and streamflow were generated with the Hydrologic Ensemble Forecast Service (HEFS) of the U.S. National Weather Service (NWS) for a 20-year period between 1979 and 1999. The hindcasts were produced for two basins in each of four River Forecast Centers (RFCs), namely the Arkansas-Red Basin RFC, the Colorado Basin RFC, the California-Nevada RFC, and the Middle Atlantic RFC. Precipitation and temperature forecasts were produced with the HEFS Meteorological Ensemble Forecast Processor (MEFP). Inputs to the MEFP comprised ;raw; precipitation and temperature forecasts from the frozen (circa 1997) version of the NWS Global Forecast System (GFS) and a climatological ensemble, which involved resampling historical observations in a moving window around the forecast valid date (;resampled climatology;). In both cases, the forecast horizon was 1-14 days. This paper outlines the hindcasting and verification strategy, and then focuses on the quality of the temperature and precipitation forecasts from the MEFP. A companion paper focuses on the quality of the streamflow forecasts from the HEFS. In general, the precipitation forecasts are more skillful than resampled climatology during the first week, but comprise little or no skill during the second week. In contrast, the temperature forecasts improve upon resampled climatology at all forecast lead times. However, there are notable differences among RFCs and for different seasons, aggregation periods and magnitudes of the observed and forecast variables, both for precipitation and temperature. For example, the MEFP-GFS precipitation forecasts show the highest correlations and greatest skill in the California Nevada RFC, particularly during the wet season (November-April). While generally reliable, the MEFP forecasts typically underestimate the largest observed precipitation amounts (a Type-II conditional bias). As a statistical technique, the MEFP cannot detect, and thus appropriately correct for, conditions that are undetected by the GFS. The calibration of the MEFP to provide reliable and skillful forecasts of a range of precipitation amounts (not only large amounts) is a secondary factor responsible for these Type-II conditional biases. Interpretation of the verification results leads to guidance on the expected performance and limitations of the MEFP, together with recommendations on future enhancements.

Hydrologic ensembles based on convection-permitting precipitation nowcasts for flash flood warnings

NASA Astrophysics Data System (ADS)

Demargne, Julie; Javelle, Pierre; Organde, Didier; de Saint Aubin, Céline; Ramos, Maria-Helena

2017-04-01

In order to better anticipate flash flood events and provide timely warnings to communities at risk, the French national service in charge of flood forecasting (SCHAPI) is implementing a national flash flood warning system for small-to-medium ungauged basins. Based on a discharge-threshold flood warning method called AIGA (Javelle et al. 2014), the current version of the system runs a simplified hourly distributed hydrologic model with operational radar-gauge QPE grids from Météo-France at a 1-km2 resolution every 15 minutes. This produces real-time peak discharge estimates along the river network, which are subsequently compared to regionalized flood frequency estimates to provide warnings according to the AIGA-estimated return period of the ongoing event. To further extend the effective warning lead time while accounting for hydrometeorological uncertainties, the flash flood warning system is being enhanced to include Météo-France's AROME-NWC high-resolution precipitation nowcasts as time-lagged ensembles and multiple sets of hydrological regionalized parameters. The operational deterministic precipitation forecasts, from the nowcasting version of the AROME convection-permitting model (Auger et al. 2015), were provided at a 2.5-km resolution for a 6-hr forecast horizon for 9 significant rain events from September 2014 to June 2016. The time-lagged approach is a practical choice of accounting for the atmospheric forecast uncertainty when no extensive forecast archive is available for statistical modelling. The evaluation on 781 French basins showed significant improvements in terms of flash flood event detection and effective warning lead-time, compared to warnings from the current AIGA setup (without any future precipitation). We also discuss how to effectively communicate verification information to help determine decision-relevant warning thresholds for flood magnitude and probability. Javelle, P., Demargne, J., Defrance, D., Arnaud, P., 2014. Evaluating flash flood warnings at ungauged locations using post-event surveys: a case study with the AIGA warning system. Hydrological Sciences Journal, doi: 10.1080/02626667.2014.923970 Auger, L., Dupont, O., Hagelin, S., Brousseau, P., Brovelli, P., 2015. AROME-NWC: a new nowcasting tool based on an operational mesoscale forecasting system. Quarterly Journal of the Royal Meteorological Society, 141: 1603-1611, doi:10.1002/qj.2463

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hong, Mei; Wang, Dong, E-mail: wangdong@nju.edu.cn; Wang, Yuankun

In recent years, the phase-space reconstruction method has usually been used for mid- and long-term runoff predictions. However, the traditional phase-space reconstruction method is still needs to be improved. Using the genetic algorithm to improve the phase-space reconstruction method, a new nonlinear model of monthly runoff is constructed. The new model does not rely heavily on embedding dimensions. Recognizing that the rainfall–runoff process is complex, affected by a number of factors, more variables (e.g. temperature and rainfall) are incorporated in the model. In order to detect the possible presence of chaos in the runoff dynamics, chaotic characteristics of the modelmore » are also analyzed, which shows the model can represent the nonlinear and chaotic characteristics of the runoff. The model is tested for its forecasting performance in four types of experiments using data from six hydrological stations on the Yellow River and the Yangtze River. Results show that the medium-and long-term runoff is satisfactorily forecasted at the hydrological stations. Not only is the forecasting trend accurate, but also the mean absolute percentage error is no more than 15%. Moreover, the forecast results of wet years and dry years are both good, which means that the improved model can overcome the traditional ‘‘wet years and dry years predictability barrier,’’ to some extent. The model forecasts for different regions are all good, showing the universality of the approach. Compared with selected conceptual and empirical methods, the model exhibits greater reliability and stability in the long-term runoff prediction. Our study provides a new thinking for research on the association between the monthly runoff and other hydrological factors, and also provides a new method for the prediction of the monthly runoff. - Highlights: • The improved phase-space reconstruction model of monthly runoff is established. • Two variables (temperature and rainfall) are incorporated in the model. • Chaotic characteristics of the model are also analyzed. • The forecast results of the mid and long-term runoff in six stations are accurate.« less

A hydrophysical database to develop pedotransfer functions for Brazilian soils: challenges and perspectives

USDA-ARS?s Scientific Manuscript database

Access to soil hydrological data is vital for hydrology projects and for supporting decision-making in issues related to the availability of food and water and the forecasting of phenomena related to soil surface stability. Brazil is a country of continental dimensions and has accumulated a signific...

Coupling high-resolution hydraulic and hydrologic models for flash flood forecasting and inundation mapping in urban areas - A case study for the City of Fort Worth

NASA Astrophysics Data System (ADS)

Nazari, B.; Seo, D.; Cannon, A.

2013-12-01

With many diverse features such as channels, pipes, culverts, buildings, etc., hydraulic modeling in urban areas for inundation mapping poses significant challenges. Identifying the practical extent of the details to be modeled in order to obtain sufficiently accurate results in a timely manner for effective emergency management is one of them. In this study we assess the tradeoffs between model complexity vs. information content for decision making in applying high-resolution hydrologic and hydraulic models for real-time flash flood forecasting and inundation mapping in urban areas. In a large urban area such as the Dallas-Fort Worth Metroplex (DFW), there exists very large spatial variability in imperviousness depending on the area of interest. As such, one may expect significant sensitivity of hydraulic model results to the resolution and accuracy of hydrologic models. In this work, we present the initial results from coupling of high-resolution hydrologic and hydraulic models for two 'hot spots' within the City of Fort Worth for real-time inundation mapping.

Coupled hydro-meteorological modelling on a HPC platform for high-resolution extreme weather impact study

NASA Astrophysics Data System (ADS)

Zhu, Dehua; Echendu, Shirley; Xuan, Yunqing; Webster, Mike; Cluckie, Ian

2016-11-01

Impact-focused studies of extreme weather require coupling of accurate simulations of weather and climate systems and impact-measuring hydrological models which themselves demand larger computer resources. In this paper, we present a preliminary analysis of a high-performance computing (HPC)-based hydrological modelling approach, which is aimed at utilizing and maximizing HPC power resources, to support the study on extreme weather impact due to climate change. Here, four case studies are presented through implementation on the HPC Wales platform of the UK mesoscale meteorological Unified Model (UM) with high-resolution simulation suite UKV, alongside a Linux-based hydrological model, Hydrological Predictions for the Environment (HYPE). The results of this study suggest that the coupled hydro-meteorological model was still able to capture the major flood peaks, compared with the conventional gauge- or radar-driving forecast, but with the added value of much extended forecast lead time. The high-resolution rainfall estimation produced by the UKV performs similarly to that of radar rainfall products in the first 2-3 days of tested flood events, but the uncertainties particularly increased as the forecast horizon goes beyond 3 days. This study takes a step forward to identify how the online mode approach can be used, where both numerical weather prediction and the hydrological model are executed, either simultaneously or on the same hardware infrastructures, so that more effective interaction and communication can be achieved and maintained between the models. But the concluding comments are that running the entire system on a reasonably powerful HPC platform does not yet allow for real-time simulations, even without the most complex and demanding data simulation part.

Performance of a coupled lagged ensemble weather and river runoff prediction model system for the Alpine Ammer River catchment

NASA Astrophysics Data System (ADS)

Smiatek, G.; Kunstmann, H.; Werhahn, J.

2012-04-01

The Ammer River catchment located in the Bavarian Ammergau Alps and alpine forelands, Germany, represents with elevations reaching 2185 m and annual mean precipitation between1100 and 2000 mm a very demanding test ground for a river runoff prediction system. Large flooding events in 1999 and 2005 motivated the development of a physically based prediction tool in this area. Such a tool is the coupled high resolution numerical weather and river runoff forecasting system AM-POE that is being studied in several configurations in various experiments starting from the year 2005. Corner stones of the coupled system are the hydrological water balance model WaSiM-ETH run at 100 m grid resolution, the numerical weather prediction model (NWP) MM5 driven at 3.5 km grid cell resolution and the Perl Object Environment (POE) framework. POE implements the input data download from various sources, the input data provision via SOAP based WEB services as well as the runs of the hydrology model both with observed and with NWP predicted meteorology input. The one way coupled system utilizes a lagged ensemble prediction system (EPS) taking into account combination of recent and previous NWP forecasts. Results obtained in the years 2005-2011 reveal that river runoff simulations depict high correlation with observed runoff when driven with monitored observations in hindcast experiments. The ability to runoff forecasts is depending on lead times in the lagged ensemble prediction and shows still limitations resulting from errors in timing and total amount of the predicted precipitation in the complex mountainous area. The presentation describes the system implementation, and demonstrates the application of the POE framework in networking, distributed computing and in the setup of various experiments as well as long term results of the system application in the years 2005 - 2011.

High resolution weather data for urban hydrological modelling and impact assessment, ICT requirements and future challenges

NASA Astrophysics Data System (ADS)

ten Veldhuis, Marie-claire; van Riemsdijk, Birna

2013-04-01

Hydrological analysis of urban catchments requires high resolution rainfall and catchment information because of the small size of these catchments, high spatial variability of the urban fabric, fast runoff processes and related short response times. Rainfall information available from traditional radar and rain gauge networks does no not meet the relevant scales of urban hydrology. A new type of weather radars, based on X-band frequency and equipped with Doppler and dual polarimetry capabilities, promises to provide more accurate rainfall estimates at the spatial and temporal scales that are required for urban hydrological analysis. Recently, the RAINGAIN project was started to analyse the applicability of this new type of radars in the context of urban hydrological modelling. In this project, meteorologists and hydrologists work closely together in several stages of urban hydrological analysis: from the acquisition procedure of novel and high-end radar products to data acquisition and processing, rainfall data retrieval, hydrological event analysis and forecasting. The project comprises of four pilot locations with various characteristics of weather radar equipment, ground stations, urban hydrological systems, modelling approaches and requirements. Access to data processing and modelling software is handled in different ways in the pilots, depending on ownership and user context. Sharing of data and software among pilots and with the outside world is an ongoing topic of discussion. The availability of high resolution weather data augments requirements with respect to the resolution of hydrological models and input data. This has led to the development of fully distributed hydrological models, the implementation of which remains limited by the unavailability of hydrological input data. On the other hand, if models are to be used in flood forecasting, hydrological models need to be computationally efficient to enable fast responses to extreme event conditions. This presentation will highlight ICT-related requirements and limitations in high resolution urban hydrological modelling and analysis. Further ICT challenges arise in provision of high resolution radar data for diverging information needs as well as in combination with other data sources in the urban environment. Different types of information are required for such diverse activities as operational flood protection, traffic management, large event organisation, business planning in shopping districts and restaurants, timing of family activities. These different information needs may require different configurations and data processing for radars and other data sources. An ICT challenge is to develop techniques for deciding how to automatically respond to these diverging information needs (e.g., through (semi-)automated negotiation). Diverse activities also provide a wide variety of information resources that can supplement traditional networks of weather sensors, such as rain sensors on cars and social media. Another ICT challenge is how to combine data from these different sources for answering a particular information need. Examples will be presented of solutions are currently being explored.

Data-Driven Model Uncertainty Estimation in Hydrologic Data Assimilation

NASA Astrophysics Data System (ADS)

Pathiraja, S.; Moradkhani, H.; Marshall, L.; Sharma, A.; Geenens, G.

2018-02-01

The increasing availability of earth observations necessitates mathematical methods to optimally combine such data with hydrologic models. Several algorithms exist for such purposes, under the umbrella of data assimilation (DA). However, DA methods are often applied in a suboptimal fashion for complex real-world problems, due largely to several practical implementation issues. One such issue is error characterization, which is known to be critical for a successful assimilation. Mischaracterized errors lead to suboptimal forecasts, and in the worst case, to degraded estimates even compared to the no assimilation case. Model uncertainty characterization has received little attention relative to other aspects of DA science. Traditional methods rely on subjective, ad hoc tuning factors or parametric distribution assumptions that may not always be applicable. We propose a novel data-driven approach (named SDMU) to model uncertainty characterization for DA studies where (1) the system states are partially observed and (2) minimal prior knowledge of the model error processes is available, except that the errors display state dependence. It includes an approach for estimating the uncertainty in hidden model states, with the end goal of improving predictions of observed variables. The SDMU is therefore suited to DA studies where the observed variables are of primary interest. Its efficacy is demonstrated through a synthetic case study with low-dimensional chaotic dynamics and a real hydrologic experiment for one-day-ahead streamflow forecasting. In both experiments, the proposed method leads to substantial improvements in the hidden states and observed system outputs over a standard method involving perturbation with Gaussian noise.

Open data for water-related operational services, the SWITCH-ON approach

NASA Astrophysics Data System (ADS)

Mazzoli, Paolo; Bagli, Stefano; Valerio, Luzzi; Broccoli, Davide; Piccinini, Francesca

2017-04-01

Recently, a collaborative project started called SWITCH-ON (EU FP7 project No 603587) coordinated by SMHI (http://water-switch-on.eu/) as part of the contemporary European movement imposed by the INSPIRE directive and the Open Data Strategy. Among It's R&D activities GECOsistema develops and expands inside SWITCH-ON a set of online services to tackle major water related issues, from reservoir and irrigation supply, to hydrological change adaptation and hydropower potential mapping. Here we present major releases of APRIL, HyCAW and High-resolution European HydroPower Atlas; all of which make intense use of open data. APRIL is a tool for seasonal run-off forecasts, that takes advantage of open datasets or low-cost data and performs forecasts through calibrated machine learning algorithms. HyCAW is a wizard that supports the assessment of adaptation options to cope with change in the temporal distribution of water availability as well as in the total water quantity. EU HPA provides all relevant information necessary to appraise the feasibility of a micro-hydropower plant at a specific site, taking into account hydrological as well as technical and economic factors. All the tools share a common vision of the project to address water concerns and currently untapped potential of open data for improved water management across the EU. Users are guided through a Web GIS interface, created using open source Web Mapping Applications, Open-Layers and Map Server, to explore available hydrological information in the area of interest, plot available data, perform analysis, and get reports and statistics.

Flood risk awareness during the 2011 floods in the central United States: showcasing the importance of hydrologic data and interagency collaboration

USGS Publications Warehouse

Holmes, Robert R.; Schwein, Noreen O.; Shadie, Charles E.

2012-01-01

Floods have long had a major impact on society and the environment, evidenced by the more than 1,500 federal disaster declarations since 1952 that were associated with flooding. Calendar year 2011 was an epic year for floods in the United States, from the flooding on the Red River of the North in late spring to the Ohio, Mississippi, and Missouri River basin floods in the spring and summer to the flooding caused by Hurricane Irene along the eastern seaboard in August. As a society, we continually seek to reduce flood impacts, with these efforts loosely grouped into two categories: mitigation and risk awareness. Mitigation involves such activities as flood assessment, flood control implementation, and regulatory activities such as storm water and floodplain ordinances. Risk awareness ranges from issuance of flood forecasts and warnings to education of lay audiences about the uncertainties inherent in assessing flood probability and risk. This paper concentrates on the issue of flood risk awareness, specifically the importance of hydrologic data and good interagency communication in providing accurate and timely flood forecasts to maximize risk awareness. The 2011 floods in the central United States provide a case study of the importance of hydrologic data and the value of proper, timely, and organized communication and collaboration around the collection and dissemination of that hydrologic data in enhancing the effectiveness of flood forecasting and flood risk awareness.

A pan-African medium-range ensemble flood forecast system

NASA Astrophysics Data System (ADS)

Thiemig, Vera; Bisselink, Bernard; Pappenberger, Florian; Thielen, Jutta

2015-04-01

The African Flood Forecasting System (AFFS) is a probabilistic flood forecast system for medium- to large-scale African river basins, with lead times of up to 15 days. The key components are the hydrological model LISFLOOD, the African GIS database, the meteorological ensemble predictions of the ECMWF and critical hydrological thresholds. In this study the predictive capability is investigated, to estimate AFFS' potential as an operational flood forecasting system for the whole of Africa. This is done in a hindcast mode, by reproducing pan-African hydrological predictions for the whole year of 2003 where important flood events were observed. Results were analysed in two ways, each with its individual objective. The first part of the analysis is of paramount importance for the assessment of AFFS as a flood forecasting system, as it focuses on the detection and prediction of flood events. Here, results were verified with reports of various flood archives such as Dartmouth Flood Observatory, the Emergency Event Database, the NASA Earth Observatory and Reliefweb. The number of hits, false alerts and missed alerts as well as the Probability of Detection, False Alarm Rate and Critical Success Index were determined for various conditions (different regions, flood durations, average amount of annual precipitations, size of affected areas and mean annual discharge). The second part of the analysis complements the first by giving a basic insight into the prediction skill of the general streamflow. For this, hydrological predictions were compared against observations at 36 key locations across Africa and the Continuous Rank Probability Skill Score (CRPSS), the limit of predictability and reliability were calculated. Results showed that AFFS detected around 70 % of the reported flood events correctly. In particular, the system showed good performance in predicting riverine flood events of long duration (> 1 week) and large affected areas (> 10 000 km2) well in advance, whereas AFFS showed limitations for small-scale and short duration flood events. Also the forecasts showed on average a good reliability, and the CRPSS helped identifying regions to focus on for future improvements. The case study for the flood event in March 2003 in the Sabi Basin (Zimbabwe and Mozambique) illustrated the good performance of AFFS in forecasting timing and severity of the floods, gave an example of the clear and concise output products, and showed that the system is capable of producing flood warnings even in ungauged river basins. Hence, from a technical perspective, AFFS shows a good prospective as an operational system, as it has demonstrated its significant potential to contribute to the reduction of flood-related losses in Africa by providing national and international aid organizations timely with medium-range flood forecast information. However, issues related to the practical implication will still need to be investigated.

MAMS: High resolution atmospheric moisture/surface properties

NASA Technical Reports Server (NTRS)

Jedlovec, Gary J.; Guillory, Anthony R.; Suggs, Ron; Atkinson, Robert J.; Carlson, Grant S.

1991-01-01

Multispectral Atmospheric Mapping Sensor (MAMS) data collected from a number of U2/ER2 aircraft flights were used to investigate atmospheric and surface (land) components of the hydrologic cycle. Algorithms were developed to retrieve surface and atmospheric geophysical parameters which describe the variability of atmospheric moisture, its role in cloud and storm development, and the influence of surface moisture and heat sources on convective activity. Techniques derived with MAMS data are being applied to existing satellite measurements to show their applicability to regional and large process studies and their impact on operational forecasting.

Local short-duration precipitation extremes in Sweden: observations, forecasts and projections

NASA Astrophysics Data System (ADS)

Olsson, Jonas; Berg, Peter; Simonsson, Lennart

2015-04-01

Local short-duration precipitation extremes (LSPEs) are a key driver of hydrological hazards, notably in steep catchments with thin soils and in urban environments. The triggered floodings, landslides, etc., have large consequences for society in terms of both economy and health. Accurate estimations of LSPEs on both climatological time-scales (past, present, future) and in real-time is thus of great importance for improved hydrological predictions as well as design of constructions and infrastructure affected by hydrological fluxes. Analysis of LSPEs is, however, associated with various limitations and uncertainties. These are to a large degree associated with the small-scale nature of the meteorological processes behind LSPEs and the associated requirements on observation sensors as well as model descriptions. Some examples of causes for the limitations involved are given in the following. - Observations: High-resolution data sets available for LSPE analyses are often limited to either relatively long series from one or a few stations or relatively short series from larger station networks. Radar data have excellent resolutions in both time and space but the estimated local precipitation intensity is still highly uncertain. New and promising techniques (e.g. microwave links) are still in their infancy. - Weather forecasts (short-range): Although forecasts with the required spatial resolution for potential generation of LSPEs (around 2-4 km) are becoming operationally available, the actual forecast precision of LSPEs is largely unknown. Forecasted LSPEs may be displaced in time or, more critically, in space which strongly affects the possibility to assess hydrological risk. - Climate projections: The spatial resolution of the current RCM generation (around 25 km) is not sufficient for proper description of LSPEs. Statistical post-processing (i.e. downscaling) is required which adds substantial uncertainty to the final result. Ensemble generation of sufficiently high-resolution RCM projections is not yet computationally feasible. In this presentation, examples of recent research in Sweden related to these aspects will be given with some main findings shown and discussed. Finally, some ongoing and future research directions will be outlined (the former hopefully accompanied by some brand-new results).

State updating of a distributed hydrological model with Ensemble Kalman Filtering: Effects of updating frequency and observation network density on forecast accuracy

NASA Astrophysics Data System (ADS)

Rakovec, O.; Weerts, A.; Hazenberg, P.; Torfs, P.; Uijlenhoet, R.

2012-12-01

This paper presents a study on the optimal setup for discharge assimilation within a spatially distributed hydrological model (Rakovec et al., 2012a). The Ensemble Kalman filter (EnKF) is employed to update the grid-based distributed states of such an hourly spatially distributed version of the HBV-96 model. By using a physically based model for the routing, the time delay and attenuation are modelled more realistically. The discharge and states at a given time step are assumed to be dependent on the previous time step only (Markov property). Synthetic and real world experiments are carried out for the Upper Ourthe (1600 km2), a relatively quickly responding catchment in the Belgian Ardennes. The uncertain precipitation model forcings were obtained using a time-dependent multivariate spatial conditional simulation method (Rakovec et al., 2012b), which is further made conditional on preceding simulations. We assess the impact on the forecasted discharge of (1) various sets of the spatially distributed discharge gauges and (2) the filtering frequency. The results show that the hydrological forecast at the catchment outlet is improved by assimilating interior gauges. This augmentation of the observation vector improves the forecast more than increasing the updating frequency. In terms of the model states, the EnKF procedure is found to mainly change the pdfs of the two routing model storages, even when the uncertainty in the discharge simulations is smaller than the defined observation uncertainty. Rakovec, O., Weerts, A. H., Hazenberg, P., Torfs, P. J. J. F., and Uijlenhoet, R.: State updating of a distributed hydrological model with Ensemble Kalman Filtering: effects of updating frequency and observation network density on forecast accuracy, Hydrol. Earth Syst. Sci. Discuss., 9, 3961-3999, doi:10.5194/hessd-9-3961-2012, 2012a. Rakovec, O., Hazenberg, P., Torfs, P. J. J. F., Weerts, A. H., and Uijlenhoet, R.: Generating spatial precipitation ensembles: impact of temporal correlation structure, Hydrol. Earth Syst. Sci. Discuss., 9, 3087-3127, doi:10.5194/hessd-9-3087-2012, 2012b.

Operational Precipitation prediction in Support of Real-Time Flash Flood Prediction and Reservoir Management

NASA Astrophysics Data System (ADS)

Georgakakos, K. P.

2006-05-01

The presentation will outline the implementation and performance evaluation of a number of national and international projects pertaining to operational precipitation estimation and prediction in the context of hydrologic warning systems and reservoir management support. In all cases, uncertainty measures of the estimates and predictions are an integral part of the precipitation models. Outstanding research issues whose resolution is likely to lead to improvements in the operational environment are presented. The presentation draws from the experience of the Hydrologic Research Center (http://www.hrc-lab.org) prototype implementation projects at the Panama Canal, Central America, Northern California, and South-Central US. References: Carpenter, T.M, and K.P. Georgakakos, "Discretization Scale Dependencies of the Ensemble Flow Range versus Catchment Area Relationship in Distributed Hydrologic Modeling," Journal of Hydrology, 2006, in press. Carpenter, T.M., and K.P. Georgakakos, "Impacts of Parametric and Radar Rainfall Uncertainty on the Ensemble Streamflow Simulations of a Distributed Hydrologic Model," Journal of Hydrology, 298, 202-221, 2004. Georgakakos, K.P., Graham, N.E., Carpenter, T.M., Georgakakos, A.P., and H. Yao, "Integrating Climate- Hydrology Forecasts and Multi-Objective Reservoir Management in Northern California," EOS, 86(12), 122,127, 2005. Georgakakos, K.P., and J.A. Sperfslage, "Operational Rainfall and Flow Forecasting for the Panama Canal Watershed," in The Rio Chagres: A Multidisciplinary Profile of a Tropical Watershed, R.S. Harmon, ed., Kluwer Academic Publishers, The Netherlands, Chapter 16, 323-334, 2005. Georgakakos, K. P., "Analytical results for operational flash flood guidance," Journal of Hydrology, doi:10.1016/j.jhydrol.2005.05.009, 2005.

MINERVE flood warning and management project. What is computed, what is required and what is visualized?

NASA Astrophysics Data System (ADS)

Garcia Hernandez, J.; Boillat, J.-L.; Schleiss, A.

2010-09-01

During last decades several flood events caused important inundations in the Upper Rhone River basin in Switzerland. As a response to such disasters, the MINERVE project aims to improve the security by reducing damages in this basin. The main goal of this project is to predict floods in advance in order to obtain a better flow control during flood peaks taking advantage from the multireservoir system of the existing hydropower schemes. The MINERVE system evaluates the hydro-meteorological situation on the watershed and provides hydrological forecasts with a horizon from three to five days. It exploits flow measurements, data from reservoirs and hydropower plants as well as deterministic (COSMO-7 and COSMO-2) and ensemble (COSMO-LEPS) meteorological forecast from MeteoSwiss. The hydrological model is based on a semi-distributed concept, dividing the watershed in 239 sub-catchments, themselves decomposed in elevation bands in order to describe the temperature-driven processes related to snow and glacier melt. The model is completed by rivers and hydraulic works such as water intakes, reservoirs, turbines and pumps. Once the hydrological forecasts are calculated, a report provides the warning level at selected control points according to time, being a support to decision-making for preventive actions. A Notice, Alert or Alarm is then activated depending on the discharge thresholds defined by the Valais Canton. Preventive operation scenarios are then generated based on observed discharge at control points, meteorological forecasts from MeteoSwiss, hydrological forecasts from MINERVE and retention possibilities in the reservoirs. An update of the situation is done every time new data or new forecasts are provided, keeping last observations and last forecasts in the warning report. The forecasts can also be used for the evaluation of priority decisions concerning the management of hydropower plants for security purposes. Considering future inflows and reservoir levels, turbine and bottom outlet preventive operations can be proposed to the hydropower plants operators in order to store water inflows and to stop turbining during the peak flow. Appropriate operations can thus reduce the peak discharges in the Rhone River and its tributaries, limiting or avoiding damages. Results presentation in a clear and understandable way is an important goal of the project and is considered as one of the main focuses. The MINERVE project is developed in partnership by the Swiss Federal Office for Environment (FOEV), Services of Roads and Water courses as well as Water Power and Energy of the Wallis Canton and Service of Water, Land and Sanitation of the Vaud Canton. The Swiss Weather Service (MeteoSwiss) provides the weather forecasts and hydroelectric companies communicate specific information regarding the hydropower plants. Scientific developments are entrusted to two entities of the Ecole Polytechnique Fédérale de Lausanne (EPFL), the Hydraulic Constructions Laboratory (LCH) and the Ecohydrology Laboratory (ECHO), as well as to the Institute of Geomatics and Analysis of Risk (IGAR) of Lausanne University (UNIL).

Ensemble hydro-meteorological forecasting for early warning of floods and scheduling of hydropower production

NASA Astrophysics Data System (ADS)

Solvang Johansen, Stian; Steinsland, Ingelin; Engeland, Kolbjørn

2016-04-01

Running hydrological models with precipitation and temperature ensemble forcing to generate ensembles of streamflow is a commonly used method in operational hydrology. Evaluations of streamflow ensembles have however revealed that the ensembles are biased with respect to both mean and spread. Thus postprocessing of the ensembles is needed in order to improve the forecast skill. The aims of this study is (i) to to evaluate how postprocessing of streamflow ensembles works for Norwegian catchments within different hydrological regimes and to (ii) demonstrate how post processed streamflow ensembles are used operationally by a hydropower producer. These aims were achieved by postprocessing forecasted daily discharge for 10 lead-times for 20 catchments in Norway by using EPS forcing from ECMWF applied the semi-distributed HBV-model dividing each catchment into 10 elevation zones. Statkraft Energi uses forecasts from these catchments for scheduling hydropower production. The catchments represent different hydrological regimes. Some catchments have stable winter condition with winter low flow and a major flood event during spring or early summer caused by snow melting. Others has a more mixed snow-rain regime, often with a secondary flood season during autumn, and in the coastal areas, the stream flow is dominated by rain, and the main flood season is autumn and winter. For post processing, a Bayesian model averaging model (BMA) close to (Kleiber et al 2011) is used. The model creates a predictive PDF that is a weighted average of PDFs centered on the individual bias corrected forecasts. The weights are here equal since all ensemble members come from the same model, and thus have the same probability. For modeling streamflow, the gamma distribution is chosen as a predictive PDF. The bias correction parameters and the PDF parameters are estimated using a 30-day sliding window training period. Preliminary results show that the improvement varies between catchments depending on where they are situated and the hydrological regime. There is an improvement in CRPS for all catchments compared to raw EPS ensembles. The improvement is up to lead-time 5-7. The postprocessing also improves the MAE for the median of the predictive PDF compared to the median of the raw EPS. But less compared to CRPS, often up to lead-time 2-3. The streamflow ensembles are to some extent used operationally in Statkraft Energi (Hydro Power company, Norway), with respect to early warning, risk assessment and decision-making. Presently all forecast used operationally for short-term scheduling are deterministic, but ensembles are used visually for expert assessment of risk in difficult situations where e.g. there is a chance of overflow in a reservoir. However, there are plans to incorporate ensembles in the daily scheduling of hydropower production.

NMME Monthly / Seasonal Forecasts for NASA SERVIR Applications Science

NASA Astrophysics Data System (ADS)

Robertson, F. R.; Roberts, J. B.

2014-12-01

This work details use of the North American Multi-Model Ensemble (NMME) experimental forecasts as drivers for Decision Support Systems (DSSs) in the NASA / USAID initiative, SERVIR (a Spanish acronym meaning "to serve"). SERVIR integrates satellite observations, ground-based data and forecast models to monitor and forecast environmental changes and to improve response to natural disasters. Through the use of DSSs whose "front ends" are physically based models, the SERVIR activity provides a natural testbed to determine the extent to which NMME monthly to seasonal projections enable scientists, educators, project managers and policy implementers in developing countries to better use probabilistic outlooks of seasonal hydrologic anomalies in assessing agricultural / food security impacts, water availability, and risk to societal infrastructure. The multi-model NMME framework provides a "best practices" approach to probabilistic forecasting. The NMME forecasts are generated at resolution more coarse than that required to support DSS models; downscaling in both space and time is necessary. The methodology adopted here applied model output statistics where we use NMME ensemble monthly projections of sea-surface temperature (SST) and precipitation from 30 years of hindcasts with observations of precipitation and temperature for target regions. Since raw model forecasts are well-known to have structural biases, a cross-validated multivariate regression methodology (CCA) is used to link the model projected states as predictors to the predictands of the target region. The target regions include a number of basins in East and South Africa as well as the Ganges / Baramaputra / Meghna basin complex. The MOS approach used address spatial downscaling. Temporal disaggregation of monthly seasonal forecasts is achieved through use of a tercile bootstrapping approach. We interpret the results of these studies, the levels of skill by several metrics, and key uncertainties.

NMME Monthly / Seasonal Forecasts for NASA SERVIR Applications Science

NASA Technical Reports Server (NTRS)

Robertson, Franklin R.; Roberts, Jason B.

2014-01-01

This work details use of the North American Multi-Model Ensemble (NMME) experimental forecasts as drivers for Decision Support Systems (DSSs) in the NASA / USAID initiative, SERVIR (a Spanish acronym meaning "to serve"). SERVIR integrates satellite observations, ground-based data and forecast models to monitor and forecast environmental changes and to improve response to natural disasters. Through the use of DSSs whose "front ends" are physically based models, the SERVIR activity provides a natural testbed to determine the extent to which NMME monthly to seasonal projections enable scientists, educators, project managers and policy implementers in developing countries to better use probabilistic outlooks of seasonal hydrologic anomalies in assessing agricultural / food security impacts, water availability, and risk to societal infrastructure. The multi-model NMME framework provides a "best practices" approach to probabilistic forecasting. The NMME forecasts are generated at resolution more coarse than that required to support DSS models; downscaling in both space and time is necessary. The methodology adopted here applied model output statistics where we use NMME ensemble monthly projections of sea-surface temperature (SST) and precipitation from 30 years of hindcasts with observations of precipitation and temperature for target regions. Since raw model forecasts are well-known to have structural biases, a cross-validated multivariate regression methodology (CCA) is used to link the model projected states as predictors to the predictands of the target region. The target regions include a number of basins in East and South Africa as well as the Ganges / Baramaputra / Meghna basin complex. The MOS approach used address spatial downscaling. Temporal disaggregation of monthly seasonal forecasts is achieved through use of a tercile bootstrapping approach. We interpret the results of these studies, the levels of skill by several metrics, and key uncertainties.

Seasonal streamflow prediction using ensemble streamflow prediction technique for the Rangitata and Waitaki River basins on the South Island of New Zealand

NASA Astrophysics Data System (ADS)

Singh, Shailesh Kumar

2014-05-01

Streamflow forecasts are essential for making critical decision for optimal allocation of water supplies for various demands that include irrigation for agriculture, habitat for fisheries, hydropower production and flood warning. The major objective of this study is to explore the Ensemble Streamflow Prediction (ESP) based forecast in New Zealand catchments and to highlights the present capability of seasonal flow forecasting of National Institute of Water and Atmospheric Research (NIWA). In this study a probabilistic forecast framework for ESP is presented. The basic assumption in ESP is that future weather pattern were experienced historically. Hence, past forcing data can be used with current initial condition to generate an ensemble of prediction. Small differences in initial conditions can result in large difference in the forecast. The initial state of catchment can be obtained by continuously running the model till current time and use this initial state with past forcing data to generate ensemble of flow for future. The approach taken here is to run TopNet hydrological models with a range of past forcing data (precipitation, temperature etc.) with current initial conditions. The collection of runs is called the ensemble. ESP give probabilistic forecasts for flow. From ensemble members the probability distributions can be derived. The probability distributions capture part of the intrinsic uncertainty in weather or climate. An ensemble stream flow prediction which provide probabilistic hydrological forecast with lead time up to 3 months is presented for Rangitata, Ahuriri, and Hooker and Jollie rivers in South Island of New Zealand. ESP based seasonal forecast have better skill than climatology. This system can provide better over all information for holistic water resource management.

Operational flood forecasting system of Umbria Region "Functional Centre

NASA Astrophysics Data System (ADS)

Berni, N.; Pandolfo, C.; Stelluti, M.; Ponziani, F.; Viterbo, A.

2009-04-01

The hydrometeorological alert office (called "Decentrate Functional Centre" - CFD) of Umbria Region, in central Italy, is the office that provides technical tools able to support decisions when significant flood/landslide events occur, furnishing 24h support for the whole duration of the emergency period, according to the national directive DPCM 27 February 2004 concerning the "Operating concepts for functional management of national and regional alert system during flooding and landslide events for civil protection activities purposes" that designs, within the Italian Civil Defence Emergency Management System, a network of 21 regional Functional Centres coordinated by a central office at the National Civil Protection Department in Rome. Due to its "linking" role between Civil Protection "real time" activities and environmental/planning "deferred time" ones, the Centre is in charge to acquire and collect both real time and quasi-static data: quantitative data from monitoring networks (hydrometeorological stations, meteo radar, ...), meteorological forecasting models output, Earth Observation data, hydraulic and hydrological simulation models, cartographic and thematic GIS data (vectorial and raster type), planning studies related to flooding areas mapping, dam managing plans during flood events, non instrumental information from direct control of "territorial presidium". A detailed procedure for the management of critical events was planned, also in order to define the different role of various authorities and institutions involved. Tiber River catchment, of which Umbria region represents the main upper-medium portion, includes also regional trans-boundary issues very important to cope with, especially for what concerns large dam behavior and management during heavy rainfall. The alert system is referred to 6 different warning areas in which the territory has been divided into and based on a threshold system of three different increasing critical levels according to the expected ground effects: ordinary, moderate and high. Particularly, hydrometric and rainfall thresholds for both floods and landslides alarms were assessed. Based on these thresholds, at the Umbria Region Functional Centre an automatic phone-call and SMS alert system is operating. For a real time flood forecasting system, at the CFD several hydrological and hydraulic models were developed. Three rainfall-runoff hydrological models, using different quantitative meteorological forecasts, are available: the event based models X-Nash (based on the Nash theory) and Mike-Drift coupled with the hydraulic model Mike-11 (developed by the Danish Hydraulic Institute - DHI); and the physically-based continuous model Mobidic (MOdello di Bilancio Idrologico DIstribuito e Continuo - Distributed and Continuous Model for the Hydrological Balance, developed by the University of Florence in cooperation with the Functional Centre of Tuscany Region). Other two hydrological models, using observed data of the real time hydrometeorological network, were implemented: the first one is the rainfall-runoff hydrological model Hec-Hms coupled with the hydraulic model Hec-Ras (United States Army Corps of Engineers - USACE). Moreover, Hec-Hms, is coupled also with a continuous soil moisture model for a more precise evaluation of the antecedent moisture condition of the basin, which is a key factor for a correct runoff volume evaluation. The second one is the routing hydrological model Stafom (STage FOrecasting Model, developed by the Italian Research Institute for Geo-Hydrological Protection of the National Research Council - IRPI-CNR). This model is an adaptive model for on-line stage forecasting for river branches where significant lateral inflow contributions occur and, up to now, it is implemented for the main Tiber River branch and it allows a forecasting lead time up to 10 hours for the downstream river section. Recently, during the period between December the 4th and the 16th 2008, Umbria region territory was interested by a severe rainfall event causing many floods and landslides. During the mainly critical phases the CFD furnished an immediate, significant 24h support for the decision support activities. The official web site (www.cfumbria.it), entirely developed with open source tools, represented a very useful device furnishing good performances for the monitoring and data dissemination to all the subjects involved, especially to the National/Regional Civil Protection offices and territorial presidium. Thresholds presented good accordance with non instrumental observations and automatic alert system was very effective. At last, during the flooding event a continuous link with the National Department, regional Civil Protection offices, territorial presidium and local public services, together with real time instrumental monitoring and now-casting hydrological activities performed by available models, represented a suitable junction between practice and science in CFD operational forecasting system at local, regional and national scale.

Assessing Variability and Errors in Historical Runoff Forecasting with Physical Models and Alternative Data Sources

NASA Astrophysics Data System (ADS)

Penn, C. A.; Clow, D. W.; Sexstone, G. A.

2017-12-01

Water supply forecasts are an important tool for water resource managers in areas where surface water is relied on for irrigating agricultural lands and for municipal water supplies. Forecast errors, which correspond to inaccurate predictions of total surface water volume, can lead to mis-allocated water and productivity loss, thus costing stakeholders millions of dollars. The objective of this investigation is to provide water resource managers with an improved understanding of factors contributing to forecast error, and to help increase the accuracy of future forecasts. In many watersheds of the western United States, snowmelt contributes 50-75% of annual surface water flow and controls both the timing and volume of peak flow. Water supply forecasts from the Natural Resources Conservation Service (NRCS), National Weather Service, and similar cooperators use precipitation and snowpack measurements to provide water resource managers with an estimate of seasonal runoff volume. The accuracy of these forecasts can be limited by available snowpack and meteorological data. In the headwaters of the Rio Grande, NRCS produces January through June monthly Water Supply Outlook Reports. This study evaluates the accuracy of these forecasts since 1990, and examines what factors may contribute to forecast error. The Rio Grande headwaters has experienced recent changes in land cover from bark beetle infestation and a large wildfire, which can affect hydrological processes within the watershed. To investigate trends and possible contributing factors in forecast error, a semi-distributed hydrological model was calibrated and run to simulate daily streamflow for the period 1990-2015. Annual and seasonal watershed and sub-watershed water balance properties were compared with seasonal water supply forecasts. Gridded meteorological datasets were used to assess changes in the timing and volume of spring precipitation events that may contribute to forecast error. Additionally, a spatially-distributed physics-based snow model was used to assess possible effects of land cover change on snowpack properties. Trends in forecasted error are variable while baseline model results show a consistent under-prediction in the recent decade, highlighting possible compounding effects of climate and land cover changes.

New and Improved GLDAS and NLDAS Data Sets and Data Services at HDISC/NASA

NASA Technical Reports Server (NTRS)

Rui, Hualan; Beaudoing, Hiroko Kato; Mocko, David M.; Rodell, Matthew; Teng, William L.; Vollmer. Bruce

2010-01-01

Terrestrial hydrological variables are important in global hydrology, climate, and carbon cycle studies. Generating global fields of these variables, however, is still a challenge. The goal of a land data assimilation system (LDAS)is to ingest satellite-and ground-based observational data products, using advanced land surface modeling and data assimilation techniques, in order to generate optimal fields of land surface states and fluxes data and, thereby, facilitate hydrology and climate modeling, research, and forecast.

Hydrologic Simulation in Mediterranean flood prone Watersheds using high-resolution quality data

NASA Astrophysics Data System (ADS)

Eirini Vozinaki, Anthi; Alexakis, Dimitrios; Pappa, Polixeni; Tsanis, Ioannis

2015-04-01

Flooding is a significant threat causing lots of inconveniencies in several societies, worldwide. The fact that the climatic change is already happening, increases the flooding risk, which is no longer a substantial menace to several societies and their economies. The improvement of spatial-resolution and accuracy of the topography and land use data due to remote sensing techniques could provide integrated flood inundation simulations. In this work hydrological analysis of several historic flood events in Mediterranean flood prone watersheds (island of Crete/Greece) takes place. Satellite images of high resolution are elaborated. A very high resolution (VHR) digital elevation model (DEM) is produced from a GeoEye-1 0.5-m-resolution satellite stereo pair and is used for floodplain management and mapping applications such as watershed delineation and river cross-section extraction. Sophisticated classification algorithms are implemented for improving Land Use/ Land Cover maps accuracy. In addition, soil maps are updated with means of Radar satellite images. The above high-resolution data are innovatively used to simulate and validate several historical flood events in Mediterranean watersheds, which have experienced severe flooding in the past. The hydrologic/hydraulic models used for flood inundation simulation in this work are HEC-HMS and HEC-RAS. The Natural Resource Conservation Service (NRCS) curve number (CN) approach is implemented to account for the effect of LULC and soil on the hydrologic response of the catchment. The use of high resolution data provides detailed validation results and results of high precision, accordingly. Furthermore, the meteorological forecasting data, which are also combined to the simulation model results, manage the development of an integrated flood forecasting and early warning system tool, which is capable of confronting or even preventing this imminent risk. The research reported in this paper was fully supported by the "ARISTEIA II" Action ("REINFORCE" program) of the "Operational Education and Life Long Learning programme" and is co-funded by the European Social Fund (ESF) and National Resources.

Parametrisation of initial conditions for seasonal stream flow forecasting in the Swiss Rhine basin

NASA Astrophysics Data System (ADS)

Schick, Simon; Rössler, Ole; Weingartner, Rolf

2016-04-01

Current climate forecast models show - to the best of our knowledge - low skill in forecasting climate variability in Central Europe at seasonal lead times. When it comes to seasonal stream flow forecasting, initial conditions thus play an important role. Here, initial conditions refer to the catchments moisture at the date of forecast, i.e. snow depth, stream flow and lake level, soil moisture content, and groundwater level. The parametrisation of these initial conditions can take place at various spatial and temporal scales. Examples are the grid size of a distributed model or the time aggregation of predictors in statistical models. Therefore, the present study aims to investigate the extent to which the parametrisation of initial conditions at different spatial scales leads to differences in forecast errors. To do so, we conduct a forecast experiment for the Swiss Rhine at Basel, which covers parts of Germany, Austria, and Switzerland and is southerly bounded by the Alps. Seasonal mean stream flow is defined for the time aggregation of 30, 60, and 90 days and forecasted at 24 dates within the calendar year, i.e. at the 1st and 16th day of each month. A regression model is employed due to the various anthropogenic effects on the basins hydrology, which often are not quantifiable but might be grasped by a simple black box model. Furthermore, the pool of candidate predictors consists of antecedent temperature, precipitation, and stream flow only. This pragmatic approach follows the fact that observations of variables relevant for hydrological storages are either scarce in space or time (soil moisture, groundwater level), restricted to certain seasons (snow depth), or regions (lake levels, snow depth). For a systematic evaluation, we therefore focus on the comprehensive archives of meteorological observations and reanalyses to estimate the initial conditions via climate variability prior to the date of forecast. The experiment itself is based on four different approaches, whose differences in model skill were estimated within a rigorous cross-validation framework for the period 1982-2013: The predictands are regressed on antecedent temperature, precipitation, and stream flow. Here, temperature and precipitation constitute basin averages out of the E-OBS gridded data set. As in 1., but temperature and precipitation are used at the E-OBS grid scale (0.25 degree in longitude and latitude) without spatial averaging. As in 1., but the regression model is applied to 66 gauged subcatchments of the Rhine basin. Forecasts for these subcatchments are then simply summed and upscaled to the area of the Rhine basin. As in 3., but the forecasts at the subcatchment scale are additionally weighted in terms of hydrological representativeness of the corresponding subcatchment.

Characterizing the utility of the TMPA real-time product for hydrologic predictions over global river basins across scales

NASA Astrophysics Data System (ADS)

Gao, H.; Zhang, S.; Nijssen, B.; Zhou, T.; Voisin, N.; Sheffield, J.; Lee, K.; Shukla, S.; Lettenmaier, D. P.

2017-12-01

Despite its errors and uncertainties, the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis real-time product (TMPA-RT) has been widely used for hydrological monitoring and forecasting due to its timely availability for real-time applications. To evaluate the utility of TMPA-RT in hydrologic predictions, many studies have compared modeled streamflows driven by TMPA-RT against gauge data. However, because of the limited availability of streamflow observations in data sparse regions, there is still a lack of comprehensive comparisons for TMPA-RT based hydrologic predictions at the global scale. Furthermore, it is expected that its skill is less optimal at the subbasin scale than the basin scale. In this study, we evaluate and characterize the utility of the TMPA-RT product over selected global river basins during the period of 1998 to 2015 using the TMPA research product (TMPA-RP) as a reference. The Variable Infiltration Capacity (VIC) model, which was calibrated and validated previously, is adopted to simulate streamflows driven by TMPA-RT and TMPA-RP, respectively. The objective of this study is to analyze the spatial and temporal characteristics of the hydrologic predictions by answering the following questions: (1) How do the precipitation errors associated with the TMPA-RT product transform into streamflow errors with respect to geographical and climatological characteristics? (2) How do streamflow errors vary across scales within a basin?

Hydrological Predictability for the Peruvian Amazon

NASA Astrophysics Data System (ADS)

Towner, Jamie; Stephens, Elizabeth; Cloke, Hannah; Bazo, Juan; Coughlan, Erin; Zsoter, Ervin

2017-04-01

Population growth in the Peruvian Amazon has prompted the expansion of livelihoods further into the floodplain and thus increasing vulnerability to the annual rise and fall of the river. This growth has coincided with a period of increasing hydrological extremes with more frequent severe flood events. The anticipation and forecasting of these events is crucial for mitigating vulnerability. Forecast-based Financing (FbF) an initiative of the German Red Cross implements risk reducing actions based on threshold exceedance within hydrometeorological forecasts using the Global Flood Awareness System (GloFAS). However, the lead times required to complete certain actions can be long (e.g. several weeks to months ahead to purchase materials and reinforce houses) and are beyond the current capabilities of GloFAS. Therefore, further calibration of the model is required in addition to understanding the climatic drivers and associated hydrological response for specific flood events, such as those observed in 2009, 2012 and 2015. This review sets out to determine the current capabilities of the GloFAS model while exploring the limits of predictability for the Amazon basin. More specifically, how the temporal patterns of flow within the main coinciding tributaries correspond to the overall Amazonian flood wave under various climatic and meteorological influences. Linking the source areas of flow to predictability within the seasonal forecasting system will develop the ability to expand the limit of predictability of the flood wave. This presentation will focus on the Iquitos region of Peru, while providing an overview of the new techniques and current challenges faced within seasonal flood prediction.

Enhancing Community Based Early Warning Systems in Nepal with Flood Forecasting Using Local and Global Models

NASA Astrophysics Data System (ADS)

Dugar, Sumit; Smith, Paul; Parajuli, Binod; Khanal, Sonu; Brown, Sarah; Gautam, Dilip; Bhandari, Dinanath; Gurung, Gehendra; Shakya, Puja; Kharbuja, RamGopal; Uprety, Madhab

2017-04-01

Operationalising effective Flood Early Warning Systems (EWS) in developing countries like Nepal poses numerous challenges, with complex topography and geology, sparse network of river and rainfall gauging stations and diverse socio-economic conditions. Despite these challenges, simple real-time monitoring based EWSs have been in place for the past decade. A key constraint of these simple systems is the very limited lead time for response - as little as 2-3 hours, especially for rivers originating from steep mountainous catchments. Efforts to increase lead time for early warning are focusing on imbedding forecasts into the existing early warning systems. In 2016, the Nepal Department of Hydrology and Meteorology (DHM) piloted an operational Probabilistic Flood Forecasting Model in major river basins across Nepal. This comprised a low data approach to forecast water levels, developed jointly through a research/practitioner partnership with Lancaster University and WaterNumbers (UK) and the International NGO Practical Action. Using Data-Based Mechanistic Modelling (DBM) techniques, the model assimilated rainfall and water levels to generate localised hourly flood predictions, which are presented as probabilistic forecasts, increasing lead times from 2-3 hours to 7-8 hours. The Nepal DHM has simultaneously started utilizing forecasts from the Global Flood Awareness System (GLoFAS) that provides streamflow predictions at the global scale based upon distributed hydrological simulations using numerical ensemble weather forecasts from the ECMWF (European Centre for Medium-Range Weather Forecasts). The aforementioned global and local models have already affected the approach to early warning in Nepal, being operational during the 2016 monsoon in the West Rapti basin in Western Nepal. On 24 July 2016, GLoFAS hydrological forecasts for the West Rapti indicated a sharp rise in river discharge above 1500 m3/sec (equivalent to the river warning level at 5 meters) with 53% probability of exceeding the Medium Level Alert in two days. Rainfall stations upstream of the West Rapti catchment recorded heavy rainfall on 26 July, and localized forecasts from the probabilistic model at 8 am suggested that the water level would cross a pre-determined warning level in the next 3 hours. The Flood Forecasting Section at DHM issued a flood advisory, and disseminated SMS flood alerts to more than 13,000 at-risk people residing along the floodplains. Water levels crossed the danger threshold (5.4 meters) at 11 am, peaking at 8.15 meters at 10 pm. Extension of the warning lead time from probabilistic forecasts was significant in minimising the risk to lives and livelihoods as communities gained extra time to prepare, evacuate and respond. Likewise, longer timescale forecasts from GLoFAS could be potentially linked with no-regret early actions leading to improved preparedness and emergency response. These forecasting tools have contributed to enhance the effectiveness and efficiency of existing community based systems, increasing the lead time for response. Nevertheless, extensive work is required on appropriate ways to interpret and disseminate probabilistic forecasts having longer (2-14 days) and shorter (3-5 hours) time horizon for operational deployment as there are numerous uncertainties associated with predictions.

A Sensor Driven Probabilistic Method for Enabling Hyper Resolution Flood Simulations

NASA Astrophysics Data System (ADS)

Fries, K. J.; Salas, F.; Kerkez, B.

2016-12-01

A reduction in the cost of sensors and wireless communications is now enabling researchers and local governments to make flow, stage and rain measurements at locations that are not covered by existing USGS or state networks. We ask the question: how should these new sources of densified, street-level sensor measurements be used to make improved forecasts using the National Water Model (NWM)? Assimilating these data "into" the NWM can be challenging due to computational complexity, as well as heterogeneity of sensor and other input data. Instead, we introduce a machine learning and statistical framework that layers these data "on top" of the NWM outputs to improve high-resolution hydrologic and hydraulic forecasting. By generalizing our approach into a post-processing framework, a rapidly repeatable blueprint is generated for for decision makers who want to improve local forecasts by coupling sensor data with the NWM. We present preliminary results based on case studies in highly instrumented watersheds in the US. Through the use of statistical learning tools and hydrologic routing schemes, we demonstrate the ability of our approach to improve forecasts while simultaneously characterizing bias and uncertainty in the NWM.

How reliable are satellite precipitation estimates for driving hydrological models: a verification study over the Mediterranean area

NASA Astrophysics Data System (ADS)

Camici, Stefania; Ciabatta, Luca; Massari, Christian; Brocca, Luca

2017-04-01

Floods are one of the most common and dangerous natural hazards, causing every year thousands of casualties and damages worldwide. The main tool for assessing flood risk and reducing damages is represented by hydrologic early warning systems that allow to forecast flood events by using real time data obtained through ground monitoring networks (e.g., raingauges and radars). However, the use of such data, mainly rainfall, presents some issues firstly related to the network density and to the limited spatial representativeness of local measurements. A way to overcome these issues may be the use of satellite-based rainfall products (SRPs) that nowadays are available on a global scale at ever increasing spatial/temporal resolution and accuracy. However, despite the large availability and increased accuracy of SRPs (e.g., the Tropical Rainfall Measurement Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA); the Satellite Application Facility on Support to Operational Hydrology and Water Management (H-SAF); and the recent Global Precipitation Measurement (GPM) mission), remotely sensed rainfall data are scarcely used in hydrological modeling and only a small number of studies have been carried out to outline some guidelines for using satellite data as input for hydrological modelling. Reasons may be related to: 1) the large bias characterizing satellite precipitation estimates, which is dependent on rainfall intensity and season, 2) the spatial/temporal resolution, 3) the timeliness, which is often insufficient for operational purposes, and 4) a general (often not justified) skepticism of the hydrological community in the use of satellite products for land applications. The objective of this study is to explore the feasibility of using SRPs in a lumped hydrologic model (MISDc, "Modello Idrologico Semi-Distribuito in continuo", Masseroni et al., 2017) over 10 basins in the Mediterranean area with different sizes and physiographic characteristics. Specifically, TMPA 3B42-RT, CMORPH, PERSIANN and a new soil moisture-derived rainfall datasets obtained through the application of SM2RAIN algorithm (Brocca et al., 2014) to ASCAT (Advanced SCATterometer) soil moisture product are used in the analysis. The performances obtained with SRPs are compared with those obtained by using ground data during the 6-year period from 2010 to 2015. In addition, the performance obtained by an integration of the above mentioned SRPs is also investigated to see whether merged rainfall observations are able to improve flood simulation. Preliminary analysis were also carried out by using the IMERG early run product of GPM mission. The results highlight that SRPs should be used with caution for rainfall-runoff modelling in the Mediterranean region. Bias correction and model recalibration are necessary steps, even though not always sufficient to achieve satisfactory performances. Indeed, some of the products provide unreliable outcomes, mainly in smaller basins (<500 km2) that, however, represent the main target for flood modelling in the Mediterranean area. The better performances are obtained by integrating different SRPs, and particularly by merging TMPA 3B42-RT and SM2RAIN-ASCAT products. The promising results of the integrated product are expected to increase the confidence on the use of SRPs in hydrological modeling, even in challenging areas as the Mediterranean. REFERENCES Brocca, L., Ciabatta, L., Massari, C., Moramarco, T., Hahn, S., Hasenauer, S., Kidd, R., Dorigo, W., Wagner, W., Levizzani, V. (2014). Soil as a natural rain gauge: estimating global rainfall from satellite soil moisture data. Journal of Geophysical Research, 119(9), 5128-5141, doi:10.1002/2014JD021489. Masseroni, D., Cislaghi, A., Camici, S., Massari, C., Brocca, L. (2017). A reliable rainfall-runoff model for flood forecasting: review and application to a semiurbanized watershed at high flood risk in Italy. Hydrology Research, in press, doi:10.2166/nh.2016.037.

Global Precipitation Measurement (GPM) Mission Applications: Activities, Challenges, and Vision

NASA Technical Reports Server (NTRS)

Kirschbaum, Dalia; Hou, Arthur

2012-01-01

Global Precipitation Measurement (GPM) is an international satellite mission to provide nextgeneration observations of rain and snow worldwide every three hours. NASA and the Japan Aerospace Exploration Agency (JAXA) will launch a "Core" satellite carrying advanced instruments that will set a new standard for precipitation measurements from space. The data they provide will be used to unify precipitation measurements made by an international network of partner satellites to quantify when, where, and how much it rains or snows around the world. The GPM mission will help advance our understanding of Earth's water and energy cycles, improve the forecasting of extreme events that cause natural disasters, and extend current capabilities of using satellite precipitation information to directly benefit society. Building upon the successful legacy of the Tropical Rainfall Measuring Mission (TRMM), GPM's next-generation global precipitation data will lead to scientific advances and societal benefits within a range of hydrologic fields including natural hazards, ecology, public health and water resources. This talk will highlight some examples from TRMM's IS-year history within these applications areas as well as discuss some existing challenges and present a look forward for GPM's contribution to applications in hydrology.

Application of seasonal forecasting for the drought forecasting in Catalonia (Spain)

NASA Astrophysics Data System (ADS)

Llasat, Maria-Carmen; Zaragoza, Albert; Aznar, Blanca; Cabot, Jordi

2010-05-01

Low flows and droughts are a hydro-climatic feature in Spain (Alvarez et al, 2008). The construction of dams as water reservoirs has been a usual tool to manage the water resources for agriculture and livestock, industries and human needs (MIMAM, 2000, 2007). The last drought that has affected Spain has last four years in Catalonia, from 2004 to the spring of 2008, and it has been particularly hard as a consequence of the precipitation deficit in the upper part of the rivers that nourish the main dams. This problem increases when the water scarcity affects very populated areas, like big cities. The Barcelona city, with more than 3.000.000 people concentrated in the downtown and surrounding areas is a clear example. One of the objectives of the SOSTAQUA project is to improve the water resources management in real time, in order to improve the water supply in the cities in the framework of sustainable development. The work presented here deals with the application of seasonal forecasting to improve the water management in Catalonia, particularly in drought conditions. A seasonal prediction index has been created as a linear combination of climatic data and the ECM4 prediction that has been validated too. This information has implemented into a hydrological model and it has been applied to the last drought considering the real water demands of population, as well as to the water storage evolution in the last months. It has been found a considerable advance in the forecasting of water volume into reservoirs. The advantage of this methodology is that it only requires seasonal forecasting free through internet. Due to the fact that the principal rivers that supply water to Barcelona, birth on the Pyrenees and Pre-Pyrenees region, the analysis and precipitation forecasting is focused on this region (Zaragoza, 2008).

Seasonal Drought Prediction: Advances, Challenges, and Future Prospects

NASA Astrophysics Data System (ADS)

Hao, Zengchao; Singh, Vijay P.; Xia, Youlong

2018-03-01

Drought prediction is of critical importance to early warning for drought managements. This review provides a synthesis of drought prediction based on statistical, dynamical, and hybrid methods. Statistical drought prediction is achieved by modeling the relationship between drought indices of interest and a suite of potential predictors, including large-scale climate indices, local climate variables, and land initial conditions. Dynamical meteorological drought prediction relies on seasonal climate forecast from general circulation models (GCMs), which can be employed to drive hydrological models for agricultural and hydrological drought prediction with the predictability determined by both climate forcings and initial conditions. Challenges still exist in drought prediction at long lead time and under a changing environment resulting from natural and anthropogenic factors. Future research prospects to improve drought prediction include, but are not limited to, high-quality data assimilation, improved model development with key processes related to drought occurrence, optimal ensemble forecast to select or weight ensembles, and hybrid drought prediction to merge statistical and dynamical forecasts.

Avenues for crowd science in Hydrology.

NASA Astrophysics Data System (ADS)

Koch, Julian; Stisen, Simon

2016-04-01

Crowd science describes research that is conducted with the participation of the general public (the crowd) and gives the opportunity to involve the crowd in research design, data collection and analysis. In various fields, scientists have already drawn on underused human resources to advance research at low cost, with high transparency and large acceptance of the public due to the bottom up structure and the participatory process. Within the hydrological sciences, crowd research has quite recently become more established in the form of crowd observatories to generate hydrological data on water quality, precipitation or river flow. These innovative observatories complement more traditional ways of monitoring hydrological data and strengthen a community-based environmental decision making. However, the full potential of crowd science lies in internet based participation of the crowd and it is not yet fully exploited in the field of Hydrology. New avenues that are not primarily based on the outsourcing of labor, but instead capitalize the full potential of human capabilities have to emerge. In multiple realms of solving complex problems, like image detection, optimization tasks, narrowing of possible solutions, humans still remain more effective than computer algorithms. The most successful online crowd science projects Foldit and Galaxy Zoo have proven that the collective of tens of thousands users could clearly outperform traditional computer based science approaches. Our study takes advantage of the well trained human perception to conduct a spatial sensitivity analysis of land-surface variables of a distributed hydrological model to identify the most sensitive spatial inputs. True spatial performance metrics, that quantitatively compare patterns, are not trivial to choose and their applicability is often not universal. On the other hand humans can quickly integrate spatial information at various scales and are therefore a trusted competence. We selected zooniverse, the most popular crowd science platform where over a million registered users contribute to various research projects, to build a survey of the human perception. The survey will be shown during the interactive discussion, but moreover for building future avenues of crowd science in Hydrology the following questions should be discussed: (1) What hydrological problems are suitable for an internet based crowd science application? (2) How to abstract the complex problem to a medium that appeals to the crowd? (3) How to secure good science with reliable results? (4) Can the crowd replace existing and established computer based applications like parameter optimization or forecasting at all?

Characterizing Macro Scale Patterns Of Uncertainty For Improved Operational Flood Forecasting Over The Conterminous United States

NASA Astrophysics Data System (ADS)

Vergara, H. J.; Kirstetter, P.; Gourley, J. J.; Flamig, Z.; Hong, Y.

2015-12-01

The macro scale patterns of simulated streamflow errors are studied in order to characterize uncertainty in a hydrologic modeling system forced with the Multi-Radar/Multi-Sensor (MRMS; http://mrms.ou.edu) quantitative precipitation estimates for flood forecasting over the Conterminous United States (CONUS). The hydrologic model is centerpiece of the Flooded Locations And Simulated Hydrograph (FLASH; http://flash.ou.edu) real-time system. The hydrologic model is implemented at 1-km/5-min resolution to generate estimates of streamflow. Data from the CONUS-wide stream gauge network of the United States' Geological Survey (USGS) were used as a reference to evaluate the discrepancies with the hydrological model predictions. Streamflow errors were studied at the event scale with particular focus on the peak flow magnitude and timing. A total of 2,680 catchments over CONUS and 75,496 events from a 10-year period are used for the simulation diagnostic analysis. Associations between streamflow errors and geophysical factors were explored and modeled. It is found that hydro-climatic factors and radar coverage could explain significant underestimation of peak flow in regions of complex terrain. Furthermore, the statistical modeling of peak flow errors shows that other geophysical factors such as basin geomorphometry, pedology, and land cover/use could also provide explanatory information. Results from this research demonstrate the utility of uncertainty characterization in providing guidance to improve model adequacy, parameter estimates, and input quality control. Likewise, the characterization of uncertainty enables probabilistic flood forecasting that can be extended to ungauged locations.

Attaining insight into interactions between hydrologic model parameters and geophysical attributes for national-scale model parameter estimation

NASA Astrophysics Data System (ADS)

Mizukami, N.; Clark, M. P.; Newman, A. J.; Wood, A.; Gutmann, E. D.

2017-12-01

Estimating spatially distributed model parameters is a grand challenge for large domain hydrologic modeling, especially in the context of hydrologic model applications such as streamflow forecasting. Multi-scale Parameter Regionalization (MPR) is a promising technique that accounts for the effects of fine-scale geophysical attributes (e.g., soil texture, land cover, topography, climate) on model parameters and nonlinear scaling effects on model parameters. MPR computes model parameters with transfer functions (TFs) that relate geophysical attributes to model parameters at the native input data resolution and then scales them using scaling functions to the spatial resolution of the model implementation. One of the biggest challenges in the use of MPR is identification of TFs for each model parameter: both functional forms and geophysical predictors. TFs used to estimate the parameters of hydrologic models typically rely on previous studies or were derived in an ad-hoc, heuristic manner, potentially not utilizing maximum information content contained in the geophysical attributes for optimal parameter identification. Thus, it is necessary to first uncover relationships among geophysical attributes, model parameters, and hydrologic processes (i.e., hydrologic signatures) to obtain insight into which and to what extent geophysical attributes are related to model parameters. We perform multivariate statistical analysis on a large-sample catchment data set including various geophysical attributes as well as constrained VIC model parameters at 671 unimpaired basins over the CONUS. We first calibrate VIC model at each catchment to obtain constrained parameter sets. Additionally, parameter sets sampled during the calibration process are used for sensitivity analysis using various hydrologic signatures as objectives to understand the relationships among geophysical attributes, parameters, and hydrologic processes.

The electrical self-potential method is a non-intrusive snow-hydrological sensor

NASA Astrophysics Data System (ADS)

Thompson, S. S.; Kulessa, B.; Essery, R. L. H.; Lüthi, M. P.

2015-08-01

Our ability to measure, quantify and assimilate hydrological properties and processes of snow in operational models is disproportionally poor compared to the significance of seasonal snowmelt as a global water resource and major risk factor in flood and avalanche forecasting. Encouraged by recent theoretical, modelling and laboratory work, we show here that the diurnal evolution of aerially-distributed self-potential magnitudes closely track those of bulk meltwater fluxes in melting in-situ snowpacks at Rhone and Jungfraujoch glaciers, Switzerland. Numerical modelling infers temporally-evolving liquid water contents in the snowpacks on successive days in close agreement with snow-pit measurements. Muting previous concerns, the governing physical and chemical properties of snow and meltwater became temporally invariant for modelling purposes. Because measurement procedure is straightforward and readily automated for continuous monitoring over significant spatial scales, we conclude that the self-potential geophysical method is a highly-promising non-intrusive snow-hydrological sensor for measurement practice, modelling and operational snow forecasting.

Statistical analysis and ANN modeling for predicting hydrological extremes under climate change scenarios: the example of a small Mediterranean agro-watershed.

PubMed

Kourgialas, Nektarios N; Dokou, Zoi; Karatzas, George P

2015-05-01

The purpose of this study was to create a modeling management tool for the simulation of extreme flow events under current and future climatic conditions. This tool is a combination of different components and can be applied in complex hydrogeological river basins, where frequent flood and drought phenomena occur. The first component is the statistical analysis of the available hydro-meteorological data. Specifically, principal components analysis was performed in order to quantify the importance of the hydro-meteorological parameters that affect the generation of extreme events. The second component is a prediction-forecasting artificial neural network (ANN) model that simulates, accurately and efficiently, river flow on an hourly basis. This model is based on a methodology that attempts to resolve a very difficult problem related to the accurate estimation of extreme flows. For this purpose, the available measurements (5 years of hourly data) were divided in two subsets: one for the dry and one for the wet periods of the hydrological year. This way, two ANNs were created, trained, tested and validated for a complex Mediterranean river basin in Crete, Greece. As part of the second management component a statistical downscaling tool was used for the creation of meteorological data according to the higher and lower emission climate change scenarios A2 and B1. These data are used as input in the ANN for the forecasting of river flow for the next two decades. The final component is the application of a meteorological index on the measured and forecasted precipitation and flow data, in order to assess the severity and duration of extreme events. Copyright © 2015 Elsevier Ltd. All rights reserved.

Application of SNODAS and hydrologic models to enhance entropy-based snow monitoring network design

NASA Astrophysics Data System (ADS)

Keum, Jongho; Coulibaly, Paulin; Razavi, Tara; Tapsoba, Dominique; Gobena, Adam; Weber, Frank; Pietroniro, Alain

2018-06-01

Snow has a unique characteristic in the water cycle, that is, snow falls during the entire winter season, but the discharge from snowmelt is typically delayed until the melting period and occurs in a relatively short period. Therefore, reliable observations from an optimal snow monitoring network are necessary for an efficient management of snowmelt water for flood prevention and hydropower generation. The Dual Entropy and Multiobjective Optimization is applied to design snow monitoring networks in La Grande River Basin in Québec and Columbia River Basin in British Columbia. While the networks are optimized to have the maximum amount of information with minimum redundancy based on entropy concepts, this study extends the traditional entropy applications to the hydrometric network design by introducing several improvements. First, several data quantization cases and their effects on the snow network design problems were explored. Second, the applicability the Snow Data Assimilation System (SNODAS) products as synthetic datasets of potential stations was demonstrated in the design of the snow monitoring network of the Columbia River Basin. Third, beyond finding the Pareto-optimal networks from the entropy with multi-objective optimization, the networks obtained for La Grande River Basin were further evaluated by applying three hydrologic models. The calibrated hydrologic models simulated discharges using the updated snow water equivalent data from the Pareto-optimal networks. Then, the model performances for high flows were compared to determine the best optimal network for enhanced spring runoff forecasting.

The New York City Operations Support Tool: Supporting Water Supply Operations for Millions in an Era of Changing Patterns in Hydrological Extreme Events

NASA Astrophysics Data System (ADS)

Matonse, A. H.; Porter, J. H.; Frei, A.

2015-12-01

Providing an average 1.1 billion gallons (~ 4.2 x 106 cubic meters) of drinking water per day to approximately nine million people in New York City (NYC) and four upstate counties, the NYC water supply is among the world's largest unfiltered systems. In addition to providing a reliable water supply in terms of water quantity and quality, the city has to fulfill other flow objectives to serve downstream communities. At times, such as during extreme hydrological events, water quality issues may restrict water usage for parts of the system. To support a risk-based water supply decision making process NYC has developed the Operations Support Tool (OST). OST combines a water supply systems model with reservoir water quality models, near real time data ingestion, data base management and an ensemble hydrological forecast. A number of reports have addressed the frequency and intensities of extreme hydrological events across the continental US. In the northeastern US studies have indicated an increase in the frequency of extremely large precipitation and streamflow events during the most recent decades. During this presentation we describe OST and, using case studies we demonstrate how this tool has been useful to support operational decisions. We also want to motivate a discussion about how undergoing changes in patterns of hydrological extreme events elevate the challenge faced by water supply managers and the role of the scientific community to integrate nonstationarity approaches in hydrologic forecast and modeling.

Hydrological time series modeling: A comparison between adaptive neuro-fuzzy, neural network and autoregressive techniques

NASA Astrophysics Data System (ADS)

Lohani, A. K.; Kumar, Rakesh; Singh, R. D.

2012-06-01

SummaryTime series modeling is necessary for the planning and management of reservoirs. More recently, the soft computing techniques have been used in hydrological modeling and forecasting. In this study, the potential of artificial neural networks and neuro-fuzzy system in monthly reservoir inflow forecasting are examined by developing and comparing monthly reservoir inflow prediction models, based on autoregressive (AR), artificial neural networks (ANNs) and adaptive neural-based fuzzy inference system (ANFIS). To take care the effect of monthly periodicity in the flow data, cyclic terms are also included in the ANN and ANFIS models. Working with time series flow data of the Sutlej River at Bhakra Dam, India, several ANN and adaptive neuro-fuzzy models are trained with different input vectors. To evaluate the performance of the selected ANN and adaptive neural fuzzy inference system (ANFIS) models, comparison is made with the autoregressive (AR) models. The ANFIS model trained with the input data vector including previous inflows and cyclic terms of monthly periodicity has shown a significant improvement in the forecast accuracy in comparison with the ANFIS models trained with the input vectors considering only previous inflows. In all cases ANFIS gives more accurate forecast than the AR and ANN models. The proposed ANFIS model coupled with the cyclic terms is shown to provide better representation of the monthly inflow forecasting for planning and operation of reservoir.

Soil Moisture Drought Monitoring and Forecasting Using Satellite and Climate Model Data over Southwestern China

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, Xuejun; Tang, Qiuhong; Liu, Xingcai

Real-time monitoring and predicting drought development with several months in advance is of critical importance for drought risk adaptation and mitigation. In this paper, we present a drought monitoring and seasonal forecasting framework based on the Variable Infiltration Capacity (VIC) hydrologic model over Southwest China (SW). The satellite precipitation data are used to force VIC model for near real-time estimate of land surface hydrologic conditions. As initialized with satellite-aided monitoring, the climate model-based forecast (CFSv2_VIC) and ensemble streamflow prediction (ESP)-based forecast (ESP_VIC) are both performed and evaluated through their ability in reproducing the evolution of the 2009/2010 severe drought overmore » SW. The results show that the satellite-aided monitoring is able to provide reasonable estimate of forecast initial conditions (ICs) in a real-time manner. Both of CFSv2_VIC and ESP_VIC exhibit comparable performance against the observation-based estimates for the first month, whereas the predictive skill largely drops beyond 1-month. Compared to ESP_VIC, CFSv2_VIC shows better performance as indicated by the smaller ensemble range. This study highlights the value of this operational framework in generating near real-time ICs and giving a reliable prediction with 1-month ahead, which has great implications for drought risk assessment, preparation and relief.« less

Application of commercial microwave link (CML) derived precipitation data in a hydrology model

NASA Astrophysics Data System (ADS)

Smiatek, Gerhard; Chwala, Christian; Kunstmann, Harald

2017-04-01

In 2016 very local and extremely intensive convective events caused severe flooding in the Alpine space. Despite the large number of monitoring stations most of the rainfall events were not captured accurately by the existing rain gauge network. As the number of traditional precipitation monitoring sites is in general decreasing, novel rain monitoring techniques are gaining attention. One of the new techniques is the rainfall estimation from signal attenuation in commercial microwave link (CML) networks operated by cellular phone companies. In this contribution, we use CML-derived rainfall information to improve the streamflow forecast of the distributed hydrology model WaSiM-ETH in hindcasting and nowcasting modes. Our model domain covers the complex terrain of the Ammer catchment located in the German Alps. The hydrology model is operated with a spatial resolution of 100m and with an hourly time step. We present two alternative methods of rainfall estimation from CMLs and compare the results to data from rain gauges and a weather radar. Finally, we show the impact of the rainfall data sets on the hydrology model initialization and in discharge simulations of the Ammer River for selected episodes in 2015 and 2016. We found that the densification of the observation network by the CML observations leads to a significant improvement of the model performance.

Real-Time Application of Multi-Satellite Precipitation Analysis for Floods and Landslides

NASA Technical Reports Server (NTRS)

Adler, Robert; Hong, Yang; Huffman, George

2007-01-01

Satellite data acquired and processed in real time now have the potential to provide the spacetime information on rainfall needed to monitor flood and landslide events around the world. This can be achieved by integrating the satellite-derived forcing data with hydrological models and landslide algorithms. Progress in using the TRMM Multi-satellite Precipitation Analysis (TMPA) as input to flood and landslide forecasts is outlined, with a focus on understanding limitations of the rainfall data and impacts of those limitations on flood/landslide analyses. Case studies of both successes and failures will be shown, as well as comparison with ground comparison data sets-- both in terms of rainfall and in terms of flood/landslide events. In addition to potential uses in real-time, the nearly ten years of TMPA data allow retrospective running of the models to examine variations in extreme events. The flood determination algorithm consists of four major components: 1) multi-satellite precipitation estimation; 2) characterization of land surface including digital elevation from NASA SRTM (Shuttle Radar Terrain Mission), topography-derived hydrologic parameters such as flow direction, flow accumulation, basin, and river network etc.; 3) a hydrological model to infiltrate rainfall and route overland runoff; and 4) an implementation interface to relay the input data to the models and display the flood inundation results to potential users and decision-makers, In terms of landslides, the satellite rainfall information is combined with a global landslide susceptibility map, derived from a combination of global surface characteristics (digital elevation topography, slope, soil types, soil texture, and land cover classification etc.) using a weighted linear combination approach. In those areas identified as "susceptible" (based on the surface characteristics), landslides are forecast where and when a rainfall intensity/duration threshold is exceeded. Results are described indicating general agreement with landslide occurrences.

Forecasting monthly inflow discharge of the Iffezheim reservoir using data-driven models

NASA Astrophysics Data System (ADS)

Zhang, Qing; Aljoumani, Basem; Hillebrand, Gudrun; Hoffmann, Thomas; Hinkelmann, Reinhard

2017-04-01

River stream flow is an essential element in hydrology study fields, especially for reservoir management, since it defines input into reservoirs. Forecasting this stream flow plays an important role in short or long-term planning and management in the reservoir, e.g. optimized reservoir and hydroelectric operation or agricultural irrigation. Highly accurate flow forecasting can significantly reduce economic losses and is always pursued by reservoir operators. Therefore, hydrologic time series forecasting has received tremendous attention of researchers. Many models have been proposed to improve the hydrological forecasting. Due to the fact that most natural phenomena occurring in environmental systems appear to behave in random or probabilistic ways, different cases may need a different methods to forecast the inflow and even a unique treatment to improve the forecast accuracy. The purpose of this study is to determine an appropriate model for forecasting monthly inflow to the Iffezheim reservoir in Germany, which is the last of the barrages in the Upper Rhine. Monthly time series of discharges, measured from 1946 to 2001 at the Plittersdorf station, which is located 6 km downstream of the Iffezheim reservoir, were applied. The accuracies of the used stochastic models - Fiering model and Auto-Regressive Integrated Moving Average models (ARIMA) are compared with Artificial Intelligence (AI) models - single Artificial Neural Network (ANN) and Wavelet ANN models (WANN). The Fiering model is a linear stochastic model and used for generating synthetic monthly data. The basic idea in modeling time series using ARIMA is to identify a simple model with as few model parameters as possible in order to provide a good statistical fit to the data. To identify and fit the ARIMA models, four phase approaches were used: identification, parameter estimation, diagnostic checking, and forecasting. An automatic selection criterion, such as the Akaike information criterion, is utilized to enhance this flexible approach to set up the model. As distinct from both stochastic models, the ANN and its related conjunction methods Wavelet-ANN (WANN) models are effective to handle non-linear systems and have been developed with antecedent flows as inputs to forecast up to 12-months lead-time for the Iffezheim reservoir. In the ANN and WANN models, the Feed Forward Back Propagation method (FFBP) is applied. The sigmoid activity and linear functions were used with several different neurons for the hidden layers and for the output layer, respectively. To compare the accuracy of the different models and identify the most suitable model for reliable forecasting, four quantitative standard statistical performance evaluation measures, the root mean square error (RMSE), the mean bias error (MAE) and the determination correlation coefficient (DC), are employed. The results reveal that the ARIMA (2, 1, 2) performs better than Fiering, ANN and WANN models. Further, the WANN model is found to be slightly better than the ANN model for forecasting monthly inflow of the Iffezheim reservoir. As a result, by using the ARIMA model, the predicted and observed values agree reasonably well.

Predictability of monthly temperature and precipitation using automatic time series forecasting methods

NASA Astrophysics Data System (ADS)

Papacharalampous, Georgia; Tyralis, Hristos; Koutsoyiannis, Demetris

2018-02-01

We investigate the predictability of monthly temperature and precipitation by applying automatic univariate time series forecasting methods to a sample of 985 40-year-long monthly temperature and 1552 40-year-long monthly precipitation time series. The methods include a naïve one based on the monthly values of the last year, as well as the random walk (with drift), AutoRegressive Fractionally Integrated Moving Average (ARFIMA), exponential smoothing state-space model with Box-Cox transformation, ARMA errors, Trend and Seasonal components (BATS), simple exponential smoothing, Theta and Prophet methods. Prophet is a recently introduced model inspired by the nature of time series forecasted at Facebook and has not been applied to hydrometeorological time series before, while the use of random walk, BATS, simple exponential smoothing and Theta is rare in hydrology. The methods are tested in performing multi-step ahead forecasts for the last 48 months of the data. We further investigate how different choices of handling the seasonality and non-normality affect the performance of the models. The results indicate that: (a) all the examined methods apart from the naïve and random walk ones are accurate enough to be used in long-term applications; (b) monthly temperature and precipitation can be forecasted to a level of accuracy which can barely be improved using other methods; (c) the externally applied classical seasonal decomposition results mostly in better forecasts compared to the automatic seasonal decomposition used by the BATS and Prophet methods; and (d) Prophet is competitive, especially when it is combined with externally applied classical seasonal decomposition.

Open Day at SHMI.

NASA Astrophysics Data System (ADS)

Jarosova, M.

2010-09-01

During the World Meteorological Day there has been preparing "Open Day" at Slovak Hydrometeorological Institute. This event has more than 10 years traditions. "Open Day" is one of a lot of possibilities to give more information about meteorology, climatology, hydrology too to public. This "Day" is executed in whole Slovakia. People can visit the laboratories, the forecasting room....and meteo and clima measuring points. The most popular is visiting forecasting room. Visitors are interested in e.g. climatologic change in Slovakia territory, preparing weather forecasting, dangerous phenomena.... Every year we have more than 500 visitors.

The Challenges of Developing a Framework for Global Water Cycle Monitoring and Prediction (Alfred Wegener Medal Lecture)

NASA Astrophysics Data System (ADS)

Wood, Eric F.

2014-05-01

The Global Earth Observation System of Systems (GEOSS) Water Strategy ("From Observations to Decisions") recognizes that "water is essential for ensuring food and energy security, for facilitating poverty reduction and health security, and for the maintenance of ecosystems and biodiversity", and that water cycle data and observations are critical for improved water management and water security - especially in less developed regions. The GEOSS Water Strategy has articulated a number of goals for improved water management, including flood and drought preparedness, that include: (i) facilitating the use of Earth Observations for water cycle observations; (ii) facilitating the acquisition, processing, and distribution of data products needed for effective management; (iii) providing expertise, information systems, and datasets to the global, regional, and national water communities. There are several challenges that must be met to advance our capability to provide near real-time water cycle monitoring, early warning of hydrological hazards (floods and droughts) and risk assessment under climate change, regionally and globally. Current approaches to monitoring and predicting hydrological hazards are limited in many parts of the world, and especially in developing countries where national capacity is limited and monitoring networks are inadequate. This presentation describes the developments at Princeton University towards a seamless monitoring and prediction framework at all time scales that allows for consistent assessment of water variability from historic to current conditions, and from seasonal and decadal predictions to climate change projections. At the center of the framework is an experimental, global water cycle monitoring and seasonal forecast system that has evolved out of regional and continental systems for the US and Africa. The system is based on land surface hydrological modeling that is driven by satellite remote sensing precipitation to predict current hydrological conditions, flood potential and the state of drought. Seasonal climate model forecasts are downscaled and bias-corrected to drive the land surface model to provide hydrological forecasts and drought products out 6-9 months. The system relies on historic reconstructions of water variability over the 20th century, which forms the background climatology to which current conditions can be assessed. Future changes in water availability and drought risk are quantified based on bias-corrected and downscaled climate model projections that are used to drive the land surface models. For regions with lack of on-the-ground data we are field-testing low-cost environmental sensors and along with new satellite products for terrestrial hydrology and vegetation, integrating these into the system for improved monitoring and prediction. At every step there are scientific challenges whose solutions are only partially being solved. In addition there are challenges in delivering such systems as "climate services", especially to societies with low technical capacity such as rural agriculturalists in sub-Saharan Africa, but whose needs for such information are great. We provide an overview of the system and some examples of real-world applications to flood and drought events, with a focus on Africa.

The flood event of 10-12 November 2013 on the Tiber River basin (central Italy): real-time flood forecasting with uncertainty supporting risk management and decision-making

NASA Astrophysics Data System (ADS)

Berni, Nicola; Brocca, Luca; Barbetta, Silvia; Pandolfo, Claudia; Stelluti, Marco; Moramarco, Tommaso

2014-05-01

The Italian national hydro-meteorological early warning system is composed by 21 regional offices (Functional Centres, CF). Umbria Region (central Italy) CF provides early warning for floods and landslides, real-time monitoring and decision support systems (DSS) for the Civil Defence Authorities when significant events occur. The alert system is based on hydrometric and rainfall thresholds with detailed procedures for the management of critical events in which different roles of authorities and institutions involved are defined. The real-time flood forecasting system is based also on different hydrological and hydraulic forecasting models. Among these, the MISDc rainfall-runoff model ("Modello Idrologico SemiDistribuito in continuo"; Brocca et al., 2011) and the flood routing model named STAFOM-RCM (STAge Forecasting Model-Rating Curve Model; Barbetta et al., 2014) are continuously operative in real-time providing discharge and stage forecasts, respectively, with lead-times up to 24 hours (when quantitative precipitation forecasts are used) in several gauged river sections in the Upper-Middle Tiber River basin. Models results are published in real-time in the open source CF web platform: www.cfumbria.it. MISDc provides discharge and soil moisture forecasts for different sub-basins while STAFOM-RCM provides stage forecasts at hydrometric sections. Moreover, through STAFOM-RCM the uncertainty of the forecast stage hydrograph is provided in terms of 95% Confidence Interval (CI) assessed by analyzing the statistical properties of model output in terms of lateral. In the period 10th-12th November 2013, a severe flood event occurred in Umbria mainly affecting the north-eastern area and causing significant economic damages, but fortunately no casualties. The territory was interested by intense and persistent rainfall; the hydro-meteorological monitoring network recorded locally rainfall depth over 400 mm in 72 hours. In the most affected area, the recorded rainfall depths correspond approximately to a return period of 200 years. Most rivers in Umbria have been involved, exceeding hydrometric thresholds and causing flooding (e.g. Chiascio river). The flood event was continuously monitored at the Umbria Region CF and the possible evolution predicted and assessed on the basis of the model forecasts. The predictions provided by MISDc and STAFOM-RCM were found useful to support real-time decision-making addressed to flood risk management. Moreover, the quantification of the uncertainty affecting the deterministic forecast stages was found consistent with the level of confidence selected and had practical utility corroborating the need of coupling deterministic forecast and 'uncertainty' when the model output is used to support decisions about flood management. REFERENCES Barbetta, S., Moramarco, T., Brocca, L., Franchini, M., Melone, F. (2014). Confidence interval of real-time forecast stages provided by the STAFOM-RCM model: the case study of the Tiber River (Italy). Hydrological Processes, 28(3), 729-743. Brocca, L., Melone, F., Moramarco, T. (2011). Distributed rainfall-runoff modelling for flood frequency estimation and flood forecasting. Hydrological Processes, 25 (18), 2801-2813

Conditional Monthly Weather Resampling Procedure for Operational Seasonal Water Resources Forecasting

NASA Astrophysics Data System (ADS)

Beckers, J.; Weerts, A.; Tijdeman, E.; Welles, E.; McManamon, A.

2013-12-01

To provide reliable and accurate seasonal streamflow forecasts for water resources management several operational hydrologic agencies and hydropower companies around the world use the Extended Streamflow Prediction (ESP) procedure. The ESP in its original implementation does not accommodate for any additional information that the forecaster may have about expected deviations from climatology in the near future. Several attempts have been conducted to improve the skill of the ESP forecast, especially for areas which are affected by teleconnetions (e,g. ENSO, PDO) via selection (Hamlet and Lettenmaier, 1999) or weighting schemes (Werner et al., 2004; Wood and Lettenmaier, 2006; Najafi et al., 2012). A disadvantage of such schemes is that they lead to a reduction of the signal to noise ratio of the probabilistic forecast. To overcome this, we propose a resampling method conditional on climate indices to generate meteorological time series to be used in the ESP. The method can be used to generate a large number of meteorological ensemble members in order to improve the statistical properties of the ensemble. The effectiveness of the method was demonstrated in a real-time operational hydrologic seasonal forecasts system for the Columbia River basin operated by the Bonneville Power Administration. The forecast skill of the k-nn resampler was tested against the original ESP for three basins at the long-range seasonal time scale. The BSS and CRPSS were used to compare the results to those of the original ESP method. Positive forecast skill scores were found for the resampler method conditioned on different indices for the prediction of spring peak flows in the Dworshak and Hungry Horse basin. For the Libby Dam basin however, no improvement of skill was found. The proposed resampling method is a promising practical approach that can add skill to ESP forecasts at the seasonal time scale. Further improvement is possible by fine tuning the method and selecting the most informative climate indices for the region of interest.

Pathways to designing and running an operational flood forecasting system: an adventure game!

NASA Astrophysics Data System (ADS)

Arnal, Louise; Pappenberger, Florian; Ramos, Maria-Helena; Cloke, Hannah; Crochemore, Louise; Giuliani, Matteo; Aalbers, Emma

2017-04-01

In the design and building of an operational flood forecasting system, a large number of decisions have to be taken. These include technical decisions related to the choice of the meteorological forecasts to be used as input to the hydrological model, the choice of the hydrological model itself (its structure and parameters), the selection of a data assimilation procedure to run in real-time, the use (or not) of a post-processor, and the computing environment to run the models and display the outputs. Additionally, a number of trans-disciplinary decisions are also involved in the process, such as the way the needs of the users will be considered in the modelling setup and how the forecasts (and their quality) will be efficiently communicated to ensure usefulness and build confidence in the forecasting system. We propose to reflect on the numerous, alternative pathways to designing and running an operational flood forecasting system through an adventure game. In this game, the player is the protagonist of an interactive story driven by challenges, exploration and problem-solving. For this presentation, you will have a chance to play this game, acting as the leader of a forecasting team at an operational centre. Your role is to manage the actions of your team and make sequential decisions that impact the design and running of the system in preparation to and during a flood event, and that deal with the consequences of the forecasts issued. Your actions are evaluated by how much they cost you in time, money and credibility. Your aim is to take decisions that will ultimately lead to a good balance between time and money spent, while keeping your credibility high over the whole process. This game was designed to highlight the complexities behind decision-making in an operational forecasting and emergency response context, in terms of the variety of pathways that can be selected as well as the timescale, cost and timing of effective actions.

Comprehensive Flood Plain Studies Using Spatial Data Management Techniques.

DTIC Science & Technology

1978-06-01

Hydrologic Engineer- ing Center computer programs that forecast urban storm water quality and dynamic in- stream water quality response to waste...determination. Water Quality The water quality analysis planned for the pilot study includes urban storm water quality forecasting and in-streamn...analysis is performed under the direction of Tony Thomas. Chief, Research Branch, by Jess Abbott for storm water quality analysis, R. G. Willey for

Assimilation of ground and satellite snow observations in a distributed hydrologic model to improve water supply forecasts in the Upper Colorado River Basin

NASA Astrophysics Data System (ADS)

Micheletty, P. D.; Day, G. N.; Quebbeman, J.; Carney, S.; Park, G. H.

2016-12-01

The Upper Colorado River Basin above Lake Powell is a major source of water supply for 25 million people and provides irrigation water for 3.5 million acres. Approximately 85% of the annual runoff is produced from snowmelt. Water supply forecasts of the April-July runoff produced by the National Weather Service (NWS) Colorado Basin River Forecast Center (CBRFC), are critical to basin water management. This project leverages advanced distributed models, datasets, and snow data assimilation techniques to improve operational water supply forecasts made by CBRFC in the Upper Colorado River Basin. The current work will specifically focus on improving water supply forecasts through the implementation of a snow data assimilation process coupled with the Hydrology Laboratory-Research Distributed Hydrologic Model (HL-RDHM). Three types of observations will be used in the snow data assimilation system: satellite Snow Covered Area (MODSCAG), satellite Dust Radiative Forcing in Snow (MODDRFS), and SNOTEL Snow Water Equivalent (SWE). SNOTEL SWE provides the main source of high elevation snowpack information during the snow season, however, these point measurement sites are carefully selected to provide consistent indices of snowpack, and may not be representative of the surrounding watershed. We address this problem by transforming the SWE observations to standardized deviates and interpolating the standardized deviates using a spatial regression model. The interpolation process will also take advantage of the MODIS Snow Covered Area and Grainsize (MODSCAG) product to inform the model on the spatial distribution of snow. The interpolated standardized deviates are back-transformed and used in an Ensemble Kalman Filter (EnKF) to update the model simulated SWE. The MODIS Dust Radiative Forcing in Snow (MODDRFS) product will be used more directly through temporary adjustments to model snowmelt parameters, which should improve melt estimates in areas affected by dust on snow. In order to assess the value of different data sources, reforecasts will be produced for a historical period and performance measures will be computed to assess forecast skill. The existing CBRFC Ensemble Streamflow Prediction (ESP) reforecasts will provide a baseline for comparison to determine the added-value of the data assimilation process.

Evaluation of Satellite and Model Precipitation Products Over Turkey

NASA Astrophysics Data System (ADS)

Yilmaz, M. T.; Amjad, M.

2017-12-01

Satellite-based remote sensing, gauge stations, and models are the three major platforms to acquire precipitation dataset. Among them satellites and models have the advantage of retrieving spatially and temporally continuous and consistent datasets, while the uncertainty estimates of these retrievals are often required for many hydrological studies to understand the source and the magnitude of the uncertainty in hydrological response parameters. In this study, satellite and model precipitation data products are validated over various temporal scales (daily, 3-daily, 7-daily, 10-daily and monthly) using in-situ measured precipitation observations from a network of 733 gauges from all over the Turkey. Tropical Rainfall Measurement Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) 3B42 version 7 and European Center of Medium-Range Weather Forecast (ECMWF) model estimates (daily, 3-daily, 7-daily and 10-daily accumulated forecast) are used in this study. Retrievals are evaluated for their mean and standard deviation and their accuracies are evaluated via bias, root mean square error, error standard deviation and correlation coefficient statistics. Intensity vs frequency analysis and some contingency table statistics like percent correct, probability of detection, false alarm ratio and critical success index are determined using daily time-series. Both ECMWF forecasts and TRMM observations, on average, overestimate the precipitation compared to gauge estimates; wet biases are 10.26 mm/month and 8.65 mm/month, respectively for ECMWF and TRMM. RMSE values of ECMWF forecasts and TRMM estimates are 39.69 mm/month and 41.55 mm/month, respectively. Monthly correlations between Gauges-ECMWF, Gauges-TRMM and ECMWF-TRMM are 0.76, 0.73 and 0.81, respectively. The model and the satellite error statistics are further compared against the gauges error statistics based on inverse distance weighting (IWD) analysis. Both the model and satellite data have less IWD errors (14.72 mm/month and 10.75 mm/month, respectively) compared to gauges IWD error (21.58 mm/month). These results show that, on average, ECMWF forecast data have higher skill than TRMM observations. Overall, both ECMWF forecast data and TRMM observations show good potential for catchment scale hydrological analysis.

European Gravity Service for Improved Emergency Management - Status and project highlights

NASA Astrophysics Data System (ADS)

Mayer-Guerr, Torsten; Adrian, Jäggi; Meyer, Ulrich; Jean, Yoomin; Susnik, Andreja; Weigelt, Matthias; van Dam, Tonie; Flechtner, Frank; Gruber, Christian; Güntner, Andreas; Gouweleeuw, Ben; Kvas, Andreas; Klinger, Beate; Flury, Jakob; Bruinsma, Sean; Lemoine, Jean-Michel; Zwenzner, Hendrik; Bourgogne, Stephane; Bandikova, Tamara

2016-04-01

The European Gravity Service for Improved Emergency Management (EGSIEM) is a project of the Horizon 2020 Framework Programme for Research and Innovation of the European Commission. EGSIEM shall demonstrate that observations of the redistribution of water and ice mass derived from the current GRACE mission, the future GRACE-FO mission, and additional data provide critical and complementary information to more traditional Earth Observation products and open the door for innovative approaches to flood and drought monitoring and forecasting. In the frame of EGSIEM three key services should established: 1) a scientific combination service to deliver the best gravity products for applications in Earth and environmental science research based on the unified knowledge of the European GRACE community, 2) a near real-time and regional service to reduce the latency and increase the temporal resolution of the mass redistribution products, and 3) a hydrological and early warning service to develop gravity-based indicators for extreme hydrological events and to demonstrate their value for flood and drought forecasting and monitoring services. All of these services shall be tailored to the various needs of the respective communities. Significant efforts shall also be devoted to transform the service products into user-friendly and easy-to-interpret data sets and the development of visualization tools. In this talk the status of the ongoing project is presented and selected results are discussed.

Improving the nowcasting of precipitation in an Alpine region with an enhanced radar echo tracking algorithm

NASA Astrophysics Data System (ADS)

Mecklenburg, S.; Joss, J.; Schmid, W.

2000-12-01

Nowcasting for hydrological applications is discussed. The tracking algorithm extrapolates radar images in space and time. It originates from the pattern recognition techniques TREC (Tracking Radar Echoes by Correlation, Rinehart and Garvey, J. Appl. Meteor., 34 (1995) 1286) and COTREC (Continuity of TREC vectors, Li et al., Nature, 273 (1978) 287). To evaluate the quality of the extrapolation, a parameter scheme is introduced, able to distinguish between errors in the position and the intensity of the predicted precipitation. The parameters for the position are the absolute error, the relative error and the error of the forecasted direction. The parameters for the intensity are the ratio of the medians and the variations of the rain rate (ratio of two quantiles) between the actual and the forecasted image. To judge the overall quality of the forecast, the correlation coefficient between the forecasted and the actual radar image has been used. To improve the forecast, three aspects have been investigated: (a) Common meteorological attributes of convective cells, derived from a hail statistics, have been determined to optimize the parameters of the tracking algorithm. Using (a), the forecast procedure modifications (b) and (c) have been applied. (b) Small-scale features have been removed by using larger tracking areas and by applying a spatial and temporal smoothing, since problems with the tracking algorithm are mainly caused by small-scale/short-term variations of the echo pattern or because of limitations caused by the radar technique itself (erroneous vectors caused by clutter or shielding). (c) The searching area and the number of searched boxes have been restricted. This limits false detections, which is especially useful in stratiform precipitation and for stationary echoes. Whereas a larger scale and the removal of small-scale features improve the forecasted position for the convective precipitation, the forecast of the stratiform event is not influenced, but limiting the search area leads to a slightly better forecast. The forecast of the intensity is successful for both precipitation events. Forecasting the variation of the rain rate calls for further investigation. Applying COTREC improves the forecast of the convective precipitation, especially for extrapolation times exceeding 30 min.

Balancing the stochastic description of uncertainties as a function of hydrologic model complexity

NASA Astrophysics Data System (ADS)

Del Giudice, D.; Reichert, P.; Albert, C.; Kalcic, M.; Logsdon Muenich, R.; Scavia, D.; Bosch, N. S.; Michalak, A. M.

2016-12-01

Uncertainty analysis is becoming an important component of forecasting water and pollutant fluxes in urban and rural environments. Properly accounting for errors in the modeling process can help to robustly assess the uncertainties associated with the inputs (e.g. precipitation) and outputs (e.g. runoff) of hydrological models. In recent years we have investigated several Bayesian methods to infer the parameters of a mechanistic hydrological model along with those of the stochastic error component. The latter describes the uncertainties of model outputs and possibly inputs. We have adapted our framework to a variety of applications, ranging from predicting floods in small stormwater systems to nutrient loads in large agricultural watersheds. Given practical constraints, we discuss how in general the number of quantities to infer probabilistically varies inversely with the complexity of the mechanistic model. Most often, when evaluating a hydrological model of intermediate complexity, we can infer the parameters of the model as well as of the output error model. Describing the output errors as a first order autoregressive process can realistically capture the "downstream" effect of inaccurate inputs and structure. With simpler runoff models we can additionally quantify input uncertainty by using a stochastic rainfall process. For complex hydrologic transport models, instead, we show that keeping model parameters fixed and just estimating time-dependent output uncertainties could be a viable option. The common goal across all these applications is to create time-dependent prediction intervals which are both reliable (cover the nominal amount of validation data) and precise (are as narrow as possible). In conclusion, we recommend focusing both on the choice of the hydrological model and of the probabilistic error description. The latter can include output uncertainty only, if the model is computationally-expensive, or, with simpler models, it can separately account for different sources of errors like in the inputs and the structure of the model.

Providing open hydrological data for decision making and research - hypeweb.smhi.se

NASA Astrophysics Data System (ADS)

Strömbäck, Lena; Andersson, Jafet; Donnelly, Chantal; Gustafsson, David; Isberg, Kristina; Pechlivanidis, Ilias; Strömqvist, Johan; Arheimer, Berit

2015-04-01

Following the EU open data strategy the Swedish Meteorological and Hydrological Institute (SMHI) is providing large parts of their databases openly available. These data are ranging from historical observations to climate predictions in various areas such as weather, oceanography and hydrology. In this presentation we will focus on the work on making hydrological data openly available. Hydrological modelling demands large amounts of spatial data, such as soil properties, land use, topography, lakes and reservoirs, ice and snow coverage, water management (e.g. irrigation patterns and regulations), meteorological data and observed water discharge in rivers. By using such data, the hydrological model will in turn provide new data that can be used for new purposes (i.e. re-purposing). In the presentation we will focus on how readily available open data from public portals have been re-purposed by using the Hydrological Predictions for the Environment (HYPE) model in a number of large-scale model applications covering numerous subbasins and rivers. HYPE is a dynamic, semi-distributed, process-based, and integrated catchment model. So far, the following regional domains have been modelled with different resolutions (number of subbasins within brackets): Sweden (37 000), Europe (35 000), Arctic basin (30 000), La Plata River (6 000), Niger River (800), Middle-East North-Africa (31 000), and the Indian subcontinent (6 000). The model output is launched as new Open Data at the web site www.hypeweb.smhi.se. The web site provides several interactive applications for exploring results from the models. The user can explore an overview of various water variables for historical and future conditions. Moreover the user can explore and download historical time series of discharge for each basin and explore the performance of the model towards observed river flow. The available results can be used for many different purposes including; (i) Climate change impact assessments on water resources and dynamics; (ii) The European Water Framework Directive (WFD) for characterization and development of measure programs to improve the ecological status of water bodies; (iii) Design variables for infrastructure constructions; (iv) Spatial water-resource mapping; (v) Operational forecasts (1-10 days and seasonal) on floods and droughts; (vi) Input to oceanographic models for operational forecasts and marine status assessments; (vii) Research. The presentation will give an overview of the functionality of the web site and the available hydrological datasets. We will also discuss a number of challenges experienced and solutions found during the construction of the website. One such is the functionality and interface design of the web site which involves cooperation between IT-specialists and hydrologists. Here, an important goal has been the software and database design to provide an efficient website which can easily be extended with new data and functionality. Another important issue is providing relevant information about the provided datasets and models to make it easy for external user to reuse the data as well as the use of standards and limitations due to dependencies of other datasets. The openly available data has been attractive by other research teams and agencies and led to new collaborations.

Highlights of advances in the field of hydrometeorological research brought about by the DRIHM project

NASA Astrophysics Data System (ADS)

Caumont, Olivier; Hally, Alan; Garrote, Luis; Richard, Évelyne; Weerts, Albrecht; Delogu, Fabio; Fiori, Elisabetta; Rebora, Nicola; Parodi, Antonio; Mihalović, Ana; Ivković, Marija; Dekić, Ljiljana; van Verseveld, Willem; Nuissier, Olivier; Ducrocq, Véronique; D'Agostino, Daniele; Galizia, Antonella; Danovaro, Emanuele; Clematis, Andrea

2015-04-01

The FP7 DRIHM (Distributed Research Infrastructure for Hydro-Meteorology, http://www.drihm.eu, 2011-2015) project intends to develop a prototype e-Science environment to facilitate the collaboration between meteorologists, hydrologists, and Earth science experts for accelerated scientific advances in Hydro-Meteorology Research (HMR). As the project comes to its end, this presentation will summarize the HMR results that have been obtained in the framework of DRIHM. The vision shaped and implemented in the framework of the DRIHM project enables the production and interpretation of numerous, complex compositions of hydrometeorological simulations of flood events from rainfall, either simulated or modelled, down to discharge. Each element of a composition is drawn from a set of various state-of-the-art models. Atmospheric simulations providing high-resolution rainfall forecasts involve different global and limited-area convection-resolving models, the former being used as boundary conditions for the latter. Some of these models can be run as ensembles, i.e. with perturbed boundary conditions, initial conditions and/or physics, thus sampling the probability density function of rainfall forecasts. In addition, a stochastic downscaling algorithm can be used to create high-resolution rainfall ensemble forecasts from deterministic lower-resolution forecasts. All these rainfall forecasts may be used as input to various rainfall-discharge hydrological models that compute the resulting stream flows for catchments of interest. In some hydrological simulations, physical parameters are perturbed to take into account model errors. As a result, six different kinds of rainfall data (either deterministic or probabilistic) can currently be compared with each other and combined with three different hydrological model engines running either in deterministic or probabilistic mode. HMR topics which are allowed or facilitated by such unprecedented sets of hydrometerological forecasts include: physical process studies, intercomparison of models and ensembles, sensitivity studies to a particular component of the forecasting chain, and design of flash-flood early-warning systems. These benefits will be illustrated with the different key cases that have been under investigation in the course of the project. These are four catastrophic cases of flooding, namely the case of 4 November 2011 in Genoa, Italy, 6 November 2011 in Catalonia, Spain, 13-16 May 2014 in eastern Europe, and 9 October 2014, again in Genoa, Italy.

Development of a Distributed Hydrologic Model Using Triangulated Irregular Networks for Continuous, Real-Time Flood Forecasting

NASA Astrophysics Data System (ADS)

Ivanov, V. Y.; Vivoni, E. R.; Bras, R. L.; Entekhabi, D.

2001-05-01

The Triangulated Irregular Networks (TINs) are widespread in many finite-element modeling applications stressing high spatial non-uniformity while describing the domain of interest in an optimized fashion that results in superior computational efficiency. TINs, being adaptive to the complexity of any terrain, are capable of maintaining topological relations between critical surface features and therefore afford higher flexibility in data manipulation. The TIN-based Real-time Integrated Basin Simulator (tRIBS) is a distributed hydrologic model that utilizes the mesh architecture and the software environment developed for the CHILD landscape evolution model and employs the hydrologic routines of its raster-oriented version, RIBS. As a totally independent software unit, the tRIBS consolidates the strengths of the distributed approach and efficient computational data platform. The current version couples the unsaturated and the saturated zones and accounts for the interaction of moving infiltration fronts with a variable groundwater surface, allowing the model to handle both storm and interstorm periods in a continuous fashion. Recent model enhancements have included the development of interstorm hydrologic fluxes through an evapotranspiration scheme as well as incorporation of a rainfall interception module. Overall, the tRIBS model has proven to properly mimic successive phases of the distributed catchment response by reproducing various runoff production mechanisms and handling their meteorological constraints. Important improvements in modeling options, robustness to data availability and overall design flexibility have also been accomplished. The current efforts are focused on further model developments as well as the application of the tRIBS to various watersheds.

On the need for long-term, on the order of a decade, hydro-climatic forecasts over large domains

NASA Astrophysics Data System (ADS)

Burges, S. J.

2012-12-01

All problems of hydrology have been influenced to some extent by the need to describe delivery of water to, and its movement through, the critical zone. The nature of the questions and the level of required quantitative description have changed with time, but all involve accurate accounting of all components of the hydrologic cycle. The broadest issues involve the temporal and spatial distributions of excess (floods) or too little (droughts) water. The spatial domains can range from small catchments to major fractions of continents. The temporal domains range from relatively short-term, on the order of hours to days to a few months, to multiple decades. Hydrologic engineers have long recognized the need to offer designs for human occupied catchments that accommodate hydrologic extremes (principally floods and droughts) that affect human and animal safety, for example, through disruptions to infrastructure and supply chains, food supplies, and water supplies. As more has been learned about the criticality of ecosystems to the well-being of the planet, water allocation issues have become those of "water for people" and "water for ecology". These latter requirements have emphasized the need for increased accuracy of estimating water budgets, and how water (and pollutants) moves through the associated critical domain. Given the now large physical demand for societal water use (it exceeds 50% of the mean annual river flow in most conterminous US river basins) hydrologic balances that include the operation of water resource infrastructure (flood damage mitigation dams and levees, storage reservoirs for municipal and industrial water, irrigation and ecological preservation) have become the norm. In most basins the storage reservoirs are relatively small (few store more than the mean annual flow of rivers) and long-term hydrological forecasting has become a major issue. Whether the issue is floods or droughts, there is now a pressing need for societally useful forecasts from seasonal to up to a decade or so ahead. I address issues that need to be considered by the ocean and hydro-climatology communities to find a way forward for this societally important issue.

Comparison of Sequential and Variational Data Assimilation

NASA Astrophysics Data System (ADS)

Alvarado Montero, Rodolfo; Schwanenberg, Dirk; Weerts, Albrecht

2017-04-01

Data assimilation is a valuable tool to improve model state estimates by combining measured observations with model simulations. It has recently gained significant attention due to its potential in using remote sensing products to improve operational hydrological forecasts and for reanalysis purposes. This has been supported by the application of sequential techniques such as the Ensemble Kalman Filter which require no additional features within the modeling process, i.e. it can use arbitrary black-box models. Alternatively, variational techniques rely on optimization algorithms to minimize a pre-defined objective function. This function describes the trade-off between the amount of noise introduced into the system and the mismatch between simulated and observed variables. While sequential techniques have been commonly applied to hydrological processes, variational techniques are seldom used. In our believe, this is mainly attributed to the required computation of first order sensitivities by algorithmic differentiation techniques and related model enhancements, but also to lack of comparison between both techniques. We contribute to filling this gap and present the results from the assimilation of streamflow data in two basins located in Germany and Canada. The assimilation introduces noise to precipitation and temperature to produce better initial estimates of an HBV model. The results are computed for a hindcast period and assessed using lead time performance metrics. The study concludes with a discussion of the main features of each technique and their advantages/disadvantages in hydrological applications.

Evaluating the performance of real-time streamflow forecasting using multi-satellite precipitation products in the Upper Zambezi, Africa

NASA Astrophysics Data System (ADS)

Demaria, E. M.; Valdes, J. B.; Wi, S.; Serrat-Capdevila, A.; Valdés-Pineda, R.; Durcik, M.

2016-12-01

In under-instrumented basins around the world, accurate and timely forecasts of river streamflows have the potential of assisting water and natural resource managers in their management decisions. The Upper Zambezi river basin is the largest basin in southern Africa and its water resources are critical to sustainable economic growth and poverty reduction in eight riparian countries. We present a real-time streamflow forecast for the basin using a multi-model-multi-satellite approach that allows accounting for model and input uncertainties. Three distributed hydrologic models with different levels of complexity: VIC, HYMOD_DS, and HBV_DS are setup at a daily time step and a 0.25 degree spatial resolution for the basin. The hydrologic models are calibrated against daily observed streamflows at the Katima-Mulilo station using a Genetic Algorithm. Three real-time satellite products: Climate Prediction Center's morphing technique (CMORPH), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), and Tropical Rainfall Measuring Mission (TRMM-3B42RT) are bias-corrected with daily CHIRPS estimates. Uncertainty bounds for predicted flows are estimated with the Inverse Variance Weighting method. Because concentration times in the basin range from a few days to more than a week, we include the use of precipitation forecasts from the Global Forecasting System (GFS) to predict daily streamflows in the basin with a 10-days lead time. The skill of GFS-predicted streamflows is evaluated and the usefulness of the forecasts for short term water allocations is presented.

Petascale Diagnostic Assessment of the Global Portfolio Rainfall Space Missions' Ability to Support Flood Forecasting

NASA Astrophysics Data System (ADS)

Reed, P. M.; Chaney, N.; Herman, J. D.; Wood, E. F.; Ferringer, M. P.

2015-12-01

This research represents a multi-institutional collaboration between Cornell University, The Aerospace Corporation, and Princeton University that has completed a Petascale diagnostic assessment of the current 10 satellite missions providing rainfall observations. Our diagnostic assessment has required four core tasks: (1) formally linking high-resolution astrodynamics design and coordination of space assets with their global hydrological impacts within a Petascale "many-objective" global optimization framework, (2) developing a baseline diagnostic evaluation of a 1-degree resolution global implementation of the Variable Infiltration Capacity (VIC) model to establish the required satellite observation frequencies and coverage to maintain acceptable global flood forecasts, (3) evaluating the limitations and vulnerabilities of the full suite of current satellite precipitation missions including the recently approved Global Precipitation Measurement (GPM) mission, and (4) conceptualizing the next generation spaced-based platforms for water cycle observation. Our team exploited over 100 Million hours of computing access on the 700,000+ core Blue Waters machine to radically advance our ability to discover and visualize key system tradeoffs and sensitivities. This project represents to our knowledge the first attempt to develop a 10,000 member Monte Carlo global hydrologic simulation at one degree resolution that characterizes the uncertain effects of changing the available frequencies of satellite precipitation on drought and flood forecasts. The simulation—optimization components of the work have set a theoretical baseline for the best possible frequencies and coverages for global precipitation given unlimited investment, broad international coordination in reconfiguring existing assets, and new satellite constellation design objectives informed directly by key global hydrologic forecasting requirements. Our research poses a step towards realizing the integrated global water cycle observatory long sought by the World Climate Research Programme, which has to date eluded the world's space agencies.

GIS model-based real-time hydrological forecasting and operation management system for the Lake Balaton and its watershed

NASA Astrophysics Data System (ADS)

Adolf Szabó, János; Zoltán Réti, Gábor; Tóth, Tünde

2017-04-01

Today, the most significant mission of the decision makers on integrated water management issues is to carry out sustainable management for sharing the resources between a variety of users and the environment under conditions of considerable uncertainty (such as climate/land-use/population/etc. change) conditions. In light of this increasing water management complexity, we consider that the most pressing needs is to develop and implement up-to-date GIS model-based real-time hydrological forecasting and operation management systems for aiding decision-making processes to improve water management. After years of researches and developments the HYDROInform Ltd. has developed an integrated, on-line IT system (DIWA-HFMS: DIstributed WAtershed - Hydrologyc Forecasting & Modelling System) which is able to support a wide-ranging of the operational tasks in water resources management such as: forecasting, operation of lakes and reservoirs, water-control and management, etc. Following a test period, the DIWA-HFMS has been implemented for the Lake Balaton and its watershed (in 500 m resolution) at Central-Transdanubian Water Directorate (KDTVIZIG). The significant pillars of the system are: - The DIWA (DIstributed WAtershed) hydrologic model, which is a 3D dynamic water-balance model that distributed both in space and its parameters, and which was developed along combined principles but its mostly based on physical foundations. The DIWA integrates 3D soil-, 2D surface-, and 1D channel-hydraulic components as well. - Lakes and reservoir-operating component; - Radar-data integration module; - fully online data collection tools; - scenario manager tool to create alternative scenarios, - interactive, intuitive, highly graphical user interface. In Vienna, the main functions, operations and results-management of the system will be presented.

Developing a calibrated CONUS-wide watershed-scale simulation platform for quantifying the influence of different sources of uncertainty on streamflow forecast skill

NASA Astrophysics Data System (ADS)

Newman, A. J.; Sampson, K. M.; Wood, A. W.; Hopson, T. M.; Brekke, L. D.; Arnold, J.; Raff, D. A.; Clark, M. P.

2013-12-01

Skill in model-based hydrologic forecasting depends on the ability to estimate a watershed's initial moisture and energy conditions, to forecast future weather and climate inputs, and on the quality of the hydrologic model's representation of watershed processes. The impact of these factors on prediction skill varies regionally, seasonally, and by model. We are investigating these influences using a watershed simulation platform that spans the continental US (CONUS), encompassing a broad range of hydroclimatic variation, and that uses the current simulation models of National Weather Service streamflow forecasting operations. The first phase of this effort centered on the implementation and calibration of the SNOW-17 and Sacramento soil moisture accounting (SAC-SMA) based hydrologic modeling system for a range of watersheds. The base configuration includes 630 basins in the United States Geological Survey's Hydro-Climatic Data Network 2009 (HCDN-2009, Lins 2012) conterminous U.S. basin subset. Retrospective model forcings were derived from Daymet (http://daymet.ornl.gov/), and where available, a priori parameter estimates were based on or compared with the operational NWS model parameters. Model calibration was accomplished by several objective, automated strategies, including the shuffled complex evolution (SCE) optimization approach developed within the NWS in the early 1990s (Duan et al. 1993). This presentation describes outcomes from this effort, including insights about measuring simulation skill, and on relationships between simulation skill and model parameters, basin characteristics (climate, topography, vegetation, soils), and the quality of forcing inputs. References: %Z Thornton, P.; Thornton, M.; Mayer, B.; Wilhelmi, N.; Wei, Y.; Devarakonda, R; Cook, R. Daymet: Daily Surface Weather on a 1 km Grid for North America. 1980-2008; Oak Ridge National Laboratory Distributed Active Archive Center: Oak Ridge, TN, USA, 2012; Volume 10.

Comparison between stochastic and machine learning methods for hydrological multi-step ahead forecasting: All forecasts are wrong!

NASA Astrophysics Data System (ADS)

Papacharalampous, Georgia; Tyralis, Hristos; Koutsoyiannis, Demetris

2017-04-01

Machine learning (ML) is considered to be a promising approach to hydrological processes forecasting. We conduct a comparison between several stochastic and ML point estimation methods by performing large-scale computational experiments based on simulations. The purpose is to provide generalized results, while the respective comparisons in the literature are usually based on case studies. The stochastic methods used include simple methods, models from the frequently used families of Autoregressive Moving Average (ARMA), Autoregressive Fractionally Integrated Moving Average (ARFIMA) and Exponential Smoothing models. The ML methods used are Random Forests (RF), Support Vector Machines (SVM) and Neural Networks (NN). The comparison refers to the multi-step ahead forecasting properties of the methods. A total of 20 methods are used, among which 9 are the ML methods. 12 simulation experiments are performed, while each of them uses 2 000 simulated time series of 310 observations. The time series are simulated using stochastic processes from the families of ARMA and ARFIMA models. Each time series is split into a fitting (first 300 observations) and a testing set (last 10 observations). The comparative assessment of the methods is based on 18 metrics, that quantify the methods' performance according to several criteria related to the accurate forecasting of the testing set, the capturing of its variation and the correlation between the testing and forecasted values. The most important outcome of this study is that there is not a uniformly better or worse method. However, there are methods that are regularly better or worse than others with respect to specific metrics. It appears that, although a general ranking of the methods is not possible, their classification based on their similar or contrasting performance in the various metrics is possible to some extent. Another important conclusion is that more sophisticated methods do not necessarily provide better forecasts compared to simpler methods. It is pointed out that the ML methods do not differ dramatically from the stochastic methods, while it is interesting that the NN, RF and SVM algorithms used in this study offer potentially very good performance in terms of accuracy. It should be noted that, although this study focuses on hydrological processes, the results are of general scientific interest. Another important point in this study is the use of several methods and metrics. Using fewer methods and fewer metrics would have led to a very different overall picture, particularly if those fewer metrics corresponded to fewer criteria. For this reason, we consider that the proposed methodology is appropriate for the evaluation of forecasting methods.

Inflow forecasting model construction with stochastic time series for coordinated dam operation

NASA Astrophysics Data System (ADS)

Kim, T.; Jung, Y.; Kim, H.; Heo, J. H.

2014-12-01

Dam inflow forecasting is one of the most important tasks in dam operation for an effective water resources management and control. In general, dam inflow forecasting with stochastic time series model is possible to apply when the data is stationary because most of stochastic process based on stationarity. However, recent hydrological data cannot be satisfied the stationarity anymore because of climate change. Therefore a stochastic time series model, which can consider seasonality and trend in the data series, named SARIMAX(Seasonal Autoregressive Integrated Average with eXternal variable) model were constructed in this study. This SARIMAX model could increase the performance of stochastic time series model by considering the nonstationarity components and external variable such as precipitation. For application, the models were constructed for four coordinated dams on Han river in South Korea with monthly time series data. As a result, the models of each dam have similar performance and it would be possible to use the model for coordinated dam operation.Acknowledgement This research was supported by a grant 'Establishing Active Disaster Management System of Flood Control Structures by using 3D BIM Technique' [NEMA-NH-12-57] from the Natural Hazard Mitigation Research Group, National Emergency Management Agency of Korea.

An assessment of the feasibility of the use of satellite-only rainfall estimates for the hydrological monitoring in central Italy

NASA Astrophysics Data System (ADS)

Campo, Lorenzo; Caparrini, Francesca

2013-04-01

The need for accurate distributed hydrological modelling has constantly increased in last years for several purposes: agricultural applications, water resources management, hydrological balance at watershed scale, floods forecast. The main input for the hydrological numerical models is rainfall data that present, at the same time, a large availability of measures (in gauged regions, with respect to other micro-meteorological variables) and the most complex spatial patterns. While also in presence of densely gauged watersheds the spatial interpolation of the rainfall is a non-trivial problem, due to the spatial intermittence of the variable (especially at finer temporal scales), ungauged regions need an alternative source of rainfall data in order to perform the hydrological modelling. Such source can be constituted by the satellite-estimated rainfall fields, with reference to both geostationary and polar-orbit platforms. In this work the rainfall product obtained by the Aqua-AIRS sensor were used in order to assess the feasibility of the use of satellite-based rainfall as input for distributed hydrological modelling. The MOBIDIC (MOdello di BIlancio Distribuito e Continuo) model, developed at the Department of civil and Environmental Engineering of the University of Florence and operationally used by Tuscany Region and Umbria Region for flood prediction and management, was used for the experiments. In particular three experiments were carried on: a) hydrological simulation with the use of rain-gauges data, b) simulation with the use of satellite-only rainfall estimates, c) simulation with the combined use of the two sources of data in order to obtain an optimal estimate of the actual rainfall fields. The domain of the study was the central Italy. Several critical events occurred in the area were analyzed. A discussion of the results is provided.

Impact of the assimilation of satellite soil moisture and LST on the hydrological cycle

NASA Astrophysics Data System (ADS)

Laiolo, Paola; Gabellani, Simone; Delogu, Fabio; Silvestro, Francesco; Rudari, Roberto; Campo, Lorenzo; Boni, Giorgio

2014-05-01

The reliable estimation of hydrological variables (e.g. soil moisture, evapotranspiration, surface temperature) in space and time is of fundamental importance in operational hydrology to improve the forecast of the rainfall-runoff response of catchments and, consequently, flood predictions. Nowadays remote sensing can offer a chance to provide good space-time estimates of several hydrological variables and then improve hydrological model performances especially in environments with scarce ground based data. The aim of this work is to investigate the impacts on the performances of a distributed hydrological model (Continuum) of the assimilation of satellite-derived soil moisture products and Land Surface (LST). In this work three different soil moisture (SM) products, derived by ASCAT sensor, are used. These data are provided by the EUMETSAT's H-SAF (Satellite Application Facility on Support to Operational Hydrology and Water Management) program. The considered soil moisture products are: large scale surface soil moisture (SM OBS 1 - H07), small scale surface soil moisture (SM OBS 2 - H08) and profile index in the roots region (SM DAS 2 - H14). These data are compared with soil moisture estimated by Continuum model on the Orba catchment (800 km2), in the northern part of Italy, for the period July 2012-June 2013. Different assimilation experiments have been performed. The first experiment consists in the assimilation of the SM products by using a simple Nudging technique; the second one is the assimilation of only LST data, derived from MSG satellite, and the third is the assimilation of both SM products and LST. The benefits on the model predictions of discharge, LST and soil moisture dynamics were tested.

Enhancements to the WRF-Hydro Hydrologic Model Structure for Semi-arid Environments

NASA Astrophysics Data System (ADS)

Lahmers, T. M.; Gupta, H.; Hazenberg, P.; Castro, C. L.; Gochis, D.; Yates, D. N.; Dugger, A. L.; Goodrich, D. C.

2017-12-01

The NOAA National Water Center (NWC) implemented an operational National Water Model (NWM) in August 2016 to simulate and forecast streamflow and soil moisture throughout the Contiguous US (CONUS). The NWM is based on the WRF-Hydro hydrologic model architecture, with a 1-km resolution Noah-MP LSM grid and a 250m routing grid. The operational NWM does not currently resolve infiltration of water from the beds of ephemeral channels, which is an important component of the water balance in semi-arid environments common in many portions of the western US. This work demonstrates the benefit of a conceptual channel infiltration function in the WRF-Hydro model architecture following calibration. The updated model structure and parameters for the NWM architecture, when implemented operationally, will permit its use in flow simulation and forecasting in the southwest US, particularly for flash floods in basins with smaller drainage areas. Our channel infiltration function is based on that of the KINEROS2 semi-distributed hydrologic model, which has been tested throughout the southwest CONUS for flash flood forecasts. Model calibration utilizes the Dynamically Dimensioned Search (DDS) algorithm, and the model is calibrated using NLDAS-2 atmospheric forcing and NCEP Stage-IV precipitation. Our results show that adding channel infiltration to WRF-Hydro can produce a physically consistent hydrologic response with a high-resolution gauge based precipitation forcing dataset in the USDA-ARS Walnut Gulch Experimental Watershed. NWM WRF-Hydro is also tested for the Babocomari River, Beaver Creek, and Sycamore Creek catchments in southern and central Arizona. In these basins, model skill is degraded due to uncertainties in the NCEP Stage-IV precipitation forcing dataset.

Probabilistic postprocessing models for flow forecasts for a system of catchments and several lead times

NASA Astrophysics Data System (ADS)

Engeland, Kolbjorn; Steinsland, Ingelin

2014-05-01

This study introduces a methodology for the construction of probabilistic inflow forecasts for multiple catchments and lead times, and investigates criterions for evaluation of multi-variate forecasts. A post-processing approach is used, and a Gaussian model is applied for transformed variables. The post processing model has two main components, the mean model and the dependency model. The mean model is used to estimate the marginal distributions for forecasted inflow for each catchment and lead time, whereas the dependency models was used to estimate the full multivariate distribution of forecasts, i.e. co-variances between catchments and lead times. In operational situations, it is a straightforward task to use the models to sample inflow ensembles which inherit the dependencies between catchments and lead times. The methodology was tested and demonstrated in the river systems linked to the Ulla-Førre hydropower complex in southern Norway, where simultaneous probabilistic forecasts for five catchments and ten lead times were constructed. The methodology exhibits sufficient flexibility to utilize deterministic flow forecasts from a numerical hydrological model as well as statistical forecasts such as persistent forecasts and sliding window climatology forecasts. It also deals with variation in the relative weights of these forecasts with both catchment and lead time. When evaluating predictive performance in original space using cross validation, the case study found that it is important to include the persistent forecast for the initial lead times and the hydrological forecast for medium-term lead times. Sliding window climatology forecasts become more important for the latest lead times. Furthermore, operationally important features in this case study such as heteroscedasticity, lead time varying between lead time dependency and lead time varying between catchment dependency are captured. Two criterions were used for evaluating the added value of the dependency model. The first one was the Energy score (ES) that is a multi-dimensional generalization of continuous rank probability score (CRPS). ES was calculated for all lead-times and catchments together, for each catchment across all lead times and for each lead time across all catchments. The second criterion was to use CRPS for forecasted inflows accumulated over several lead times and catchments. The results showed that ES was not very sensitive to correct covariance structure, whereas CRPS for accumulated flows where more suitable for evaluating the dependency model. This indicates that it is more appropriate to evaluate relevant univariate variables that depends on the dependency structure then to evaluate the multivariate forecast directly.

A gain-loss framework based on ensemble flow forecasts to switch the urban drainage-wastewater system management towards energy optimization during dry periods

NASA Astrophysics Data System (ADS)

Courdent, Vianney; Grum, Morten; Munk-Nielsen, Thomas; Mikkelsen, Peter S.

2017-05-01

Precipitation is the cause of major perturbation to the flow in urban drainage and wastewater systems. Flow forecasts, generated by coupling rainfall predictions with a hydrologic runoff model, can potentially be used to optimize the operation of integrated urban drainage-wastewater systems (IUDWSs) during both wet and dry weather periods. Numerical weather prediction (NWP) models have significantly improved in recent years, having increased their spatial and temporal resolution. Finer resolution NWP are suitable for urban-catchment-scale applications, providing longer lead time than radar extrapolation. However, forecasts are inevitably uncertain, and fine resolution is especially challenging for NWP. This uncertainty is commonly addressed in meteorology with ensemble prediction systems (EPSs). Handling uncertainty is challenging for decision makers and hence tools are necessary to provide insight on ensemble forecast usage and to support the rationality of decisions (i.e. forecasts are uncertain and therefore errors will be made; decision makers need tools to justify their choices, demonstrating that these choices are beneficial in the long run). This study presents an economic framework to support the decision-making process by providing information on when acting on the forecast is beneficial and how to handle the EPS. The relative economic value (REV) approach associates economic values with the potential outcomes and determines the preferential use of the EPS forecast. The envelope curve of the REV diagram combines the results from each probability forecast to provide the highest relative economic value for a given gain-loss ratio. This approach is traditionally used at larger scales to assess mitigation measures for adverse events (i.e. the actions are taken when events are forecast). The specificity of this study is to optimize the energy consumption in IUDWS during low-flow periods by exploiting the electrical smart grid market (i.e. the actions are taken when no events are forecast). Furthermore, the results demonstrate the benefit of NWP neighbourhood post-processing methods to enhance the forecast skill and increase the range of beneficial uses.

Impact of TRMM and SSM/I-derived Precipitation and Moisture Data on the GEOS Global Analysis

NASA Technical Reports Server (NTRS)

Hou, Arthur Y.; Zhang, Sara Q.; daSilva, Arlindo M.; Olson, William S.

1999-01-01

Current global analyses contain significant errors in primary hydrological fields such as precipitation, evaporation, and related cloud and moisture in the tropics. The Data Assimilation Office at NASA's Goddard Space Flight Center has been exploring the use of space-based rainfall and total precipitable water (TPW) estimates to constrain these hydrological parameters in the Goddard Earth Observing System (GEOS) global data assimilation system. We present results showing that assimilating the 6-hour averaged rain rates and TPW estimates from the Tropical Rainfall Measuring Mission (TRMM) and Special Sensor Microwave/Imager (SSM/I) instruments improves not only the precipitation and moisture estimates but also reduce state-dependent systematic errors in key climate parameters directly linked to convection such as the outgoing longwave radiation, clouds, and the large-scale circulation. The improved analysis also improves short-range forecasts beyond 1 day, but the impact is relatively modest compared with improvements in the time-averaged analysis. The study shows that, in the presence of biases and other errors of the forecast model, improving the short-range forecast is not necessarily prerequisite for improving the assimilation as a climate data set. The full impact of a given type of observation on the assimilated data set should not be measured solely in terms of forecast skills.

Climate Scenarios for the NASA / USAID SERVIR Project: Challenges for Multiple Planning Horizons

NASA Technical Reports Server (NTRS)

Robertson, Franklin R.; Roberts, J. B.; Lyon, B.; Funk, C.; Bosilovich, M. G.

2014-01-01

SERVIR, an acronym meaning "to serve" in Spanish, is a joint venture between NASA and the U.S. Agency for International Development (USAID) which provides satellite-based Earth observation data, modeling, and science applications to help developing nations in Central America, East Africa and the Himalayas improve environmental decision making. Anticipating climate variability / climate change impacts has now become an important component of the SERVIR efforts to build capacity in these regions. Uncertainty in hydrometeorological components of climate variations and exposure to extreme events across scales from weather to climate are of particular concern. We report here on work to construct scenarios or outlooks that are being developed as input drivers for decision support systems (DSSs) in a variety of settings. These DSSs are being developed jointly by a broad array NASA Applied Science Team (AST) Investigations and user communities in the three SERVIR Hub Regions, Central America, East Africa and the Himalayas. Issues span hydrologic / water resources modeling, agricultural productivity, and forest carbon reserves. The scenarios needed for these efforts encompass seasonal forecasts, interannual outlooks, and likely decadal / multi-decadal trends. Providing these scenarios across the different AST efforts enables some level of integration in considering regional responses to climate events. We will discuss a number of challenges in developing this continuum of scenarios including the identification and "mining" of predictability, addressing multiple continental regions, issues of downscaling global model integrations to regional / local applications (i.e. hydrologic and crop modeling). We compare / contrast the role of the U.S. National Multi- Model Experiment initiative in seasonal forecasts and the CMIP-5 climate model experiments in supporting these efforts. Examples of these scenarios, their use, and an assessment of their utility as well as limitations will be presented.

A real-time evaluation and demonstration of strategies for 'Over-The-Loop' ensemble streamflow forecasting in US watersheds

NASA Astrophysics Data System (ADS)

Wood, Andy; Clark, Elizabeth; Mendoza, Pablo; Nijssen, Bart; Newman, Andy; Clark, Martyn; Nowak, Kenneth; Arnold, Jeffrey

2017-04-01

Many if not most national operational streamflow prediction systems rely on a forecaster-in-the-loop approach that require the hands-on-effort of an experienced human forecaster. This approach evolved from the need to correct for long-standing deficiencies in the models and datasets used in forecasting, and the practice often leads to skillful flow predictions despite the use of relatively simple, conceptual models. Yet the 'in-the-loop' forecast process is not reproducible, which limits opportunities to assess and incorporate new techniques systematically, and the effort required to make forecasts in this way is an obstacle to expanding forecast services - e.g., though adding new forecast locations or more frequent forecast updates, running more complex models, or producing forecast and hindcasts that can support verification. In the last decade, the hydrologic forecasting community has begun develop more centralized, 'over-the-loop' systems. The quality of these new forecast products will depend on their ability to leverage research in areas including earth system modeling, parameter estimation, data assimilation, statistical post-processing, weather and climate prediction, verification, and uncertainty estimation through the use of ensembles. Currently, many national operational streamflow forecasting and water management communities have little experience with the strengths and weaknesses of over-the-loop approaches, even as such systems are beginning to be deployed operationally in centers such as ECMWF. There is thus a need both to evaluate these forecasting advances and to demonstrate their potential in a public arena, raising awareness in forecast user communities and development programs alike. To address this need, the US National Center for Atmospheric Research is collaborating with the University of Washington, the Bureau of Reclamation and the US Army Corps of Engineers, using the NCAR 'System for Hydromet Analysis Research and Prediction Applications' (SHARP) to implement, assess and demonstrate real-time over-the-loop ensemble flow forecasts in a range of US watersheds. The system relies on fully ensemble techniques, including: an 100-member ensemble of meteorological model forcings and an ensemble particle filter data assimilation for initializing watershed states; analog/regression-based downscaling of ensemble weather forecasts from GEFS; and statistical post-processing of ensemble forecast outputs, all of which run in real-time within a workflow managed by ECWMF's ecFlow libraries over large US regional domains. We describe SHARP and present early hindcast and verification results for short to seasonal range streamflow forecasts in a number of US case study watersheds.

Perturbations in the initial soil moisture conditions: Impacts on hydrologic simulation in a large river basin

NASA Astrophysics Data System (ADS)

Niroula, Sundar; Halder, Subhadeep; Ghosh, Subimal

2018-06-01

Real time hydrologic forecasting requires near accurate initial condition of soil moisture; however, continuous monitoring of soil moisture is not operational in many regions, such as, in Ganga basin, extended in Nepal, India and Bangladesh. Here, we examine the impacts of perturbation/error in the initial soil moisture conditions on simulated soil moisture and streamflow in Ganga basin and its propagation, during the summer monsoon season (June to September). This provides information regarding the required minimum duration of model simulation for attaining the model stability. We use the Variable Infiltration Capacity model for hydrological simulations after validation. Multiple hydrologic simulations are performed, each of 21 days, initialized on every 5th day of the monsoon season for deficit, surplus and normal monsoon years. Each of these simulations is performed with the initial soil moisture condition obtained from long term runs along with positive and negative perturbations. The time required for the convergence of initial errors is obtained for all the cases. We find a quick convergence for the year with high rainfall as well as for the wet spells within a season. We further find high spatial variations in the time required for convergence; the region with high precipitation such as Lower Ganga basin attains convergence at a faster rate. Furthermore, deeper soil layers need more time for convergence. Our analysis is the first attempt on understanding the sensitivity of hydrological simulations of Ganga basin on initial soil moisture conditions. The results obtained here may be useful in understanding the spin-up requirements for operational hydrologic forecasts.

The total probabilities from high-resolution ensemble forecasting of floods

NASA Astrophysics Data System (ADS)

Olav Skøien, Jon; Bogner, Konrad; Salamon, Peter; Smith, Paul; Pappenberger, Florian

2015-04-01

Ensemble forecasting has for a long time been used in meteorological modelling, to give an indication of the uncertainty of the forecasts. As meteorological ensemble forecasts often show some bias and dispersion errors, there is a need for calibration and post-processing of the ensembles. Typical methods for this are Bayesian Model Averaging (Raftery et al., 2005) and Ensemble Model Output Statistics (EMOS) (Gneiting et al., 2005). There are also methods for regionalizing these methods (Berrocal et al., 2007) and for incorporating the correlation between lead times (Hemri et al., 2013). To make optimal predictions of floods along the stream network in hydrology, we can easily use the ensemble members as input to the hydrological models. However, some of the post-processing methods will need modifications when regionalizing the forecasts outside the calibration locations, as done by Hemri et al. (2013). We present a method for spatial regionalization of the post-processed forecasts based on EMOS and top-kriging (Skøien et al., 2006). We will also look into different methods for handling the non-normality of runoff and the effect on forecasts skills in general and for floods in particular. Berrocal, V. J., Raftery, A. E. and Gneiting, T.: Combining Spatial Statistical and Ensemble Information in Probabilistic Weather Forecasts, Mon. Weather Rev., 135(4), 1386-1402, doi:10.1175/MWR3341.1, 2007. Gneiting, T., Raftery, A. E., Westveld, A. H. and Goldman, T.: Calibrated Probabilistic Forecasting Using Ensemble Model Output Statistics and Minimum CRPS Estimation, Mon. Weather Rev., 133(5), 1098-1118, doi:10.1175/MWR2904.1, 2005. Hemri, S., Fundel, F. and Zappa, M.: Simultaneous calibration of ensemble river flow predictions over an entire range of lead times, Water Resour. Res., 49(10), 6744-6755, doi:10.1002/wrcr.20542, 2013. Raftery, A. E., Gneiting, T., Balabdaoui, F. and Polakowski, M.: Using Bayesian Model Averaging to Calibrate Forecast Ensembles, Mon. Weather Rev., 133(5), 1155-1174, doi:10.1175/MWR2906.1, 2005. Skøien, J. O., Merz, R. and Blöschl, G.: Top-kriging - Geostatistics on stream networks, Hydrol. Earth Syst. Sci., 10(2), 277-287, 2006.

WOD - Weather On Demand forecasting system

NASA Astrophysics Data System (ADS)

Rognvaldsson, Olafur; Ragnarsson, Logi; Stanislawska, Karolina

2017-04-01

The backbone of the Belgingur forecasting system (called WOD - Weather On Demand) is the WRF-Chem atmospheric model, with a number of in-house customisations. Initial and boundary data are taken from the Global Forecasting System, operated by the National Oceanic and Atmospheric Administration (NOAA). Operational forecasts use cycling of a number of parameters, mainly deep soil and surface fields. This is done to minimise spin-up effects and to ensure proper book-keeping of hydrological fields such as snow accumulation and runoff, as well as the constituents of various chemical parameters. The WOD system can be used to create conventional short- to medium-range weather forecasts for any location on the globe. The WOD system can also be used for air quality purposes (e.g. dispersion forecasts from volcanic eruptions) and as a tool to provide input to other modelling systems, such as hydrological models. A wide variety of post-processing options are also available, making WOD an ideal tool for creating highly customised output that can be tailored to the specific needs of individual end-users. The most recent addition to the WOD system is an integrated verification system where forecasts can be compared to surface observations from chosen locations. Forecast visualisation, such as weather charts, meteograms, weather icons and tables, is done via number of web components that can be configured to serve the varying needs of different end-users. The WOD system itself can be installed in an automatic way on hardware running a range of Linux based OS. System upgrades can also be done in semi-automatic fashion, i.e. upgrades and/or bug-fixes can be pushed to the end-user hardware without system downtime. Importantly, the WOD system requires only rudimentary knowledge of the WRF modelling, and the Linux operating systems on behalf of the end-user, making it an ideal NWP tool in locations with limited IT infrastructure.

Updating of states in operational hydrological models

NASA Astrophysics Data System (ADS)

Bruland, O.; Kolberg, S.; Engeland, K.; Gragne, A. S.; Liston, G.; Sand, K.; Tøfte, L.; Alfredsen, K.

2012-04-01

Operationally the main purpose of hydrological models is to provide runoff forecasts. The quality of the model state and the accuracy of the weather forecast together with the model quality define the runoff forecast quality. Input and model errors accumulate over time and may leave the model in a poor state. Usually model states can be related to observable conditions in the catchment. Updating of these states, knowing their relation to observable catchment conditions, influence directly the forecast quality. Norway is internationally in the forefront in hydropower scheduling both on short and long terms. The inflow forecasts are fundamental to this scheduling. Their quality directly influence the producers profit as they optimize hydropower production to market demand and at the same time minimize spill of water and maximize available hydraulic head. The quality of the inflow forecasts strongly depends on the quality of the models applied and the quality of the information they use. In this project the focus has been to improve the quality of the model states which the forecast is based upon. Runoff and snow storage are two observable quantities that reflect the model state and are used in this project for updating. Generally the methods used can be divided in three groups: The first re-estimates the forcing data in the updating period; the second alters the weights in the forecast ensemble; and the third directly changes the model states. The uncertainty related to the forcing data through the updating period is due to both uncertainty in the actual observation and to how well the gauging stations represent the catchment both in respect to temperatures and precipitation. The project looks at methodologies that automatically re-estimates the forcing data and tests the result against observed response. Model uncertainty is reflected in a joint distribution of model parameters estimated using the Dream algorithm.

GASAKe: forecasting landslide activations by a genetic-algorithms-based hydrological model

NASA Astrophysics Data System (ADS)

Terranova, O. G.; Gariano, S. L.; Iaquinta, P.; Iovine, G. G. R.

2015-07-01

GASAKe is a new hydrological model aimed at forecasting the triggering of landslides. The model is based on genetic algorithms and allows one to obtain thresholds for the prediction of slope failures using dates of landslide activations and rainfall series. It can be applied to either single landslides or a set of similar slope movements in a homogeneous environment. Calibration of the model provides families of optimal, discretized solutions (kernels) that maximize the fitness function. Starting from the kernels, the corresponding mobility functions (i.e., the predictive tools) can be obtained through convolution with the rain series. The base time of the kernel is related to the magnitude of the considered slope movement, as well as to the hydro-geological complexity of the site. Generally, shorter base times are expected for shallow slope instabilities compared to larger-scale phenomena. Once validated, the model can be applied to estimate the timing of future landslide activations in the same study area, by employing measured or forecasted rainfall series. Examples of application of GASAKe to a medium-size slope movement (the Uncino landslide at San Fili, in Calabria, southern Italy) and to a set of shallow landslides (in the Sorrento Peninsula, Campania, southern Italy) are discussed. In both cases, a successful calibration of the model has been achieved, despite unavoidable uncertainties concerning the dates of occurrence of the slope movements. In particular, for the Sorrento Peninsula case, a fitness of 0.81 has been obtained by calibrating the model against 10 dates of landslide activation; in the Uncino case, a fitness of 1 (i.e., neither missing nor false alarms) has been achieved using five activations. As for temporal validation, the experiments performed by considering further dates of activation have also proved satisfactory. In view of early-warning applications for civil protection, the capability of the model to simulate the occurrences of the Uncino landslide has been tested by means of a progressive, self-adaptive procedure. Finally, a sensitivity analysis has been performed by taking into account the main parameters of the model. The obtained results are quite promising, given the high performance of the model against different types of slope instabilities characterized by several historical activations. Nevertheless, further refinements are still needed for application to landslide risk mitigation within early-warning and decision-support systems.

GASAKe: forecasting landslide activations by a genetic-algorithms based hydrological model

NASA Astrophysics Data System (ADS)

Terranova, O. G.; Gariano, S. L.; Iaquinta, P.; Iovine, G. G. R.

2015-02-01

GASAKe is a new hydrological model aimed at forecasting the triggering of landslides. The model is based on genetic-algorithms and allows to obtaining thresholds of landslide activation from the set of historical occurrences and from the rainfall series. GASAKe can be applied to either single landslides or set of similar slope movements in a homogeneous environment. Calibration of the model is based on genetic-algorithms, and provides for families of optimal, discretized solutions (kernels) that maximize the fitness function. Starting from these latter, the corresponding mobility functions (i.e. the predictive tools) can be obtained through convolution with the rain series. The base time of the kernel is related to the magnitude of the considered slope movement, as well as to hydro-geological complexity of the site. Generally, smaller values are expected for shallow slope instabilities with respect to large-scale phenomena. Once validated, the model can be applied to estimate the timing of future landslide activations in the same study area, by employing recorded or forecasted rainfall series. Example of application of GASAKe to a medium-scale slope movement (the Uncino landslide at San Fili, in Calabria, Southern Italy) and to a set of shallow landslides (in the Sorrento Peninsula, Campania, Southern Italy) are discussed. In both cases, a successful calibration of the model has been achieved, despite unavoidable uncertainties concerning the dates of landslide occurrence. In particular, for the Sorrento Peninsula case, a fitness of 0.81 has been obtained by calibrating the model against 10 dates of landslide activation; in the Uncino case, a fitness of 1 (i.e. neither missing nor false alarms) has been achieved against 5 activations. As for temporal validation, the experiments performed by considering the extra dates of landslide activation have also proved satisfactory. In view of early-warning applications for civil protection purposes, the capability of the model to simulate the occurrences of the Uncino landslide has been tested by means of a progressive, self-adaptive procedure. Finally, a sensitivity analysis has been performed by taking into account the main parameters of the model. The obtained results are quite promising, given the high performance of the model obtained against different types of slope instabilities, characterized by several historical activations. Nevertheless, further refinements are still needed for applications to landslide risk mitigation within early-warning and decision-support systems.

Recent advances in the multimodel hydrologic ensemble forecasting using the HydroProg system in the Nysa Klodzka river basin (southwestern Poland)

NASA Astrophysics Data System (ADS)

Niedzielski, Tomasz; Mizinski, Bartlomiej; Swierczynska-Chlasciak, Malgorzata

2017-04-01

The HydroProg system, the real-time multimodel hydrologic ensemble system elaborated at the University of Wroclaw (Poland) in frame of the research grant no. 2011/01/D/ST10/04171 financed by National Science Centre of Poland, has been experimentally launched in 2013 in the Nysa Klodzka river basin (southwestern Poland). Since that time the system has been working operationally to provide water level predictions in real time. At present, depending on a hydrologic gauge, up to eight hydrologic models are run. They are data- and physically-based solutions, with the majority of them being the data-based ones. The paper aims to report on the performance of the implementation of the HydroProg system for the basin in question. We focus on several high flows episodes and discuss the skills of the individual models in forecasting them. In addition, we present the performance of the multimodel ensemble solution. We also introduce a new prognosis which is determined in the following way: for a given lead time we select the most skillful prediction (from the set of all individual models running at a given gauge and their multimodel ensemble) using the performance statistics computed operationally in real time as a function of lead time.

Analysis of extreme rain and flood events using a regional hydrologically enhanced hydrometeorological system

NASA Astrophysics Data System (ADS)

Yucel, Ismail; Onen, Alper

2013-04-01

Evidence is showing that global warming or climate change has a direct influence on changes in precipitation and the hydrological cycle. Extreme weather events such as heavy rainfall and flooding are projected to become much more frequent as climate warms. Regional hydrometeorological system model which couples the atmosphere with physical and gridded based surface hydrology provide efficient predictions for extreme hydrological events. This modeling system can be used for flood forecasting and warning issues as they provide continuous monitoring of precipitation over large areas at high spatial resolution. This study examines the performance of the Weather Research and Forecasting (WRF-Hydro) model that performs the terrain, sub-terrain, and channel routing in producing streamflow from WRF-derived forcing of extreme precipitation events. The capability of the system with different options such as data assimilation is tested for number of flood events observed in basins of western Black Sea Region in Turkey. Rainfall event structures and associated flood responses are evaluated with gauge and satellite-derived precipitation and measured streamflow values. The modeling system shows skills in capturing the spatial and temporal structure of extreme rainfall events and resulted flood hydrographs. High-resolution routing modules activated in the model enhance the simulated discharges.

Hydrologic Forecasting in the 21st Century: Challenges and Directions of Research

NASA Astrophysics Data System (ADS)

Restrepo, P.; Schaake, J.

2009-04-01

Traditionally, the role of the Hydrology program of the National Weather Service has been centered around forecasting floods, in order to minimize loss of lives and damage to property as a result of floods as well as water levels for navigable rivers, and water supply in some areas of the country. A number of factors, including shifting population patterns, widespread drought and concerns about climate change have made it imperative to widen the focus to cover forecasting flows ranging from drought to floods and anything in between. Because of these concerns, it is imperative to develop models that rely more on the physical characteristics of the watershed for parameterization and less on historical observations. Furthermore, it is also critical to consider explicitly the sources of uncertainty in the forecasting process, including parameter values, model structure, forcings (both observations and forecasts), initial conditions, and streamflow observations. A consequence of more widespread occurrence of low flows as a result either of the already evident earlier snowmelt in the Western United States, or of the predicted changes in precipitation patterns, is the issue of water quality: lower flows will have higher concentrations of certain pollutants. This paper describes the current projects and future directions of research for hydrologic forecasting in the United States. Ongoing projects on quantitative precipitation and temperature estimates and forecasts, uncertainty modeling by the use of ensembles, data assimilation, verification, distributed conceptual modeling will be reviewed. Broad goals of the research directions are: 1) reliable modeling of the different sources of uncertainty. 2) a more expeditious and cost-effective approach by reducing the effort required in model calibration; 3) improvements in forecast lead-time and accuracy; 4) an approach for rapid adjustment of model parameters to account for changes in the watershed, both rapid as the result from forest fires or levee breaches, and slow, as the result of watershed reforestation, reforestation or urban development; 5) an expanded suite of products, including soil moisture and temperature forecasts, and water quality constituents; and 6) a comprehensive verification system to assess the effectiveness of the other 5 goals. To this end, the research plan places an emphasis on research of models with parameters that can be derived from physical watershed characteristics. Purely physically based models may be unattainable or impractical, and, therefore, models resulting from a combination of physically and conceptually approached processes may be required With respect to the hydrometeorological forcings the research plan emphasizes the development of improved precipitation estimation techniques through the synthesis of radar, rain gauge, satellite, and numerical weather prediction model output, particularly in those areas where ground-based sensors are inadequate to detect spatial variability in precipitation. Better estimation and forecasting of precipitation are most likely to be achieved by statistical merging of remote-sensor observations and forecasts from high-resolution numerical prediction models. Enhancements to the satellite-based precipitation products will include use of TRMM precipitation data in preparation for information to be supplied by the Global Precipitation Mission satellites not yet deployed. Because of a growing need for services in water resources, including low-flow forecasts for water supply customers, we will be directing research into coupled surface-groundwater models that will eventually replace the groundwater component of the existing models, and will be part of the new generation of models. Finally, the research plan covers the directions of research for probabilistic forecasting using ensembles, data assimilation and the verification and validation of both deterministic and probabilistic forecasts.

Understanding and Seasonal Forecasting of multiscale droughts in China

NASA Astrophysics Data System (ADS)

Yuan, X.; Wang, L.; Wang, S.; Zhang, M.

2016-12-01

Droughts were climate anomalies that occurred naturally. But they have been altered by climate change and human interventions, and have covered a variety of spatiotemporal scales from seasonal/decadal droughts at regional to continental scales that are associated with large-scale climate anomalies and certain atmospheric circulation patterns, to flash droughts at local scales that are usually concurrent with heat extremes. Droughts have quite different implications across a number of sectors, with the considerations augmented from meteorological droughts to agricultural and hydrological droughts, where the latter could be affected by human activities directly. This raises a grand challenge to understand and predict droughts across scales in a changing environment. This presentation will be started by diagnosing an El Niño-induced meteorological drought that occurred over northern China (NC) last year, where the oceanic and atmospheric background are investigated, and the real-time prediction from Climate Forecast System version 2 (CFSv2) are diagnosed. The comparison between 2015 NC drought and other historical droughts are discussed, and a dynamical-statistical forecasting approach is being developed. Secondly, a rapidly developing agricultural drought event that termed as "flash droughts" accompanied by extreme heat, low soil moisture and high evapotranspiration (ET), occurred frequently around the world, and caused devastating impacts on crop yields and water supply. The increasing trend of flash droughts over China was tripled after the big El Niño event in 1997/98, but the warming hiatus does exist over many regions of China. The changes in flash droughts over China are being attributed by using multiple reanalysis data and the CMIP5 simulations. Lastly, the effects of human interventions on the drought propagation will be investigated over Yellow River basin in northern China. A comparison between SPI and standardized streamflow index indicates that the response of hydrological droughts to meteorological droughts becomes longer, and the duration and severity of hydrological droughts could be doubled or tripled with human interventions. The impact of human intervention on the hydrological drought predictability is being explored within the NMME/VIC forecasting framework.

Global Drought Monitoring and Forecasting based on Satellite Data and Land Surface Modeling

NASA Astrophysics Data System (ADS)

Sheffield, J.; Lobell, D. B.; Wood, E. F.

2010-12-01

Monitoring drought globally is challenging because of the lack of dense in-situ hydrologic data in many regions. In particular, soil moisture measurements are absent in many regions and in real time. This is especially problematic for developing regions such as Africa where water information is arguably most needed, but virtually non-existent on the ground. With the emergence of remote sensing estimates of all components of the water cycle there is now the potential to monitor the full terrestrial water cycle from space to give global coverage and provide the basis for drought monitoring. These estimates include microwave-infrared merged precipitation retrievals, evapotranspiration based on satellite radiation, temperature and vegetation data, gravity recovery measurements of changes in water storage, microwave based retrievals of soil moisture and altimetry based estimates of lake levels and river flows. However, many challenges remain in using these data, especially due to biases in individual satellite retrieved components, their incomplete sampling in time and space, and their failure to provide budget closure in concert. A potential way forward is to use modeling to provide a framework to merge these disparate sources of information to give physically consistent and spatially and temporally continuous estimates of the water cycle and drought. Here we present results from our experimental global water cycle monitor and its African drought monitor counterpart (http://hydrology.princeton.edu/monitor). The system relies heavily on satellite data to drive the Variable Infiltration Capacity (VIC) land surface model to provide near real-time estimates of precipitation, evapotranspiraiton, soil moisture, snow pack and streamflow. Drought is defined in terms of anomalies of soil moisture and other hydrologic variables relative to a long-term (1950-2000) climatology. We present some examples of recent droughts and how they are identified by the system, including objective quantification and tracking of their spatial-temporal characteristics. Further we present strategies for merging various sources of information, including bias correction of satellite precipitation and assimilation of remotely sensed soil moisture, which can augment the monitoring in regions where satellite precipitation is most uncertain. Ongoing work is adding a drought forecast component based on a successful implementation over the U.S. and agricultural productivity estimates based on output from crop yield models. The forecast component uses seasonal global climate forecasts from the NCEP Climate Forecast System (CFS). These are merged with observed climatology in a Bayesian framework to produce ensemble atmospheric forcings that better capture the uncertainties. At the same time, the system bias corrects and downscales the monthly CFS data. We show some initial seasonal (up to 6-month lead) hydrologic forecast results for the African system. Agricultural monitoring is based on the precipitation, temperature and soil moisture from the system to force statistical and process based crop yield models. We demonstrate the feasibility of monitoring major crop types across the world and show a strategy for providing predictions of yields within our drought forecast mode.

Coupling impervious surface rate derived from satellite remote sensing with distributed hydrological model for highly urbanized watershed flood forecasting

NASA Astrophysics Data System (ADS)

Dong, L.

2017-12-01

Abstract: The original urban surface structure changed a lot because of the rapid development of urbanization. Impermeable area has increased a lot. It causes great pressure for city flood control and drainage. Songmushan reservoir basin with high degree of urbanization is taken for an example. Pixel from Landsat is decomposed by Linear spectral mixture model and the proportion of urban area in it is considered as impervious rate. Based on impervious rate data before and after urbanization, an physically based distributed hydrological model, Liuxihe Model, is used to simulate the process of hydrology. The research shows that the performance of the flood forecasting of high urbanization area carried out with Liuxihe Model is perfect and can meet the requirement of the accuracy of city flood control and drainage. The increase of impervious area causes conflux speed more quickly and peak flow to be increased. It also makes the time of peak flow advance and the runoff coefficient increase. Key words: Liuxihe Model; Impervious rate; City flood control and drainage; Urbanization; Songmushan reservoir basin

Application of the Haines Index in the fire warning system

NASA Astrophysics Data System (ADS)

Kalin, Lovro; Marija, Mokoric; Tomislav, Kozaric

2016-04-01

Croatia, as all Mediterranean countries, is strongly affected by large wildfires, particularly in the coastal region. In the last two decades the number and intensity of fires has been significantly increased, which is unanimously associated with climate change, e.g. global warming. More extreme fires are observed, and the fire-fighting season has been expanded to June and September. The meteorological support for fire protection and planning is therefore even more important. At the Meteorological and Hydrological Service of Croatia a comprehensive monitoring and warning system has been established. It includes standard components, such as short term forecast of Fire Weather Index (FWI), but long range forecast as well. However, due to more frequent hot and dry seasons, FWI index often does not provide additional information of extremely high fire danger, since it regularly takes the highest values for long periods. Therefore the additional tools have been investigated. One of widely used meteorological products is the Haines index (HI). It provides information of potential fire growth, taking into account only the vertical instability of the atmosphere, and not the state of the fuel. Several analyses and studies carried out at the Service confirmed the correlation of high HI values with large and extreme fires. The Haines index forecast has been used at the Service for several years, employing European Centre for Medium Range Weather Forecast (ECMWF) global prediction model, as well as the limited-area Aladin model. The verification results show that these forecast are reliable, when compared to radiosonde measurements. All these results provided the introduction of the additional fire warnings, that are issued by the Service's Forecast Department.

Assessment of SWE data assimilation for ensemble streamflow predictions

NASA Astrophysics Data System (ADS)

Franz, Kristie J.; Hogue, Terri S.; Barik, Muhammad; He, Minxue

2014-11-01

An assessment of data assimilation (DA) for Ensemble Streamflow Prediction (ESP) using seasonal water supply hindcasting in the North Fork of the American River Basin (NFARB) and the National Weather Service (NWS) hydrologic forecast models is undertaken. Two parameter sets, one from the California Nevada River Forecast Center (RFC) and one from the Differential Evolution Adaptive Metropolis (DREAM) algorithm, are tested. For each parameter set, hindcasts are generated using initial conditions derived with and without the inclusion of a DA scheme that integrates snow water equivalent (SWE) observations. The DREAM-DA scenario uses an Integrated Uncertainty and Ensemble-based data Assimilation (ICEA) framework that also considers model and parameter uncertainty. Hindcasts are evaluated using deterministic and probabilistic forecast verification metrics. In general, the impact of DA on the skill of the seasonal water supply predictions is mixed. For deterministic (ensemble mean) predictions, the Percent Bias (PBias) is improved with integration of the DA. DREAM-DA and the RFC-DA have the lowest biases and the RFC-DA has the lowest Root Mean Squared Error (RMSE). However, the RFC and DREAM-DA have similar RMSE scores. For the probabilistic predictions, the RFC and DREAM have the highest Continuous Ranked Probability Skill Scores (CRPSS) and the RFC has the best discrimination for low flows. Reliability results are similar between the non-DA and DA tests and the DREAM and DREAM-DA have better reliability than the RFC and RFC-DA for forecast dates February 1 and later. Despite producing improved streamflow simulations in previous studies, the hindcast analysis suggests that the DA method tested may not result in obvious improvements in streamflow forecasts. We advocate that integration of hindcasting and probabilistic metrics provides more rigorous insight on model performance for forecasting applications, such as in this study.

Evaluation of Flood Forecast and Warning in Elbe river basin - Impact of Forecaster's Strategy

NASA Astrophysics Data System (ADS)

Danhelka, Jan; Vlasak, Tomas

2010-05-01

Czech Hydrometeorological Institute (CHMI) is responsible for flood forecasting and warning in the Czech Republic. To meet that issue CHMI operates hydrological forecasting systems and publish flow forecast in selected profiles. Flood forecast and warning is an output of system that links observation (flow and atmosphere), data processing, weather forecast (especially NWP's QPF), hydrological modeling and modeled outputs evaluation and interpretation by forecaster. Forecast users are interested in final output without separating uncertainties of separate steps of described process. Therefore an evaluation of final operational forecasts was done for profiles within Elbe river basin produced by AquaLog forecasting system during period 2002 to 2008. Effects of uncertainties of observation, data processing and especially meteorological forecasts were not accounted separately. Forecast of flood levels exceedance (peak over the threshold) during forecasting period was the main criterion as flow increase forecast is of the highest importance. Other evaluation criteria included peak flow and volume difference. In addition Nash-Sutcliffe was computed separately for each time step (1 to 48 h) of forecasting period to identify its change with the lead time. Textual flood warnings are issued for administrative regions to initiate flood protection actions in danger of flood. Flood warning hit rate was evaluated at regions level and national level. Evaluation found significant differences of model forecast skill between forecasting profiles, particularly less skill was evaluated at small headwater basins due to domination of QPF uncertainty in these basins. The average hit rate was 0.34 (miss rate = 0.33, false alarm rate = 0.32). However its explored spatial difference is likely to be influenced also by different fit of parameters sets (due to different basin characteristics) and importantly by different impact of human factor. Results suggest that the practice of interactive model operation, experience and forecasting strategy differs between responsible forecasting offices. Warning is based on model outputs interpretation by hydrologists-forecaster. Warning hit rate reached 0.60 for threshold set to lowest flood stage of which 0.11 was underestimation of flood degree (miss 0.22, false alarm 0.28). Critical success index of model forecast was 0.34, while the same criteria for warning reached 0.55. We assume that the increase accounts not only to change of scale from single forecasting point to region for warning, but partly also to forecaster's added value. There is no official warning strategy preferred in the Czech Republic (f.e. tolerance towards higher false alarm rate). Therefore forecaster decision and personal strategy is of great importance. Results show quite successful warning for 1st flood level exceedance, over-warning for 2nd flood level, but under-warning for 3rd (highest) flood level. That suggests general forecaster's preference of medium level warning (2nd flood level is legally determined to be the start of the flood and flood protection activities). In conclusion human forecaster's experience and analysis skill increases flood warning performance notably. However society preference should be specifically addressed in the warning strategy definition to support forecaster's decision making.

European In-Situ Snow Measurements: Practices and Purposes.

PubMed

Pirazzini, Roberta; Leppänen, Leena; Picard, Ghislain; Lopez-Moreno, Juan Ignacio; Marty, Christoph; Macelloni, Giovanni; Kontu, Anna; von Lerber, Annakaisa; Tanis, Cemal Melih; Schneebeli, Martin; de Rosnay, Patricia; Arslan, Ali Nadir

2018-06-22

In-situ snow measurements conducted by European institutions for operational, research, and energy business applications were surveyed in the framework of the European Cooperation in Science and Technology (COST) Action ES1404, called "A European network for a harmonised monitoring of snow for the benefit of climate change scenarios, hydrology, and numerical weather prediction". Here we present the results of this survey, which was answered by 125 participants from 99 operational and research institutions, belonging to 38 European countries. The typologies of environments where the snow measurements are performed range from mountain to low elevated plains, including forests, bogs, tundra, urban areas, glaciers, lake ice, and sea ice. Of the respondents, 93% measure snow macrophysical parameters, such as snow presence, snow depth (HS), snow water equivalent (SWE), and snow density. These describe the bulk characteristics of the whole snowpack or of a snow layer, and they are the primary snow properties that are needed for most operational applications (such as hydrological monitoring, avalanche forecast, and weather forecast). In most cases, these measurements are done with manual methods, although for snow presence, HS, and SWE, automatized methods are also applied by some respondents. Parameters characterizing precipitating and suspended snow (such as the height of new snow, precipitation intensity, flux of drifting/blowing snow, and particle size distribution), some of which are crucial for the operational services, are measured by 74% of the respondents. Parameters characterizing the snow microstructural properties (such as the snow grain size and shape, and specific surface area), the snow electromagnetic properties (such as albedo, brightness temperature, and backscatter), and the snow composition (such as impurities and isotopes) are measured by 41%, 26%, and 13% of the respondents, respectively, mostly for research applications. The results of this survey are discussed from the perspective of the need of enhancing the efficiency and coverage of the in-situ observational network applying automatic and cheap measurement methods. Moreover, recommendations for the enhancement and harmonization of the observational network and measurement practices are provided.

Use and availability of continuous streamflow records in Wyoming

USGS Publications Warehouse

Schuetz, J.R.

1986-01-01

This report documents a survey that identifies local, State, and Federal uses of data from 139 continuous-record, surface-water stations, presently (1984) operated by the Wyoming District of the U. S. Geological Survey; identifies sources of funding pertaining to collections of streamflow data; and presents frequency of data availability. Uses of data from the 139 stations are categorized into seven classes: Regional Hydrology, Hydrology Systems, Legal Obligations, Planning and Design, Project Operation, Hydrologic Forecasts, and Water Quality Monitoring. Sufficient use of surface water data collected from the stations justifies the continued operation of all stations. (USGS)

Development of an Experimental African Drought Monitoring and Seasonal Forecasting System: A First Step towards a Global Drought Information System

NASA Astrophysics Data System (ADS)

Wood, E. F.; Chaney, N.; Sheffield, J.; Yuan, X.

2012-12-01

Extreme hydrologic events in the form of droughts are a significant source of social and economic damage. Internationally, organizations such as UNESCO, the Group on Earth Observations (GEO), and the World Climate Research Programme (WCRP) have recognized the need for drought monitoring, especially for the developing world where drought has had devastating impacts on local populations through food insecurity and famine. Having the capacity to monitor droughts in real-time, and to provide drought forecasts with sufficient warning will help developing countries and international programs move from the management of drought crises to the management of drought risk. While observation-based assessments, such as those produced by the US Drought Monitor, are effective for monitoring in countries with extensive observation networks (of precipitation in particular), their utility is lessened in areas (e.g., Africa) where observing networks are sparse. For countries with sparse networks and weak reporting systems, remote sensing observations can provide the real-time data for the monitoring of drought. More importantly, these datasets are now available for at least a decade, which allows for the construction of a climatology against which current conditions can be compared. In this presentation we discuss the development of our multi-lingual experimental African Drought Monitor (ADM) (see http://hydrology.princeton.edu/~nchaney/ADM_ML). At the request of UNESCO, the ADM system has been installed at AGRHYMET, a regional climate and agricultural center in Niamey, Niger and at the ICPAC climate center in Nairobi, Kenya. The ADM system leverages off our U.S. drought monitoring and forecasting system (http://hydrology.princeton.edu/forecasting) that uses the NLDAS data to force the VIC land surface model (LSM) at 1/8th degree spatial resolution for the estimation of our soil moisture drought index (Sheffield et al., 2004). For the seasonal forecast of drought, CFSv2 climate forecasts are bias corrected, downscaled and used as inputs to the VIC LSM as well as forecasts based on ESP and CPC official seasonal outlook. For Africa, data from a combination of remote sensing (TMPA-based precipitation, land cover characteristics) and GFS analysis fields (temperature and wind) are used to monitor drought using our soil moisture drought index as well as 1, 3 and and 6-month SPI. River discharge is also estimated at over 900 locations. Seasonal forecasts have been developed using CFSv2 climate forecasts following the approaches used over CONUS. We will discuss the performance of the system to evaluate the depiction of drought over various scales, from regional to the African continent, and over a number of years to capture multiple drought events. Furthermore, the hindcasts from the seasonal drought forecast system are analyzed to assess the ability of seasonal climate models to detect drought on-set and its recovery. Finally, we will discuss whether our ADM provides a pathway to a Global Drought Information System, a goal of the WCRP Drought Task Force.

Post-processing ECMWF precipitation and temperature ensemble reforecasts for operational hydrologic forecasting at various spatial scales

NASA Astrophysics Data System (ADS)

Verkade, J. S.; Brown, J. D.; Reggiani, P.; Weerts, A. H.

2013-09-01

The ECMWF temperature and precipitation ensemble reforecasts are evaluated for biases in the mean, spread and forecast probabilities, and how these biases propagate to streamflow ensemble forecasts. The forcing ensembles are subsequently post-processed to reduce bias and increase skill, and to investigate whether this leads to improved streamflow ensemble forecasts. Multiple post-processing techniques are used: quantile-to-quantile transform, linear regression with an assumption of bivariate normality and logistic regression. Both the raw and post-processed ensembles are run through a hydrologic model of the river Rhine to create streamflow ensembles. The results are compared using multiple verification metrics and skill scores: relative mean error, Brier skill score and its decompositions, mean continuous ranked probability skill score and its decomposition, and the ROC score. Verification of the streamflow ensembles is performed at multiple spatial scales: relatively small headwater basins, large tributaries and the Rhine outlet at Lobith. The streamflow ensembles are verified against simulated streamflow, in order to isolate the effects of biases in the forcing ensembles and any improvements therein. The results indicate that the forcing ensembles contain significant biases, and that these cascade to the streamflow ensembles. Some of the bias in the forcing ensembles is unconditional in nature; this was resolved by a simple quantile-to-quantile transform. Improvements in conditional bias and skill of the forcing ensembles vary with forecast lead time, amount, and spatial scale, but are generally moderate. The translation to streamflow forecast skill is further muted, and several explanations are considered, including limitations in the modelling of the space-time covariability of the forcing ensembles and the presence of storages.

Comment on "Can assimilation of crowdsourced data in hydrological modelling improve flood prediction?" by Mazzoleni et al. (2017)

NASA Astrophysics Data System (ADS)

Viero, Daniele P.

2018-01-01

Citizen science and crowdsourcing are gaining increasing attention among hydrologists. In a recent contribution, Mazzoleni et al. (2017) investigated the integration of crowdsourced data (CSD) into hydrological models to improve the accuracy of real-time flood forecasts. The authors used synthetic CSD (i.e. not actually measured), because real CSD were not available at the time of the study. In their work, which is a proof-of-concept study, Mazzoleni et al. (2017) showed that assimilation of CSD improves the overall model performance; the impact of irregular frequency of available CSD, and that of data uncertainty, were also deeply assessed. However, the use of synthetic CSD in conjunction with (semi-)distributed hydrological models deserves further discussion. As a result of equifinality, poor model identifiability, and deficiencies in model structure, internal states of (semi-)distributed models can hardly mimic the actual states of complex systems away from calibration points. Accordingly, the use of synthetic CSD that are drawn from model internal states under best-fit conditions can lead to overestimation of the effectiveness of CSD assimilation in improving flood prediction. Operational flood forecasting, which results in decisions of high societal value, requires robust knowledge of the model behaviour and an in-depth assessment of both model structure and forcing data. Additional guidelines are given that are useful for the a priori evaluation of CSD for real-time flood forecasting and, hopefully, for planning apt design strategies for both model calibration and collection of CSD.

The role of hydrology in water resources management

NASA Astrophysics Data System (ADS)

Shamir, U.

2011-12-01

Modern water resources management developed as a branch of science based engineering since the landmark publication of Mass et al. (1962&1967) which emerged from the Harvard Water Program. Clearly, water was managed much earlier, in fact since the early days of civilization, as evidenced by the publication of Vitruvius on architecture in the 1st Century BC, but the 1950s marked the advent of modeling enabled by computers, which transformed the field we call Water Resources Management (WRM). Since then, thousands of papers have been published and thousands of decisions and projects have been aided by WRM methodologies and model results. This presentation is not an historical review of water resources management, although it appears in a session titled The Evolution of WRM Paradigms. Instead, it is an attempt to discuss the role of hydrology as a feeder of information for the management domain. The issues faced by hydrologists who work to serve and support WRM will be discussed and elucidated by case studies. For hydrologists, some of the important points in this regard are: - Planning, design and operation are three interconnected "layers" of WRM. Planning is where the sources and consumers are identified, the overall "architecture" of a proposed system is laid out, including its topology and connectivity. Design is where sizes of facilities are fixed. Operational policy determines the operation of the system under a selected forecasted set of typical and/or critical conditions, while real-time operation means setting the operational variables for a defined time period ahead (hour, day, week, month, year). The three "layers" are inter-connected and inter-dependent, but still can be addressed differently. - Hydrological data of different types are required, according to the management issue being addressed. They range from short term now-casting/forecasting for real-time operation and response, e.g., for flood protection, to long-term time probabilistic series and ensembles for planning, which consider changing natural and anthropogenic drivers (land use, climate change). Since hydrology is a continuous process that is not divided internally according to the needs of management, the hydrological analysis must be geared to produce the suitable information for the different management issues. - Aggregation and disaggregation in space and time: selection of the level of detail in time and space should begin from the needs of the management issue being addressed, and dictate the monitoring, collection and processing. - Water quality: should receive more attention, as it is playing an ever increasing role in management, including its importance in ecological services. - Optimization, simulation and combining the two: optimization for WRM is used extensively. Some optimization models are able to address uncertainty internally, and further development continues. Simulation is easier to employ, but it merely produces "if-then" analysis. Combination of optimization and simulation is a common way to combine the advantages of the two. - Uncertainty, forecasting, ensembles: the uncertainties inherent in hydrological analysis and forecasting lead to the requirement for generating forecasts with a probabilistic characterization. This can be in the form of PDFs, time series, ensembles.

Spatial Modeling for Resources Framework (SMRF): A modular framework for developing spatial forcing data in mountainous terrain

NASA Astrophysics Data System (ADS)

Havens, S.; Marks, D. G.; Kormos, P.; Hedrick, A. R.; Johnson, M.; Robertson, M.; Sandusky, M.

2017-12-01

In the Western US, operational water supply managers rely on statistical techniques to forecast the volume of water left to enter the reservoirs. As the climate changes and the demand increases for stored water utilized for irrigation, flood control, power generation, and ecosystem services, water managers have begun to move from statistical techniques towards using physically based models. To assist with the transition, a new open source framework was developed, the Spatial Modeling for Resources Framework (SMRF), to automate and simplify the most common forcing data distribution methods. SMRF is computationally efficient and can be implemented for both research and operational applications. Currently, SMRF is able to generate all of the forcing data required to run physically based snow or hydrologic models at 50-100 m resolution over regions of 500-10,000 km2, and has been successfully applied in real time and historical applications for the Boise River Basin in Idaho, USA, the Tuolumne River Basin and San Joaquin in California, USA, and Reynolds Creek Experimental Watershed in Idaho, USA. These applications use meteorological station measurements and numerical weather prediction model outputs as input data. SMRF has significantly streamlined the modeling workflow, decreased model set up time from weeks to days, and made near real-time application of physics-based snow and hydrologic models possible.

Soil Moisture Initialization Error and Subgrid Variability of Precipitation in Seasonal Streamflow Forecasting

NASA Technical Reports Server (NTRS)

Koster, Randal D.; Walker, Gregory K.; Mahanama, Sarith P.; Reichle, Rolf H.

2013-01-01

Offline simulations over the conterminous United States (CONUS) with a land surface model are used to address two issues relevant to the forecasting of large-scale seasonal streamflow: (i) the extent to which errors in soil moisture initialization degrade streamflow forecasts, and (ii) the extent to which a realistic increase in the spatial resolution of forecasted precipitation would improve streamflow forecasts. The addition of error to a soil moisture initialization field is found to lead to a nearly proportional reduction in streamflow forecast skill. The linearity of the response allows the determination of a lower bound for the increase in streamflow forecast skill achievable through improved soil moisture estimation, e.g., through satellite-based soil moisture measurements. An increase in the resolution of precipitation is found to have an impact on large-scale streamflow forecasts only when evaporation variance is significant relative to the precipitation variance. This condition is met only in the western half of the CONUS domain. Taken together, the two studies demonstrate the utility of a continental-scale land surface modeling system as a tool for addressing the science of hydrological prediction.

Evaluation of Gridded Precipitation Data for Driving SWAT Model in Area Upstream of Three Gorges Reservoir

PubMed Central

Yang, Yan; Wang, Guoqiang; Wang, Lijing; Yu, Jingshan; Xu, Zongxue

2014-01-01

Gridded precipitation data are becoming an important source for driving hydrologic models to achieve stable and valid simulation results in different regions. Thus, evaluating different sources of precipitation data is important for improving the applicability of gridded data. In this study, we used three gridded rainfall datasets: 1) National Centers for Environmental Prediction - Climate Forecast System Reanalysis (NCEP-CFSR); 2) Asian Precipitation - Highly-Resolved Observational Data Integration Towards Evaluation (APHRODITE); and 3) China trend - surface reanalysis (trend surface) data. These are compared with monitoring precipitation data for driving the Soil and Water Assessment Tool in two basins upstream of Three Gorges Reservoir (TGR) in China. The results of one test basin with significant topographic influence indicates that all the gridded data have poor abilities in reproducing hydrologic processes with the topographic influence on precipitation quantity and distribution. However, in a relatively flat test basin, the APHRODITE and trend surface data can give stable and desirable results. The results of this study suggest that precipitation data for future applications should be considered comprehensively in the TGR area, including the influence of data density and topography. PMID:25409467

Performance assessment and parameterization of the SWAP-WOFOST model for peat soil under agricultural use in northern Europe.

NASA Astrophysics Data System (ADS)

Bertram, Sascha; Bechtold, Michel; Hendriks, Rob; Piayda, Arndt; Regina, Kristiina; Myllys, Merja; Tiemeyer, Bärbel

2017-04-01

Peat soils form a major share of soil suitable for agriculture in northern Europe. Successful agricultural production depends on hydrological and pedological conditions, local climate and agricultural management. Climate change impact assessment on food production and development of mitigation and adaptation strategies require reliable yield forecasts under given emission scenarios. Coupled soil hydrology - crop growth models, driven by regionalized future climate scenarios are a valuable tool and widely used for this purpose. Parameterization on local peat soil conditions and crop breed or grassland specie performance, however, remains a major challenge. The subject of this study is to evaluate the performance and sensitivity of the SWAP-WOFOST coupled soil hydrology and plant growth model with respect to the application on peat soils under different regional conditions across northern Europe. Further, the parameterization of region-specific crop and grass species is discussed. First results of the model application and parameterization at deep peat sites in southern Finland are presented. The model performed very well in reproducing two years of observed, daily ground water level data on four hydrologically contrasting sites. Naturally dry and wet sites could be modelled with the same performance as sites with active water table management by regulated drains in order to improve peat conservation. A simultaneous multi-site calibration scheme was used to estimate plant growth parameters of the local oat breed. Cross-site validation of the modelled yields against two years of observations proved the robustness of the chosen parameter set and gave no indication of possible overparameterization. This study proves the suitability of the coupled SWAP-WOFOST model for the prediction of crop yields and water table dynamics of peat soils in agricultural use under given climate conditions.

A Participatory Modeling Application of a Distributed Hydrologic Model in Nuevo Leon, Mexico for the 2010 Hurricane Alex Flood Event

NASA Astrophysics Data System (ADS)

Baish, A. S.; Vivoni, E. R.; Payan, J. G.; Robles-Morua, A.; Basile, G. M.

2011-12-01

A distributed hydrologic model can help bring consensus among diverse stakeholders in regional flood planning by producing quantifiable sets of alternative futures. This value is acute in areas with high uncertainties in hydrologic conditions and sparse observations. In this study, we conduct an application of the Triangulated Irregular Network (TIN)-based Real-time Integrated Basin Simulator (tRIBS) in the Santa Catarina basin of Nuevo Leon, Mexico, where Hurricane Alex in July 2010 led to catastrophic flooding of the capital city of Monterrey. Distributed model simulations utilize best-available information on the regional topography, land cover, and soils obtained from Mexican government agencies or analysis of remotely-sensed imagery from MODIS and ASTER. Furthermore, we developed meteorological forcing for the flood event based on multiple data sources, including three local gauge networks, satellite-based estimates from TRMM and PERSIANN, and the North American Land Data Assimilation System (NLDAS). Remotely-sensed data allowed us to quantify rainfall distributions in the upland, rural portions of the Santa Catarina that are sparsely populated and ungauged. Rural areas had significant contributions to the flood event and as a result were considered by stakeholders for flood control measures, including new reservoirs and upland vegetation management. Participatory modeling workshops with the stakeholders revealed a disconnect between urban and rural populations in regard to understanding the hydrologic conditions of the flood event and the effectiveness of existing and potential flood control measures. Despite these challenges, the use of the distributed flood forecasts developed within this participatory framework facilitated building consensus among diverse stakeholders and exploring alternative futures in the basin.

Seasonal forecasting of hydrological drought in the Limpopo Basin: a comparison of statistical methods

NASA Astrophysics Data System (ADS)

Seibert, Mathias; Merz, Bruno; Apel, Heiko

2017-03-01

The Limpopo Basin in southern Africa is prone to droughts which affect the livelihood of millions of people in South Africa, Botswana, Zimbabwe and Mozambique. Seasonal drought early warning is thus vital for the whole region. In this study, the predictability of hydrological droughts during the main runoff period from December to May is assessed using statistical approaches. Three methods (multiple linear models, artificial neural networks, random forest regression trees) are compared in terms of their ability to forecast streamflow with up to 12 months of lead time. The following four main findings result from the study. 1. There are stations in the basin at which standardised streamflow is predictable with lead times up to 12 months. The results show high inter-station differences of forecast skill but reach a coefficient of determination as high as 0.73 (cross validated). 2. A large range of potential predictors is considered in this study, comprising well-established climate indices, customised teleconnection indices derived from sea surface temperatures and antecedent streamflow as a proxy of catchment conditions. El Niño and customised indices, representing sea surface temperature in the Atlantic and Indian oceans, prove to be important teleconnection predictors for the region. Antecedent streamflow is a strong predictor in small catchments (with median 42 % explained variance), whereas teleconnections exert a stronger influence in large catchments. 3. Multiple linear models show the best forecast skill in this study and the greatest robustness compared to artificial neural networks and random forest regression trees, despite their capabilities to represent nonlinear relationships. 4. Employed in early warning, the models can be used to forecast a specific drought level. Even if the coefficient of determination is low, the forecast models have a skill better than a climatological forecast, which is shown by analysis of receiver operating characteristics (ROCs). Seasonal statistical forecasts in the Limpopo show promising results, and thus it is recommended to employ them as complementary to existing forecasts in order to strengthen preparedness for droughts.

Optimal selection and placement of green infrastructure to reduce impacts of land use change and climate change on hydrology and water quality: An application to the Trail Creek Watershed, Indiana.

PubMed

Liu, Yaoze; Theller, Lawrence O; Pijanowski, Bryan C; Engel, Bernard A

2016-05-15

The adverse impacts of urbanization and climate change on hydrology and water quality can be mitigated by applying green infrastructure practices. In this study, the impacts of land use change and climate change on hydrology and water quality in the 153.2 km(2) Trail Creek watershed located in northwest Indiana were estimated using the Long-Term Hydrologic Impact Assessment-Low Impact Development 2.1 (L-THIA-LID 2.1) model for the following environmental concerns: runoff volume, Total Suspended Solids (TSS), Total Phosphorous (TP), Total Kjeldahl Nitrogen (TKN), and Nitrate+Nitrite (NOx). Using a recent 2001 land use map and 2050 land use forecasts, we found that land use change resulted in increased runoff volume and pollutant loads (8.0% to 17.9% increase). Climate change reduced runoff and nonpoint source pollutant loads (5.6% to 10.2% reduction). The 2050 forecasted land use with current rainfall resulted in the largest runoff volume and pollutant loads. The optimal selection and placement of green infrastructure practices using L-THIA-LID 2.1 model were conducted. Costs of applying green infrastructure were estimated using the L-THIA-LID 2.1 model considering construction, maintenance, and opportunity costs. To attain the same runoff volume and pollutant loads as in 2001 land uses for 2050 land uses, the runoff volume, TSS, TP, TKN, and NOx for 2050 needed to be reduced by 10.8%, 14.4%, 13.1%, 15.2%, and 9.0%, respectively. The corresponding annual costs of implementing green infrastructure to achieve the goals were $2.1, $0.8, $1.6, $1.9, and $0.8 million, respectively. Annual costs of reducing 2050 runoff volume/pollutant loads were estimated, and results show green infrastructure annual cost greatly increased for larger reductions in runoff volume and pollutant loads. During optimization, the most cost-efficient green infrastructure practices were selected and implementation levels increased for greater reductions of runoff and nonpoint source pollutants. Copyright © 2016 Elsevier B.V. All rights reserved.

Development and Use of the Hydrologic Ensemble Forecast System by the National Weather Service to Support the New York City Water Supply

NASA Astrophysics Data System (ADS)

Shedd, R.; Reed, S. M.; Porter, J. H.

2015-12-01

The National Weather Service (NWS) has been working for several years on the development of the Hydrologic Ensemble Forecast System (HEFS). The objective of HEFS is to provide ensemble river forecasts incorporating the best precipitation and temperature forcings at any specific time horizon. For the current implementation, this includes the Global Ensemble Forecast System (GEFS) and the Climate Forecast System (CFSv2). One of the core partners that has been working with the NWS since the beginning of the development phase of HEFS is the New York City Department of Environmental Protection (NYCDEP) which is responsible for the complex water supply system for New York City. The water supply system involves a network of reservoirs in both the Delaware and Hudson River basins. At the same time that the NWS was developing HEFS, NYCDEP was working on enhancing the operations of their water supply reservoirs through the development of a new Operations Support Tool (OST). OST is designed to guide reservoir system operations to ensure an adequate supply of high-quality drinking water for the city, as well as to meet secondary objectives for reaches downstream of the reservoirs assuming the primary water supply goals can be met. These secondary objectives include fisheries and ecosystem support, enhanced peak flow attenuation beyond that provided natively by the reservoirs, salt front management, and water supply for other cities. Since January 2014, the NWS Northeast and Middle Atlantic River Forecast Centers have provided daily one year forecasts from HEFS to NYCDEP. OST ingests these forecasts, couples them with near-real-time environmental and reservoir system data, and drives models of the water supply system. The input of ensemble forecasts results in an ensemble of model output, from which information on the range and likelihood of possible future system states can be extracted. This type of probabilistic information provides system managers with additional information not available from deterministic forecasts and allows managers to better assess risk, and provides greater context for decision-making than has been available in the past. HEFS has allowed NYCDEP water supply managers to make better decisions on reservoir operations than they likely would have in the past, using only deterministic forecasts.

Modeling hydrologic and geomorphic hazards across post-fire landscapes using a self-organizing map approach

USGS Publications Warehouse

Friedel, Michael J.

2011-01-01

Few studies attempt to model the range of possible post-fire hydrologic and geomorphic hazards because of the sparseness of data and the coupled, nonlinear, spatial, and temporal relationships among landscape variables. In this study, a type of unsupervised artificial neural network, called a self-organized map (SOM), is trained using data from 540 burned basins in the western United States. The sparsely populated data set includes variables from independent numerical landscape categories (climate, land surface form, geologic texture, and post-fire condition), independent landscape classes (bedrock geology and state), and dependent initiation processes (runoff, landslide, and runoff and landslide combination) and responses (debris flows, floods, and no events). Pattern analysis of the SOM-based component planes is used to identify and interpret relations among the variables. Application of the Davies-Bouldin criteria following k-means clustering of the SOM neurons identified eight conceptual regional models for focusing future research and empirical model development. A split-sample validation on 60 independent basins (not included in the training) indicates that simultaneous predictions of initiation process and response types are at least 78% accurate. As climate shifts from wet to dry conditions, forecasts across the burned landscape reveal a decreasing trend in the total number of debris flow, flood, and runoff events with considerable variability among individual basins. These findings suggest the SOM may be useful in forecasting real-time post-fire hazards, and long-term post-recovery processes and effects of climate change scenarios.

Radar-driven High-resolution Hydrometeorological Forecasts of the 26 September 2007 Venice flash flood

NASA Astrophysics Data System (ADS)

Massimo Rossa, Andrea; Laudanna Del Guerra, Franco; Borga, Marco; Zanon, Francesco; Settin, Tommaso; Leuenberger, Daniel

2010-05-01

Space and time scales of flash floods are such that flash flood forecasting and warning systems depend upon the accurate real-time provision of rainfall information, high-resolution numerical weather prediction (NWP) forecasts and the use of hydrological models. Currently available high-resolution NWP model models can potentially provide warning forecasters information on the future evolution of storms and their internal structure, thereby increasing convective-scale warning lead times. However, it is essential that the model be started with a very accurate representation of on-going convection, which calls for assimilation of high-resolution rainfall data. This study aims to assess the feasibility of using carefully checked radar-derived quantitative precipitation estimates (QPE) for assimilation into NWP and hydrological models. The hydrometeorological modeling chain includes the convection-permitting NWP model COSMO-2 and a hydrologic-hydraulic models built upon the concept of geomorphological transport. Radar rainfall observations are assimilated into the NWP model via the latent heat nudging method. The study is focused on 26 September 2007 extreme flash flood event which impacted the coastal area of north-eastern Italy around Venice. The hydro-meteorological modeling system is implemented over the Dese river, a 90 km2 catchment flowing to the Venice lagoon. The radar rainfall observations are carefully checked for artifacts, including beam attenuation, by means of physics-based correction procedures and comparison with a dense network of raingauges. The impact of the radar QPE in the assimilation cycle of the NWP model is very significant, in that the main individual organized convective systems were successfully introduced into the model state, both in terms of timing and localization. Also, incorrectly localized precipitation in the model reference run without rainfall assimilation was correctly reduced to about the observed levels. On the other hand, the highest rainfall intensities were underestimated by 20% at a scale of 1000 km2, and the local peaks by 50%. The positive impact of the assimilated radar rainfall was carried over into the free forecast for about 2-5 hours, depending on when this forecast was started, and was larger, when the main mesoscale convective system was present in the initial conditions. The improvements of the meteorological model simulations were directly propagated to the river flow simulations, with an extension of the warning lead time up to three hours.

Distinguishing high and low flow domains in urban drainage systems 2 days ahead using numerical weather prediction ensembles

NASA Astrophysics Data System (ADS)

Courdent, Vianney; Grum, Morten; Mikkelsen, Peter Steen

2018-01-01

Precipitation constitutes a major contribution to the flow in urban storm- and wastewater systems. Forecasts of the anticipated runoff flows, created from radar extrapolation and/or numerical weather predictions, can potentially be used to optimize operation in both wet and dry weather periods. However, flow forecasts are inevitably uncertain and their use will ultimately require a trade-off between the value of knowing what will happen in the future and the probability and consequence of being wrong. In this study we examine how ensemble forecasts from the HIRLAM-DMI-S05 numerical weather prediction (NWP) model subject to three different ensemble post-processing approaches can be used to forecast flow exceedance in a combined sewer for a wide range of ratios between the probability of detection (POD) and the probability of false detection (POFD). We use a hydrological rainfall-runoff model to transform the forecasted rainfall into forecasted flow series and evaluate three different approaches to establishing the relative operating characteristics (ROC) diagram of the forecast, which is a plot of POD against POFD for each fraction of concordant ensemble members and can be used to select the weight of evidence that matches the desired trade-off between POD and POFD. In the first approach, the rainfall input to the model is calculated for each of 25 ensemble members as a weighted average of rainfall from the NWP cells over the catchment where the weights are proportional to the areal intersection between the catchment and the NWP cells. In the second approach, a total of 2825 flow ensembles are generated using rainfall input from the neighbouring NWP cells up to approximately 6 cells in all directions from the catchment. In the third approach, the first approach is extended spatially by successively increasing the area covered and for each spatial increase and each time step selecting only the cell with the highest intensity resulting in a total of 175 ensemble members. While the first and second approaches have the disadvantage of not covering the full range of the ROC diagram and being computationally heavy, respectively, the third approach leads to both a broad coverage of the ROC diagram range at a relatively low computational cost. A broad coverage of the ROC diagram offers a larger selection of prediction skill to choose from to best match to the prediction purpose. The study distinguishes itself from earlier research in being the first application to urban hydrology, with fast runoff and small catchments that are highly sensitive to local extremes. Furthermore, no earlier reference has been found on the highly efficient third approach using only neighbouring cells with the highest threat to expand the range of the ROC diagram. This study provides an efficient and robust approach to using ensemble rainfall forecasts affected by bias and misplacement errors for predicting flow threshold exceedance in urban drainage systems.

Innovative Tools for Water Quality/Quantity Management: New York City's Operations Support Tool

NASA Astrophysics Data System (ADS)

Wang, L.; Schaake, J. C.; Day, G. N.; Porter, J.; Sheer, D. P.; Pyke, G.

2011-12-01

The New York City Department of Environmental Protection (DEP) manages New York City's water supply, which is comprised of over 20 reservoirs and supplies more than 1 billion gallons of water per day to over 9 million customers. Recently, DEP has initiated design of an Operations Support Tool (OST), a state-of-the-art decision support system to provide computational and predictive support for water supply operations and planning. This presentation describes the technical structure of OST, including the underlying water supply and water quality models, data sources and database management, reservoir inflow forecasts, and the functionalities required to meet the needs of a diverse group of end users. OST is a major upgrade of DEP's current water supply - water quality model, developed to evaluate alternatives for controlling turbidity in NYC's Catskill reservoirs. While the current model relies on historical hydrologic and meteorological data, OST can be driven by forecasted future conditions. It will receive a variety of near-real-time data from a number of sources. OST will support two major types of simulations: long-term, for evaluating policy or infrastructure changes over an extended period of time; and short-term "position analysis" (PA) simulations, consisting of multiple short simulations, all starting from the same initial conditions. Typically, the starting conditions for a PA run will represent those for the current day and traces of forecasted hydrology will drive the model for the duration of the simulation period. The result of these simulations will be a distribution of future system states based on system operating rules and the range of input ensemble streamflow predictions. DEP managers will analyze the output distributions and make operation decisions using risk-based metrics such as probability of refill. Currently, in the developmental stages of OST, forecasts are based on antecedent hydrologic conditions and are statistical in nature. The statistical algorithm is a relatively simple and versatile, but lacks short-term skill critical for water quality and spill management. To improve short-term skill, OST will ultimately operate with meteorologically driven hydrologic forecasts provided by the National Weather Service (NWS). OST functionalities will support a wide range of DEP uses, including short term operational projections, outage planning and emergency management, operating rule development, and water supply planning. A core use of OST will be to inform reservoir management strategies to control and mitigate turbidity events while ensuring water supply reliability. OST will also allow DEP to manage its complex reservoir system to meet multiple objectives, including ecological flows, tailwater fisheries and recreational releases, and peak flow mitigation for downstream communities.

Climate Change Impacts on Sediment Transport In a Lowland Watershed System: Controlling Processes and Projection

NASA Astrophysics Data System (ADS)

al Aamery, N. M. H.; Mahoney, D. T.; Fox, J.

2017-12-01

Future climate change projections suggest extreme impacts on watershed hydrologic systems for some regions of the world including pronounced increases in surface runoff and instream flows. Yet, there remains a lack of research focused on how future changes in hydrologic extremes, as well as relative hydrologic mean changes, impact sediment redistribution within a watershed and sediment flux from a watershed. The authors hypothesized that variations in mean and extreme changes in turn may impact sediments in depositional and erosional dominance in a manner that may not be obvious to the watershed manager. Therefore, the objectives of this study were to investigate the inner processes connecting the combined effect of extreme climate change projections on the vegetation, upland erosion, and instream processes to produce changes in sediment redistribution within watersheds. To do so, research methods were carried out by the authors including simulating sediment processes in forecast and hindcast periods for a lowland watershed system. Publically available climate realizations from several climate factors and the Soil Water Assessment Tool (SWAT) were used to predict hydrologic conditions for the South Elkhorn Watershed in central Kentucky, USA to 2050. The results of the simulated extreme and mean hydrological components were used in simulating upland erosion with the connectivity processes consideration and thereafter used in building and simulating the instream erosion and deposition of sediment processes with the consideration of surface fine grain lamina (SFGL) layer controlling the benthic ecosystem. Results are used to suggest the dominance of erosional and depositional redistribution of sediments under different scenarios associated with extreme and mean hydrologic forecasting. The results are discussed in reference to the benthic ecology of the stream system providing insight on how water managers might consider sediment redistribution in a changing climate.

The utility of satellite precipitation products for hydrologic prediction in topographically complex regions: The Chehalis River Basin, WA as a case study

NASA Astrophysics Data System (ADS)

Cao, Q.; Mehran, A.; Lettenmaier, D. P.; Mass, C.; Johnson, N.

2015-12-01

Accurate measurements of precipitation are of great importance in hydrologic predictions especially for floods, which are a pervasive natural hazard. One of the primary objectives of Global Precipitation Measurement (GPM) mission is to provide a basis for hydrologic predictions using satellite sensors. A major advance in GPM relative to the Tropical Rainfall Measuring Mission (TRMM) is that it observes atmospheric river (AR) events, most of which have landfall too far north to be tracked by TRMM. These events are responsible for most major floods along the U.S. West Coast. We address the question of whether, for hydrologic modeling purposes, it is better to use precipitation products derived directly from GPM and/or other precipitation fields from weather models that have assimilated satellite data. Our overall strategy is to compare different methods for prediction of flood and/or high flow events by different forcings on the hydrologic model. We examine four different configurations of the Distroibute Hydrology Soil Vegetation Model (DHSVM) over the Chehalis River Basin that use a) precipitation forcings based on gridded station data; b) precipitation forcings based on NWS WSR-88D data, c) forcings based from short-term precipitation forecasts using the Weather Research and Forecasting (WRF) mesoscale atmospheric model, and d) satellite-based precipitation estimates (TMPA and IMERG). We find that in general, biases in the radar and satellite products result in much larger errors than with either gridded station data or WRF forcings, but if these biases are removed, comparable performance in flood predictions can be achieved by Satellite-based precipitation estimates (TMPA and IMERG).