Parcel-scale urban coastal flood mapping: Leveraging the multi-scale CoSMoS model for coastal flood forecasting

NASA Astrophysics Data System (ADS)

Gallien, T.; Barnard, P. L.; Sanders, B. F.

2011-12-01

California coastal sea levels are projected to rise 1-1.4 meters in the next century and evidence suggests mean tidal range, and consequently, mean high water (MHW) is increasing along portions of Southern California Bight. Furthermore, emerging research indicates wind stress patterns associated with the Pacific Decadal Oscillation (PDO) have suppressed sea level rise rates along the West Coast since 1980, and a reversal in this pattern would result in the resumption of regional sea level rise rates equivalent to or exceeding global mean sea level rise rates, thereby enhancing coastal flooding. Newport Beach is a highly developed, densely populated lowland along the Southern California coast currently subject to episodic flooding from coincident high tides and waves, and the frequency and intensity of flooding is expected to increase with projected future sea levels. Adaptation to elevated sea levels will require flood mapping and forecasting tools that are sensitive to the dominant factors affecting flooding including extreme high tides, waves and flood control infrastructure. Considerable effort has been focused on the development of nowcast and forecast systems including Scripps Institute of Oceanography's Coastal Data Information Program (CDIP) and the USGS Multi-hazard model, the Southern California Coastal Storm Modeling System (CoSMoS). However, fine scale local embayment dynamics and overtopping flows are needed to map unsteady flooding effects in coastal lowlands protected by dunes, levees and seawalls. Here, a recently developed two dimensional Godunov non-linear shallow water solver is coupled to water level and wave forecasts from the CoSMoS model to investigate the roles of tides, waves, sea level changes and flood control infrastructure in accurate flood mapping and forecasting. The results of this study highlight the important roles of topographic data, embayment hydrodynamics, water level uncertainties and critical flood processes required for meaningful prediction of sea level rise impacts and coastal flood forecasting.

Daily water level forecasting using wavelet decomposition and artificial intelligence techniques

NASA Astrophysics Data System (ADS)

Seo, Youngmin; Kim, Sungwon; Kisi, Ozgur; Singh, Vijay P.

2015-01-01

Reliable water level forecasting for reservoir inflow is essential for reservoir operation. The objective of this paper is to develop and apply two hybrid models for daily water level forecasting and investigate their accuracy. These two hybrid models are wavelet-based artificial neural network (WANN) and wavelet-based adaptive neuro-fuzzy inference system (WANFIS). Wavelet decomposition is employed to decompose an input time series into approximation and detail components. The decomposed time series are used as inputs to artificial neural networks (ANN) and adaptive neuro-fuzzy inference system (ANFIS) for WANN and WANFIS models, respectively. Based on statistical performance indexes, the WANN and WANFIS models are found to produce better efficiency than the ANN and ANFIS models. WANFIS7-sym10 yields the best performance among all other models. It is found that wavelet decomposition improves the accuracy of ANN and ANFIS. This study evaluates the accuracy of the WANN and WANFIS models for different mother wavelets, including Daubechies, Symmlet and Coiflet wavelets. It is found that the model performance is dependent on input sets and mother wavelets, and the wavelet decomposition using mother wavelet, db10, can further improve the efficiency of ANN and ANFIS models. Results obtained from this study indicate that the conjunction of wavelet decomposition and artificial intelligence models can be a useful tool for accurate forecasting daily water level and can yield better efficiency than the conventional forecasting models.

Particle swarm optimization based artificial neural network model for forecasting groundwater level in Udupi district

NASA Astrophysics Data System (ADS)

Balavalikar, Supreetha; Nayak, Prabhakar; Shenoy, Narayan; Nayak, Krishnamurthy

2018-04-01

The decline in groundwater is a global problem due to increase in population, industries, and environmental aspects such as increase in temperature, decrease in overall rainfall, loss of forests etc. In Udupi district, India, the water source fully depends on the River Swarna for drinking and agriculture purposes. Since the water storage in Bajae dam is declining day-by-day and the people of Udupi district are under immense pressure due to scarcity of drinking water, alternatively depend on ground water. As the groundwater is being heavily used for drinking and agricultural purposes, there is a decline in its water table. Therefore, the groundwater resources must be identified and preserved for human survival. This research proposes a data driven approach for forecasting the groundwater level. The monthly variations in groundwater level and rainfall data in three observation wells located in Brahmavar, Kundapur and Hebri were investigated and the scenarios were examined for 2000-2013. The focus of this research work is to develop an ANN based groundwater level forecasting model and compare with hybrid ANN-PSO forecasting model. The model parameters are tested using different combinations of the data. The results reveal that PSO-ANN based hybrid model gives a better prediction accuracy, than ANN alone.

Forecasting in an integrated surface water-ground water system: The Big Cypress Basin, South Florida

NASA Astrophysics Data System (ADS)

Butts, M. B.; Feng, K.; Klinting, A.; Stewart, K.; Nath, A.; Manning, P.; Hazlett, T.; Jacobsen, T.

2009-04-01

The South Florida Water Management District (SFWMD) manages and protects the state's water resources on behalf of 7.5 million South Floridians and is the lead agency in restoring America's Everglades - the largest environmental restoration project in US history. Many of the projects to restore and protect the Everglades ecosystem are part of the Comprehensive Everglades Restoration Plan (CERP). The region has a unique hydrological regime, with close connection between surface water and groundwater, and a complex managed drainage network with many structures. Added to the physical complexity are the conflicting needs of the ecosystem for protection and restoration, versus the substantial urban development with the accompanying water supply, water quality and flood control issues. In this paper a novel forecasting and real-time modelling system is presented for the Big Cypress Basin. The Big Cypress Basin includes 272 km of primary canals and 46 water control structures throughout the area that provide limited levels of flood protection, as well as water supply and environmental quality management. This system is linked to the South Florida Water Management District's extensive real-time (SCADA) data monitoring and collection system. Novel aspects of this system include the use of a fully distributed and integrated modeling approach and a new filter-based updating approach for accurately forecasting river levels. Because of the interaction between surface- and groundwater a fully integrated forecast modeling approach is required. Indeed, results for the Tropical Storm Fay in 2008, the groundwater levels show an extremely rapid response to heavy rainfall. Analysis of this storm also shows that updating levels in the river system can have a direct impact on groundwater levels.

Non-parametric data-based approach for the quantification and communication of uncertainties in river flood forecasts

NASA Astrophysics Data System (ADS)

Van Steenbergen, N.; Willems, P.

2012-04-01

Reliable flood forecasts are the most important non-structural measures to reduce the impact of floods. However flood forecasting systems are subject to uncertainty originating from the input data, model structure and model parameters of the different hydraulic and hydrological submodels. To quantify this uncertainty a non-parametric data-based approach has been developed. This approach analyses the historical forecast residuals (differences between the predictions and the observations at river gauging stations) without using a predefined statistical error distribution. Because the residuals are correlated with the value of the forecasted water level and the lead time, the residuals are split up into discrete classes of simulated water levels and lead times. For each class, percentile values are calculated of the model residuals and stored in a 'three dimensional error' matrix. By 3D interpolation in this error matrix, the uncertainty in new forecasted water levels can be quantified. In addition to the quantification of the uncertainty, the communication of this uncertainty is equally important. The communication has to be done in a consistent way, reducing the chance of misinterpretation. Also, the communication needs to be adapted to the audience; the majority of the larger public is not interested in in-depth information on the uncertainty on the predicted water levels, but only is interested in information on the likelihood of exceedance of certain alarm levels. Water managers need more information, e.g. time dependent uncertainty information, because they rely on this information to undertake the appropriate flood mitigation action. There are various ways in presenting uncertainty information (numerical, linguistic, graphical, time (in)dependent, etc.) each with their advantages and disadvantages for a specific audience. A useful method to communicate uncertainty of flood forecasts is by probabilistic flood mapping. These maps give a representation of the probability of flooding of a certain area, based on the uncertainty assessment of the flood forecasts. By using this type of maps, water managers can focus their attention on the areas with the highest flood probability. Also the larger public can consult these maps for information on the probability of flooding for their specific location, such that they can take pro-active measures to reduce the personal damage. The method of quantifying the uncertainty was implemented in the operational flood forecasting system for the navigable rivers in the Flanders region of Belgium. The method has shown clear benefits during the floods of the last two years.

Exploring What Determines the Use of Forecasts of Varying Time Periods in Guanacaste, Costa Rica

NASA Astrophysics Data System (ADS)

Babcock, M.; Wong-Parodi, G.; Grossmann, I.; Small, M. J.

2016-12-01

Weather and climate forecasts are promoted as ways to improve water management, especially in the face of changing environmental conditions. However, studies indicate many stakeholders who may benefit from such information do not use it. This study sought to better understand which personal factors (e.g., trust in forecast sources, perceptions of accuracy) were important determinants of the use of 4-day, 3-month, and 12-month rainfall forecasts by stakeholders in water management-related sectors in the seasonally dry province of Guanacaste, Costa Rica. From August to October 2015, we surveyed 87 stakeholders from a mix of government agencies, local water committees, large farms, tourist businesses, environmental NGO's, and the public. The result of an exploratory factor analysis suggests that trust in "informal" forecast sources (traditional methods, family advice) and in "formal" sources (government, university and private company science) are independent of each other. The result of logistic regression analyses suggest that 1) greater understanding of forecasts is associated with a greater probability of 4-day and 3-month forecast use, but not 12-month forecast use, 2) a greater probability of 3-month forecast use is associated with a lower level of trust in "informal" sources, and 3), feeling less secure about water resources, and regularly using many sources of information (and specifically formal meetings and reports) are each associated with a greater probability of using 12-month forecasts. While limited by the sample size, and affected by the factoring method and regression model assumptions, these results do appear to suggest that while forecasts of all times scales are used to some extent, local decision makers' decisions to use 4-day and 3-month forecasts appear to be more intrinsically motivated (based on their level of understanding and trust) and the use of 12-month forecasts seems to be more motivated by a sense of requirement or mandate.

iFLOOD: A Real Time Flood Forecast System for Total Water Modeling in the National Capital Region

NASA Astrophysics Data System (ADS)

Sumi, S. J.; Ferreira, C.

2017-12-01

Extreme flood events are the costliest natural hazards impacting the US and frequently cause extensive damages to infrastructure, disruption to economy and loss of lives. In 2016, Hurricane Matthew brought severe damage to South Carolina and demonstrated the importance of accurate flood hazard predictions that requires the integration of riverine and coastal model forecasts for total water prediction in coastal and tidal areas. The National Weather Service (NWS) and the National Ocean Service (NOS) provide flood forecasts for almost the entire US, still there are service-gap areas in tidal regions where no official flood forecast is available. The National capital region is vulnerable to multi-flood hazards including high flows from annual inland precipitation events and surge driven coastal inundation along the tidal Potomac River. Predicting flood levels on such tidal areas in river-estuarine zone is extremely challenging. The main objective of this study is to develop the next generation of flood forecast systems capable of providing accurate and timely information to support emergency management and response in areas impacted by multi-flood hazards. This forecast system is capable of simulating flood levels in the Potomac and Anacostia River incorporating the effects of riverine flooding from the upstream basins, urban storm water and tidal oscillations from the Chesapeake Bay. Flood forecast models developed so far have been using riverine data to simulate water levels for Potomac River. Therefore, the idea is to use forecasted storm surge data from a coastal model as boundary condition of this system. Final output of this validated model will capture the water behavior in river-estuary transition zone far better than the one with riverine data only. The challenge for this iFLOOD forecast system is to understand the complex dynamics of multi-flood hazards caused by storm surges, riverine flow, tidal oscillation and urban storm water. Automated system simulations will help to develop a seamless integration with the boundary systems in the service-gap area with new insights into our scientific understanding of such complex systems. A visualization system is being developed to allow stake holders and the community to have access to the flood forecasting for their region with sufficient lead time.

Improved water-level forecasting for the Northwest European Shelf and North Sea through direct modelling of tide, surge and non-linear interaction

NASA Astrophysics Data System (ADS)

Zijl, Firmijn; Verlaan, Martin; Gerritsen, Herman

2013-07-01

In real-time operational coastal forecasting systems for the northwest European shelf, the representation accuracy of tide-surge models commonly suffers from insufficiently accurate tidal representation, especially in shallow near-shore areas with complex bathymetry and geometry. Therefore, in conventional operational systems, the surge component from numerical model simulations is used, while the harmonically predicted tide, accurately known from harmonic analysis of tide gauge measurements, is added to forecast the full water-level signal at tide gauge locations. Although there are errors associated with this so-called astronomical correction (e.g. because of the assumption of linearity of tide and surge), for current operational models, astronomical correction has nevertheless been shown to increase the representation accuracy of the full water-level signal. The simulated modulation of the surge through non-linear tide-surge interaction is affected by the poor representation of the tide signal in the tide-surge model, which astronomical correction does not improve. Furthermore, astronomical correction can only be applied to locations where the astronomic tide is known through a harmonic analysis of in situ measurements at tide gauge stations. This provides a strong motivation to improve both tide and surge representation of numerical models used in forecasting. In the present paper, we propose a new generation tide-surge model for the northwest European Shelf (DCSMv6). This is the first application on this scale in which the tidal representation is such that astronomical correction no longer improves the accuracy of the total water-level representation and where, consequently, the straightforward direct model forecasting of total water levels is better. The methodology applied to improve both tide and surge representation of the model is discussed, with emphasis on the use of satellite altimeter data and data assimilation techniques for reducing parameter uncertainty. Historic DCSMv6 model simulations are compared against shelf wide observations for a full calendar year. For a selection of stations, these results are compared to those with astronomical correction, which confirms that the tide representation in coastal regions has sufficient accuracy, and that forecasting total water levels directly yields superior results.

Optimization of autoregressive, exogenous inputs-based typhoon inundation forecasting models using a multi-objective genetic algorithm

NASA Astrophysics Data System (ADS)

Ouyang, Huei-Tau

2017-07-01

Three types of model for forecasting inundation levels during typhoons were optimized: the linear autoregressive model with exogenous inputs (LARX), the nonlinear autoregressive model with exogenous inputs with wavelet function (NLARX-W) and the nonlinear autoregressive model with exogenous inputs with sigmoid function (NLARX-S). The forecast performance was evaluated by three indices: coefficient of efficiency, error in peak water level and relative time shift. Historical typhoon data were used to establish water-level forecasting models that satisfy all three objectives. A multi-objective genetic algorithm was employed to search for the Pareto-optimal model set that satisfies all three objectives and select the ideal models for the three indices. Findings showed that the optimized nonlinear models (NLARX-W and NLARX-S) outperformed the linear model (LARX). Among the nonlinear models, the optimized NLARX-W model achieved a more balanced performance on the three indices than the NLARX-S models and is recommended for inundation forecasting during typhoons.

Complex relationship between seasonal streamflow forecast skill and value in reservoir operations

NASA Astrophysics Data System (ADS)

Turner, Sean W. D.; Bennett, James C.; Robertson, David E.; Galelli, Stefano

2017-09-01

Considerable research effort has recently been directed at improving and operationalising ensemble seasonal streamflow forecasts. Whilst this creates new opportunities for improving the performance of water resources systems, there may also be associated risks. Here, we explore these potential risks by examining the sensitivity of forecast value (improvement in system performance brought about by adopting forecasts) to changes in the forecast skill for a range of hypothetical reservoir designs with contrasting operating objectives. Forecast-informed operations are simulated using rolling horizon, adaptive control and then benchmarked against optimised control rules to assess performance improvements. Results show that there exists a strong relationship between forecast skill and value for systems operated to maintain a target water level. But this relationship breaks down when the reservoir is operated to satisfy a target demand for water; good forecast accuracy does not necessarily translate into performance improvement. We show that the primary cause of this behaviour is the buffering role played by storage in water supply reservoirs, which renders the forecast superfluous for long periods of the operation. System performance depends primarily on forecast accuracy when critical decisions are made - namely during severe drought. As it is not possible to know in advance if a forecast will perform well at such moments, we advocate measuring the consistency of forecast performance, through bootstrap resampling, to indicate potential usefulness in storage operations. Our results highlight the need for sensitivity assessment in value-of-forecast studies involving reservoirs with supply objectives.

Complex relationship between seasonal streamflow forecast skill and value in reservoir operations

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

Turner, Sean W. D.; Bennett, James C.; Robertson, David E.

Considerable research effort has recently been directed at improving and operationalising ensemble seasonal streamflow forecasts. Whilst this creates new opportunities for improving the performance of water resources systems, there may also be associated risks. Here, we explore these potential risks by examining the sensitivity of forecast value (improvement in system performance brought about by adopting forecasts) to changes in the forecast skill for a range of hypothetical reservoir designs with contrasting operating objectives. Forecast-informed operations are simulated using rolling horizon, adaptive control and then benchmarked against optimised control rules to assess performance improvements. Results show that there exists a strongmore » relationship between forecast skill and value for systems operated to maintain a target water level. But this relationship breaks down when the reservoir is operated to satisfy a target demand for water; good forecast accuracy does not necessarily translate into performance improvement. We show that the primary cause of this behaviour is the buffering role played by storage in water supply reservoirs, which renders the forecast superfluous for long periods of the operation. System performance depends primarily on forecast accuracy when critical decisions are made – namely during severe drought. As it is not possible to know in advance if a forecast will perform well at such moments, we advocate measuring the consistency of forecast performance, through bootstrap resampling, to indicate potential usefulness in storage operations. Our results highlight the need for sensitivity assessment in value-of-forecast studies involving reservoirs with supply objectives.« less

Complex relationship between seasonal streamflow forecast skill and value in reservoir operations

DOE PAGES

Turner, Sean W. D.; Bennett, James C.; Robertson, David E.; ...

2017-09-28

Considerable research effort has recently been directed at improving and operationalising ensemble seasonal streamflow forecasts. Whilst this creates new opportunities for improving the performance of water resources systems, there may also be associated risks. Here, we explore these potential risks by examining the sensitivity of forecast value (improvement in system performance brought about by adopting forecasts) to changes in the forecast skill for a range of hypothetical reservoir designs with contrasting operating objectives. Forecast-informed operations are simulated using rolling horizon, adaptive control and then benchmarked against optimised control rules to assess performance improvements. Results show that there exists a strongmore » relationship between forecast skill and value for systems operated to maintain a target water level. But this relationship breaks down when the reservoir is operated to satisfy a target demand for water; good forecast accuracy does not necessarily translate into performance improvement. We show that the primary cause of this behaviour is the buffering role played by storage in water supply reservoirs, which renders the forecast superfluous for long periods of the operation. System performance depends primarily on forecast accuracy when critical decisions are made – namely during severe drought. As it is not possible to know in advance if a forecast will perform well at such moments, we advocate measuring the consistency of forecast performance, through bootstrap resampling, to indicate potential usefulness in storage operations. Our results highlight the need for sensitivity assessment in value-of-forecast studies involving reservoirs with supply objectives.« less

Evaluation of weather forecast systems for storm surge modeling in the Chesapeake Bay

NASA Astrophysics Data System (ADS)

Garzon, Juan L.; Ferreira, Celso M.; Padilla-Hernandez, Roberto

2018-01-01

Accurate forecast of sea-level heights in coastal areas depends, among other factors, upon a reliable coupling of a meteorological forecast system to a hydrodynamic and wave system. This study evaluates the predictive skills of the coupled circulation and wind-wave model system (ADCIRC+SWAN) for simulating storm tides in the Chesapeake Bay, forced by six different products: (1) Global Forecast System (GFS), (2) Climate Forecast System (CFS) version 2, (3) North American Mesoscale Forecast System (NAM), (4) Rapid Refresh (RAP), (5) European Center for Medium-Range Weather Forecasts (ECMWF), and (6) the Atlantic hurricane database (HURDAT2). This evaluation is based on the hindcasting of four events: Irene (2011), Sandy (2012), Joaquin (2015), and Jonas (2016). By comparing the simulated water levels to observations at 13 monitoring stations, we have found that the ADCIR+SWAN System forced by the following: (1) the HURDAT2-based system exhibited the weakest statistical skills owing to a noteworthy overprediction of the simulated wind speed; (2) the ECMWF, RAP, and NAM products captured the moment of the peak and moderately its magnitude during all storms, with a correlation coefficient ranging between 0.98 and 0.77; (3) the CFS system exhibited the worst averaged root-mean-square difference (excepting HURDAT2); (4) the GFS system (the lowest horizontal resolution product tested) resulted in a clear underprediction of the maximum water elevation. Overall, the simulations forced by NAM and ECMWF systems induced the most accurate results best accuracy to support water level forecasting in the Chesapeake Bay during both tropical and extra-tropical storms.

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.

Subseasonal to Seasonal Predictions of U.S. West Coast High Water Levels

NASA Astrophysics Data System (ADS)

Khouakhi, A.; Villarini, G.; Zhang, W.; Slater, L. J.

2017-12-01

Extreme sea levels pose a significant threat to coastal communities, ecosystems, and assets, as they are conducive to coastal flooding, coastal erosion and inland salt-water intrusion. As sea levels continue to rise, these sea level extremes - including occasional minor coastal flooding experienced during high tide (nuisance floods) - are of concern. Extreme sea levels are increasing at many locations around the globe and have been attributed largely to rising mean sea levels associated with intra-seasonal to interannual climate processes such as the El Niño-Southern Oscillation (ENSO). Here, intra-seasonal to seasonal probabilistic forecasts of high water levels are computed at the Toke Point tide gage station on the US west coast. We first identify the main climate drivers that are responsible for high water levels and examine their predictability using General Circulation Models (GCMs) from the North American Multi-Model Ensemble (NMME). These drivers are then used to develop a probabilistic framework for the seasonal forecasting of high water levels. We focus on the climate controls on the frequency of high water levels using the number of exceedances above the 99.5th percentile and above the nuisance flood level established by the National Weather Service. Our findings indicate good forecast skill at the shortest lead time, with the skill that decreases as we increase the lead time. In general, these models aptly capture the year-to-year variability in the observational records.

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.

Using an Artificial Neural Network to forecast groundwater levels following the removal of a large dam, Milltown Montana Ashley Marks

NASA Astrophysics Data System (ADS)

Marks, A. M.

2010-12-01

Fifty percent of the world’s population depends upon groundwater as their main source of drinking water (Hirata et al., 2007). Scarcity of groundwater clearly affects the entire world. One quarter of the world’s people live in areas characterized by physical water scarcity, making competition for water resources intense (International Water Management Institute (IWMI), 2006; World Water Council, 2008). Tools that forecast groundwater levels have been progressively developed over time, from the Boussinesq equation in 1871 to present day. However, complex three dimensional numerical flow models are the standard for determining groundwater behavior in most settings. These often require excessive field work, data collection, expense, and computational expertise. Artificial Neural Networks (ANNs) have been successfully used in other disciplines as a more practical and cost effective alternative for predicting outcomes dependant on multiple, complex, varying inputs. This research investigates the utility of ANNs to forecast groundwater levels from common data acquired on national data bases. Around Missoula in west central Montana, groundwater levels play an important role especially in the East Missoula and Turah areas, since groundwater levels were recently affected by the removal of the 28 ft Milltown Dam. The dam had impounded contaminated sediments which were polluting the Clark Fork River and nearby wells. Prior to dam removal engineers lowered the reservoir by 12 feet to examine the submerged portion of the dam. Water levels declined in wells during this initial drawdown and local citizens reported dry wells. This prompted a one million dollar well replacement response by the EPA to proactively protect water supplies in the 500+ domestic wells proximal to the reservoir. ANN’s can be an invaluable tool for forecasting groundwater behavior and have been successful for predicting groundwater levels within a foot of observed levels in several Milltown wells.

Application of Jason-2/3 Altimetry for Virtual Gauging and Flood Forecasting in Mekong Basin

NASA Astrophysics Data System (ADS)

Lee, H.; Hossain, F.; Okeowo, M. A.; Nguyen, L. D.; Bui, D. D.; Chang, C. H.

2016-12-01

Vietnam suffers from both flood and drought during the rainy and dry seasons, respectively, due to its highly varying surface water resources. However, the National Center for Water Resources Planning and Investigation (NAWAPI) states that only 7 surface water monitoring stations have been constructed in Central and Highland Central regions with 100 station planned to be constructed by 2030 throughout Vietnam. For the Mekong Delta (MD), the Mekong River Commission (MRC) provides 7-day river level forecasting, but only at the two gauge stations located near the border between Cambodia and Vietnam (http://ffw.mrcmekong.org/south.htm). In order to help stakeholder agencies monitor upstream processes in the rivers and manage their impacts on the agricultural sector and densely populated delta cities, we, first of all, construct the so-called virtual stations throughout the entire Mekong River using the fully automated river level extraction tool with Jason-2/3 Geophysical Research Record (GDR) data. Then, we discuss the potentials and challenges of river level forecasting using Jason-2/3 Interim GDR (IGDR) data, which has 1 - 2 days of latency, over the Mekong River. Finally, based on our analyses, we propose a forecasting system for the Mekong River by drawing from our experience in operationalizing Jason-2 altimetry for Bangladesh flood forecasting.

Evaluation of an operational water cycle prediction system for the Laurentian Great Lakes and St. Lawrence River

NASA Astrophysics Data System (ADS)

Fortin, Vincent; Durnford, Dorothy; Smith, Gregory; Dyck, Sarah; Martinez, Yosvany; Mackay, Murray; Winter, Barbara

2017-04-01

Environment and Climate Change Canada (ECCC) is implementing new numerical guidance products based on fully coupled numerical models to better inform the public as well as specialized users on the current and future state of various components of the water cycle, including stream flow and water levels. Outputs from this new system, named the Water Cycle Prediction System (WCPS), have been available for the Great Lakes and St. Lawrence River watershed since June 2016. WCPS links together ECCC's weather forecasting model, GEM, the 2-D ice model C-ICE, the 3-D lake and ocean model NEMO, and a 2-D hydrological model, WATROUTE. Information concerning the water cycle is passed between the models at intervals varying from a few minutes to one hour. It currently produces two forecasts per day for the next three days of the complete water cycle in the Great Lakes region, the largest freshwater lake system in the world. Products include spatially-varying precipitation, evaporation, river discharge, water level anomalies, surface water temperatures, ice coverage, and surface currents. These new products are of interest to water resources and management authority, flood forecasters, hydroelectricity producers, navigation, environmental disaster managers, search and rescue teams, agriculture, and the general public. This presentation focuses on the evaluation of various elements forecasted by the system, and weighs the advantages and disadvantages of running the system fully coupled.

Near-real-time Estimation and Forecast of Total Precipitable Water in Europe

NASA Astrophysics Data System (ADS)

Bartholy, J.; Kern, A.; Barcza, Z.; Pongracz, R.; Ihasz, I.; Kovacs, R.; Ferencz, C.

2013-12-01

Information about the amount and spatial distribution of atmospheric water vapor (or total precipitable water) is essential for understanding weather and the environment including the greenhouse effect, the climate system with its feedbacks and the hydrological cycle. Numerical weather prediction (NWP) models need accurate estimations of water vapor content to provide realistic forecasts including representation of clouds and precipitation. In the present study we introduce our research activity for the estimation and forecast of atmospheric water vapor in Central Europe using both observations and models. The Eötvös Loránd University (Hungary) operates a polar orbiting satellite receiving station in Budapest since 2002. This station receives Earth observation data from polar orbiting satellites including MODerate resolution Imaging Spectroradiometer (MODIS) Direct Broadcast (DB) data stream from satellites Terra and Aqua. The received DB MODIS data are automatically processed using freely distributed software packages. Using the IMAPP Level2 software total precipitable water is calculated operationally using two different methods. Quality of the TPW estimations is a crucial question for further application of the results, thus validation of the remotely sensed total precipitable water fields is presented using radiosonde data. In a current research project in Hungary we aim to compare different estimations of atmospheric water vapor content. Within the frame of the project we use a NWP model (DBCRAS; Direct Broadcast CIMSS Regional Assimilation System numerical weather prediction software developed by the University of Wisconsin, Madison) to forecast TPW. DBCRAS uses near real time Level2 products from the MODIS data processing chain. From the wide range of the derived Level2 products the MODIS TPW parameter found within the so-called mod07 results (Atmospheric Profiles Product) and the cloud top pressure and cloud effective emissivity parameters from the so-called mod06 results (Cloud Product) are assimilated twice a day (at 00 and 12 UTC) by DBCRAS. DBCRAS creates 72 hours long weather forecasts with 48 km horizontal resolution. DBCRAS is operational at the University since 2009 which means that by now sufficient data is available for the verification of the model. In the present study verification results for the DBCRAS total precipitable water forecasts are presented based on analysis data from the European Centre for Medium-Range Weather Forecasts (ECMWF). Numerical indices are calculated to quantify the performance of DBCRAS. During a limited time period DBCRAS was also ran without assimilating MODIS products which means that there is possibility to quantify the effect of assimilating MODIS physical products on the quality of the forecasts. For this limited time period verification indices are compared to decide whether MODIS data improves forecast quality or not.

Urban flood early warning systems: approaches to hydrometeorological forecasting and communicating risk

NASA Astrophysics Data System (ADS)

Cranston, Michael; Speight, Linda; Maxey, Richard; Tavendale, Amy; Buchanan, Peter

2015-04-01

One of the main challenges for the flood forecasting community remains the provision of reliable early warnings of surface (or pluvial) flooding. The Scottish Flood Forecasting Service has been developing approaches for forecasting the risk of surface water flooding including capitalising on the latest developments in quantitative precipitation forecasting from the Met Office. A probabilistic Heavy Rainfall Alert decision support tool helps operational forecasters assess the likelihood of surface water flooding against regional rainfall depth-duration estimates from MOGREPS-UK linked to historical short-duration flooding in Scotland. The surface water flood risk is communicated through the daily Flood Guidance Statement to emergency responders. A more recent development is an innovative risk-based hydrometeorological approach that links 24-hour ensemble rainfall forecasts through a hydrological model (Grid-to-Grid) to a library of impact assessments (Speight et al., 2015). The early warning tool - FEWS Glasgow - presents the risk of flooding to people, property and transport across a 1km grid over the city of Glasgow with a lead time of 24 hours. Communication of the risk was presented in a bespoke surface water flood forecast product designed based on emergency responder requirements and trialled during the 2014 Commonwealth Games in Glasgow. The development of new approaches to surface water flood forecasting are leading to improved methods of communicating the risk and better performance in early warning with a reduction in false alarm rates with summer flood guidance in 2014 (67%) compared to 2013 (81%) - although verification of instances of surface water flooding remains difficult. However the introduction of more demanding hydrometeorological capabilities with associated greater levels of uncertainty does lead to an increased demand on operational flood forecasting skills and resources. Speight, L., Cole, S.J., Moore, R.J., Pierce, C., Wright, B., Golding, B., Cranston, M., Tavendale, A., Ghimire, S., and Dhondia, J. (2015) Developing surface water flood forecasting capabilities in Scotland: an operational pilot for the 2014 Commonwealth Games in Glasgow. Journal of Flood Risk Management, In Press.

Evaluation of the fast orthogonal search method for forecasting chloride levels in the Deltona groundwater supply (Florida, USA)

NASA Astrophysics Data System (ADS)

El-Jaat, Majda; Hulley, Michael; Tétreault, Michel

2018-02-01

Despite the broad impact and importance of saltwater intrusion in coastal aquifers, little research has been directed towards forecasting saltwater intrusion in areas where the source of saltwater is uncertain. Saline contamination in inland groundwater supplies is a concern for numerous communities in the southern US including the city of Deltona, Florida. Furthermore, conventional numerical tools for forecasting saltwater contamination are heavily dependent on reliable characterization of the physical characteristics of underlying aquifers, information that is often absent or challenging to obtain. To overcome these limitations, a reliable alternative data-driven model for forecasting salinity in a groundwater supply was developed for Deltona using the fast orthogonal search (FOS) method. FOS was applied on monthly water-demand data and corresponding chloride concentrations at water supply wells. Groundwater salinity measurements from Deltona water supply wells were applied to evaluate the forecasting capability and accuracy of the FOS model. Accurate and reliable groundwater salinity forecasting is necessary to support effective and sustainable coastal-water resource planning and management. The available (27) water supply wells for Deltona were randomly split into three test groups for the purposes of FOS model development and performance assessment. Based on four performance indices (RMSE, RSR, NSEC, and R), the FOS model proved to be a reliable and robust forecaster of groundwater salinity. FOS is relatively inexpensive to apply, is not based on rigorous physical characterization of the water supply aquifer, and yields reliable estimates of groundwater salinity in active water supply wells.

Optimizing multiple reliable forward contracts for reservoir allocation using multitime scale streamflow forecasts

NASA Astrophysics Data System (ADS)

Lu, Mengqian; Lall, Upmanu; Robertson, Andrew W.; Cook, Edward

2017-03-01

Streamflow forecasts at multiple time scales provide a new opportunity for reservoir management to address competing objectives. Market instruments such as forward contracts with specified reliability are considered as a tool that may help address the perceived risk associated with the use of such forecasts in lieu of traditional operation and allocation strategies. A water allocation process that enables multiple contracts for water supply and hydropower production with different durations, while maintaining a prescribed level of flood risk reduction, is presented. The allocation process is supported by an optimization model that considers multitime scale ensemble forecasts of monthly streamflow and flood volume over the upcoming season and year, the desired reliability and pricing of proposed contracts for hydropower and water supply. It solves for the size of contracts at each reliability level that can be allocated for each future period, while meeting target end of period reservoir storage with a prescribed reliability. The contracts may be insurable, given that their reliability is verified through retrospective modeling. The process can allow reservoir operators to overcome their concerns as to the appropriate skill of probabilistic forecasts, while providing water users with short-term and long-term guarantees as to how much water or energy they may be allocated. An application of the optimization model to the Bhakra Dam, India, provides an illustration of the process. The issues of forecast skill and contract performance are examined. A field engagement of the idea is useful to develop a real-world perspective and needs a suitable institutional environment.

Using NMME in Region-Specific Operational Seasonal Climate Forecasts

NASA Astrophysics Data System (ADS)

Gronewold, A.; Bolinger, R. A.; Fry, L. M.; Kompoltowicz, K.

2015-12-01

The National Oceanic and Atmospheric Administration's Climate Prediction Center (NOAA/CPC) provides access to a suite of real-time monthly climate forecasts that comprise the North American Multi-Model Ensemble (NMME) in an attempt to meet increasing demands for monthly to seasonal climate prediction. While the graphical map forecasts of the NMME are informative, there is a need to provide decision-makers with probabilistic forecasts specific to their region of interest. Here, we demonstrate the potential application of the NMME to address regional climate projection needs by developing new forecasts of temperature and precipitation for the North American Great Lakes, the largest system of lakes on Earth. Regional opertional water budget forecasts rely on these outlooks to initiate monthly forecasts not only of the water budget, but of monthly lake water levels as well. More specifically, we present an alternative for improving existing operational protocols that currently involve a relatively time-consuming and subjective procedure based on interpreting the maps of the NMME. In addition, all forecasts are currently presented in the NMME in a probabilistic format, with equal weighting given to each member of the ensemble. In our new evolution of this product, we provide historical context for the forecasts by superimposing them (in an on-line graphical user interface) with the historical range of observations. Implementation of this new tool has already led to noticeable advantages in regional water budget forecasting, and has the potential to be transferred to other regional decision-making authorities as well.

Multiobjective hedging rules for flood water conservation

NASA Astrophysics Data System (ADS)

Ding, Wei; Zhang, Chi; Cai, Ximing; Li, Yu; Zhou, Huicheng

2017-03-01

Flood water conservation can be beneficial for water uses especially in areas with water stress but also can pose additional flood risk. The potential of flood water conservation is affected by many factors, especially decision makers' preference for water conservation and reservoir inflow forecast uncertainty. This paper discusses the individual and joint effects of these two factors on the trade-off between flood control and water conservation, using a multiobjective, two-stage reservoir optimal operation model. It is shown that hedging between current water conservation and future flood control exists only when forecast uncertainty or decision makers' preference is within a certain range, beyond which, hedging is trivial and the multiobjective optimization problem is reduced to a single objective problem with either flood control or water conservation. Different types of hedging rules are identified with different levels of flood water conservation preference, forecast uncertainties, acceptable flood risk, and reservoir storage capacity. Critical values of decision preference (represented by a weight) and inflow forecast uncertainty (represented by standard deviation) are identified. These inform reservoir managers with a feasible range of their preference to water conservation and thresholds of forecast uncertainty, specifying possible water conservation within the thresholds. The analysis also provides inputs for setting up an optimization model by providing the range of objective weights and the choice of hedging rule types. A case study is conducted to illustrate the concepts and analyses.

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.

Improving the Predictability of Severe Water Levels along the Coasts of Marginal Seas

NASA Astrophysics Data System (ADS)

Ridder, N. N.; de Vries, H.; van den Brink, H.; De Vries, H.

2016-12-01

Extreme water levels can lead to catastrophic consequences with severe societal and economic repercussions. Particularly vulnerable are countries that are largely situated below sea level. To support and optimize forecast models, as well as future adaptation efforts, this study assesses the modeled contribution of storm surges and astronomical tides to total water levels under different air-sea momentum transfer parameterizations in a numerical surge model (WAQUA/DCSMv5) of the North Sea. It particularly focuses on the implications for the representation of extreme and rapidly recurring severe water levels over the past decades based on the example of the Netherlands. For this, WAQUA/DCSMv5, which is currently used to forecast coastal water levels in the Netherlands, is forced with ERA Interim reanalysis data. Model results are obtained from two different methodologies to parameterize air-sea momentum transfer. The first calculates the governing wind stress forcing using a drag coefficient derived from the conventional approach of wind speed dependent Charnock constants. The other uses instantaneous wind stress from the parameterization of the quasi-linear theory applied within the ECMWF wave model which is expected to deliver a more realistic forcing. The performance of both methods is tested by validating the model output with observations, paying particular attention to their ability to reproduce rapidly succeeding high water levels and extreme events. In a second step, the common features of and connections between these events are analyzed. The results of this study will allow recommendations for the improvement of water level forecasts within marginal seas and support decisions by policy makers. Furthermore, they will strengthen the general understanding of severe and extreme water levels as a whole and help to extend the currently limited knowledge about clustering events.

Seasonal forecasts of groundwater levels in Lanyang Plain in Taiwan

NASA Astrophysics Data System (ADS)

Chang, Ya-Chi; Lin, Yi-Chiu

2017-04-01

Groundwater plays a critical and important role in world's freshwater resources and it is also an important part of Taiwan's water supply for domestic, agricultural and industrial use. Prolonged dry climatic conditions can induce groundwater drought and may have huge impact on water resources. Therefore, this study utilizes seasonal rainfall forecasts from the Model for Prediction Across Scales (MPAS) to simulate groundwater levels in Lanyang Plain in Taiwan up to three months into future. The MPAS is setup with 120 km uniform grid and the physics schemes including WSM6 micorphysics scheme, Kain-Fritsch cumulus scheme, RRTMG radiation scheme, and YSU planetary boundary layer scheme are used to provide the rainfall forecasts. Results of this study can provide a reference for water resources management to ensure the sustainability of groundwater resources in Lanyang Plain.

Understanding Variability in Beach Slope to Improve Forecasts of Storm-induced Water Levels

NASA Astrophysics Data System (ADS)

Doran, K. S.; Stockdon, H. F.; Long, J.

2014-12-01

The National Assessment of Hurricane-Induced Coastal Erosion Hazards combines measurements of beach morphology with storm hydrodynamics to produce forecasts of coastal change during storms for the Gulf of Mexico and Atlantic coastlines of the United States. Wave-induced water levels are estimated using modeled offshore wave height and period and measured beach slope (from dune toe to shoreline) through the empirical parameterization of Stockdon et al. (2006). Spatial and temporal variability in beach slope leads to corresponding variability in predicted wave setup and swash. Seasonal and storm-induced changes in beach slope can lead to differences on the order of a meter in wave runup elevation, making accurate specification of this parameter essential to skillful forecasts of coastal change. Spatial variation in beach slope is accounted for through alongshore averaging, but temporal variability in beach slope is not included in the final computation of the likelihood of coastal change. Additionally, input morphology may be years old and potentially very different than the conditions present during forecast storm. In order to improve our forecasts of hurricane-induced coastal erosion hazards, the temporal variability of beach slope must be included in the final uncertainty of modeled wave-induced water levels. Frequently collected field measurements of lidar-based beach morphology are examined for study sites in Duck, North Carolina, Treasure Island, Florida, Assateague Island, Virginia, and Dauphin Island, Alabama, with some records extending over a period of 15 years. Understanding the variability of slopes at these sites will help provide estimates of associated water level uncertainty which can then be applied to other areas where lidar observations are infrequent, and improve the overall skill of future forecasts of storm-induced coastal change. Stockdon, H. F., Holman, R. A., Howd, P. A., and Sallenger Jr, A. H. (2006). Empirical parameterization of setup,swash, and runup. Coastal engineering, 53(7), 573-588.

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.

A Drought Early Warning System Using System Dynamics Model and Seasonal Climate Forecasts: a case study in Hsinchu, Taiwan.

NASA Astrophysics Data System (ADS)

Tien, Yu-Chuan; Tung, Ching-Ping; Liu, Tzu-Ming; Lin, Chia-Yu

2016-04-01

In the last twenty years, Hsinchu, a county of Taiwan, has experienced a tremendous growth in water demand due to the development of Hsinchu Science Park. In order to fulfill the water demand, the government has built the new reservoir, Baoshan second reservoir. However, short term droughts still happen. One of the reasons is that the water level of the reservoirs in Hsinchu cannot be reasonably forecasted, which sometimes even underestimates the severity of drought. The purpose of this study is to build a drought early warning system that projects the water levels of two important reservoirs, Baoshan and Baoshan second reservoir, and also the spatial distribution of water shortagewith the lead time of three months. Furthermore, this study also attempts to assist the government to improve water resources management. Hence, a system dynamics model of Touchien River, which is the most important river for public water supply in Hsinchu, is developed. The model consists of several important subsystems, including two reservoirs, water treatment plants and agricultural irrigation districts. Using the upstream flow generated by seasonal weather forecasting data, the model is able to simulate the storage of the two reservoirs and the distribution of water shortage. Moreover, the model can also provide the information under certain emergency scenarios, such as the accident or failure of a water treatment plant. At last, the performance of the proposed method and the original water resource management method that the government used were also compared. Keyword: Water Resource Management, Hydrology, Seasonal Climate Forecast, Reservoir, Early Warning, Drought

Assimilation of CryoSat-2 altimetry to a hydrodynamic model of the Brahmaputra river

NASA Astrophysics Data System (ADS)

Schneider, Raphael; Nygaard Godiksen, Peter; Ridler, Marc-Etienne; Madsen, Henrik; Bauer-Gottwein, Peter

2016-04-01

Remote sensing provides valuable data for parameterization and updating of hydrological models, for example water level measurements of inland water bodies from satellite radar altimeters. Satellite altimetry data from repeat-orbit missions such as Envisat, ERS or Jason has been used in many studies, also synthetic wide-swath altimetry data as expected from the SWOT mission. This study is one of the first hydrologic applications of altimetry data from a drifting orbit satellite mission, namely CryoSat-2. CryoSat-2 is equipped with the SIRAL instrument, a new type of radar altimeter similar to SRAL on Sentinel-3. CryoSat-2 SARIn level 2 data is used to improve a 1D hydrodynamic model of the Brahmaputra river basin in South Asia set up in the DHI MIKE 11 software. CryoSat-2 water levels were extracted over river masks derived from Landsat imagery. After discharge calibration, simulated water levels were fitted to the CryoSat-2 data along the Assam valley by adapting cross section shapes and datums. The resulting hydrodynamic model shows accurate spatio-temporal representation of water levels, which is a prerequisite for real-time model updating by assimilation of CryoSat-2 altimetry or multi-mission data in general. For this task, a data assimilation framework has been developed and linked with the MIKE 11 model. It is a flexible framework that can assimilate water level data which are arbitrarily distributed in time and space. Different types of error models, data assimilation methods, etc. can easily be used and tested. Furthermore, it is not only possible to update the water level of the hydrodynamic model, but also the states of the rainfall-runoff models providing the forcing of the hydrodynamic model. The setup has been used to assimilate CryoSat-2 observations over the Assam valley for the years 2010 to 2013. Different data assimilation methods and localizations were tested, together with different model error representations. Furthermore, the impact of different filtering and clustering methods and error descriptions of the CryoSat-2 observations was evaluated. Performance improvement in terms of discharge and water level forecast due to the assimilation of satellite altimetry data was then evaluated. The model forecasts were also compared to climatology and persistence forecasts. Using ensemble based filters, the evaluation was done not only based on performance criteria for the central forecast such as root-mean-square error (RMSE) and Nash-Sutcliffe model efficiency (NSE), but also based on sharpness, reliability and continuous ranked probability score (CRPS) of the ensemble of probabilistic forecasts.

Water Level Prediction of Lake Cascade Mahakam Using Adaptive Neural Network Backpropagation (ANNBP)

NASA Astrophysics Data System (ADS)

Mislan; Gaffar, A. F. O.; Haviluddin; Puspitasari, N.

2018-04-01

A natural hazard information and flood events are indispensable as a form of prevention and improvement. One of the causes is flooding in the areas around the lake. Therefore, forecasting the surface of Lake water level to anticipate flooding is required. The purpose of this paper is implemented computational intelligence method namely Adaptive Neural Network Backpropagation (ANNBP) to forecasting the Lake Cascade Mahakam. Based on experiment, performance of ANNBP indicated that Lake water level prediction have been accurate by using mean square error (MSE) and mean absolute percentage error (MAPE). In other words, computational intelligence method can produce good accuracy. A hybrid and optimization of computational intelligence are focus in the future work.

Operational early warning of shallow landslides in Norway: Evaluation of landslide forecasts and associated challenges

NASA Astrophysics Data System (ADS)

Dahl, Mads-Peter; Colleuille, Hervé; Boje, Søren; Sund, Monica; Krøgli, Ingeborg; Devoli, Graziella

2015-04-01

The Norwegian Water Resources and Energy Directorate (NVE) runs a national early warning system (EWS) for shallow landslides in Norway. Slope failures included in the EWS are debris slides, debris flows, debris avalanches and slush flows. The EWS has been operational on national scale since 2013 and consists of (a) quantitative landslide thresholds and daily hydro-meteorological prognosis; (b) daily qualitative expert evaluation of prognosis / additional data in decision to determine warning levels; (c) publication of warning levels through various custom build internet platforms. The effectiveness of an EWS depends on both the quality of forecasts being issued, and the communication of forecasts to the public. In this analysis a preliminary evaluation of landslide forecasts from the Norwegian EWS within the period 2012-2014 is presented. Criteria for categorizing forecasts as correct, missed events or false alarms are discussed and concrete examples of forecasts falling into the latter two categories are presented. The evaluation show a rate of correct forecasts exceeding 90%. However correct forecast categorization is sometimes difficult, particularly due to poorly documented landslide events. Several challenges has to be met in the process of further lowering rates of missed events of false alarms in the EWS. Among others these include better implementation of susceptibility maps in landslide forecasting, more detailed regionalization of hydro-meteorological landslide thresholds, improved prognosis on precipitation, snowmelt and soil water content as well as the build-up of more experience among the people performing landslide forecasting.

State of the Science for Sub-Seasonal to Seasonal Precipitation Forecasting in Support of Water Resource Managers

NASA Astrophysics Data System (ADS)

DeWitt, D. G.

2017-12-01

Water resource managers are one of the communities that would strongly benefit from highly-skilled sub-seasonal to seasonal precipitation forecasts. Unfortunately, the current state of the art prediction tools frequently fail to provide a level of skill sufficient to meet the stakeholders needs, especially on the monthly and seasonal timescale. On the other hand, the skill of precipitation forecasts on the week-2 timescale are relatively high and arguably useful in many decision-making contexts. This talk will present a comparison of forecast skill for the week-2 through the first season timescale and describe current efforts within NOAA and elsewhere to try to improve forecast skill beyond week-2, including research gaps that need to be addressed in order to make progress.

Simulating Glacial Outburst Lake Releases for Suicide Basin, Mendenhall Glacier, Juneau, Alaska

NASA Astrophysics Data System (ADS)

Jacobs, A. B.; Moran, T.; Hood, E. W.

2017-12-01

Glacial Lake outbursts from Suicide Basin are recent phenomenon first characterized in 2011. The 2014 event resulted in record river stage and moderate flooding on the Mendenhall River in Juneau. Recognizing that these events can adversely impact residential areas of Juneau's Mendenhall Valley, the Alaska-Pacific River Forecast Center developed a real-time modeling technique capable of forecasting the timing and magnitude of the flood-wave crest due to releases from Suicide Basin. The 2014 event was estimated at about 37,000 acre feet with water levels cresting within 36 hours from the time the flood wave hit Mendenhall Lake. Given the magnitude of possible impacts to the public, accurate hydrological forecasting is essential for public safety and Emergency Managers. However, the data needed to effectively forecast magnitudes of specific jökulhlaup events are limited. Estimating this event as related to river stage depended upon three variables: 1) the timing of the lag between Suicide Basin water level declines and the related rise of Mendenhall Lake, 2) continuous monitoring of Mendenhall Lake water levels, and 3) estimating the total water volume stored in Suicide Basin. Real-time modeling of the event utilized a Time of Concentration hydrograph with independent power equations representing the rising and falling limbs of the hydrograph. The initial accuracy of the model — as forecasted about 24 hours prior to crest — resulted in an estimated crest within 0.5 feet of the actual with a timing error of about six hours later than the actual crest.

Chesapeake Bay Forecast System: Oxygen Prediction for the Sustainable Ecosystem Management

NASA Astrophysics Data System (ADS)

Mathukumalli, B.; Long, W.; Zhang, X.; Wood, R.; Murtugudde, R. G.

2010-12-01

The Chesapeake Bay Forecast System (CBFS) is a flexible, end-to-end expert prediction tool for decision makers that will provide customizable, user-specified predictions and projections of the region’s climate, air and water quality, local chemistry, and ecosystems at days to decades. As a part of CBFS, the long-term water quality data were collected and assembled to develop ecological models for the sustainable management of the Chesapeake Bay. Cultural eutrophication depletes oxygen levels in this ecosystem particularly in summer which has several negative implications on the structure and function of ecosystem. In order to understand dynamics and prediction of spatially-explicit oxygen levels in the Bay, an empirical process based ecological model is developed with long-term control variables (water temperature, salinity, nitrogen and phosphorus). Statistical validation methods were employed to demonstrate usability of predictions for management purposes and the predicted oxygen levels are quite faithful to observations. The predicted oxygen values and other physical outputs from downscaling of regional weather and climate predictions, or forecasts from hydrodynamic models can be used to forecast various ecological components. Such forecasts would be useful for both recreational and commercial users of the bay (for example, bass fishing). Furthermore, this work can also be used to predict extent of hypoxia/anoxia not only from anthropogenic nutrient pollution, but also from global warming. Some hindcasts and forecasts are discussed along with the ongoing efforts at a mechanistic ecosystem model to provide prognostic oxygen predictions and projections and upper trophic modeling using an energetics approach.

Risk Based Reservoir Operations Using Ensemble Streamflow Predictions for Lake Mendocino in Mendocino County, California

NASA Astrophysics Data System (ADS)

Delaney, C.; Mendoza, J.; Whitin, B.; Hartman, R. K.

2017-12-01

Ensemble Forecast Operations (EFO) is a risk based approach of reservoir flood operations that incorporates ensemble streamflow predictions (ESPs) made by NOAA's California-Nevada River Forecast Center (CNRFC). With the EFO approach, each member of an ESP is individually modeled to forecast system conditions and calculate risk of reaching critical operational thresholds. Reservoir release decisions are computed which seek to manage forecasted risk to established risk tolerance levels. A water management model was developed for Lake Mendocino, a 111,000 acre-foot reservoir located near Ukiah, California, to evaluate the viability of the EFO alternative to improve water supply reliability but not increase downstream flood risk. Lake Mendocino is a dual use reservoir, which is owned and operated for flood control by the United States Army Corps of Engineers and is operated for water supply by the Sonoma County Water Agency. Due to recent changes in the operations of an upstream hydroelectric facility, this reservoir has suffered from water supply reliability issues since 2007. The EFO alternative was simulated using a 26-year (1985-2010) ESP hindcast generated by the CNRFC, which approximates flow forecasts for 61 ensemble members for a 15-day horizon. Model simulation results of the EFO alternative demonstrate a 36% increase in median end of water year (September 30) storage levels over existing operations. Additionally, model results show no increase in occurrence of flows above flood stage for points downstream of Lake Mendocino. This investigation demonstrates that the EFO alternative may be a viable approach for managing Lake Mendocino for multiple purposes (water supply, flood mitigation, ecosystems) and warrants further investigation through additional modeling and analysis.

Application of artificial neural network model for groundwater level forecasting in a river island with artificial influencing factors

NASA Astrophysics Data System (ADS)

Lee, Sanghoon; Yoon, Heesung; Park, Byeong-Hak; Lee, Kang-Kun

2017-04-01

Groundwater use has been increased for various purposes like agriculture, industry or drinking water in recent years, the issue related to sustainability on the groundwater use also has been raised. Accordingly, forecasting the groundwater level is of great importance for planning sustainable use of groundwater. In a small island surrounded by the Han River, South Korea, seasonal fluctuation of the groundwater level is characterized by multiple factors such as recharge/discharge event of the Paldang dam, Water Curtain Cultivation (WCC) during the winter season, operation of Groundwater Heat Pump System (GWHP). For a period when the dam operation is only occurred in the study area, a prediction of the groundwater level can be easily achieved by a simple cross-correlation model. However, for a period when the WCC and the GWHP systems are working together, the groundwater level prediction is challenging due to its unpredictable operation of the two systems. This study performed Artificial Neural Network (ANN) model to forecast the groundwater level in the river area reflecting the various predictable/unpredictable factors. For constructing the ANN models, two monitoring wells, YSN1 and YSO8, which are located near the injection and abstraction wells for the GWHP system were selected, respectively. By training with the groundwater level data measured in January 2015 to August 2015, response of groundwater level by each of the surface water level, the WCC and the GWHP system were evaluated. Consequentially, groundwater levels in December 2015 to March 2016 were predicted by ANN models, providing optimal fits in comparison to the observed water levels. This study suggests that the ANN model is a useful tool to forecast the groundwater level in terms of the management of groundwater. Acknowledgement : Financial support was provided by the "R&D Project on Environmental Management of Geologic CO2 Storage" from the KEITI (Project Number: 2014001810003) This research was supported by "BK 21plus project of the Korean Government"

Adapting National Water Model Forecast Data to Local Hyper-Resolution H&H Models During Hurricane Irma

NASA Astrophysics Data System (ADS)

Singhofen, P.

2017-12-01

The National Water Model (NWM) is a remarkable undertaking. The foundation of the NWM is a 1 square kilometer grid which is used for near real-time modeling and flood forecasting of most rivers and streams in the contiguous United States. However, the NWM falls short in highly urbanized areas with complex drainage infrastructure. To overcome these shortcomings, the presenter proposes to leverage existing local hyper-resolution H&H models and adapt the NWM forcing data to them. Gridded near real-time rainfall, short range forecasts (18-hour) and medium range forecasts (10-day) during Hurricane Irma are applied to numerous detailed H&H models in highly urbanized areas of the State of Florida. Coastal and inland models are evaluated. Comparisons of near real-time rainfall data are made with observed gaged data and the ability to predict flooding in advance based on forecast data is evaluated. Preliminary findings indicate that the near real-time rainfall data is consistently and significantly lower than observed data. The forecast data is more promising. For example, the medium range forecast data provides 2 - 3 days advanced notice of peak flood conditions to a reasonable level of accuracy in most cases relative to both timing and magnitude. Short range forecast data provides about 12 - 14 hours advanced notice. Since these are hyper-resolution models, flood forecasts can be made at the street level, providing emergency response teams with valuable information for coordinating and dispatching limited resources.

Modeling influence of tide stages on forecasts of the 2010 Chilean tsunami

NASA Astrophysics Data System (ADS)

Uslu, B. U.; Chamberlin, C.; Walsh, D.; Eble, M. C.

2010-12-01

The impact of the 2010 Chilean tsunami is studied using the NOAA high-resolution tsunami forecast model augmented to include modeled tide heights in addition to deep-water tsunami propagation as boundary-condition input. The Chilean tsunami was observed at the Los Angeles tide station at mean low water, Hilo at low, Pago Pago at mid tide and Wake Island near high tide. Because the tsunami arrived at coastal communities at a representative variety of tide stages, 2010 Chile tsunami provides opportunity to study the tsunami impacts at different tide levels to different communities. The current forecast models are computed with a constant tidal stage, and this study evaluates techniques for adding an additional varying predicted tidal component in a forecasting context. Computed wave amplitudes, wave currents and flooding are compared at locations around the Pacific, and the difference in tsunami impact due to tidal stage is studied. This study focuses on how tsunami impacts vary with different tide levels, and helps us understand how the inclusion of tidal components can improve real-time forecast accuracy.

Disaggregating residential water demand for improved forecasts and decision making

NASA Astrophysics Data System (ADS)

Woodard, G.; Brookshire, D.; Chermak, J.; Krause, K.; Roach, J.; Stewart, S.; Tidwell, V.

2003-04-01

Residential water demand is the product of population and per capita demand. Estimates of per capita demand often are based on econometric models of demand, usually based on time series data of demand aggregated at the water provider level. Various studies have examined the impact of such factors as water pricing, weather, and income, with many other factors and details of water demand remaining unclear. Impacts of water conservation programs often are estimated using simplistic engineering calculations. Partly as a result of this, policy discussions regarding water demand management often focus on water pricing, water conservation, and growth control. Projecting water demand is often a straight-forward, if fairly uncertain process of forecasting population and per capita demand rates. SAHRA researchers are developing improved forecasts of residential water demand by disaggregating demand to the level of individuals, households, and specific water uses. Research results based on high-resolution water meter loggers, household-level surveys, economic experiments and recent census data suggest that changes in wealth, household composition, and individual behavior may affect demand more than changes in population or the stock of landscape plants, water-using appliances and fixtures, generally considered the primary determinants of demand. Aging populations and lower fertility rates are dramatically reducing household size, thereby increasing the number of households and residences for a given population. Recent prosperity and low interest rates have raised home ownership rates to unprecented levels. These two trends are leading to increased per capita outdoor water demand. Conservation programs have succeeded in certain areas, such as promoting drought-tolerant native landscaping, but have failed in other areas, such as increasing irrigation efficiency or curbing swimming pool water usage. Individual behavior often is more important than the household's stock of water-using fixtures, and ranges from hedonism (installing pools and whirlpool tubs) to satisficing (adjusting irrigation timers only twice per year) to acting on deeply-held conservation ethics in ways that not only fail any benefit-cost test, but are discouraged, or even illegal (reuse of gray water and black water). Research findings are being captured in dynamic simulation models that integrate social and natural science to create tools to assist water resource managers in providing sustainable water supplies and improving residential water demand forecasts. These models feature simple, graphical user interfaces and output screens that provide decision makers with visual, easy-to-understand information at the basin level. The models reveal connections between various supply and demand components, and highlight direct impacts and feedback mechanisms associated with various policy options.

Forecasted Flood Depth Grids Providing Early Situational Awareness to FEMA during the 2017 Atlantic Hurricane Season

NASA Astrophysics Data System (ADS)

Jones, M.; Longenecker, H. E., III

2017-12-01

The 2017 hurricane season brought the unprecedented landfall of three Category 4 hurricanes (Harvey, Irma and Maria). FEMA is responsible for coordinating the federal response and recovery efforts for large disasters such as these. FEMA depends on timely and accurate depth grids to estimate hazard exposure, model damage assessments, plan flight paths for imagery acquisition, and prioritize response efforts. In order to produce riverine or coastal depth grids based on observed flooding, the methodology requires peak crest water levels at stream gauges, tide gauges, high water marks, and best-available elevation data. Because peak crest data isn't available until the apex of a flooding event and high water marks may take up to several weeks for field teams to collect for a large-scale flooding event, final observed depth grids are not available to FEMA until several days after a flood has begun to subside. Within the last decade NOAA's National Weather Service (NWS) has implemented the Advanced Hydrologic Prediction Service (AHPS), a web-based suite of accurate forecast products that provide hydrograph forecasts at over 3,500 stream gauge locations across the United States. These forecasts have been newly implemented into an automated depth grid script tool, using predicted instead of observed water levels, allowing FEMA access to flood hazard information up to 3 days prior to a flooding event. Water depths are calculated from the AHPS predicted flood stages and are interpolated at 100m spacing along NHD hydrolines within the basin of interest. A water surface elevation raster is generated from these water depths using an Inverse Distance Weighted interpolation. Then, elevation (USGS NED 30m) is subtracted from the water surface elevation raster so that the remaining values represent the depth of predicted flooding above the ground surface. This automated process requires minimal user input and produced forecasted depth grids that were comparable to post-event observed depth grids and remote sensing-derived flood extents for the 2017 hurricane season. These newly available forecasted models were used for pre-event response planning and early estimated hazard exposure counts, allowing FEMA to plan for and stand up operations several days sooner than previously possible.

Land use and water use in the Antelope Valley, California

USGS Publications Warehouse

Templin, William E.; Phillips, Steven P.; Cherry, Daniel E.; DeBortoli, Myrna L.; Haltom, T.C.; McPherson, Kelly R.; Mrozek, C.A.

1995-01-01

Urban land use and water use in the Antelope Valley, California, have increased significantly since development of the valley began in the late 1800's.. Ground water has been a major source of water in this area because of limited local surface-water resources. Ground-water pumpage is reported to have increased from about 29,000 acre-feet in 1919 to about 400,000 acre-feet in the 1950's. Completion of the California Aqueduct to this area in the early 1970's conveyed water from the Sacramento-San Joaquin Delta, about 400 miles to the north. Declines in groundwater levels and increased costs of electrical power in the 1970's resulted in a reduction in the quantity of ground water that was pumped annually for irrigation uses. Total annual reported ground-water pumpage decreased to a low of about 53,200 acre-feet in 1983 and increased to about 91,700 acre-feet in 1991 as a result of rapid urban development and the 1987-92 drought. This increased urban development, in combination with several years of drought, renewed concern about a possible return to extensive depletion of ground-water storage and increased land subsidence.Increased water demands are expected to continue as a result of increased urban development. Water-demand forecasts in 1980 for the Antelope Valley indicated that total annual water demand by 2020 was expected to be about 250,000 acre-feet, with agricultural demand being about 65 percent of this total. In 1990, total water demand was projected to be about 175,000 acre-feet by 2010; however, agricultural water demand was expected to account for only 37 percent of the total demand. New and existing land- and water-use data were collected and compiled during 1992-93 to identify present and historical land and water uses. In 1993, preliminary forecasts for total water demand by 2010 ranged from about 127,500 to 329,000 acre-feet. These wide-ranging estimates indicate that forecasts can change with time as factors that affect water demand change and different forecasting methods are used. The forecasts using the MWD_MAIN (Metropolitan Water District of Southern California Municipal and Industrial Needs) water-demand forecasting system yielded the largest estimates of water demand. These forecasts were based on projections of population growth and other socioeconomic variables. Initial forecasts using the MWD_MAIN forecasting system commonly are considered "interim" or preliminary. Available historical and future socioeconomic data required for the forecasting system are limited for this area. Decisions on local water-resources demand management may be made by members of the Antelope Valley Water Group and other interested parties based on this report, other studies, their best judgement, and cumulative knowledge of local conditions. Potential water-resource management actions in the Antelope Valley include (1) increasing artificial ground-water recharge when excess local runoff (or imported water supplies) are available; (2) implementing water-conservation best-management practices; and (3) optimizing ground-water pumpage throughout the basin.

Forecasting Caspian Sea level changes using satellite altimetry data (June 1992-December 2013) based on evolutionary support vector regression algorithms and gene expression programming

NASA Astrophysics Data System (ADS)

Imani, Moslem; You, Rey-Jer; Kuo, Chung-Yen

2014-10-01

Sea level forecasting at various time intervals is of great importance in water supply management. Evolutionary artificial intelligence (AI) approaches have been accepted as an appropriate tool for modeling complex nonlinear phenomena in water bodies. In the study, we investigated the ability of two AI techniques: support vector machine (SVM), which is mathematically well-founded and provides new insights into function approximation, and gene expression programming (GEP), which is used to forecast Caspian Sea level anomalies using satellite altimetry observations from June 1992 to December 2013. SVM demonstrates the best performance in predicting Caspian Sea level anomalies, given the minimum root mean square error (RMSE = 0.035) and maximum coefficient of determination (R2 = 0.96) during the prediction periods. A comparison between the proposed AI approaches and the cascade correlation neural network (CCNN) model also shows the superiority of the GEP and SVM models over the CCNN.

7 CFR 612.6 - Application for water supply forecast service.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 7 Agriculture 6 2010-01-01 2010-01-01 false Application for water supply forecast service. 612.6... CONSERVATION SERVICE, DEPARTMENT OF AGRICULTURE CONSERVATION OPERATIONS SNOW SURVEYS AND WATER SUPPLY FORECASTS § 612.6 Application for water supply forecast service. Requests for obtaining water supply forecasts or...

Flood Forecast Accuracy and Decision Support System Approach: the Venice Case

NASA Astrophysics Data System (ADS)

Canestrelli, A.; Di Donato, M.

2016-02-01

In the recent years numerical models for weather predictions have experienced continuous advances in technology. As a result, all the disciplines making use of weather forecasts have made significant steps forward. In the case of the Safeguard of Venice, a large effort has been put in order to improve the forecast of tidal levels. In this context, the Istituzione Centro Previsioni e Segnalazioni Maree (ICPSM) of the Venice Municipality has developed and tested many different forecast models, both of the statistical and deterministic type, and has shown to produce very accurate forecasts. For Venice, the maximum admissible forecast error should be (ideally) of the order of ten centimeters at 24 hours. The entity of the forecast error clearly affects the decisional process, which mainly consists of alerting the population, activating the movable barriers installed at the three tidal inlets and contacting the port authority. This process becomes more challenging whenever the weather predictions, and therefore the water level forecasts, suddenly change. These new forecasts have to be quickly transformed into operational tasks. Therefore, it is of the utter importance to set up scheduled alerts and emergency plans by means of easy-to-follow procedures. On this direction, Technital has set up a Decision Support System based on expert procedures that minimizes the human mistakes and, as a consequence, reduces the risk of flooding of the historical center. Moreover, the Decision Support System can communicate predefined alerts to all the interested subjects. The System uses the water levels forecasts produced by the ICPSM by taking into account the accuracy at different leading times. The Decision Support System has been successfully tested with 8 years of data, 6 of them in real time. Venice experience shows that the Decision Support System is an essential tool which assesses the risks associated with a particular event, provides clear operational procedures and minimizes the impact of natural floods on human lives, private properties and historical monuments.

Experiences from coordinated national-level landslide and flood forecasting in Norway

NASA Astrophysics Data System (ADS)

Krøgli, Ingeborg; Fleig, Anne; Glad, Per; Dahl, Mads-Peter; Devoli, Graziella; Colleuille, Hervé

2015-04-01

While flood forecasting at national level is quite well established and operational in many countries worldwide, landslide forecasting at national level is still seldom. Examples of coordinated flood and landslide forecasting are even rarer. Most of the time flood and landslide forecasters work separately (investigating, defining thresholds, and developing models) and most of the time without communication with each other. One example of coordinated operational early warning systems (EWS) for flooding and shallow landslides is found at the Norwegian Water Resources and Energy Directorate (NVE) in Norway. In this presentation we give an introduction to the two separate but tightly collaborative EWSs and to the coordination of these. The two EWSs are being operated from the same office, every day using similar hydro-meteorological prognosis and hydrological models. Prognosis and model outputs on e.g. discharge, snow melt, soil water content and exceeded landslide thresholds are evaluated in a web based decision-making tool (xgeo.no). The experts performing forecasts are hydrologists, geologists and physical geographers. A similar warning scale, based on colors (green, yellow, orange and red) is used for both EWSs, however thresholds for flood and landslide warning levels are defined differently. Also warning areas may not necessary be the same for both hazards and depending on the specific meteorological event, duration of the warning periods can differ. We present how knowledge, models and tools, but also human and economic resources are being shared between the two EWSs. Moreover, we discuss challenges faced in the communication of warning messages using recent flood and landslide events as examples.

7 CFR 612.5 - Dissemination of water supply forecasts and basic data.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 7 Agriculture 6 2010-01-01 2010-01-01 false Dissemination of water supply forecasts and basic data... SUPPLY FORECASTS § 612.5 Dissemination of water supply forecasts and basic data. Water supply outlook reports prepared by NRCS and its cooperators containing water supply forecasts and basic data are usually...

Season-ahead water quality forecasts for the Schuylkill River, Pennsylvania

NASA Astrophysics Data System (ADS)

Block, P. J.; Leung, K.

2013-12-01

Anticipating and preparing for elevated water quality parameter levels in critical water sources, using weather forecasts, is not uncommon. In this study, we explore the feasibility of extending this prediction scale to a season-ahead for the Schuylkill River in Philadelphia, utilizing both statistical and dynamical prediction models, to characterize the season. This advance information has relevance for recreational activities, ecosystem health, and water treatment, as the Schuylkill provides 40% of Philadelphia's water supply. The statistical model associates large-scale climate drivers with streamflow and water quality parameter levels; numerous variables from NOAA's CFSv2 model are evaluated for the dynamical approach. A multi-model combination is also assessed. Results indicate moderately skillful prediction of average summertime total coliform and wintertime turbidity, using season-ahead oceanic and atmospheric variables, predominantly from the North Atlantic Ocean. Models predicting the number of elevated turbidity events across the wintertime season are also explored.

National Weather Service Forecast Office - Honolulu, Hawai`i

Science.gov Websites

Locations - Coastal Forecast Kauai Northwest Waters Kauai Windward Waters Kauai Leeward Waters Kauai Channel Oahu Forecast Oahu Surf Forecast Coastal Wind Observations Buoy Reports, and current weather conditions for selected locations tides, sunrise and sunset information Coastal Waters Forecast general weather

Predicting groundwater level fluctuations with meteorological effect implications—A comparative study among soft computing techniques

NASA Astrophysics Data System (ADS)

Shiri, Jalal; Kisi, Ozgur; Yoon, Heesung; Lee, Kang-Kun; Hossein Nazemi, Amir

2013-07-01

The knowledge of groundwater table fluctuations is important in agricultural lands as well as in the studies related to groundwater utilization and management levels. This paper investigates the abilities of Gene Expression Programming (GEP), Adaptive Neuro-Fuzzy Inference System (ANFIS), Artificial Neural Networks (ANN) and Support Vector Machine (SVM) techniques for groundwater level forecasting in following day up to 7-day prediction intervals. Several input combinations comprising water table level, rainfall and evapotranspiration values from Hongcheon Well station (South Korea), covering a period of eight years (2001-2008) were used to develop and test the applied models. The data from the first six years were used for developing (training) the applied models and the last two years data were reserved for testing. A comparison was also made between the forecasts provided by these models and the Auto-Regressive Moving Average (ARMA) technique. Based on the comparisons, it was found that the GEP models could be employed successfully in forecasting water table level fluctuations up to 7 days beyond data records.

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.

Online decision support system for surface irrigation management

NASA Astrophysics Data System (ADS)

Wang, Wenchao; Cui, Yuanlai

2017-04-01

Irrigation has played an important role in agricultural production. Irrigation decision support system is developed for irrigation water management, which can raise irrigation efficiency with few added engineering services. An online irrigation decision support system (OIDSS), in consist of in-field sensors and central computer system, is designed for surface irrigation management in large irrigation district. Many functions have acquired in OIDSS, such as data acquisition and detection, real-time irrigation forecast, water allocation decision and irrigation information management. The OIDSS contains four parts: Data acquisition terminals, Web server, Client browser and Communication system. Data acquisition terminals are designed to measure paddy water level, soil water content in dry land, ponds water level, underground water level, and canals water level. A web server is responsible for collecting meteorological data, weather forecast data, the real-time field data, and manager's feedback data. Water allocation decisions are made in the web server. Client browser is responsible for friendly displaying, interacting with managers, and collecting managers' irrigation intention. Communication system includes internet and the GPRS network used by monitoring stations. The OIDSS's model is based on water balance approach for both lowland paddy and upland crops. Considering basic database of different crops water demands in the whole growth stages and irrigation system engineering information, the OIDSS can make efficient decision of water allocation with the help of real-time field water detection and weather forecast. This system uses technical methods to reduce requirements of user's specialized knowledge and can also take user's managerial experience into account. As the system is developed by the Browser/Server model, it is possible to make full use of the internet resources, to facilitate users at any place where internet exists. The OIDSS has been applied in Zhanghe Irrigation District (Center China) to manage the required irrigation deliveries. Two years' application indicates that the proposed OIDSS can achieve promising performance for surface irrigation. Historical data of rice growing period in 2014 has been applied to test the OIDSS: it gives out 3 irrigation decisions, which is consistent with actual irrigation times and the forecast irrigation dates are well fit with the actual situations; the corresponding amount of total irrigation decreases by 15.13% compared to those without using the OIDSS.

7 CFR 612.2 - Snow survey and water supply forecast activities.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 7 Agriculture 6 2010-01-01 2010-01-01 false Snow survey and water supply forecast activities. 612... SUPPLY FORECASTS § 612.2 Snow survey and water supply forecast activities. To carry out the cooperative snow survey and water supply forecast program, NRCS: (a) Establishes, maintains, and operates manual...

7 CFR 612.2 - Snow survey and water supply forecast activities.

Code of Federal Regulations, 2014 CFR

2014-01-01

... 7 Agriculture 6 2014-01-01 2014-01-01 false Snow survey and water supply forecast activities. 612... SUPPLY FORECASTS § 612.2 Snow survey and water supply forecast activities. To carry out the cooperative snow survey and water supply forecast program, NRCS: (a) Establishes, maintains, and operates manual...

7 CFR 612.2 - Snow survey and water supply forecast activities.

Code of Federal Regulations, 2013 CFR

2013-01-01

... 7 Agriculture 6 2013-01-01 2013-01-01 false Snow survey and water supply forecast activities. 612... SUPPLY FORECASTS § 612.2 Snow survey and water supply forecast activities. To carry out the cooperative snow survey and water supply forecast program, NRCS: (a) Establishes, maintains, and operates manual...

7 CFR 612.2 - Snow survey and water supply forecast activities.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 7 Agriculture 6 2011-01-01 2011-01-01 false Snow survey and water supply forecast activities. 612... SUPPLY FORECASTS § 612.2 Snow survey and water supply forecast activities. To carry out the cooperative snow survey and water supply forecast program, NRCS: (a) Establishes, maintains, and operates manual...

7 CFR 612.2 - Snow survey and water supply forecast activities.

Code of Federal Regulations, 2012 CFR

2012-01-01

... 7 Agriculture 6 2012-01-01 2012-01-01 false Snow survey and water supply forecast activities. 612... SUPPLY FORECASTS § 612.2 Snow survey and water supply forecast activities. To carry out the cooperative snow survey and water supply forecast program, NRCS: (a) Establishes, maintains, and operates manual...

The US Navy Coastal Surge and Inundation Prediction System (CSIPS): Making Forecasts Easier

DTIC Science & Technology

2013-02-14

produced the best results Peak Water Level Percent Error CD Formulation LAWMA , Amerada Pass Freshwater Canal Locks Calcasieu Pass Sabine Pass...Conclusions Ongoing Work 16 Baseline Simulation Results Peak Water Level Percent Error LAWMA , Amerada Pass Freshwater Canal Locks Calcasieu Pass...Conclusions Ongoing Work 20 Sensitivity Studies Waves Run Water Level – Percent Error of Peak HWM MAPE Lawma , Armeda Pass Freshwater

Optimal Search Strategy for the Definition of a DNAPL Source

DTIC Science & Technology

2009-08-01

29. Flow field results for stochastic model (colored contours) and potentiometric map created by hydrogeologist using well water level measurements...potentiometric map created by hydrogeologist using well water level measurements (black contours). 5.1.3. Source search algorithm Figure 30 shows the 15...and C. D. Tankersley, “Forecasting piezometric head levels in the Floridian aquifer: A Kalman filtering approach”, Water Resources Research, 29(11

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.

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.

Improving medium-range and seasonal hydroclimate forecasts in the southeast USA

NASA Astrophysics Data System (ADS)

Tian, Di

Accurate hydro-climate forecasts are important for decision making by water managers, agricultural producers, and other stake holders. Numerical weather prediction models and general circulation models may have potential for improving hydro-climate forecasts at different scales. In this study, forecast analogs of the Global Forecast System (GFS) and Global Ensemble Forecast System (GEFS) based on different approaches were evaluated for medium-range reference evapotranspiration (ETo), irrigation scheduling, and urban water demand forecasts in the southeast United States; the Climate Forecast System version 2 (CFSv2) and the North American national multi-model ensemble (NMME) were statistically downscaled for seasonal forecasts of ETo, precipitation (P) and 2-m temperature (T2M) at the regional level. The GFS mean temperature (Tmean), relative humidity, and wind speed (Wind) reforecasts combined with the climatology of Reanalysis 2 solar radiation (Rs) produced higher skill than using the direct GFS output only. Constructed analogs showed slightly higher skill than natural analogs for deterministic forecasts. Both irrigation scheduling driven by the GEFS-based ETo forecasts and GEFS-based ETo forecast skill were generally positive up to one week throughout the year. The GEFS improved ETo forecast skill compared to the GFS. The GEFS-based analog forecasts for the input variables of an operational urban water demand model were skillful when applied in the Tampa Bay area. The modified operational models driven by GEFS analog forecasts showed higher forecast skill than the operational model based on persistence. The results for CFSv2 seasonal forecasts showed maximum temperature (Tmax) and Rs had the greatest influence on ETo. The downscaled Tmax showed the highest predictability, followed by Tmean, Tmin, Rs, and Wind. The CFSv2 model could better predict ETo in cold seasons during El Nino Southern Oscillation (ENSO) events only when the forecast initial condition was in ENSO. Downscaled P and T2M forecasts were produced by directly downscaling the NMME P and T2M output or indirectly using the NMME forecasts of Nino3.4 sea surface temperatures to predict local-scale P and T2M. The indirect method generally showed the highest forecast skill which occurs in cold seasons. The bias-corrected NMME ensemble forecast skill did not outperform the best single model.

Sensitivity of Hydrologic Response to Climate Model Debiasing Procedures

NASA Astrophysics Data System (ADS)

Channell, K.; Gronewold, A.; Rood, R. B.; Xiao, C.; Lofgren, B. M.; Hunter, T.

2017-12-01

Climate change is already having a profound impact on the global hydrologic cycle. In the Laurentian Great Lakes, changes in long-term evaporation and precipitation can lead to rapid water level fluctuations in the lakes, as evidenced by unprecedented change in water levels seen in the last two decades. These fluctuations often have an adverse impact on the region's human, environmental, and economic well-being, making accurate long-term water level projections invaluable to regional water resources management planning. Here we use hydrological components from a downscaled climate model (GFDL-CM3/WRF), to obtain future water supplies for the Great Lakes. We then apply a suite of bias correction procedures before propagating these water supplies through a routing model to produce lake water levels. Results using conventional bias correction methods suggest that water levels will decline by several feet in the coming century. However, methods that reflect the seasonal water cycle and explicitly debias individual hydrological components (overlake precipitation, overlake evaporation, runoff) imply that future water levels may be closer to their historical average. This discrepancy between debiased results indicates that water level forecasts are highly influenced by the bias correction method, a source of sensitivity that is commonly overlooked. Debiasing, however, does not remedy misrepresentation of the underlying physical processes in the climate model that produce these biases and contribute uncertainty to the hydrological projections. This uncertainty coupled with the differences in water level forecasts from varying bias correction methods are important for water management and long term planning in the Great Lakes region.

Research on water shortage risks and countermeasures in North China

NASA Astrophysics Data System (ADS)

Cheng, Yuxiang; Fang, Wenxuan; Wu, Ziqin

2017-05-01

In the paper, a grey forecasting model and a population growth model are established for forecasting water resources supply and demand situation in the region, and evaluating the scarcity of water resources thereof in order to solve the problem of water shortage in North China. A concrete plan for alleviating water resources pressure is proposed with AHP as basis, thereby discussing the feasibility of the plan. Firstly, water resources supply and demand in the future 15 years are predicted. There are four sources for the demand of water resources mainly: industry, agriculture, ecology and resident living. Main supply sources include surface water and underground water resources. A grey forecasting method is adopted for predicting in the paper aiming at water resources demands since industrial, agricultural and ecological water consumption data have excessive decision factors and the correlation is relatively fuzzy. Since residents' water consumption is determined by per capita water consumption and local population, a logistic growth model is adopted to forecast the population. The grey forecasting method is used for predicting per capita water consumption, and total water demand can be obtained finally. International calculation standards are adopted as reference aiming at water supply. The grey forecasting method is adopted for forecasting surface water quantity and underground water quantity, and water resources supply is obtained finally. Per capita water availability in the region is calculated by comparing the water resources supply and demand. Results show that per capita water availability in the region is only 283 cubic meters this year, people live in serious water shortage region, who will suffer from water shortage state for long time. Then, sensitivity analysis is applied for model test. The test result is excellent, and the prediction results are more accurate. In the paper, the following measures are proposed for improving water resources condition in the region according to prediction results, such as construction of reservoirs, sewage treatment, water diversion project and other measures. A detailed water supply plan is formulated. Water supply weights of all measures are determined according to the AHP model. Solution is sought after original models are improved. Results show that water resources quantity per capita will be up to 2170 cubic meters or so this year, people suffer from moderate water shortage in the region, which can meet people's life needs and economic development needs basically. In addition, water resources quantity per capita is increased year by year, and it can reach mild water shortage level after 2030. In a word, local water resources dilemma can be effectively solved by the plan actually, and thoughts can be provided for decision makers.

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.

Ensemble Flow Forecasts for Risk Based Reservoir Operations of Lake Mendocino in Mendocino County, California

NASA Astrophysics Data System (ADS)

Delaney, C.; Hartman, R. K.; Mendoza, J.; Evans, K. M.; Evett, S.

2016-12-01

Forecast informed reservoir operations (FIRO) is a methodology that incorporates short to mid-range precipitation or flow forecasts to inform the flood operations of reservoirs. Previous research and modeling for flood control reservoirs has shown that FIRO can reduce flood risk and increase water supply for many reservoirs. The risk-based method of FIRO presents a unique approach that incorporates flow forecasts made by NOAA's California-Nevada River Forecast Center (CNRFC) to model and assess risk of meeting or exceeding identified management targets or thresholds. Forecasted risk is evaluated against set risk tolerances to set reservoir flood releases. A water management model was developed for Lake Mendocino, a 116,500 acre-foot reservoir located near Ukiah, California. Lake Mendocino is a dual use reservoir, which is owned and operated for flood control by the United State Army Corps of Engineers and is operated by the Sonoma County Water Agency for water supply. Due to recent changes in the operations of an upstream hydroelectric facility, this reservoir has been plagued with water supply reliability issues since 2007. FIRO is applied to Lake Mendocino by simulating daily hydrologic conditions from 1985 to 2010 in the Upper Russian River from Lake Mendocino to the City of Healdsburg approximately 50 miles downstream. The risk-based method is simulated using a 15-day, 61 member streamflow hindcast by the CNRFC. Model simulation results of risk-based flood operations demonstrate a 23% increase in average end of water year (September 30) storage levels over current operations. Model results show no increase in occurrence of flood damages for points downstream of Lake Mendocino. This investigation demonstrates that FIRO may be a viable flood control operations approach for Lake Mendocino and warrants further investigation through additional modeling and analysis.

Flood forecasting using non-stationarity in a river with tidal influence - a feasibility study

NASA Astrophysics Data System (ADS)

Killick, Rebecca; Kretzschmar, Ann; Ilic, Suzi; Tych, Wlodek

2017-04-01

Flooding is the most common natural hazard causing damage, disruption and loss of life worldwide. Despite improvements in modelling and forecasting of water levels and flood inundation (Kretzschmar et al., 2014; Hoitink and Jay, 2016), there are still large discrepancies between predictions and observations particularly during storm events when accurate predictions are most important. Many models exist for forecasting river levels (Smith et al., 2013; Leedal et al., 2013) however they commonly assume that the errors in the data are independent, stationary and normally distributed. This is generally not the case especially during storm events suggesting that existing models are not describing the drivers of river level in an appropriate fashion. Further challenges exist in the lower sections of a river influenced by both river and tidal flows and their interaction and there is scope for improvement in prediction. This paper investigates the use of a powerful statistical technique to adaptively forecast river levels by modelling the process as locally stationary. The proposed methodology takes information on both upstream and downstream river levels and incorporates meteorological information (rainfall forecasts) and tidal levels when required to forecast river levels at a specified location. Using this approach, a single model will be capable of predicting water levels in both tidal and non-tidal river reaches. In this pilot project, the methodology of Smith et al. (2013) using harmonic tidal analysis and data based mechanistic modelling is compared with the methodology developed by Killick et al. (2016) utilising data-driven wavelet decomposition to account for the information contained in the upstream and downstream river data to forecast a non-stationary time-series. Preliminary modelling has been carried out using the tidal stretch of the River Lune in North-west England and initial results are presented here. Future work includes expanding the methodology to forecast river levels at a network of locations simultaneously. References Hoitink, A. J. F., and D. A. Jay (2016), Tidal river dynamics: Implications for deltas, Rev. Geophys., 54, 240-272 Killick, R., Knight, M., Nason, G.P., Eckley, I.A. (2016) The Local Partial Autocorrelation Function and its Application to the Forecasting of Locally Stationary Time Series. Submitted Kretzschmar, Ann and Tych, Wlodek and Chappell, Nick A (2014) Reversing hydrology: estimation of sub-hourly rainfall time-series from streamflow. Env. Modell Softw., 60. pp. 290-301 D. Leedal, A. H. Weerts, P. J. Smith, & K. J. Beven. (2013). Application of data-based mechanistic modelling for flood forecasting at multiple locations in the Eden catchment in the National Flood Forecasting System (England and Wales). HESS, 17(1), 177-185. Smith, P., Beven, K., Horsburgh, K., Hardaker, P., & Collier, C. (2013). Data-based mechanistic modelling of tidally affected river reaches for flood warning purposes: An example on the River Dee, UK. , Q.J.R. Meteorol. Soc. 139(671), 340-349.

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.

Exploring Options for an Integrated Water Level Observation Network in Alaska

NASA Astrophysics Data System (ADS)

McCammon, M.

2016-02-01

Portions' of Alaska's remote coastlines are among the Nation's most vulnerable to geohazards such as tsunami, extra-tropical storm surge, and erosion; and the availability of observations of water levels, ocean waves, and river discharge are severely lacking to support water level warnings and forecasts. Alaska is experiencing dramatic reductions in sea ice cover, changes in extra-tropical storm surge patterns, and thawing permafrost. These conditions are endangering coastal populations throughout the State. Gaps in the ocean observing system limit our State's ability to provide useful marine and sea ice forecasts, especially in the Arctic. A spectrum of observation platforms may provide an optimal solution for filling the most critical gaps in these coastal and ocean areas. The collaborations described in this talk and better leveraging of resources and capabilities across federal, state, and academic partners will provide the best opportunity for advancing our science capacity and capabilities in this remote region.

Bootstrap position analysis for forecasting low flow frequency

USGS Publications Warehouse

Tasker, Gary D.; Dunne, P.

1997-01-01

A method of random resampling of residuals from stochastic models is used to generate a large number of 12-month-long traces of natural monthly runoff to be used in a position analysis model for a water-supply storage and delivery system. Position analysis uses the traces to forecast the likelihood of specified outcomes such as reservoir levels falling below a specified level or streamflows falling below statutory passing flows conditioned on the current reservoir levels and streamflows. The advantages of this resampling scheme, called bootstrap position analysis, are that it does not rely on the unverifiable assumption of normality, fewer parameters need to be estimated directly from the data, and accounting for parameter uncertainty is easily done. For a given set of operating rules and water-use requirements for a system, water managers can use such a model as a decision-making tool to evaluate different operating rules. ?? ASCE,.

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.

Water balance models in one-month-ahead streamflow forecasting

USGS Publications Warehouse

Alley, William M.

1985-01-01

Techniques are tested that incorporate information from water balance models in making 1-month-ahead streamflow forecasts in New Jersey. The results are compared to those based on simple autoregressive time series models. The relative performance of the models is dependent on the month of the year in question. The water balance models are most useful for forecasts of April and May flows. For the stations in northern New Jersey, the April and May forecasts were made in order of decreasing reliability using the water-balance-based approaches, using the historical monthly means, and using simple autoregressive models. The water balance models were useful to a lesser extent for forecasts during the fall months. For the rest of the year the improvements in forecasts over those obtained using the simpler autoregressive models were either very small or the simpler models provided better forecasts. When using the water balance models, monthly corrections for bias are found to improve minimum mean-square-error forecasts as well as to improve estimates of the forecast conditional distributions.

An Integrated Urban Flood Analysis System in South Korea

NASA Astrophysics Data System (ADS)

Moon, Young-Il; Kim, Min-Seok; Yoon, Tae-Hyung; Choi, Ji-Hyeok

2017-04-01

Due to climate change and the rapid growth of urbanization, the frequency of concentrated heavy rainfall has caused urban floods. As a result, we studied climate change in Korea and developed an integrated flood analysis system that systematized technology to quantify flood risk and flood forecasting in urban areas. This system supports synthetic decision-making through real-time monitoring and prediction on flash rain or short-term rainfall by using radar and satellite information. As part of the measures to deal with the increase of inland flood damage, we have found it necessary to build a systematic city flood prevention system that systematizes technology to quantify flood risk as well as flood forecast, taking into consideration both inland and river water. This combined inland-river flood analysis system conducts prediction on flash rain or short-term rainfall by using radar and satellite information and performs prompt and accurate prediction on the inland flooded area. In addition, flood forecasts should be accurate and immediate. Accurate flood forecasts signify that the prediction of the watch, warning time and water level is precise. Immediate flood forecasts represent the forecasts lead time which is the time needed to evacuate. Therefore, in this study, in order to apply rainfall-runoff method to medium and small urban stream for flood forecasts, short-term rainfall forecasting using radar is applied to improve immediacy. Finally, it supports synthetic decision-making for prevention of flood disaster through real-time monitoring. Keywords: Urban Flood, Integrated flood analysis system, Rainfall forecasting, Korea Acknowledgments This research was supported by a grant (16AWMP-B066744-04) from Advanced Water Management Research Program (AWMP) funded by Ministry of Land, Infrastructure and Transport of Korean government.

Using statistical and artificial neural network models to forecast potentiometric levels at a deep well in South Texas

NASA Astrophysics Data System (ADS)

Uddameri, V.

2007-01-01

Reliable forecasts of monthly and quarterly fluctuations in groundwater levels are necessary for short- and medium-term planning and management of aquifers to ensure proper service of seasonal demands within a region. Development of physically based transient mathematical models at this time scale poses considerable challenges due to lack of suitable data and other uncertainties. Artificial neural networks (ANN) possess flexible mathematical structures and are capable of mapping highly nonlinear relationships. Feed-forward neural network models were constructed and trained using the back-percolation algorithm to forecast monthly and quarterly time-series water levels at a well that taps into the deeper Evangeline formation of the Gulf Coast aquifer in Victoria, TX. Unlike unconfined formations, no causal relationships exist between water levels and hydro-meteorological variables measured near the vicinity of the well. As such, an endogenous forecasting model using dummy variables to capture short-term seasonal fluctuations and longer-term (decadal) trends was constructed. The root mean square error, mean absolute deviation and correlation coefficient ( R) were noted to be 1.40, 0.33 and 0.77 m, respectively, for an evaluation dataset of quarterly measurements and 1.17, 0.46, and 0.88 m for an evaluative monthly dataset not used to train or test the model. These statistics were better for the ANN model than those developed using statistical regression techniques.

Using ensembles in water management: forecasting dry and wet episodes

NASA Astrophysics Data System (ADS)

van het Schip-Haverkamp, Tessa; van den Berg, Wim; van de Beek, Remco

2015-04-01

Extreme weather situations as droughts and extensive precipitation are becoming more frequent, which makes it more important to obtain accurate weather forecasts for the short and long term. Ensembles can provide a solution in terms of scenario forecasts. MeteoGroup uses ensembles in a new forecasting technique which presents a number of weather scenarios for a dynamical water management project, called Water-Rijk, in which water storage and water retention plays a large role. The Water-Rijk is part of Park Lingezegen, which is located between Arnhem and Nijmegen in the Netherlands. In collaboration with the University of Wageningen, Alterra and Eijkelkamp a forecasting system is developed for this area which can provide water boards with a number of weather and hydrology scenarios in order to assist in the decision whether or not water retention or water storage is necessary in the near future. In order to make a forecast for drought and extensive precipitation, the difference 'precipitation- evaporation' is used as a measurement of drought in the weather forecasts. In case of an upcoming drought this difference will take larger negative values. In case of a wet episode, this difference will be positive. The Makkink potential evaporation is used which gives the most accurate potential evaporation values during the summer, when evaporation plays an important role in the availability of surface water. Scenarios are determined by reducing the large number of forecasts in the ensemble to a number of averaged members with each its own likelihood of occurrence. For the Water-Rijk project 5 scenario forecasts are calculated: extreme dry, dry, normal, wet and extreme wet. These scenarios are constructed for two forecasting periods, each using its own ensemble technique: up to 48 hours ahead and up to 15 days ahead. The 48-hour forecast uses an ensemble constructed from forecasts of multiple high-resolution regional models: UKMO's Euro4 model,the ECMWF model, WRF and Hirlam. Using multiple model runs and additional post processing, an ensemble can be created from non-ensemble models. The 15-day forecast uses the ECMWF Ensemble Prediction System forecast from which scenarios can be deduced directly. A combination of the ensembles from the two forecasting periods is used in order to have the highest possible resolution of the forecast for the first 48 hours followed by the lower resolution long term forecast.

Seasonal Water Balance Forecasts for Drought Early Warning in Ethiopia

NASA Astrophysics Data System (ADS)

Spirig, Christoph; Bhend, Jonas; Liniger, Mark

2016-04-01

Droughts severely impact Ethiopian agricultural production. Successful early warning for drought conditions in the upcoming harvest season therefore contributes to better managing food shortages arising from adverse climatic conditions. So far, however, meteorological seasonal forecasts have not been used in Ethiopia's national food security early warning system (i.e. the LEAP platform). Here we analyse the forecast quality of seasonal forecasts of total rainfall and of the meteorological water balance as a proxy for plant available water. We analyse forecast skill of June to September rainfall and water balance from dynamical seasonal forecast systems, the ECMWF System4 and EC-EARTH global forecasting systems. Rainfall forecasts outperform forecasts assuming a stationary climate mainly in north-eastern Ethiopia - an area that is particularly vulnerable to droughts. Forecasts of the water balance index seem to be even more skilful and thus more useful than pure rainfall forecasts. The results vary though for different lead times and skill measures employed. We further explore the potential added value of dynamically downscaling the forecasts through several dynamical regional climate models made available through the EU FP7 project EUPORIAS. Preliminary results suggest that dynamically downscaled seasonal forecasts are not significantly better compared with seasonal forecasts from the global models. We conclude that seasonal forecasts of a simple climate index such as the water balance have the potential to benefit drought early warning in Ethiopia, both due to its positive predictive skill and higher usefulness than seasonal mean quantities.

Flood Risk Assessment and Forecasting for the Ganges-Brahmaputra-Meghna River Basins

NASA Astrophysics Data System (ADS)

Hopson, T. M.; Priya, S.; Young, W.; Avasthi, A.; Clayton, T. D.; Brakenridge, G. R.; Birkett, C. M.; Riddle, E. E.; Broman, D.; Boehnert, J.; Sampson, K. M.; Kettner, A.; Singh, D.

2017-12-01

During the 2017 South Asia monsoon, torrential rains and catastrophic floods affected more than 45 million people, including 16 million children, across the Ganges-Brahmaputra-Meghna (GBM) basins. The basin is recognized as one of the world's most disaster-prone regions, with severe floods occurring almost annually causing extreme loss of life and property. In light of this vulnerability, the World Bank and collaborators have contributed toward reducing future flood impacts through recent developments to improve operational preparedness for such events, as well as efforts in more general preparedness and resilience building through planning based on detailed risk assessments. With respect to improved event-specific flood preparedness through operational warnings, we discuss a new forecasting system that provides probability-based flood forecasts developed for more than 85 GBM locations. Forecasts are available online, along with near-real-time data maps of rainfall (predicted and actual) and river levels. The new system uses multiple data sets and multiple models to enhance forecasting skill, and provides improved forecasts up to 16 days in advance of the arrival of high waters. These longer lead times provide the opportunity to save both lives and livelihoods. With sufficient advance notice, for example, farmers can harvest a threatened rice crop or move vulnerable livestock to higher ground. Importantly, the forecasts not only predict future water levels but indicate the level of confidence in each forecast. Knowing whether the probability of a danger-level flood is 10 percent or 90 percent helps people to decide what, if any, action to take. With respect to efforts in general preparedness and resilience building, we also present a recent flood risk assessment, and how it provides, for the first time, a numbers-based view of the impacts of different size floods across the Ganges basin. The findings help identify priority areas for tackling flood risks (for example, relocating levees, improving flood warning systems, or boosting overall economic resilience). The assessment includes the locations and numbers of people at risk, as well as the locations and value of buildings, roads and railways, and crops at risk. An accompanying atlas includes easy-to-use risk maps and tables for the Ganges basins.

Implementation of a state of the art automated system for the production of cloud/water vapor motion winds from geostationary satellites

NASA Technical Reports Server (NTRS)

Velden, Christopher S.

1994-01-01

The thrust of the proposed effort under this contract is aimed at improving techniques to track water vapor data in sequences of imagery from geostationary satellites. In regards to this task, significant testing, evaluation, and progress was accomplished during this period. Sets of winds derived from Meteosat data were routinely produced during Atlantic hurricane events in the 1993 season. These wind sets were delivered via Internet in real time to the Hurricane Research Division in Miami for their evaluation in a track forecast model. For eighteen cases in which 72-hour forecasts were produced, thirteen resulted in track forecast improvements (some quite significant). In addition, quality-controlled Meteosat water vapor winds produced by NESDIS were validated against rawinsondes, yielding an 8 m/s RMS. This figure is comparable to upper-level cloud drift wind accuracies. Given the complementary horizontal coverage in cloud-free areas, we believe that water vapor vectors can supplement cloud-drift wind information to provide good full-disk coverage of the upper tropospheric flow. The impact of these winds on numerical analysis and forecasts will be tested in the next reporting period.

Identifying needs for streamflow forecasting in the Incomati basin, Southern Africa

NASA Astrophysics Data System (ADS)

Sunday, Robert; Werner, Micha; Masih, Ilyas; van der Zaag, Pieter

2013-04-01

Despite being widely recognised as an efficient tool in the operational management of water resources, rainfall and streamflow forecasts are currently not utilised in water management practice in the Incomati Basin in Southern Africa. Although, there have been initiatives for forecasting streamflow in the Sabie and Crocodile sub-basins, the outputs of these have found little use because of scepticism on the accuracy and reliability of the information, or the relevance of the information provided to the needs of the water managers. The process of improving these forecasts is underway, but as yet the actual needs of the forecasts are unclear and scope of the ongoing initiatives remains very limited. In this study questionnaires and focused group interviews were used to establish the need, potential use, benefit and required accuracy of rainfall and streamflow forecasts in the Incomati Basin. Thirty five interviews were conducted with professionals engaged in water sector and detailed discussions were held with water institutions, including the Inkomati Catchment Management Agency (ICMA), Komati Basin Water Authority (KOBWA), South African Weather Service (SAWS), water managers, dam operators, water experts, farmers and other water users in the Basin. Survey results show that about 97% of the respondents receive weather forecasts. In contrast to expectations, only 5% have access to the streamflow forecast. In the weather forecast, the most important variables were considered to be rainfall and temperature at daily and weekly time scales. Moreover, forecasts of global climatic indices such as El Niño or La Niña were neither received nor demanded. There was limited demand and/or awareness of flood and drought forecasts including the information on their linkages with global climatic indices. While the majority of respondents indicate the need and indeed use the weather forecast, the provision, communication and interpretation were in general found to be with too little detail and clarity. In some cases this was attributed to the short time and space allotted in media such as television and newspapers respectively. Major uses of the weather forecast were made in personal planning i.e., travelling (29%) and dressing (23%). The usefulness in water sector was reported for water allocation (23%), farming (11%) and flood monitoring (9%), but was considered as a factor having minor influence on the actual decision making in operational water management mainly due to uncertainty of the weather forecast, difference in the time scale and institutional arrangements. In the incidences where streamflow forecasts were received (5% of the cases), its application in decision making was not carried out due to high uncertainty. Moreover, dam operators indicated weekly streamflow forecast as very important in releasing water for agriculture but this was not the format in which forecasts were available to them. Generally, users affirmed the accuracy and benefits of weather forecasts and had no major concerns on the impacts of wrong forecasts. However, respondents indicated the need to improve the accuracy and accessibility of the forecast. Likewise, water managers expressed the need for both rainfall and flow forecasts but indicated that they face hindrances due to financial and human resource constraints. This shows that there is a need to strengthen water related forecasts and the consequent uses in the basin. This can be done through collaboration among forecasting and water organisations such as the SAWS, Research Institutions and users like ICMA, KOBWA and farmers. Collaboration between the meteorology and water resources sectors is important to establish consistent forecast information. The forecasts themselves should be detailed and user specific to ensure these are indeed used and can answer to the needs of the users.

Forecasting cyanobacteria dominance in Canadian temperate lakes.

PubMed

Persaud, Anurani D; Paterson, Andrew M; Dillon, Peter J; Winter, Jennifer G; Palmer, Michelle; Somers, Keith M

2015-03-15

Predictive models based on broad scale, spatial surveys typically identify nutrients and climate as the most important predictors of cyanobacteria abundance; however these models generally have low predictive power because at smaller geographic scales numerous other factors may be equally or more important. At the lake level, for example, the ability to forecast cyanobacteria dominance is of tremendous value to lake managers as they can use such models to communicate exposure risks associated with recreational and drinking water use, and possible exposure to algal toxins, in advance of bloom occurrence. We used detailed algal, limnological and meteorological data from two temperate lakes in south-central Ontario, Canada to determine the factors that are closely linked to cyanobacteria dominance, and to develop easy to use models to forecast cyanobacteria biovolume. For Brandy Lake (BL), the strongest and most parsimonious model for forecasting % cyanobacteria biovolume (% CB) included water column stability, hypolimnetic TP, and % cyanobacteria biovolume two weeks prior. For Three Mile Lake (TML), the best model for forecasting % CB included water column stability, hypolimnetic TP concentration, and 7-d mean wind speed. The models for forecasting % CB in BL and TML are fundamentally different in their lag periods (BL = lag 1 model and TML = lag 2 model) and in some predictor variables despite the close proximity of the study lakes. We speculate that three main factors (nutrient concentrations, water transparency and lake morphometry) may have contributed to differences in the models developed, and may account for variation observed in models derived from large spatial surveys. Our results illustrate that while forecast models can be developed to determine when cyanobacteria will dominate within two temperate lakes, the models require detailed, lake-specific calibration to be effective as risk-management tools. Copyright © 2015 Elsevier Ltd. All rights reserved.

Skill assessment of a real-time forecast system utilizing a coupled hydrologic and coastal hydrodynamic model during Hurricane Irene (2011)

NASA Astrophysics Data System (ADS)

Dresback, Kendra M.; Fleming, Jason G.; Blanton, Brian O.; Kaiser, Carola; Gourley, Jonathan J.; Tromble, Evan M.; Luettich, Richard A.; Kolar, Randall L.; Hong, Yang; Van Cooten, Suzanne; Vergara, Humberto J.; Flamig, Zac L.; Lander, Howard M.; Kelleher, Kevin E.; Nemunaitis-Monroe, Kodi L.

2013-12-01

Due to the devastating effects of recent hurricanes in the Gulf of Mexico (e.g., Katrina, Rita, Ike and Gustav), the development of a high-resolution, real-time, total water level prototype system has been accelerated. The fully coupled model system that includes hydrology is an extension of the ADCIRC Surge Guidance System (ASGS), and will henceforth be referred to as ASGS-STORM (Scalable, Terrestrial, Ocean, River, Meteorological) to emphasize the major processes that are represented by the system.The ASGS-STORM system incorporates tides, waves, winds, rivers and surge to produce a total water level, which provides a holistic representation of coastal flooding. ASGS-STORM was rigorously tested during Hurricane Irene, which made landfall in late August 2011 in North Carolina. All results from ASGS-STORM for the advisories were produced in real-time, forced by forecast wind and pressure fields computed using a parametric tropical cyclone model, and made available via the web. Herein, a skill assessment, analyzing wind speed and direction, significant wave heights, and total water levels, is used to evaluate ASGS-STORM's performance during Irene for three advisories and the best track from the National Hurricane Center (NHC). ASGS-STORM showed slight over-prediction for two advisories (Advisory 23 and 25) due to the over-estimation of the storm intensity. However, ASGS-STORM shows notable skill in capturing total water levels, wind speed and direction, and significant wave heights in North Carolina when utilizing Advisory 28, which had a slight shift in the track but provided a more accurate estimation of the storm intensity, along with the best track from the NHC. Results from ASGS-STORM have shown that as the forecast of the advisories improves, so does the accuracy of the models used in the study; therefore, accurate input from the weather forecast is a necessary, but not sufficient, condition to ensure the accuracy of the guidance provided by the system. While Irene provided a real-time test of the viability of a total water level system, the relatively insignificant freshwater discharges precludes definitive conclusions about the role of freshwater discharges on total water levels in estuarine zones. Now that the system has been developed, on-going work will examine storms (e.g., Floyd) for which the freshwater discharge played a more meaningful role.

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.

Transforming National Oceanic and Atmospheric Administration (NOAA) Water Prediction

NASA Astrophysics Data System (ADS)

Graziano, T. M.; Clark, E. P.

2016-12-01

As a significant step forward to transform NOAA's water prediction services, NOAA plans to implement a new National Water Model (NWM) Version 1.0 in August 2016. A continental scale water resources model, the NWM is an evolution of the WRF-Hydro architecture developed by the National Center for Atmospheric Research (NCAR). It represents NOAA's first foray into high performance computing for water prediction and will expand NOAA's current water quantity forecasts, at approximately 4000 U.S. Geological Survey (USGS) stream gage sites across the country, to forecasts of flow, soil moisture, evapotranspiration, runoff, snow water equivalent and other parameters for 2.7 million stream reaches nationwide. This new guidance will be provided to NOAA's River Forecast Centers around the country and other field offices, along with guidance for evaluation and validation, and tools to visualize these data and enhance decision support. Initially, a subset if these data will be available via NOAA's Office of Water Prediction web site and the full output of the NWM simulations will be available via the NOAA Operational Model Archive and Distribution System (NOMADS). These enhancements in turn will improve NWS' ability to deliver impact-based decision support services nationwide through the provision of short through extended range, high fidelity "street level" water forecasts and warnings. Subsequent planned out-year enhancements to the NWM include the expanded assimilation of anthropogenic data, an operational nest to provide higher resolution forecasts needed for inundation mapping, and tackling the deeper challenges associated with drought and other water resources issues. The NWM is a NOAA-led interagency effort that relies on the National Hydrographic Dataset of the USGS and EPA, as well as the National Streamflow Information Program of the USGS. Its development continues to be advanced in partnership with NCAR, and a partnership with the Consortium for the Advancement of Hydrologic Sciences, Inc. (CUASHI) and the National Science Foundation. This presentation will highlight the policy, programmatic, and service transformation of NOAA's water resources mission with the NWM.

Water demand forecasting: review of soft computing methods.

PubMed

Ghalehkhondabi, Iman; Ardjmand, Ehsan; Young, William A; Weckman, Gary R

2017-07-01

Demand forecasting plays a vital role in resource management for governments and private companies. Considering the scarcity of water and its inherent constraints, demand management and forecasting in this domain are critically important. Several soft computing techniques have been developed over the last few decades for water demand forecasting. This study focuses on soft computing methods of water consumption forecasting published between 2005 and 2015. These methods include artificial neural networks (ANNs), fuzzy and neuro-fuzzy models, support vector machines, metaheuristics, and system dynamics. Furthermore, it was discussed that while in short-term forecasting, ANNs have been superior in many cases, but it is still very difficult to pick a single method as the overall best. According to the literature, various methods and their hybrids are applied to water demand forecasting. However, it seems soft computing has a lot more to contribute to water demand forecasting. These contribution areas include, but are not limited, to various ANN architectures, unsupervised methods, deep learning, various metaheuristics, and ensemble methods. Moreover, it is found that soft computing methods are mainly used for short-term demand forecasting.

Assimilating InSAR Maps of Water Vapor to Improve Heavy Rainfall Forecasts: A Case Study With Two Successive Storms

NASA Astrophysics Data System (ADS)

Mateus, Pedro; Miranda, Pedro M. A.; Nico, Giovanni; Catalão, João.; Pinto, Paulo; Tomé, Ricardo

2018-04-01

Very high resolution precipitable water vapor maps obtained by the Sentinel-1 A synthetic aperture radar (SAR), using the SAR interferometry (InSAR) technique, are here shown to have a positive impact on the performance of severe weather forecasts. A case study of deep convection which affected the city of Adra, Spain, on 6-7 September 2015, is successfully forecasted by the Weather Research and Forecasting model initialized with InSAR data assimilated by the three-dimensional variational technique, with improved space and time distributions of precipitation, as observed by the local weather radar and rain gauge. This case study is exceptional because it consisted of two severe events 12 hr apart, with a timing that allows for the assimilation of both the ascending and descending satellite images, each for the initialization of each event. The same methodology applied to the network of Global Navigation Satellite System observations in Iberia, at the same times, failed to reproduce observed precipitation, although it also improved, in a more modest way, the forecast skill. The impact of precipitable water vapor data is shown to result from a direct increment of convective available potential energy, associated with important adjustments in the low-level wind field, favoring its release in deep convection. It is suggested that InSAR images, complemented by dense Global Navigation Satellite System data, may provide a new source of water vapor data for weather forecasting, since their sampling frequency could reach the subdaily scale by merging different SAR platforms, or when future geosynchronous radar missions become operational.

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.

Improving the performance of streamflow forecasting model using data-preprocessing technique in Dungun River Basin

NASA Astrophysics Data System (ADS)

Khai Tiu, Ervin Shan; Huang, Yuk Feng; Ling, Lloyd

2018-03-01

An accurate streamflow forecasting model is important for the development of flood mitigation plan as to ensure sustainable development for a river basin. This study adopted Variational Mode Decomposition (VMD) data-preprocessing technique to process and denoise the rainfall data before putting into the Support Vector Machine (SVM) streamflow forecasting model in order to improve the performance of the selected model. Rainfall data and river water level data for the period of 1996-2016 were used for this purpose. Homogeneity tests (Standard Normal Homogeneity Test, the Buishand Range Test, the Pettitt Test and the Von Neumann Ratio Test) and normality tests (Shapiro-Wilk Test, Anderson-Darling Test, Lilliefors Test and Jarque-Bera Test) had been carried out on the rainfall series. Homogenous and non-normally distributed data were found in all the stations, respectively. From the recorded rainfall data, it was observed that Dungun River Basin possessed higher monthly rainfall from November to February, which was during the Northeast Monsoon. Thus, the monthly and seasonal rainfall series of this monsoon would be the main focus for this research as floods usually happen during the Northeast Monsoon period. The predicted water levels from SVM model were assessed with the observed water level using non-parametric statistical tests (Biased Method, Kendall's Tau B Test and Spearman's Rho Test).

Evaluation of Ensemble Water Supply and Demands Forecasts for Water Management in the Klamath River Basin

NASA Astrophysics Data System (ADS)

Broman, D.; Gangopadhyay, S.; McGuire, M.; Wood, A.; Leady, Z.; Tansey, M. K.; Nelson, K.; Dahm, K.

2017-12-01

The Upper Klamath River Basin in south central Oregon and north central California is home to the Klamath Irrigation Project, which is operated by the Bureau of Reclamation and provides water to around 200,000 acres of agricultural lands. The project is managed in consideration of not only water deliveries to irrigators, but also wildlife refuge water demands, biological opinion requirements for Endangered Species Act (ESA) listed fish, and Tribal Trust responsibilities. Climate change has the potential to impact water management in terms of volume and timing of water and the ability to meet multiple objectives. Current operations use a spreadsheet-based decision support tool, with water supply forecasts from the National Resources Conservation Service (NRCS) and California-Nevada River Forecast Center (CNRFC). This tool is currently limited in its ability to incorporate in ensemble forecasts, which offer the potential for improved operations by quantifying forecast uncertainty. To address these limitations, this study has worked to develop a RiverWare based water resource systems model, flexible enough to use across multiple decision time-scales, from short-term operations out to long-range planning. Systems model development has been accompanied by operational system development to handle data management and multiple modeling components. Using a set of ensemble hindcasts, this study seeks to answer several questions: A) Do a new set of ensemble streamflow forecasts have additional skill beyond what?, and allow for improved decision making under changing conditions? B) Do net irrigation water requirement forecasts developed in this project to quantify agricultural demands and reservoir evaporation forecasts provide additional benefits to decision making beyond water supply forecasts? C) What benefit do ensemble forecasts have in the context of water management decisions?

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.

Using additional external inputs to forecast water quality with an artificial neural network for contamination event detection in source water

NASA Astrophysics Data System (ADS)

Schmidt, F.; Liu, S.

2016-12-01

Source water quality plays an important role for the safety of drinking water and early detection of its contamination is vital to taking appropriate countermeasures. However, compared to drinking water, it is more difficult to detect contamination events because its environment is less controlled and numerous natural causes contribute to a high variability of the background values. In this project, Artificial Neural Networks (ANNs) and a Contamination Event Detection Process (CED Process) were used to identify events in river water. The ANN models the response of basic water quality sensors obtained in laboratory experiments in an off-line learning stage and continuously forecasts future values of the time line in an on-line forecasting step. During this second stage, the CED Process compares the forecast to the measured value and classifies it as regular background or event value, which modifies the ANN's continuous learning and influences its forecasts. In addition to this basic setup, external information is fed to the CED Process: A so-called Operator Input (OI) is provided to inform about unusual water quality levels that are unrelated to the presence of contamination, for example due to cooling water discharge from a nearby power plant. This study's primary goal is to evaluate how well the OI fits into the design of the combined forecasting ANN and CED Process and to understand its effects on the online forecasting stage. To test this, data from laboratory experiments conducted previously at the School of Environment, Tsinghua University, have been used to perform simulations highlighting features and drawbacks of this method. Applying the OI has been shown to have a positive influence on the ANN's ability to handle a sudden change in background values, which is unrelated to contamination. However, it might also mask the presence of an event, an issue that underlines the necessity to have several instances of the algorithm run in parallel. Other difficulties addressed in this study include the source and the format of the OI. This project tries to add to the ongoing research into algorithms for CED. It provides ideas for how results from the binary classification of time series could be evaluated in a more realistic fashion and shows what the advantages and limitations of such a method would be.

An Approach to Remove the Systematic Bias from the Storm Surge forecasts in the Venice Lagoon

NASA Astrophysics Data System (ADS)

Canestrelli, A.

2017-12-01

In this work a novel approach is proposed for removing the systematic bias from the storm surge forecast computed by a two-dimensional shallow-water model. The model covers both the Adriatic and Mediterranean seas and provides the forecast at the entrance of the Venice Lagoon. The wind drag coefficient at the water-air interface is treated as a calibration parameter, with a different value for each range of wind velocities and wind directions. This sums up to a total of 16-64 parameters to be calibrated, depending on the chosen resolution. The best set of parameters is determined by means of an optimization procedure, which minimizes the RMS error between measured and modeled water level in Venice for the period 2011-2015. It is shown that a bias is present, for which the peaks of wind velocities provided by the weather forecast are largely underestimated, and that the calibration procedure removes this bias. When the calibrated model is used to reproduce events not included in the calibration dataset, the forecast error is strongly reduced, thus confirming the quality of our procedure. The proposed approach it is not site-specific and could be applied to different situations, such as storm surges caused by intense hurricanes.

Challenges and potential solutions for European coastal ocean modelling

NASA Astrophysics Data System (ADS)

She, Jun; Stanev, Emil

2017-04-01

Coastal operational oceanography is a science and technological platform to integrate and transform the outcomes in marine monitoring, new knowledge generation and innovative technologies into operational information products and services in the coastal ocean. It has been identified as one of the four research priorities by EuroGOOS (She et al. 2016). Coastal modelling plays a central role in such an integration and transformation. A next generation coastal ocean forecasting system should have following features: i) being able to fully exploit benefits from future observations, ii) generate meaningful products in finer scales e.g., sub-mesoscale and in estuary-coast-sea continuum, iii) efficient parallel computing and model grid structure, iv) provide high quality forecasts as forcing to NWP and coastal climate models, v) resolving correctly inter-basin and inter-sub-basin water exchange, vi) resolving synoptic variability and predictability in marine ecosystems, e.g., for algae bloom, vi) being able to address critical and relevant issues in coastal applications, e.g., marine spatial planning, maritime safety, marine pollution protection, disaster prevention, offshore wind energy, climate change adaptation and mitigation, ICZM (integrated coastal zone management), the WFD (Water Framework Directive), and the MSFD (Marine Strategy Framework Directive), especially on habitat, eutrophication, and hydrographic condition descriptors. This presentation will address above challenges, identify limits of current models and propose correspondent research needed. The proposed roadmap will address an integrated monitoring-modelling approach and developing Unified European Coastal Ocean Models. In the coming years, a few new developments in European Sea observations can expected, e.g., more near real time delivering on profile observations made by research vessels, more shallow water Argo floats and bio-Argo floats deployed, much more high resolution sea level data from SWOT and on-going altimetry missions, contributing to resolving (sub-)mesoscale eddies, more currents measurements from ADCPs and HF radars, geostationary data for suspended sediment and diurnal observations from satellite SST products. These developments will make it possible to generate new knowledge and build up new capacities for modelling and forecasting systems, e.g., improved currents forecast, improved water skin temperature and surface winds forecast, improved modelling and forecast of (sub) mesoscale activities and drift forecast, new forecast capabilities on SPM (Suspended Particle Matter) and algae bloom. There will be much more in-situ and satellite data available for assimilation. The assimilation of sea level, chl-a, ferrybox and profile observations will greatly improves the ocean-ice-ecosystem forecast quality.

Determining effective forecast horizons for multi-purpose reservoirs with short- and long-term operating objectives

NASA Astrophysics Data System (ADS)

Luchner, Jakob; Anghileri, Daniela; Castelletti, Andrea

2017-04-01

Real-time control of multi-purpose reservoirs can benefit significantly from hydro-meteorological forecast products. Because of their reliability, the most used forecasts range on time scales from hours to few days and are suitable for short-term operation targets such as flood control. In recent years, hydro-meteorological forecasts have become more accurate and reliable on longer time scales, which are more relevant to long-term reservoir operation targets such as water supply. While the forecast quality of such products has been studied extensively, the forecast value, i.e. the operational effectiveness of using forecasts to support water management, has been only relatively explored. It is comparatively easy to identify the most effective forecasting information needed to design reservoir operation rules for flood control but it is not straightforward to identify which forecast variable and lead time is needed to define effective hedging rules for operational targets with slow dynamics such as water supply. The task is even more complex when multiple targets, with diverse slow and fast dynamics, are considered at the same time. In these cases, the relative importance of different pieces of information, e.g. magnitude and timing of peak flow rate and accumulated inflow on different time lags, may vary depending on the season or the hydrological conditions. In this work, we analyze the relationship between operational forecast value and streamflow forecast horizon for different multi-purpose reservoir trade-offs. We use the Information Selection and Assessment (ISA) framework to identify the most effective forecast variables and horizons for informing multi-objective reservoir operation over short- and long-term temporal scales. The ISA framework is an automatic iterative procedure to discriminate the information with the highest potential to improve multi-objective reservoir operating performance. Forecast variables and horizons are selected using a feature selection technique. The technique determines the most informative combination in a multi-variate regression model to the optimal reservoir releases based on perfect information at a fixed objective trade-off. The improved reservoir operation is evaluated against optimal reservoir operation conditioned upon perfect information on future disturbances and basic reservoir operation using only the day of the year and the reservoir level. Different objective trade-offs are selected for analyzing resulting differences in improved reservoir operation and selected forecast variables and horizons. For comparison, the effective streamflow forecast horizon determined by the ISA framework is benchmarked against the performances obtained with a deterministic model predictive control (MPC) optimization scheme. Both the ISA framework and the MPC optimization scheme are applied to the real-world case study of Lake Como, Italy, using perfect streamflow forecast information. The principal operation targets for Lake Como are flood control and downstream water supply which makes its operation a suitable case study. Results provide critical feedback to reservoir operators on the use of long-term streamflow forecasts and to the hydro-meteorological forecasting community with respect to the forecast horizon needed from reliable streamflow forecasts.

The diagnosis and forecast system of hydrometeorological characteristics for the White, Barents, Kara and Pechora Seas

NASA Astrophysics Data System (ADS)

Fomin, Vladimir; Diansky, Nikolay; Gusev, Anatoly; Kabatchenko, Ilia; Panasenkova, Irina

2017-04-01

The diagnosis and forecast system for simulating hydrometeorological characteristics of the Russian Western Arctic seas is presented. It performs atmospheric forcing computation with the regional non-hydrostatic atmosphere model Weather Research and Forecasting model (WRF) with spatial resolution 15 km, as well as computation of circulation, sea level, temperature, salinity and sea ice with the marine circulation model INMOM (Institute of Numerical Mathematics Ocean Model) with spatial resolution 2.7 km, and the computation of wind wave parameters using the Russian wind-wave model (RWWM) with spatial resolution 5 km. Verification of the meteorological characteristics is done for air temperature, air pressure, wind velocity, water temperature, currents, sea level anomaly, wave characteristics such as wave height and wave period. The results of the hydrometeorological characteristic verification are presented for both retrospective and forecast computations. The retrospective simulation of the hydrometeorological characteristics for the White, Barents, Kara and Pechora Seas was performed with the diagnosis and forecast system for the period 1986-2015. The important features of the Kara Sea circulation are presented. Water exchange between Pechora and Kara Seas is described. The importance is shown of using non-hydrostatic atmospheric circulation model for the atmospheric forcing computation in coastal areas. According to the computation results, extreme values of hydrometeorological characteristics were obtained for the Russian Western Arctic seas.

Application of RBFN network and GM (1, 1) for groundwater level simulation

NASA Astrophysics Data System (ADS)

Li, Zijun; Yang, Qingchun; Wang, Luchen; Martín, Jordi Delgado

2017-10-01

Groundwater is a prominent resource of drinking and domestic water in the world. In this context, a feasible water resources management plan necessitates acceptable predictions of groundwater table depth fluctuations, which can help ensure the sustainable use of a watershed's aquifers for urban and rural water supply. Due to the difficulties of identifying non-linear model structure and estimating the associated parameters, in this study radial basis function neural network (RBFNN) and GM (1, 1) models are used for the prediction of monthly groundwater level fluctuations in the city of Longyan, Fujian Province (South China). The monthly groundwater level data monitored from January 2003 to December 2011 are used in both models. The error criteria are estimated using the coefficient of determination ( R 2), mean absolute error (E) and root mean squared error (RMSE). The results show that both the models can forecast the groundwater level with fairly high accuracy, but the RBFN network model can be a promising tool to simulate and forecast groundwater level since it has a relatively smaller RMSE and MAE.

Increased performance in the short-term water demand forecasting through the use of a parallel adaptive weighting strategy

NASA Astrophysics Data System (ADS)

Sardinha-Lourenço, A.; Andrade-Campos, A.; Antunes, A.; Oliveira, M. S.

2018-03-01

Recent research on water demand short-term forecasting has shown that models using univariate time series based on historical data are useful and can be combined with other prediction methods to reduce errors. The behavior of water demands in drinking water distribution networks focuses on their repetitive nature and, under meteorological conditions and similar consumers, allows the development of a heuristic forecast model that, in turn, combined with other autoregressive models, can provide reliable forecasts. In this study, a parallel adaptive weighting strategy of water consumption forecast for the next 24-48 h, using univariate time series of potable water consumption, is proposed. Two Portuguese potable water distribution networks are used as case studies where the only input data are the consumption of water and the national calendar. For the development of the strategy, the Autoregressive Integrated Moving Average (ARIMA) method and a short-term forecast heuristic algorithm are used. Simulations with the model showed that, when using a parallel adaptive weighting strategy, the prediction error can be reduced by 15.96% and the average error by 9.20%. This reduction is important in the control and management of water supply systems. The proposed methodology can be extended to other forecast methods, especially when it comes to the availability of multiple forecast models.

7 CFR 612.1 - Purpose and scope.

Code of Federal Regulations, 2010 CFR

2010-01-01

... AGRICULTURE CONSERVATION OPERATIONS SNOW SURVEYS AND WATER SUPPLY FORECASTS § 612.1 Purpose and scope. This... administration of a cooperative snow survey and water supply forecast program. The program provides agricultural water users and other water management groups in the western states area with water supply forecasts to...

Introduction of Drought Monitoring and Forecasting System based on Real-time Water Information Using ICT

NASA Astrophysics Data System (ADS)

Lee, Y., II; Kim, H. S.; Chun, G.

2016-12-01

There were severe damages such as restriction on water supply caused by continuous drought from 2014 to 2015 in South Korea. Through this drought event, government of South Korea decided to establish National Drought Information Analysis Center in K-water(Korea Water Resources Corporation) and introduce a national drought monitoring and early warning system to mitigate those damages. Drought index such as SPI(Standard Precipitation Index), PDSI(Palmer Drought Severity Index) and SMI(Soil Moisture Index) etc. have been developed and are widely used to provide drought information in many countries. However, drought indexes are not appropriate for drought monitoring and early warning in civilized countries with high population density such as South Korea because it could not consider complicated water supply network. For the national drought monitoring and forecasting of South Korea, `Drought Information Analysis System' (D.I.A.S) which is based on the real time data(storage, flowrate, waterlevel etc.) was developed. Based on its advanced methodology, `DIAS' is changing the paradigm of drought monitoring and early warning systems. Because `D.I.A.S' contains the information of water supply network from water sources to the people across the nation and provides drought information considering the real-time hydrological conditions of each and every water source. For instance, in case the water level of a specific dam declines to predetermined level of caution, `D.I.A.S' will notify people who uses the dam as a source of residential or industrial water. It is expected to provide credible drought monitoring and forecasting information with a strong relationship between drought information and the feelings of people rely on water users by `D.I.A.S'.

Validation of a weather forecast model at radiance level against satellite observations allowing quantification of temperature, humidity, and cloud-related biases

NASA Astrophysics Data System (ADS)

Bani Shahabadi, Maziar; Huang, Yi; Garand, Louis; Heilliette, Sylvain; Yang, Ping

2016-09-01

An established radiative transfer model (RTM) is adapted for simulating all-sky infrared radiance spectra from the Canadian Global Environmental Multiscale (GEM) model in order to validate its forecasts at the radiance level against Atmospheric InfraRed Sounder (AIRS) observations. Synthetic spectra are generated for 2 months from short-term (3-9 h) GEM forecasts. The RTM uses a monthly climatological land surface emissivity/reflectivity atlas. An updated ice particle optical property library was introduced for cloudy radiance calculations. Forward model brightness temperature (BT) biases are assessed to be of the order of ˜1 K for both clear-sky and overcast conditions. To quantify GEM forecast meteorological variables biases, spectral sensitivity kernels are generated and used to attribute radiance biases to surface and atmospheric temperatures, atmospheric humidity, and clouds biases. The kernel method, supplemented with retrieved profiles based on AIRS observations in collocation with a microwave sounder, achieves good closure in explaining clear-sky radiance biases, which are attributed mostly to surface temperature and upper tropospheric water vapor biases. Cloudy-sky radiance biases are dominated by cloud-induced radiance biases. Prominent GEM biases are identified as: (1) too low surface temperature over land, causing about -5 K bias in the atmospheric window region; (2) too high upper tropospheric water vapor, inducing about -3 K bias in the water vapor absorption band; (3) too few high clouds in the convective regions, generating about +10 K bias in window band and about +6 K bias in the water vapor band.

Coupling Radar Rainfall to Hydrological Models for Water Abstraction Management

NASA Astrophysics Data System (ADS)

Asfaw, Alemayehu; Shucksmith, James; Smith, Andrea; MacDonald, Ken

2015-04-01

The impacts of climate change and growing water use are likely to put considerable pressure on water resources and the environment. In the UK, a reform to surface water abstraction policy has recently been proposed which aims to increase the efficiency of using available water resources whilst minimising impacts on the aquatic environment. Key aspects to this reform include the consideration of dynamic rather than static abstraction licensing as well as introducing water trading concepts. Dynamic licensing will permit varying levels of abstraction dependent on environmental conditions (i.e. river flow and quality). The practical implementation of an effective dynamic abstraction strategy requires suitable flow forecasting techniques to inform abstraction asset management. Potentially the predicted availability of water resources within a catchment can be coupled to predicted demand and current storage to inform a cost effective water resource management strategy which minimises environmental impacts. The aim of this work is to use a historical analysis of UK case study catchment to compare potential water resource availability using modelled dynamic abstraction scenario informed by a flow forecasting model, against observed abstraction under a conventional abstraction regime. The work also demonstrates the impacts of modelling uncertainties on the accuracy of predicted water availability over range of forecast lead times. The study utilised a conceptual rainfall-runoff model PDM - Probability-Distributed Model developed by Centre for Ecology & Hydrology - set up in the Dove River catchment (UK) using 1km2 resolution radar rainfall as inputs and 15 min resolution gauged flow data for calibration and validation. Data assimilation procedures are implemented to improve flow predictions using observed flow data. Uncertainties in the radar rainfall data used in the model are quantified using artificial statistical error model described by Gaussian distribution and propagated through the model to assess its influence on the forecasted flow uncertainty. Furthermore, the effects of uncertainties at different forecast lead times on potential abstraction strategies are assessed. The results show that over a 10 year period, an average of approximately 70 ML/d of potential water is missed in the study catchment under a convention abstraction regime. This indicates a considerable potential for the use of flow forecasting models to effectively implement advanced abstraction management and more efficiently utilize available water resources in the study catchment.

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.

On the use of wave parameterizations and a storm impact scaling model in National Weather Service Coastal Flood and decision support operations

USGS Publications Warehouse

Mignone, Anthony; Stockdon, H.; Willis, M.; Cannon, J.W.; Thompson, R.

2012-01-01

National Weather Service (NWS) Weather Forecast Offices (WFO) are responsible for issuing coastal flood watches, warnings, advisories, and local statements to alert decision makers and the general public when rising water levels may lead to coastal impacts such as inundation, erosion, and wave battery. Both extratropical and tropical cyclones can generate the prerequisite rise in water level to set the stage for a coastal impact event. Forecasters use a variety of tools including computer model guidance and local studies to help predict the potential severity of coastal flooding. However, a key missing component has been the incorporation of the effects of waves in the prediction of total water level and the associated coastal impacts. Several recent studies have demonstrated the importance of incorporating wave action into the NWS coastal flood program. To follow up on these studies, this paper looks at the potential of applying recently developed empirical parameterizations of wave setup, swash, and runup to the NWS forecast process. Additionally, the wave parameterizations are incorporated into a storm impact scaling model that compares extreme water levels to beach elevation data to determine the mode of coastal change at predetermined “hotspots” of interest. Specifically, the storm impact model compares the approximate storm-induced still water level, which includes contributions from tides, storm surge, and wave setup, to dune crest elevation to determine inundation potential. The model also compares the combined effects of tides, storm surge, and the 2 % exceedance level for vertical wave runup (including both wave setup and swash) to dune toe and crest elevations to determine if erosion and/or ocean overwash may occur. The wave parameterizations and storm impact model are applied to two cases in 2009 that led to significant coastal impacts and unique forecast challenges in North Carolina: the extratropical “Nor'Ida” event during 11-14 November and the large swell event from distant Hurricane Bill on 22 August. The coastal impacts associated with Nor'Ida were due to the combined effects of surge, tide, and wave processes and led to an estimated 5.8 million dollars in damage. While the impacts from Hurricane Bill were not as severe as Nor'Ida, they were mainly associated with wave processes. Thus, this event exemplifies the importance of incorporating waves into the total water level and coastal impact prediction process. These examples set the stage for potential future applications including adaption to the more complex topography along the New England coast.

Sources of seasonal water-supply forecast skill in the western US

USGS Publications Warehouse

Dettinger, Michael

2007-01-01

Many water supplies in the western US depend on water that is stored in snowpacks and reservoirs during the cool, wet seasons for release and use in the following warm seasons. Managers of these water supplies must decide each winter how much water will be available in subsequent seasons so that they can proactively capture and store water and can make reliable commitments for later deliveries. Long-lead water-supply forecasts are thus important components of water managers' decisionmaking. Present-day operational water-supply forecasts draw skill from observations of the amount of water in upland snowpacks, along with estimates of the amount of water otherwise available (often via surrogates for antecedent precipitation, soil moisture or baseflows). Occasionally, the historical hydroclimatic influences of various global climate conditions may be factored in to forecasts. The relative contributions of (potential) forecast skill for January-March and April-July seasonal water- supply availability from these sources are mapped across the western US as lag correlations among elements of the inputs and outputs from a physically based, regional land-surface hydrology model of the western US from 1950-1999. Information about snow-water contents is the most valuable predictor for forecasts made through much of the cool-season but, before the snows begin to fall, indices of El Nino-Southern Oscillation are the primary source of whatever meager skill is available. The contributions to forecast skill made available by knowledge of antecedent flows (a traditional predictor) and soil moisture at the time the long-lead forecast is issued are compared, to gain insights into the potential usefulness of new soil-moisture monitoring options in the region. When similar computations are applied to simulated flows under historical conditions, but with a uniform +2°C warming imposed, the widespread diminution of snowpacks reduces forecast skills, although skill contributed by measures of antecedent moisture conditions (soil moisture or baseflows) grow in stature, relative to snowpacks, in partial compensation. Forecast skills, e.g., of March forecasts for April-July water supplies from those parts of the region that yield the majority of the runoff, decline by an average of about 15% of captured variance in response to the imposed warming.

Nonlinear autoregressive neural networks with external inputs for forecasting of typhoon inundation level.

PubMed

Ouyang, Huei-Tau

2017-08-01

Accurate inundation level forecasting during typhoon invasion is crucial for organizing response actions such as the evacuation of people from areas that could potentially flood. This paper explores the ability of nonlinear autoregressive neural networks with exogenous inputs (NARX) to predict inundation levels induced by typhoons. Two types of NARX architecture were employed: series-parallel (NARX-S) and parallel (NARX-P). Based on cross-correlation analysis of rainfall and water-level data from historical typhoon records, 10 NARX models (five of each architecture type) were constructed. The forecasting ability of each model was assessed by considering coefficient of efficiency (CE), relative time shift error (RTS), and peak water-level error (PE). The results revealed that high CE performance could be achieved by employing more model input variables. Comparisons of the two types of model demonstrated that the NARX-S models outperformed the NARX-P models in terms of CE and RTS, whereas both performed exceptionally in terms of PE and without significant difference. The NARX-S and NARX-P models with the highest overall performance were identified and their predictions were compared with those of traditional ARX-based models. The NARX-S model outperformed the ARX-based models in all three indexes, whereas the NARX-P model exhibited comparable CE performance and superior RTS and PE performance.

A temporal and spatial analysis of ground-water levels for effective monitoring in Huron County, Michigan

USGS Publications Warehouse

Holtschlag, David J.; Sweat, M.J.

1999-01-01

Quarterly water-level measurements were analyzed to assess the effectiveness of a monitoring network of 26 wells in Huron County, Michigan. Trends were identified as constant levels and autoregressive components were computed at all wells on the basis of data collected from 1993 to 1997, using structural time series analysis. Fixed seasonal components were identified at 22 wells and outliers were identified at 23 wells. The 95- percent confidence intervals were forecast for water-levels during the first and second quarters of 1998. Intervals in the first quarter were consistent with 92.3 percent of the measured values. In the second quarter, measured values were within the forecast intervals only 65.4 percent of the time. Unusually low precipitation during the second quarter is thought to have contributed to the reduced reliability of the second-quarter forecasts. Spatial interrelations among wells were investigated on the basis of the autoregressive components, which were filtered to create a set of innovation sequences that were temporally uncorrelated. The empirical covariance among the innovation sequences indicated both positive and negative spatial interrelations. The negative covariance components are considered to be physically implausible and to have resulted from random sampling error. Graphical modeling, a form of multivariate analysis, was used to model the covariance structure. Results indicate that only 29 of the 325 possible partial correlations among the water-level innovations were statistically significant. The model covariance matrix, corresponding to the model partial correlation structure, contained only positive elements. This model covariance was sequentially partitioned to compute a set of partial covariance matrices that were used to rank the effectiveness of the 26 monitoring wells from greatest to least. Results, for example, indicate that about 50 percent of the uncertainty of the water-level innovations currently monitored by the 26- well network could be described by the 6 most effective wells.

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.

National Weather Service Forecast Office - Honolulu, Hawai`i

Science.gov Websites

Locations - Coastal Forecast Kauai Northwest Waters Kauai Windward Waters Kauai Leeward Waters Kauai Channel Coastal Wind Observations Buoy Reports, and current weather conditions for selected locations tides , sunrise and sunset information Coastal Waters Forecast general weather overview Tropical information

Reservoir water level forecasting using group method of data handling

NASA Astrophysics Data System (ADS)

Zaji, Amir Hossein; Bonakdari, Hossein; Gharabaghi, Bahram

2018-06-01

Accurately forecasted reservoir water level is among the most vital data for efficient reservoir structure design and management. In this study, the group method of data handling is combined with the minimum description length method to develop a very practical and functional model for predicting reservoir water levels. The models' performance is evaluated using two groups of input combinations based on recent days and recent weeks. Four different input combinations are considered in total. The data collected from Chahnimeh#1 Reservoir in eastern Iran are used for model training and validation. To assess the models' applicability in practical situations, the models are made to predict a non-observed dataset for the nearby Chahnimeh#4 Reservoir. According to the results, input combinations (L, L -1) and (L, L -1, L -12) for recent days with root-mean-squared error (RMSE) of 0.3478 and 0.3767, respectively, outperform input combinations (L, L -7) and (L, L -7, L -14) for recent weeks with RMSE of 0.3866 and 0.4378, respectively, with the dataset from https://www.typingclub.com/st. Accordingly, (L, L -1) is selected as the best input combination for making 7-day ahead predictions of reservoir water levels.

Stakeholder Application of NOAA/NWS River Forecasts: Oil and Water?

NASA Astrophysics Data System (ADS)

Werner, K.; Averyt, K.; Bardlsey, T.; Owen, G.

2011-12-01

The literature strongly suggests that water management seldom uses forecasts for decision making despite the proven skill of the prediction system and the obvious application of these forecasts to mitigate risk. The literature also suggests that forecast usage is motivated most strongly by risk of failure of the water management objectives. In the semi-arid western United States where water demand has grown such that it roughly equals the long term supply, risk of failure has become pervasive. In the Colorado Basin, the US National Weather Service's Colorado Basin River Forecast Center (CBRFC) has partnered with the Western Water Assessment (WWA) and the Climate Assessment for the Southwest (CLIMAS) to develop a toolkit for stakeholder engagement and application of seasonal streamflow predictions. This toolkit has been used to facilitate several meetings both in the Colorado Basin and elsewhere to assess the factors that motivate, deter, and improve the application of forecasts in this region. The toolkit includes idealized (1) scenario exercises where participants are asked to apply forecasts to real world water management problems, (2) web based exercises where participants gain experience with forecasts and other online forecast tools, and (3) surveys that assess respondents' experience with and perceptions of forecasts and climate science. This talk will present preliminary results from this effort as well as how the CBRFC has adopted the results into its stakeholder engagement strategies.

Contribution of piezometric measurement on knowledge and management of low water levels

NASA Astrophysics Data System (ADS)

Bessiere, Hélène; Stollsteiner, Philippe; Allier, Delphine; Nicolas, Jérôme; Gourcy, Laurence

2014-05-01

This article is based on a BRGM study on piezometric indicators, threshold values of discharges and groundwater levels for the assessment of potentially pumpable volumes of chalky watersheds. A method for estimating low water levels from groundwater levels is presented from three examples of chalk aquifer; the first one is located in Picardy and the two other in the Champagne Ardennes region. Piezometers with "annual" cycles, used in these examples, are supposed to be representative of the aquifer hydrodynamics. The analysis leads to relatively precise and satisfactory relationships between groundwater levels and observed discharges for this chalky context. These relationships may be useful for monitoring, validation, extension or reconstruction of the low water flow. On the one hand, they allow defining the piezometric levels corresponding to the different alert thresholds of river discharges. On the other hand, they clarify the distribution of low water flow from runoff or the draining of the aquifer. Finally, these correlations give an assessment of the minimum flow for the coming weeks using of the rate of draining of the aquifer. Nevertheless the use of these correlations does not allow to optimize the value of pumpable volumes because it seems to be difficult to integrate the amount of the effective rainfall that may occur during the draining period. In addition, these relationships cannot be exploited for multi-annual cycle systems. In these cases, the solution seems to lie on the realization of a rainfall-runoff-piezometric level model. Therefore, two possibilities are possible. The first one is to achieve each year, on a given date, a forecast for the days or months to come with various frequential distributions rainfalls. However, the forecast must be reiterated each year depending on climatic conditions. The principle of the second method is to simulate forecasts for different rainfall intensities and following different initial conditions. The results are presented in chart form. In addition, this last method is currently tested for the problem of floods by groundwater level rise.

Decision Support on the Sediments Flushing of Aimorés Dam Using Medium-Range Ensemble Forecasts

NASA Astrophysics Data System (ADS)

Mainardi Fan, Fernando; Schwanenberg, Dirk; Collischonn, Walter; Assis dos Reis, Alberto; Alvarado Montero, Rodolfo; Alencar Siqueira, Vinicius

2015-04-01

In the present study we investigate the use of medium-range streamflow forecasts in the Doce River basin (Brazil), at the reservoir of Aimorés Hydro Power Plant (HPP). During daily operations this reservoir acts as a "trap" to the sediments that originate from the upstream basin of the Doce River. This motivates a cleaning process called "pass through" to periodically remove the sediments from the reservoir. The "pass through" or "sediments flushing" process consists of a decrease of the reservoir's water level to a certain flushing level when a determined reservoir inflow threshold is forecasted. Then, the water in the approaching inflow is used to flush the sediments from the reservoir through the spillway and to recover the original reservoir storage. To be triggered, the sediments flushing operation requires an inflow larger than 3000m³/s in a forecast horizon of 7 days. This lead-time of 7 days is far beyond the basin's concentration time (around 2 days), meaning that the forecasts for the pass through procedure highly depends on Numerical Weather Predictions (NWP) models that generate Quantitative Precipitation Forecasts (QPF). This dependency creates an environment with a high amount of uncertainty to the operator. To support the decision making at Aimorés HPP we developed a fully operational hydrological forecasting system to the basin. The system is capable of generating ensemble streamflow forecasts scenarios when driven by QPF data from meteorological Ensemble Prediction Systems (EPS). This approach allows accounting for uncertainties in the NWP at a decision making level. This system is starting to be used operationally by CEMIG and is the one shown in the present study, including a hindcasting analysis to assess the performance of the system for the specific flushing problem. The QPF data used in the hindcasting study was derived from the TIGGE (THORPEX Interactive Grand Global Ensemble) database. Among all EPS available on TIGGE, three were selected: ECMWF, GEFS, and CPTEC. As a deterministic reference forecast, we adopt the high resolution ECMWF forecast for comparison. The experiment consisted on running retrospective forecasts for a full five-year period. To verify the proposed objectives of the study, we use different metrics to evaluate the forecast: ROC Curves, Exceedance Diagrams, Forecast Convergence Score (FCS). Metrics results enabled to understand the benefits of the hydrological ensemble prediction system as a decision making tool for the HPP operation. The ROC scores indicate that the use of the lower percentiles of the ensemble scenarios issues for a true alarm rate around 0,5 to 0,8 (depending on the model and on the percentile), for the lead time of seven days. While the false alarm rate is between 0 and 0,3. Those rates were better than the ones resulting from the deterministic reference forecast. Exceedance diagrams and forecast convergence scores indicate that the ensemble scenarios provide an early signal about the threshold crossing. Furthermore, the ensemble forecasts are more consistent between two subsequent forecasts in comparison to the deterministic forecast. The assessments results also give more credibility to CEMIG in the realization and communication of flushing operation with the stakeholders involved.

Nambe Pueblo Water Budget and Forecasting model.

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

Brainard, James Robert

2009-10-01

This report documents The Nambe Pueblo Water Budget and Water Forecasting model. The model has been constructed using Powersim Studio (PS), a software package designed to investigate complex systems where flows and accumulations are central to the system. Here PS has been used as a platform for modeling various aspects of Nambe Pueblo's current and future water use. The model contains three major components, the Water Forecast Component, Irrigation Scheduling Component, and the Reservoir Model Component. In each of the components, the user can change variables to investigate the impacts of water management scenarios on future water use. The Watermore » Forecast Component includes forecasting for industrial, commercial, and livestock use. Domestic demand is also forecasted based on user specified current population, population growth rates, and per capita water consumption. Irrigation efficiencies are quantified in the Irrigated Agriculture component using critical information concerning diversion rates, acreages, ditch dimensions and seepage rates. Results from this section are used in the Water Demand Forecast, Irrigation Scheduling, and the Reservoir Model components. The Reservoir Component contains two sections, (1) Storage and Inflow Accumulations by Categories and (2) Release, Diversion and Shortages. Results from both sections are derived from the calibrated Nambe Reservoir model where historic, pre-dam or above dam USGS stream flow data is fed into the model and releases are calculated.« less

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).

Integrating predictive information into an agro-economic model to guide agricultural planning

NASA Astrophysics Data System (ADS)

Block, Paul; Zhang, Ying; You, Liangzhi

2017-04-01

Seasonal climate forecasts can inform long-range planning, including water resources utilization and allocation, however quantifying the value of this information on the economy is often challenging. For rain-fed farmers, skillful season-ahead predictions may lead to superior planning, as compared to business as usual strategies, resulting in additional benefits or reduced losses. In this study, regional-level probabilistic precipitation forecasts of the major rainy season in Ethiopia are fed into an agro-economic model, adapted from the International Food Policy Research Institute, to evaluate economic outcomes (GDP, poverty rates, etc.) as compared with a no-forecast approach. Based on forecasted conditions, farmers can select various actions: adjusting crop area and crop type, purchasing drought resistant seed, or applying additional fertilizer. Preliminary results favor the forecast-based approach, particularly through crop area reallocation.

Tsunami Forecasting and Monitoring in New Zealand

NASA Astrophysics Data System (ADS)

Power, William; Gale, Nora

2011-06-01

New Zealand is exposed to tsunami threats from several sources that vary significantly in their potential impact and travel time. One route for reducing the risk from these tsunami sources is to provide advance warning based on forecasting and monitoring of events in progress. In this paper the National Tsunami Warning System framework, including the responsibilities of key organisations and the procedures that they follow in the event of a tsunami threatening New Zealand, are summarised. A method for forecasting threat-levels based on tsunami models is presented, similar in many respects to that developed for Australia by Allen and Greenslade (Nat Hazards 46:35-52, 2008), and a simple system for easy access to the threat-level forecasts using a clickable pdf file is presented. Once a tsunami enters or initiates within New Zealand waters, its progress and evolution can be monitored in real-time using a newly established network of online tsunami gauge sensors placed at strategic locations around the New Zealand coasts and offshore islands. Information from these gauges can be used to validate and revise forecasts, and assist in making the all-clear decision.

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.

Data Assimilation of AIRS Water Vapor Profiles: Impact on Precipitation Forecasts for Atmospheric River Cases Affecting the Western of the United States

NASA Technical Reports Server (NTRS)

Blankenship, Clay; Zavodsky, Bradley; Jedlovec, Gary; Wick, Gary; Neiman, Paul

2013-01-01

Atmospheric rivers are transient, narrow regions in the atmosphere responsible for the transport of large amounts of water vapor. These phenomena can have a large impact on precipitation. In particular, they can be responsible for intense rain events on the western coast of North America during the winter season. This paper focuses on attempts to improve forecasts of heavy precipitation events in the Western US due to atmospheric rivers. Profiles of water vapor derived from from Atmospheric Infrared Sounder (AIRS) observations are combined with GFS forecasts by a three-dimensional variational data assimilation in the Gridpoint Statistical Interpolation (GSI). Weather Research and Forecasting (WRF) forecasts initialized from the combined field are compared to forecasts initialized from the GFS forecast only for 3 test cases in the winter of 2011. Results will be presented showing the impact of the AIRS profile data on water vapor and temperature fields, and on the resultant precipitation forecasts.

Developing the remote sensing-based early warning system for monitoring TSS concentrations in Lake Mead.

PubMed

Imen, Sanaz; Chang, Ni-Bin; Yang, Y Jeffrey

2015-09-01

Adjustment of the water treatment process to changes in water quality is a focus area for engineers and managers of water treatment plants. The desired and preferred capability depends on timely and quantitative knowledge of water quality monitoring in terms of total suspended solids (TSS) concentrations. This paper presents the development of a suite of nowcasting and forecasting methods by using high-resolution remote-sensing-based monitoring techniques on a daily basis. First, the integrated data fusion and mining (IDFM) technique was applied to develop a near real-time monitoring system for daily nowcasting of the TSS concentrations. Then a nonlinear autoregressive neural network with external input (NARXNET) model was selected and applied for forecasting analysis of the changes in TSS concentrations over time on a rolling basis onward using the IDFM technique. The implementation of such an integrated forecasting and nowcasting approach was assessed by a case study at Lake Mead hosting the water intake for Las Vegas, Nevada, in the water-stressed western U.S. Long-term monthly averaged results showed no simultaneous impact from forest fire events on accelerating the rise of TSS concentration. However, the results showed a probable impact of a decade of drought on increasing TSS concentration in the Colorado River Arm and Overton Arm. Results of the forecasting model highlight the reservoir water level as a significant parameter in predicting TSS in Lake Mead. In addition, the R-squared value of 0.98 and the root mean square error of 0.5 between the observed and predicted TSS values demonstrates the reliability and application potential of this remote sensing-based early warning system in terms of TSS projections at a drinking water intake. Copyright © 2015 Elsevier Ltd. All rights reserved.

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.

Municipal water consumption forecast accuracy

NASA Astrophysics Data System (ADS)

Fullerton, Thomas M.; Molina, Angel L.

2010-06-01

Municipal water consumption planning is an active area of research because of infrastructure construction and maintenance costs, supply constraints, and water quality assurance. In spite of that, relatively few water forecast accuracy assessments have been completed to date, although some internal documentation may exist as part of the proprietary "grey literature." This study utilizes a data set of previously published municipal consumption forecasts to partially fill that gap in the empirical water economics literature. Previously published municipal water econometric forecasts for three public utilities are examined for predictive accuracy against two random walk benchmarks commonly used in regional analyses. Descriptive metrics used to quantify forecast accuracy include root-mean-square error and Theil inequality statistics. Formal statistical assessments are completed using four-pronged error differential regression F tests. Similar to studies for other metropolitan econometric forecasts in areas with similar demographic and labor market characteristics, model predictive performances for the municipal water aggregates in this effort are mixed for each of the municipalities included in the sample. Given the competitiveness of the benchmarks, analysts should employ care when utilizing econometric forecasts of municipal water consumption for planning purposes, comparing them to recent historical observations and trends to insure reliability. Comparative results using data from other markets, including regions facing differing labor and demographic conditions, would also be helpful.

Linking Science of Flood Forecasts to Humanitarian Actions for Improved Preparedness and Effective Response

NASA Astrophysics Data System (ADS)

Uprety, M.; Dugar, S.; Gautam, D.; Kanel, D.; Kshetri, M.; Kharbuja, R. G.; Acharya, S. H.

2017-12-01

Advances in flood forecasting have provided opportunities for humanitarian responders to employ a range of preparedness activities at different forecast time horizons. Yet, the science of prediction is less understood and realized across the humanitarian landscape, and often preparedness plans are based upon average level of flood risk. Working under the remit of Forecast Based Financing (FbF), we present a pilot from Nepal on how available flood and weather forecast products are informing specific pre-emptive actions in the local preparedness and response plans, thereby supporting government stakeholders and humanitarian agencies to take early actions before an impending flood event. In Nepal, forecasting capabilities are limited but in a state of positive flux. Whilst local flood forecasts based upon rainfall-runoff models are yet to be operationalized, streamflow predictions from Global Flood Awareness System (GLoFAS) can be utilized to plan and implement preparedness activities several days in advance. Likewise, 3-day rainfall forecasts from Nepal Department of Hydrology and Meteorology (DHM) can further inform specific set of early actions for potential flash floods due to heavy precipitation. Existing community based early warning systems in the major river basins of Nepal are utilizing real time monitoring of water levels and rainfall together with localised probabilistic flood forecasts which has increased warning lead time from 2-3 hours to 7-8 hours. Based on these available forecast products, thresholds and trigger levels have been determined for different flood scenarios. Matching these trigger levels and assigning responsibilities to relevant actors for early actions, a set of standard operating procedures (SOPs) are being developed, broadly covering general preparedness activities and science informed anticipatory actions for different forecast lead times followed by the immediate response activities. These SOPs are currently being rolled out and tested by the Ministry of Home Affairs (MoHA) through its district emergency operation centres in West Nepal. Potential scale up and successful implementation of this science based approach would be instrumental to take forward global commitments on disaster risk reduction, climate change adaptation and sustainable goals in Nepal.

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.

Visualizing complex hydrodynamic features

NASA Astrophysics Data System (ADS)

Kempf, Jill L.; Marshall, Robert E.; Yen, Chieh-Cheng

1990-08-01

The Lake Erie Forecasting System is a cooperative project by university, private and governmental institutions to provide continuous forecasting of three-dimensional structure within the lake. The forecasts will include water velocity and temperature distributions throughout the body of water, as well as water level and wind-wave distributions at the lake's surface. Many hydrodynamic features can be extracted from this data, including coastal jets, large-scale thermocline motion and zones of upwelling and downwelling. A visualization system is being developed that will aid in understanding these features and their interactions. Because of the wide variety of features, they cannot all be adequately represented by a single rendering technique. Particle tracing, surface rendering, and volumetric techniques are all necessary. This visualization effortis aimed towards creating a system that will provide meaningful forecasts for those using the lake for recreational and commercial purposes. For example, the fishing industry needs to know about large-scale thermocline motion in order to find the best fishing areas and power plants need to know water intAke temperatures. The visualization system must convey this information in a manner that is easily understood by these users. Scientists must also be able to use this system to verify their hydrodynamic simulation. The focus of the system, therefore, is to provide the information to serve these diverse interests, without overwhelming any single user with unnecessary data.

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.

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

Carson, K.S.

The presence of overpopulation or unsustainable population growth may place pressure on the food and water supplies of countries in sensitive areas of the world. Severe air or water pollution may place additional pressure on these resources. These pressures may generate both internal and international conflict in these areas as nations struggle to provide for their citizens. Such conflicts may result in United States intervention, either unilaterally, or through the United Nations. Therefore, it is in the interests of the United States to identify potential areas of conflict in order to properly train and allocate forces. The purpose of thismore » research is to forecast the probability of conflict in a nation as a function of it s environmental conditions. Probit, logit and ordered probit models are employed to forecast the probability of a given level of conflict. Data from 95 countries are used to estimate the models. Probability forecasts are generated for these 95 nations. Out-of sample forecasts are generated for an additional 22 nations. These probabilities are then used to rank nations from highest probability of conflict to lowest. The results indicate that the dependence of a nation`s economy on agriculture, the rate of deforestation, and the population density are important variables in forecasting the probability and level of conflict. These results indicate that environmental variables do play a role in generating or exacerbating conflict. It is unclear that the United States military has any direct role in mitigating the environmental conditions that may generate conflict. A more important role for the military is to aid in data gathering to generate better forecasts so that the troops are adequntely prepared when conflicts arises.« less

Forecasting effects of climate change on Great Lakes fisheries: models that link habitat supply to population dynamics can help

USGS Publications Warehouse

Jones, Michael L.; Shuter, Brian J.; Zhao, Yingming; Stockwell, Jason D.

2006-01-01

Future changes to climate in the Great Lakes may have important consequences for fisheries. Evidence suggests that Great Lakes air and water temperatures have risen and the duration of ice cover has lessened during the past century. Global circulation models (GCMs) suggest future warming and increases in precipitation in the region. We present new evidence that water temperatures have risen in Lake Erie, particularly during summer and winter in the period 1965–2000. GCM forecasts coupled with physical models suggest lower annual runoff, less ice cover, and lower lake levels in the future, but the certainty of these forecasts is low. Assessment of the likely effects of climate change on fish stocks will require an integrative approach that considers several components of habitat rather than water temperature alone. We recommend using mechanistic models that couple habitat conditions to population demographics to explore integrated effects of climate-caused habitat change and illustrate this approach with a model for Lake Erie walleye (Sander vitreum). We show that the combined effect on walleye populations of plausible changes in temperature, river hydrology, lake levels, and light penetration can be quite different from that which would be expected based on consideration of only a single factor.

Verification of an ensemble prediction system for storm surge forecast in the Adriatic Sea

NASA Astrophysics Data System (ADS)

Mel, Riccardo; Lionello, Piero

2014-12-01

In the Adriatic Sea, storm surges present a significant threat to Venice and to the flat coastal areas of the northern coast of the basin. Sea level forecast is of paramount importance for the management of daily activities and for operating the movable barriers that are presently being built for the protection of the city. In this paper, an EPS (ensemble prediction system) for operational forecasting of storm surge in the northern Adriatic Sea is presented and applied to a 3-month-long period (October-December 2010). The sea level EPS is based on the HYPSE (hydrostatic Padua Sea elevation) model, which is a standard single-layer nonlinear shallow water model, whose forcings (mean sea level pressure and surface wind fields) are provided by the ensemble members of the ECMWF (European Center for Medium-Range Weather Forecasts) EPS. Results are verified against observations at five tide gauges located along the Croatian and Italian coasts of the Adriatic Sea. Forecast uncertainty increases with the predicted value of the storm surge and with the forecast lead time. The EMF (ensemble mean forecast) provided by the EPS has a rms (root mean square) error lower than the DF (deterministic forecast), especially for short (up to 3 days) lead times. Uncertainty for short lead times of the forecast and for small storm surges is mainly caused by uncertainty of the initial condition of the hydrodynamical model. Uncertainty for large lead times and large storm surges is mainly caused by uncertainty in the meteorological forcings. The EPS spread increases with the rms error of the forecast. For large lead times the EPS spread and the forecast error substantially coincide. However, the EPS spread in this study, which does not account for uncertainty in the initial condition, underestimates the error during the early part of the forecast and for small storm surge values. On the contrary, it overestimates the rms error for large surge values. The PF (probability forecast) of the EPS has a clear skill in predicting the actual probability distribution of sea level, and it outperforms simple "dressed" PF methods. A probability estimate based on the single DF is shown to be inadequate. However, a PF obtained with a prescribed Gaussian distribution and centered on the DF value performs very similarly to the EPS-based PF.

Sub-Seasonal Climate Forecast Rodeo

NASA Astrophysics Data System (ADS)

Webb, R. S.; Nowak, K.; Cifelli, R.; Brekke, L. D.

2017-12-01

The Bureau of Reclamation, as the largest water wholesaler and the second largest producer of hydropower in the United States, benefits from skillful forecasts of future water availability. Researchers, water managers from local, regional, and federal agencies, and groups such as the Western States Water Council agree that improved precipitation and temperature forecast information at the sub-seasonal to seasonal (S2S) timescale is an area with significant potential benefit to water management. In response, and recognizing NOAA's leadership in forecasting, Reclamation has partnered with NOAA to develop and implement a real-time S2S forecasting competition. For a year, solvers are submitting forecasts of temperature and precipitation for weeks 3&4 and 5&6 every two weeks on a 1x1 degree grid for the 17 western state domain where Reclamation operates. The competition began on April 18, 2017 and the final real-time forecast is due April 3, 2018. Forecasts are evaluated once observational data become available using spatial anomaly correlation. Scores are posted on a competition leaderboard hosted by the National Integrated Drought Information System (NIDIS). The leaderboard can be accessed at: https://www.drought.gov/drought/sub-seasonal-climate-forecast-rodeo. To be eligible for cash prizes - which total $800,000 - solvers must outperform two benchmark forecasts during the real-time competition as well as in a required 11-year hind-cast. To receive a prize, competitors must grant a non-exclusive license to practice their forecast technique and make it available as open source software. At approximately one quarter complete, there are teams outperforming the benchmarks in three of the four competition categories. With prestige and monetary incentives on the line, it is hoped that the competition will spur innovation of improved S2S forecasts through novel approaches, enhancements to established models, or otherwise. Additionally, the competition aims to raise awareness on the S2S forecast need and the potential benefits- which extend beyond water management - to drought preparedness, public health, and other sectors.

Forecasting United States heartworm Dirofilaria immitis prevalence in dogs.

PubMed

Bowman, Dwight D; Liu, Yan; McMahan, Christopher S; Nordone, Shila K; Yabsley, Michael J; Lund, Robert B

2016-10-10

This paper forecasts next year's canine heartworm prevalence in the United States from 16 climate, geographic and societal factors. The forecast's construction and an assessment of its performance are described. The forecast is based on a spatial-temporal conditional autoregressive model fitted to over 31 million antigen heartworm tests conducted in the 48 contiguous United States during 2011-2015. The forecast uses county-level data on 16 predictive factors, including temperature, precipitation, median household income, local forest and surface water coverage, and presence/absence of eight mosquito species. Non-static factors are extrapolated into the forthcoming year with various statistical methods. The fitted model and factor extrapolations are used to estimate next year's regional prevalence. The correlation between the observed and model-estimated county-by-county heartworm prevalence for the 5-year period 2011-2015 is 0.727, demonstrating reasonable model accuracy. The correlation between 2015 observed and forecasted county-by-county heartworm prevalence is 0.940, demonstrating significant skill and showing that heartworm prevalence can be forecasted reasonably accurately. The forecast presented herein can a priori alert veterinarians to areas expected to see higher than normal heartworm activity. The proposed methods may prove useful for forecasting other diseases.

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.

Forecasting of Water Consumptions Expenditure Using Holt-Winter’s and ARIMA

NASA Astrophysics Data System (ADS)

Razali, S. N. A. M.; Rusiman, M. S.; Zawawi, N. I.; Arbin, N.

2018-04-01

This study is carried out to forecast water consumption expenditure of Malaysian university specifically at University Tun Hussein Onn Malaysia (UTHM). The proposed Holt-Winter’s and Auto-Regressive Integrated Moving Average (ARIMA) models were applied to forecast the water consumption expenditure in Ringgit Malaysia from year 2006 until year 2014. The two models were compared and performance measurement of the Mean Absolute Percentage Error (MAPE) and Mean Absolute Deviation (MAD) were used. It is found that ARIMA model showed better results regarding the accuracy of forecast with lower values of MAPE and MAD. Analysis showed that ARIMA (2,1,4) model provided a reasonable forecasting tool for university campus water usage.

Skillful Spring Forecasts of September Arctic Sea Ice Extent Using Passive Microwave Data

NASA Technical Reports Server (NTRS)

Petty, A. A.; Schroder, D.; Stroeve, J. C.; Markus, Thorsten; Miller, Jeffrey A.; Kurtz, Nathan Timothy; Feltham, D. L.; Flocco, D.

2017-01-01

In this study, we demonstrate skillful spring forecasts of detrended September Arctic sea ice extent using passive microwave observations of sea ice concentration (SIC) and melt onset (MO). We compare these to forecasts produced using data from a sophisticated melt pond model, and find similar to higher skill values, where the forecast skill is calculated relative to linear trend persistence. The MO forecasts shows the highest skill in March-May, while the SIC forecasts produce the highest skill in June-August, especially when the forecasts are evaluated over recent years (since 2008). The high MO forecast skill in early spring appears to be driven primarily by the presence and timing of open water anomalies, while the high SIC forecast skill appears to be driven by both open water and surface melt processes. Spatial maps of detrended anomalies highlight the drivers of the different forecasts, and enable us to understand regions of predictive importance. Correctly capturing sea ice state anomalies, along with changes in open water coverage appear to be key processes in skillfully forecasting summer Arctic sea ice.

7 CFR 612.4 - Eligible individuals or groups.

Code of Federal Regulations, 2010 CFR

2010-01-01

..., DEPARTMENT OF AGRICULTURE CONSERVATION OPERATIONS SNOW SURVEYS AND WATER SUPPLY FORECASTS § 612.4 Eligible individuals or groups. (a) Any individual or group who is a significant water user and who would benefit from a water supply forecast may obtain forecasts from NRCS on a regular basis provided data are...

7 CFR 612.3 - Data collected and forecasts.

Code of Federal Regulations, 2010 CFR

2010-01-01

..., DEPARTMENT OF AGRICULTURE CONSERVATION OPERATIONS SNOW SURVEYS AND WATER SUPPLY FORECASTS § 612.3 Data..., and wind. (b) Water supply forecasts in the western states area are generally made monthly from.... Data sites generally include a snow course where both snow depth and water equivalent of snow are...

A method for determining average beach slope and beach slope variability for U.S. sandy coastlines

USGS Publications Warehouse

Doran, Kara S.; Long, Joseph W.; Overbeck, Jacquelyn R.

2015-01-01

The U.S. Geological Survey (USGS) National Assessment of Hurricane-Induced Coastal Erosion Hazards compares measurements of beach morphology with storm-induced total water levels to produce forecasts of coastal change for storms impacting the Gulf of Mexico and Atlantic coastlines of the United States. The wave-induced water level component (wave setup and swash) is estimated by using modeled offshore wave height and period and measured beach slope (from dune toe to shoreline) through the empirical parameterization of Stockdon and others (2006). Spatial and temporal variability in beach slope leads to corresponding variability in predicted wave setup and swash. For instance, seasonal and storm-induced changes in beach slope can lead to differences on the order of 1 meter (m) in wave-induced water level elevation, making accurate specification of this parameter and its associated uncertainty essential to skillful forecasts of coastal change. A method for calculating spatially and temporally averaged beach slopes is presented here along with a method for determining total uncertainty for each 200-m alongshore section of coastline.

Predicting Airspace Capacity Impacts Using the Consolidated Storm Prediction for Aviation

NASA Technical Reports Server (NTRS)

Russell, Carl

2010-01-01

Convective weather is currently the largest contributor to air traffic delays in the United States. In order to make effective traffic flow management decisions to mitigate these delays, weather forecasts must be made as early and as accurately as possible. A forecast product that could be used to mitigate convective weather impacts is the Consolidated Storm Prediction for Aviation. This product provides forecasts of cloud water content and convective top heights at 0- to 8-hour look-ahead times. The objective of this study was to examine a method of predicting the impact of convective weather on air traffic sector capacities using these forecasts. Polygons representing forecast convective weather were overlaid at multiple flight levels on a sector map to calculate the fraction of each sector covered by weather. The fractional volume coverage was used as the primary metric to determine convection s impact on sectors. Results reveal that the forecasts can be used to predict the probability and magnitude of weather impacts on sector capacity up to eight hours in advance.

NOAA Propagation Database Value in Tsunami Forecast Guidance

NASA Astrophysics Data System (ADS)

Eble, M. C.; Wright, L. M.

2016-02-01

The National Oceanic and Atmospheric Administration (NOAA) Center for Tsunami Research (NCTR) has developed a tsunami forecasting capability that combines a graphical user interface with data ingestion and numerical models to produce estimates of tsunami wave arrival times, amplitudes, current or water flow rates, and flooding at specific coastal communities. The capability integrates several key components: deep-ocean observations of tsunamis in real-time, a basin-wide pre-computed propagation database of water level and flow velocities based on potential pre-defined seismic unit sources, an inversion or fitting algorithm to refine the tsunami source based on the observations during an event, and tsunami forecast models. As tsunami waves propagate across the ocean, observations from the deep ocean are automatically ingested into the application in real-time to better define the source of the tsunami itself. Since passage of tsunami waves over a deep ocean reporting site is not immediate, we explore the value of the NOAA propagation database in providing placeholder forecasts in advance of deep ocean observations. The propagation database consists of water elevations and flow velocities pre-computed for 50 x 100 [km] unit sources in a continuous series along all known ocean subduction zones. The 2011 Japan Tohoku tsunami is presented as the case study

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.

An Operational Short-Term Forecasting System for Regional Hydropower Management

NASA Astrophysics Data System (ADS)

Gronewold, A.; Labuhn, K. A.; Calappi, T. J.; MacNeil, A.

2017-12-01

The Niagara River is the natural outlet of Lake Erie and drains four of the five Great lakes. The river is used to move commerce and is home to both sport fishing and tourism industries. It also provides nearly 5 million kilowatts of hydropower for approximately 3.9 million homes. Due to a complex international treaty and the necessity of balancing water needs for an extensive tourism industry, the power entities operating on the river require detailed and accurate short-term river flow forecasts to maximize power output. A new forecast system is being evaluated that takes advantage of several previously independent components including the NOAA Lake Erie operational Forecast System (LEOFS), a previously developed HEC-RAS model, input from the New York Power Authority(NYPA) and Ontario Power Generation (OPG) and lateral flow forecasts for some of the tributaries provided by the NOAA Northeast River Forecast Center (NERFC). The Corps of Engineers updated the HEC-RAS model of the upper Niagara River to use the output forcing from LEOFS and a planned Grass Island Pool elevation provided by the power entities. The entire system has been integrated at the NERFC; it will be run multiple times per day with results provided to the Niagara River Control Center operators. The new model helps improve discharge forecasts by better accounting for dynamic conditions on Lake Erie. LEOFS captures seiche events on the lake that are often several meters of displacement from still water level. These seiche events translate into flow spikes that HEC-RAS routes downstream. Knowledge of the peak arrival time helps improve operational decisions at the Grass Island Pool. This poster will compare and contrast results from the existing operational flow forecast and the new integrated LEOFS/HEC-RAS forecast. This additional model will supply the Niagara River Control Center operators with multiple forecasts of flow to help improve forecasting under a wider variety of conditions.

Forecasting urban water demand: A meta-regression analysis.

PubMed

Sebri, Maamar

2016-12-01

Water managers and planners require accurate water demand forecasts over the short-, medium- and long-term for many purposes. These range from assessing water supply needs over spatial and temporal patterns to optimizing future investments and planning future allocations across competing sectors. This study surveys the empirical literature on the urban water demand forecasting using the meta-analytical approach. Specifically, using more than 600 estimates, a meta-regression analysis is conducted to identify explanations of cross-studies variation in accuracy of urban water demand forecasting. Our study finds that accuracy depends significantly on study characteristics, including demand periodicity, modeling method, forecasting horizon, model specification and sample size. The meta-regression results remain robust to different estimators employed as well as to a series of sensitivity checks performed. The importance of these findings lies in the conclusions and implications drawn out for regulators and policymakers and for academics alike. Copyright © 2016. Published by Elsevier Ltd.

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.

Improved water allocation utilizing probabilistic climate forecasts: Short-term water contracts in a risk management framework

NASA Astrophysics Data System (ADS)

Sankarasubramanian, A.; Lall, Upmanu; Souza Filho, Francisco Assis; Sharma, Ashish

2009-11-01

Probabilistic, seasonal to interannual streamflow forecasts are becoming increasingly available as the ability to model climate teleconnections is improving. However, water managers and practitioners have been slow to adopt such products, citing concerns with forecast skill. Essentially, a management risk is perceived in "gambling" with operations using a probabilistic forecast, while a system failure upon following existing operating policies is "protected" by the official rules or guidebook. In the presence of a prescribed system of prior allocation of releases under different storage or water availability conditions, the manager has little incentive to change. Innovation in allocation and operation is hence key to improved risk management using such forecasts. A participatory water allocation process that can effectively use probabilistic forecasts as part of an adaptive management strategy is introduced here. Users can express their demand for water through statements that cover the quantity needed at a particular reliability, the temporal distribution of the "allocation," the associated willingness to pay, and compensation in the event of contract nonperformance. The water manager then assesses feasible allocations using the probabilistic forecast that try to meet these criteria across all users. An iterative process between users and water manager could be used to formalize a set of short-term contracts that represent the resulting prioritized water allocation strategy over the operating period for which the forecast was issued. These contracts can be used to allocate water each year/season beyond long-term contracts that may have precedence. Thus, integrated supply and demand management can be achieved. In this paper, a single period multiuser optimization model that can support such an allocation process is presented. The application of this conceptual model is explored using data for the Jaguaribe Metropolitan Hydro System in Ceara, Brazil. The performance relative to the current allocation process is assessed in the context of whether such a model could support the proposed short-term contract based participatory process. A synthetic forecasting example is also used to explore the relative roles of forecast skill and reservoir storage in this framework.

Smart Irrigation From Soil Moisture Forecast Using Satellite And Hydro -Meteorological Modelling

NASA Astrophysics Data System (ADS)

Corbari, Chiara; Mancini, Marco; Ravazzani, Giovanni; Ceppi, Alessandro; Salerno, Raffaele; Sobrino, Josè

2017-04-01

Increased water demand and climate change impacts have recently enhanced the need to improve water resources management, even in those areas which traditionally have an abundant supply of water. The highest consumption of water is devoted to irrigation for agricultural production, and so it is in this area that efforts have to be focused to study possible interventions. The SIM project funded by EU in the framework of the WaterWorks2014 - Water Joint Programming Initiative aims at developing an operational tool for real-time forecast of crops irrigation water requirements to support parsimonious water management and to optimize irrigation scheduling providing real-time and forecasted soil moisture behavior at high spatial and temporal resolutions with forecast horizons from few up to thirty days. This study discusses advances in coupling satellite driven soil water balance model and meteorological forecast as support for precision irrigation use comparing different case studies in Italy, in the Netherlands, in China and Spain, characterized by different climatic conditions, water availability, crop types and irrigation techniques and water distribution rules. Herein, the applications in two operative farms in vegetables production in the South of Italy where semi-arid climatic conditions holds, two maize fields in Northern Italy in a more water reach environment with flood irrigation will be presented. This system combines state of the art mathematical models and new technologies for environmental monitoring, merging ground observed data with Earth observations. Discussion on the methodology approach is presented, comparing for a reanalysis periods the forecast system outputs with observed soil moisture and crop water needs proving the reliability of the forecasting system and its benefits. The real-time visualization of the implemented system is also presented through web-dashboards.

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.

Spatial forecast of landslides in three gorges based on spatial data mining.

PubMed

Wang, Xianmin; Niu, Ruiqing

2009-01-01

The Three Gorges is a region with a very high landslide distribution density and a concentrated population. In Three Gorges there are often landslide disasters, and the potential risk of landslides is tremendous. In this paper, focusing on Three Gorges, which has a complicated landform, spatial forecasting of landslides is studied by establishing 20 forecast factors (spectra, texture, vegetation coverage, water level of reservoir, slope structure, engineering rock group, elevation, slope, aspect, etc). China-Brazil Earth Resources Satellite (Cbers) images were adopted based on C4.5 decision tree to mine spatial forecast landslide criteria in Guojiaba Town (Zhigui County) in Three Gorges and based on this knowledge, perform intelligent spatial landslide forecasts for Guojiaba Town. All landslides lie in the dangerous and unstable regions, so the forecast result is good. The method proposed in the paper is compared with seven other methods: IsoData, K-Means, Mahalanobis Distance, Maximum Likelihood, Minimum Distance, Parallelepiped and Information Content Model. The experimental results show that the method proposed in this paper has a high forecast precision, noticeably higher than that of the other seven methods.

Spatial Forecast of Landslides in Three Gorges Based On Spatial Data Mining

PubMed Central

Wang, Xianmin; Niu, Ruiqing

2009-01-01

The Three Gorges is a region with a very high landslide distribution density and a concentrated population. In Three Gorges there are often landslide disasters, and the potential risk of landslides is tremendous. In this paper, focusing on Three Gorges, which has a complicated landform, spatial forecasting of landslides is studied by establishing 20 forecast factors (spectra, texture, vegetation coverage, water level of reservoir, slope structure, engineering rock group, elevation, slope, aspect, etc). China-Brazil Earth Resources Satellite (Cbers) images were adopted based on C4.5 decision tree to mine spatial forecast landslide criteria in Guojiaba Town (Zhigui County) in Three Gorges and based on this knowledge, perform intelligent spatial landslide forecasts for Guojiaba Town. All landslides lie in the dangerous and unstable regions, so the forecast result is good. The method proposed in the paper is compared with seven other methods: IsoData, K-Means, Mahalanobis Distance, Maximum Likelihood, Minimum Distance, Parallelepiped and Information Content Model. The experimental results show that the method proposed in this paper has a high forecast precision, noticeably higher than that of the other seven methods. PMID:22573999

Value of long-term streamflow forecast to reservoir operations for water supply in snow-dominated catchments

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

Anghileri, Daniela; Voisin, Nathalie; Castelletti, Andrea F.

In this study, we develop a forecast-based adaptive control framework for Oroville reservoir, California, to assess the value of seasonal and inter-annual forecasts for reservoir operation.We use an Ensemble Streamflow Prediction (ESP) approach to generate retrospective, one-year-long streamflow forecasts based on the Variable Infiltration Capacity hydrology model. The optimal sequence of daily release decisions from the reservoir is then determined by Model Predictive Control, a flexible and adaptive optimization scheme.We assess the forecast value by comparing system performance based on the ESP forecasts with that based on climatology and a perfect forecast. In addition, we evaluate system performance based onmore » a synthetic forecast, which is designed to isolate the contribution of seasonal and inter-annual forecast skill to the overall value of the ESP forecasts.Using the same ESP forecasts, we generalize our results by evaluating forecast value as a function of forecast skill, reservoir features, and demand. Our results show that perfect forecasts are valuable when the water demand is high and the reservoir is sufficiently large to allow for annual carry-over. Conversely, ESP forecast value is highest when the reservoir can shift water on a seasonal basis.On average, for the system evaluated here, the overall ESP value is 35% less than the perfect forecast value. The inter-annual component of the ESP forecast contributes 20-60% of the total forecast value. Improvements in the seasonal component of the ESP forecast would increase the overall ESP forecast value between 15 and 20%.« less

Stakeholders' perceptions of social-ecological systems and the information they use in the management of freshwater resources in Guanacaste, Costa Rica

NASA Astrophysics Data System (ADS)

Wong-Parodi, G.; Babcock, M.; Small, M.; Grossmann, I.

2014-12-01

Climate change is expected to increase the chances of drought, and shift precipitation patterns in seasonally dry places. In some places, the heuristics or "rules of thumb" that stakeholders use may no longer be reliable for the effective management of water resources. This can have dire consequences for social and ecological systems, especially in developing countries. Scientists and policymakers view climate forecasts as one way for improving informed decision-making about freshwater resources. However, successful communication requires that stakeholders understand and are able to use such information. To develop effective communications, it is critical to characterize stakeholders' understanding of social-ecological systems as related to water, the type of information used to inform management decisions, and the perceived value of forecast information. To achieve our objective, we conducted 40 semi-structured interviews with farmers, water managers, hydroelectric utilities, local climate experts, tourism industry representatives, and members of the general public in the semi-arid region of Guanacaste, Costa Rica. People believe that they have enough water at this time however they believe that the region will become much drier in the future, which they attribute to climate change, El Nino/La Nina, and deforestation. With respect to the value of forecast information, we found that the scale of decision-making (e.g., irrigation district versus small farmer) was associated with a stakeholders' level of "technical sophistication" and trust in government. In future work, we will evaluate the prevalence of these beliefs and practices in the larger population in order to identify effective ways to tailor the presentation of forecast information for different audiences. This work provides insight into the development of forecast communications to improve the management of resources in development countries in the face of a changing climate.

Improved regional water management utilizing climate forecasts: An interbasin transfer model with a risk management framework

NASA Astrophysics Data System (ADS)

Li, Weihua; Sankarasubramanian, A.; Ranjithan, R. S.; Brill, E. D.

2014-08-01

Regional water supply systems undergo surplus and deficit conditions due to differences in inflow characteristics as well as due to their seasonal demand patterns. This study proposes a framework for regional water management by proposing an interbasin transfer (IBT) model that uses climate-information-based inflow forecast for minimizing the deviations from the end-of-season target storage across the participating pools. Using the ensemble streamflow forecast, the IBT water allocation model was applied for two reservoir systems in the North Carolina Triangle Area. Results show that interbasin transfers initiated by the ensemble streamflow forecast could potentially improve the overall water supply reliability as the demand continues to grow in the Triangle Area. To further understand the utility of climate forecasts in facilitating IBT under different spatial correlation structures between inflows and between the initial storages of the two systems, a synthetic experiment was designed to evaluate the framework under inflow forecast having different skills. Findings from the synthetic study can be summarized as follows: (a) inflow forecasts combined with the proposed IBT optimization model provide improved allocation in comparison to the allocations obtained under the no-transfer scenario as well as under transfers obtained with climatology; (b) spatial correlations between inflows and between initial storages among participating reservoirs could also influence the potential benefits that could be achieved through IBT; (c) IBT is particularly beneficial for systems that experience low correlations between inflows or between initial storages or on both attributes of the regional water supply system. Thus, if both infrastructure and permitting structures exist for promoting interbasin transfers, season-ahead inflow forecasts could provide added benefits in forecasting surplus/deficit conditions among the participating pools in the regional water supply system.

Improved Regional Water Management Utilizing Climate Forecasts: An Inter-basin Transfer Model with a Risk Management Framework

NASA Astrophysics Data System (ADS)

Li, W.; Arumugam, S.; Ranjithan, R. S.; Brill, E. D., Jr.

2014-12-01

Regional water supply systems undergo surplus and deficit conditions due to differences in inflow characteristics as well as due to their seasonal demand patterns. This study presents a framework for regional water management by proposing an Inter-Basin Transfer (IBT) model that uses climate-information-based inflow forecast for minimizing the deviations from the end- of-season target storage across the participating reservoirs. Using the ensemble streamflow forecast, the IBT water allocation model was applied for two reservoir systems in the North Carolina Triangle area. Results show that inter-basin transfers initiated by the ensemble streamflow forecast could potentially improve the overall water supply reliability as the demand continues to grow in the Triangle Area. To further understand the utility of climate forecasts in facilitating IBT under different spatial correlation structures between inflows and between the initial storages of the two systems, a synthetic experiment was designed to evaluate the framework under inflow forecast having different skills. Findings from the synthetic study can be summarized as follows: (a) Inflow forecasts combined with the proposed IBT optimization model provide improved allocation in comparison to the allocations obtained under the no- transfer scenario as well as under transfers obtained with climatology; (b) Spatial correlations between inflows and between initial storages among participating reservoirs could also influence the potential benefits that could be achieved through IBT; (c) IBT is particularly beneficial for systems that experience low correlations between inflows or between initial storages or on both attributes of the regional water supply system. Thus, if both infrastructure and permitting structures exist for promoting inter-basin transfers, season-ahead inflow forecasts could provide added benefits in forecasting surplus/deficit conditions among the participating reservoirs in the regional water supply system.

REAL-TIME high-resolution urban surface water flood mapping to support flood emergency management

NASA Astrophysics Data System (ADS)

Guan, M.; Yu, D.; Wilby, R.

2016-12-01

Strong evidence has shown that urban flood risks will substantially increase because of urbanisation, economic growth, and more frequent weather extremes. To effectively manage these risks require not only traditional grey engineering solutions, but also a green management solution. Surface water flood risk maps based on return period are useful for planning purposes, but are limited for application in flood emergencies, because of the spatiotemporal heterogeneity of rainfall and complex urban topography. Therefore, a REAL-TIME urban surface water mapping system is highly beneficial to increasing urban resilience to surface water flooding. This study integrated numerical weather forecast and high-resolution urban surface water modelling into a real-time multi-level surface water mapping system for Leicester City in the UK. For rainfall forecast, the 1km composite rain radar from the Met Office was used, and we used the advanced rainfall-runoff model - FloodMap to predict urban surface water at both city-level (10m-20m) and street-level (2m-5m). The system is capable of projecting 3-hour urban surface water flood, driven by rainfall derived from UK Met Office radar. Moreover, this system includes real-time accessibility mapping to assist the decision-making of emergency responders. This will allow accessibility (e.g. time to travel) from individual emergency service stations (e.g. Fire & Rescue; Ambulance) to vulnerable places to be evaluated. The mapping results will support contingency planning by emergency responders ahead of potential flood events.

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.

Risky Business: Development, Communication and Use of Hydroclimatic Forecasts

NASA Astrophysics Data System (ADS)

Lall, U.

2012-12-01

Inter-seasonal and longer hydroclimatic forecasts have been made increasingly in the last two decades following the increase in ENSO activity since the early 1980s and the success in seasonal ENSO forecasting. Yet, the number of examples of systematic use of these forecasts and their incorporation into water systems operation continue to be few. This may be due in part to the limited skill in such forecasts over much of the world, but is also likely due to the limited evolution of methods and opportunities to "safely" use uncertain forecasts. There has been a trend to rely more on "physically based" rather than "physically informed" empirical forecasts, and this may in part explain the limited success in developing usable products in more locations. Given the limited skill, forecasters have tended to "dumb" down their forecasts - either formally or subjectively shrinking the forecasts towards climatology, or reducing them to tercile forecasts that serve to obscure the potential information in the forecast. Consequently, the potential utility of such forecasts for decision making is compromised. Water system operating rules are often designed to be robust in the face of historical climate variability, and consequently are adapted to the potential conditions that a forecast seeks to inform. In such situations, there is understandable reluctance by managers to use the forecasts as presented, except in special cases where an alternate course of action is pragmatically appealing in any case. In this talk, I review opportunities to present targeted forecasts for use with decision systems that directly address climate risk and the risk induced by unbiased yet uncertain forecasts, focusing especially on extreme events and water allocation in a competitive environment. Examples from Brazil and India covering surface and ground water conjunctive use strategies that could potentially be insured and lead to improvements over the traditional system operation and resource allocation are provided.

Towards water vapor assimilation into mesoscale models for improved precipitation forecast

NASA Astrophysics Data System (ADS)

Demoz, B.; Whiteman, D.; Venable, D.; Joseph, E.

2006-05-01

Atmospheric water vapor plays a primary role in the life cycle of clouds, precipitation and is crucial in understanding many aspects of the water cycle. It is very important to short-range mesoscale and storm-scale weather prediction. Specifically, accurate characterization of water vapor at low levels is a necessary condition for quantitative precipitation forecast (QPF), the initiation of convection and various thermodynamic and microphysical processes in mesoscale severe weather systems. However, quantification of its variability (both temporal and spatial) and integration of high quality and high frequency water vapor profiles into mesoscale models have been challenging. We report on a conceptual proposal that attempts to 1) define approporiate lidar-based data and instrumentation required for mesoscale data assimilation and 2) a possible federated network of ground-based lidars that may be capable of acquiring such high resolution water vapor data sets and 3) a possible frame work of assimilation of the data into a mesoscale model.

An operational global ocean forecast system and its applications

NASA Astrophysics Data System (ADS)

Mehra, A.; Tolman, H. L.; Rivin, I.; Rajan, B.; Spindler, T.; Garraffo, Z. D.; Kim, H.

2012-12-01

A global Real-Time Ocean Forecast System (RTOFS) was implemented in operations at NCEP/NWS/NOAA on 10/25/2011. This system is based on an eddy resolving 1/12 degree global HYCOM (HYbrid Coordinates Ocean Model) and is part of a larger national backbone capability of ocean modeling at NWS in strong partnership with US Navy. The forecast system is run once a day and produces a 6 day long forecast using the daily initialization fields produced at NAVOCEANO using NCODA (Navy Coupled Ocean Data Assimilation), a 3D multi-variate data assimilation methodology. As configured within RTOFS, HYCOM has a horizontal equatorial resolution of 0.08 degrees or ~9 km. The HYCOM grid is on a Mercator projection from 78.64 S to 47 N and north of this it employs an Arctic dipole patch where the poles are shifted over land to avoid a singularity at the North Pole. This gives a mid-latitude (polar) horizontal resolution of approximately 7 km (3.5 km). The coastline is fixed at 10 m isobath with open Bering Straits. This version employs 32 hybrid vertical coordinate surfaces with potential density referenced to 2000 m. Vertical coordinates can be isopycnals, often best for resolving deep water masses, levels of equal pressure (fixed depths), best for the well mixed unstratified upper ocean and sigma-levels (terrain-following), often the best choice in shallow water. The dynamic ocean model is coupled to a thermodynamic energy loan ice model and uses a non-slab mixed layer formulation. The forecast system is forced with 3-hourly momentum, radiation and precipitation fluxes from the operational Global Forecast System (GFS) fields. Results include global sea surface height and three dimensional fields of temperature, salinity, density and velocity fields used for validation and evaluation against available observations. Several downstream applications of this forecast system will also be discussed which include search and rescue operations at US Coast Guard, navigation safety information provided by OPC using real time ocean model guidance from Global RTOFS surface ocean currents, operational guidance on radionuclide dispersion near Fukushima using 3D tracers, boundary conditions for various operational coastal ocean forecast systems (COFS) run by NOS etc.

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.

National Water Model: Providing the Nation with Actionable Water Intelligence

NASA Astrophysics Data System (ADS)

Aggett, G. R.; Bates, B.

2017-12-01

The National Water Model (NWM) provides national, street-level detail of water movement through time and space. Operating hourly, this flood of information offers enormous benefits in the form of water resource management, natural disaster preparedness, and the protection of life and property. The Geo-Intelligence Division at the NOAA National Water Center supplies forecasters and decision-makers with timely, actionable water intelligence through the processing of billions of NWM data points every hour. These datasets include current streamflow estimates, short and medium range streamflow forecasts, and many other ancillary datasets. The sheer amount of NWM data produced yields a dataset too large to allow for direct human comprehension. As such, it is necessary to undergo model data post-processing, filtering, and data ingestion by visualization web apps that make use of cartographic techniques to bring attention to the areas of highest urgency. This poster illustrates NWM output post-processing and cartographic visualization techniques being developed and employed by the Geo-Intelligence Division at the NOAA National Water Center to provide national actionable water intelligence.

From Research to Operations: Transitioning Noaa's Lake Erie Harmful Algal Bloom Forecast System

NASA Astrophysics Data System (ADS)

Kavanaugh, K. E.; Stumpf, R. P.

2016-02-01

A key priority of NOAA's Harmful Algal Bloom Operational Forecast System (HAB-OFS) is to leverage the Ecological Forecasting Roadmap to systematically transition to operations scientifically mature HAB forecasts in regions of the country where there is a strong user need identified and an operational framework can be supported. While in the demonstration phase, the Lake Erie HAB forecast has proven its utility. Over the next two years, NOAA will be transitioning the Lake Erie HAB forecast to operations with an initial operating capability established in the HAB OFS' operational infrastructure by the 2016 bloom season. Blooms of cyanobacteria are a recurring problem in Lake Erie, and the dominant bloom forming species, Microcystis aeruginosa, produces a toxin called microcystin that is poisonous to humans, livestock and pets. Once the toxins have contaminated the source water used for drinking water, it is costly for public water suppliers to remove them. As part of the Lake Erie HAB forecast demonstration, NOAA has provided information regarding the cyanobacterial blooms in a biweekly Experimental HAB Bulletin, which includes information about the current and forecasted distribution, toxicity, potential for vertical mixing or scum formation, mixing of the water column, and predictions of bloom decline. Coastal resource managers, public water suppliers and public health officials use the Experimental HAB Bulletins to respond to and mitigate the impacts of cyanobacterial blooms. The transition to operations will benefit stakeholders through ensuring that future Lake Erie HAB forecast products are sustained, systematic, reliable, and robust. Once operational, the forecasts will continue to be assessed and improvements will be made based on the results of emerging scientific research. In addition, the lessons learned from the Lake Erie transition will be used to streamline the process for future HAB forecasts presently in development.

Evaluation of model-based seasonal streamflow and water allocation forecasts for the Elqui Valley, Chile

NASA Astrophysics Data System (ADS)

Delorit, Justin; Cristian Gonzalez Ortuya, Edmundo; Block, Paul

2017-09-01

In many semi-arid regions, multisectoral demands often stress available water supplies. Such is the case in the Elqui River valley of northern Chile, which draws on a limited-capacity reservoir to allocate 25 000 water rights. Delayed infrastructure investment forces water managers to address demand-based allocation strategies, particularly in dry years, which are realized through reductions in the volume associated with each water right. Skillful season-ahead streamflow forecasts have the potential to inform managers with an indication of future conditions to guide reservoir allocations. This work evaluates season-ahead statistical prediction models of October-January (growing season) streamflow at multiple lead times associated with manager and user decision points, and links predictions with a reservoir allocation tool. Skillful results (streamflow forecasts outperform climatology) are produced for short lead times (1 September: ranked probability skill score (RPSS) of 0.31, categorical hit skill score of 61 %). At longer lead times, climatological skill exceeds forecast skill due to fewer observations of precipitation. However, coupling the 1 September statistical forecast model with a sea surface temperature phase and strength statistical model allows for equally skillful categorical streamflow forecasts to be produced for a 1 May lead, triggered for 60 % of years (1950-2015), suggesting forecasts need not be strictly deterministic to be useful for water rights holders. An early (1 May) categorical indication of expected conditions is reinforced with a deterministic forecast (1 September) as more observations of local variables become available. The reservoir allocation model is skillful at the 1 September lead (categorical hit skill score of 53 %); skill improves to 79 % when categorical allocation prediction certainty exceeds 80 %. This result implies that allocation efficiency may improve when forecasts are integrated into reservoir decision frameworks. The methods applied here advance the understanding of the mechanisms and timing responsible for moisture transport to the Elqui Valley and provide a unique application of streamflow forecasting in the prediction of water right allocations.

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.

Ensemble Flow Forecasts for Risk Based Reservoir Operations of Lake Mendocino in Mendocino County, California: A Framework for Objectively Leveraging Weather and Climate Forecasts in a Decision Support Environment

NASA Astrophysics Data System (ADS)

Delaney, C.; Hartman, R. K.; Mendoza, J.; Whitin, B.

2017-12-01

Forecast informed reservoir operations (FIRO) is a methodology that incorporates short to mid-range precipitation and flow forecasts to inform the flood operations of reservoirs. The Ensemble Forecast Operations (EFO) alternative is a probabilistic approach of FIRO that incorporates ensemble streamflow predictions (ESPs) made by NOAA's California-Nevada River Forecast Center (CNRFC). With the EFO approach, release decisions are made to manage forecasted risk of reaching critical operational thresholds. A water management model was developed for Lake Mendocino, a 111,000 acre-foot reservoir located near Ukiah, California, to evaluate the viability of the EFO alternative to improve water supply reliability but not increase downstream flood risk. Lake Mendocino is a dual use reservoir, which is owned and operated for flood control by the United States Army Corps of Engineers and is operated for water supply by the Sonoma County Water Agency. Due to recent changes in the operations of an upstream hydroelectric facility, this reservoir has suffered from water supply reliability issues since 2007. The EFO alternative was simulated using a 26-year (1985-2010) ESP hindcast generated by the CNRFC. The ESP hindcast was developed using Global Ensemble Forecast System version 10 precipitation reforecasts processed with the Hydrologic Ensemble Forecast System to generate daily reforecasts of 61 flow ensemble members for a 15-day forecast horizon. Model simulation results demonstrate that the EFO alternative may improve water supply reliability for Lake Mendocino yet not increase flood risk for downstream areas. The developed operations framework can directly leverage improved skill in the second week of the forecast and is extendable into the S2S time domain given the demonstration of improved skill through a reliable reforecast of adequate historical duration and consistent with operationally available numerical weather predictions.

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.

Multi-Model Validation in the Chesapeake Bay Region During Frontier Sentinel 2010

DTIC Science & Technology

2012-09-28

which a 72-hr forecast took approximately 1 hr. Identical runs were performed on the DoD Supercomputing Resources Center (DSRC) host “ DaVinci ” at the...performance Navy DSRC host DaVinci . Products of water level and horizontal current maps as well as station time series, identical to those produced by the...forecast meteorological fields. The NCOM simulations were run daily on 128 CPUs at the Navy DSRC host DaVinci and required approximately 5 hrs of wall

Water and Power Systems Co-optimization under a High Performance Computing Framework

NASA Astrophysics Data System (ADS)

Xuan, Y.; Arumugam, S.; DeCarolis, J.; Mahinthakumar, K.

2016-12-01

Water and energy systems optimizations are traditionally being treated as two separate processes, despite their intrinsic interconnections (e.g., water is used for hydropower generation, and thermoelectric cooling requires a large amount of water withdrawal). Given the challenges of urbanization, technology uncertainty and resource constraints, and the imminent threat of climate change, a cyberinfrastructure is needed to facilitate and expedite research into the complex management of these two systems. To address these issues, we developed a High Performance Computing (HPC) framework for stochastic co-optimization of water and energy resources to inform water allocation and electricity demand. The project aims to improve conjunctive management of water and power systems under climate change by incorporating improved ensemble forecast models of streamflow and power demand. First, by downscaling and spatio-temporally disaggregating multimodel climate forecasts from General Circulation Models (GCMs), temperature and precipitation forecasts are obtained and input into multi-reservoir and power systems models. Extended from Optimus (Optimization Methods for Universal Simulators), the framework drives the multi-reservoir model and power system model, Temoa (Tools for Energy Model Optimization and Analysis), and uses Particle Swarm Optimization (PSO) algorithm to solve high dimensional stochastic problems. The utility of climate forecasts on the cost of water and power systems operations is assessed and quantified based on different forecast scenarios (i.e., no-forecast, multimodel forecast and perfect forecast). Analysis of risk management actions and renewable energy deployments will be investigated for the Catawba River basin, an area with adequate hydroclimate predicting skill and a critical basin with 11 reservoirs that supplies water and generates power for both North and South Carolina. Further research using this scalable decision supporting framework will provide understanding and elucidate the intricate and interdependent relationship between water and energy systems and enhance the security of these two critical public infrastructures.

Seasonal Drought Prediction in East Africa: Can National Multi-Model Ensemble Forecasts Help?

NASA Technical Reports Server (NTRS)

Shukla, Shraddhanand; Roberts, J. B.; Funk, Christopher; Robertson, F. R.; Hoell, Andrew

2015-01-01

The increasing food and water demands of East Africa's growing population are stressing the region's inconsistent water resources and rain-fed agriculture. As recently as in 2011 part of this region underwent one of the worst famine events in its history. Timely and skillful drought forecasts at seasonal scale for this region can inform better water and agro-pastoral management decisions, support optimal allocation of the region's water resources, and mitigate socio-economic losses incurred by droughts. However seasonal drought prediction in this region faces several challenges. Lack of skillful seasonal rainfall forecasts; the focus of this presentation, is one of those major challenges. In the past few decades, major strides have been taken towards improvement of seasonal scale dynamical climate forecasts. The National Centers for Environmental Prediction's (NCEP) National Multi-model Ensemble (NMME) is one such state-of-the-art dynamical climate forecast system. The NMME incorporates climate forecasts from 6+ fully coupled dynamical models resulting in 100+ ensemble member forecasts. Recent studies have indicated that in general NMME offers improvement over forecasts from any single model. However thus far the skill of NMME for forecasting rainfall in a vulnerable region like the East Africa has been unexplored. In this presentation we report findings of a comprehensive analysis that examines the strength and weakness of NMME in forecasting rainfall at seasonal scale in East Africa for all three of the prominent seasons for the region. (i.e. March-April-May, July-August-September and October-November- December). Simultaneously we also describe hybrid approaches; that combine statistical approaches with NMME forecasts; to improve rainfall forecast skill in the region when raw NMME forecasts lack in skill.

Seasonal Drought Prediction in East Africa: Can National Multi-Model Ensemble Forecasts Help?

NASA Technical Reports Server (NTRS)

Shukla, Shraddhanand; Roberts, J. B.; Funk, Christopher; Robertson, F. R.; Hoell, Andrew

2014-01-01

The increasing food and water demands of East Africa's growing population are stressing the region's inconsistent water resources and rain-fed agriculture. As recently as in 2011 part of this region underwent one of the worst famine events in its history. Timely and skillful drought forecasts at seasonal scale for this region can inform better water and agro-pastoral management decisions, support optimal allocation of the region's water resources, and mitigate socio-economic losses incurred by droughts. However seasonal drought prediction in this region faces several challenges. Lack of skillful seasonal rainfall forecasts; the focus of this presentation, is one of those major challenges. In the past few decades, major strides have been taken towards improvement of seasonal scale dynamical climate forecasts. The National Centers for Environmental Prediction's (NCEP) National Multi-model Ensemble (NMME) is one such state-of-the-art dynamical climate forecast system. The NMME incorporates climate forecasts from 6+ fully coupled dynamical models resulting in 100+ ensemble member forecasts. Recent studies have indicated that in general NMME offers improvement over forecasts from any single model. However thus far the skill of NMME for forecasting rainfall in a vulnerable region like the East Africa has been unexplored. In this presentation we report findings of a comprehensive analysis that examines the strength and weakness of NMME in forecasting rainfall at seasonal scale in East Africa for all three of the prominent seasons for the region. (i.e. March-April-May, July-August-September and October-November- December). Simultaneously we also describe hybrid approaches; that combine statistical approaches with NMME forecasts; to improve rainfall forecast skill in the region when raw NMME forecasts lack in skill.

Improving groundwater predictions utilizing seasonal precipitation forecasts from general circulation models forced with sea surface temperature forecasts

USGS Publications Warehouse

Almanaseer, Naser; Sankarasubramanian, A.; Bales, Jerad

2014-01-01

Recent studies have found a significant association between climatic variability and basin hydroclimatology, particularly groundwater levels, over the southeast United States. The research reported in this paper evaluates the potential in developing 6-month-ahead groundwater-level forecasts based on the precipitation forecasts from ECHAM 4.5 General Circulation Model Forced with Sea Surface Temperature forecasts. Ten groundwater wells and nine streamgauges from the USGS Groundwater Climate Response Network and Hydro-Climatic Data Network were selected to represent groundwater and surface water flows, respectively, having minimal anthropogenic influences within the Flint River Basin in Georgia, United States. The writers employ two low-dimensional models [principle component regression (PCR) and canonical correlation analysis (CCA)] for predicting groundwater and streamflow at both seasonal and monthly timescales. Three modeling schemes are considered at the beginning of January to predict winter (January, February, and March) and spring (April, May, and June) streamflow and groundwater for the selected sites within the Flint River Basin. The first scheme (model 1) is a null model and is developed using PCR for every streamflow and groundwater site using previous 3-month observations (October, November, and December) available at that particular site as predictors. Modeling schemes 2 and 3 are developed using PCR and CCA, respectively, to evaluate the role of precipitation forecasts in improving monthly and seasonal groundwater predictions. Modeling scheme 3, which employs a CCA approach, is developed for each site by considering observed groundwater levels from nearby sites as predictands. The performance of these three schemes is evaluated using two metrics (correlation coefficient and relative RMS error) by developing groundwater-level forecasts based on leave-five-out cross-validation. Results from the research reported in this paper show that using precipitation forecasts in climate models improves the ability to predict the interannual variability of winter and spring streamflow and groundwater levels over the basin. However, significant conditional bias exists in all the three modeling schemes, which indicates the need to consider improved modeling schemes as well as the availability of longer time-series of observed hydroclimatic information over the basin.

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.

Promoting inclusive water governance and forecasting the structure of water consumption based on compositional data: A case study of Beijing.

PubMed

Wei, Yigang; Wang, Zhichao; Wang, Huiwen; Yao, Tang; Li, Yan

2018-09-01

Water is centrally important for agricultural security, environment, people's livelihoods, and socio-economic development, particularly in the face of extreme climate changes. Due to water shortages in many cities, the conflicts between various stakeholders and sectors over water use and allocation are becoming more common and intense. Effective inclusive governance of water use is critical for relieving water use conflicts. In addition, reliable forecasting of the structure of water usage among different sectors is a basic need for effective water governance planning. Although a large number of studies have attempted to forecast water use, little is known about the forecasted structure and trends of water use in the future. This paper aims to develop a forecasting model for the structure of water usage based on compositional data. Compositional data analysis is an effective approach for investigating the internal structure of a system. A host of data transformation methods and forecasting models were adopted and compared in order to derive the best-performing model. According to mean absolute percent error for compositional data (CoMAPE), a hyperspherical-transformation-based vector autoregression model for compositional data (VAR-DRHT) is the best-performing model. The proportions of the agricultural, industrial, domestic and environmental water will be 6.11%, 5.01%, 37.48% and 51.4% by 2020. Several recommendations for water inclusive development are provided to give a better account for the optimization of the water use structure, alleviation of water shortages, and improving stake holders' wellbeing. Overall, although we focus on groundwater, this study presents a powerful framework broadly applicable to resource management. Copyright © 2018 Elsevier B.V. All rights reserved.

Development of Water Quality Forecasting Models Based on the SOM-ANN on TMDL Unit Watershed in Nakdong River

NASA Astrophysics Data System (ADS)

KIM, M.; Kim, J.; Baek, J.; Kim, C.; Shin, H.

2013-12-01

It has being happened as flush flood or red/green tide in various natural phenomena due to climate change and indiscreet development of river or land. Especially, water being very important to man should be protected and managed from water quality pollution, and in water resources management, real-time watershed monitoring system is being operated with the purpose of keeping watch and managing on rivers. It is especially important to monitor and forecast water quality in watershed. A study area selected Nak_K as one site among TMDL unit watershed in Nakdong River. This study is to develop a water quality forecasting model connected with making full use of observed data of 8 day interval from Nakdong River Environment Research Center. When forecasting models for each of the BOD, DO, COD, and chlorophyll-a are established considering correlation of various water quality factors, it is needed to select water quality factors showing highly considerable correlation with each water quality factor which is BOD, DO, COD, and chlorophyll-a. For analyzing the correlation of the factors (reservoir discharge, precipitation, air temperature, DO, BOD, COD, Tw, TN, TP, chlorophyll-a), in this study, self-organizing map was used and cross correlation analysis method was also used for comparing results drawn. Based on the results, each forecasting model for BOD, DO, COD, and chlorophyll-a was developed during the short period as 8, 16, 24, 32 days at 8 day interval. The each forecasting model is based on neural network with back propagation algorithm. That is, the study is connected with self-organizing map for analyzing correlation among various factors and neural network model for forecasting of water quality. It is considerably effective to manage the water quality in plenty of rivers, then, it specially is possible to monitor a variety of accidents in water quality. It will work well to protect water quality and to prevent destruction of the environment becoming more and more serious before occurring.

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.

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.

Distortion Representation of Forecast Errors for Model Skill Assessment and Objective Analysis

NASA Technical Reports Server (NTRS)

Hoffman, Ross N.; Nehrkorn, Thomas; Grassotti, Christopher

1998-01-01

We proposed a novel characterization of errors for numerical weather predictions. A general distortion representation allows for the displacement and amplification or bias correction of forecast anomalies. Characterizing and decomposing forecast error in this way has several important applications, including the model assessment application and the objective analysis application. In this project, we have focused on the assessment application, restricted to a realistic but univariate 2-dimensional situation. Specifically, we study the forecast errors of the sea level pressure (SLP), the 500 hPa geopotential height, and the 315 K potential vorticity fields for forecasts of the short and medium range. The forecasts are generated by the Goddard Earth Observing System (GEOS) data assimilation system with and without ERS-1 scatterometer data. A great deal of novel work has been accomplished under the current contract. In broad terms, we have developed and tested an efficient algorithm for determining distortions. The algorithm and constraints are now ready for application to larger data sets to be used to determine the statistics of the distortion as outlined above, and to be applied in data analysis by using GEOS water vapor imagery to correct short-term forecast errors.

Expert forecasts and the emergence of water scarcity on public agendas

USGS Publications Warehouse

Graffy, E.A.

2006-01-01

Expert forecasts of worldwide water scarcity depict conditions that call for proactive, preventive, coordinated water governance, but they have not been matched by public agendas of commensurate scope and urgency in the United States. This disconnect can not be adequately explained without some attention to attributes of forecasts themselves. I propose that the institutional fragmentation of water expertise and prevailing patterns of communication about water scarcity militate against the formulation of a common public definition of the problem and encourage reliance on unambiguous crises to stimulate social and policy agenda setting. I do not argue that expert forecasts should drive public agendas deterministically, but if their purpose is to help prevent water crises (not just predict them), then a greater effort is needed to overcome the barriers to meaningful public scrutiny of expert claims and evaluation of water strategies presently in place. Copyright ?? 2006 Taylor & Francis Group, LLC.

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.

Forecasting land cover change impacts on drinking water treatment costs in Minneapolis, Minnesota

EPA Science Inventory

Source protection is a critical aspect of drinking water treatment. The benefits of protecting source water quality in reducing drinking water treatment costs are clear. However, forecasting the impacts of environmental change on source water quality and its potential to influenc...

Coastal and Riverine Flood Forecast Model powered by ADCIRC

NASA Astrophysics Data System (ADS)

Khalid, A.; Ferreira, C.

2017-12-01

Coastal flooding is becoming a major threat to increased population in the coastal areas. To protect coastal communities from tropical storms & hurricane damages, early warning systems are being developed. These systems have the capability of real time flood forecasting to identify hazardous coastal areas and aid coastal communities in rescue operations. State of the art hydrodynamic models forced by atmospheric forcing have given modelers the ability to forecast storm surge, water levels and currents. This helps to identify the areas threatened by intense storms. Study on Chesapeake Bay area has gained national importance because of its combined riverine and coastal phenomenon, which leads to greater uncertainty in flood predictions. This study presents an automated flood forecast system developed by following Advanced Circulation (ADCIRC) Surge Guidance System (ASGS) guidelines and tailored to take in riverine and coastal boundary forcing, thus includes all the hydrodynamic processes to forecast total water in the Potomac River. As studies on tidal and riverine flow interaction are very scarce in number, our forecast system would be a scientific tool to examine such area and fill the gaps with precise prediction for Potomac River. Real-time observations from National Oceanic and Atmospheric Administration (NOAA) and field measurements have been used as model boundary feeding. The model performance has been validated by using major historical riverine and coastal flooding events. Hydrodynamic model ADCIRC produced promising predictions for flood inundation areas. As better forecasts can be achieved by using coupled models, this system is developed to take boundary conditions from Global WaveWatchIII for the research purposes. Wave and swell propagation will be fed through Global WavewatchIII model to take into account the effects of swells and currents. This automated forecast system is currently undergoing rigorous testing to include any missing parameters which might provide better and more reliable forecast for the flood affected communities.

A Bayesian spatio-temporal model for forecasting the prevalence of antibodies to Borrelia burgdorferi, causative agent of Lyme disease, in domestic dogs within the contiguous United States.

PubMed

Watson, Stella C; Liu, Yan; Lund, Robert B; Gettings, Jenna R; Nordone, Shila K; McMahan, Christopher S; Yabsley, Michael J

2017-01-01

This paper models the prevalence of antibodies to Borrelia burgdorferi in domestic dogs in the United States using climate, geographic, and societal factors. We then use this model to forecast the prevalence of antibodies to B. burgdorferi in dogs for 2016. The data available for this study consists of 11,937,925 B. burgdorferi serologic test results collected at the county level within the 48 contiguous United States from 2011-2015. Using the serologic data, a baseline B. burgdorferi antibody prevalence map was constructed through the use of spatial smoothing techniques after temporal aggregation; i.e., head-banging and Kriging. In addition, several covariates purported to be associated with B. burgdorferi prevalence were collected on the same spatio-temporal granularity, and include forestation, elevation, water coverage, temperature, relative humidity, precipitation, population density, and median household income. A Bayesian spatio-temporal conditional autoregressive (CAR) model was used to analyze these data, for the purposes of identifying significant risk factors and for constructing disease forecasts. The fidelity of the forecasting technique was assessed using historical data, and a Lyme disease forecast for dogs in 2016 was constructed. The correlation between the county level model and baseline B. burgdorferi antibody prevalence estimates from 2011 to 2015 is 0.894, illustrating that the Bayesian spatio-temporal CAR model provides a good fit to these data. The fidelity of the forecasting technique was assessed in the usual fashion; i.e., the 2011-2014 data was used to forecast the 2015 county level prevalence, with comparisons between observed and predicted being made. The weighted (to acknowledge sample size) correlation between 2015 county level observed prevalence and 2015 forecasted prevalence is 0.978. A forecast for the prevalence of B. burgdorferi antibodies in domestic dogs in 2016 is also provided. The forecast presented from this model can be used to alert veterinarians in areas likely to see above average B. burgdorferi antibody prevalence in dogs in the upcoming year. In addition, because dogs and humans can be exposed to ticks in similar habitats, these data may ultimately prove useful in predicting areas where human Lyme disease risk may emerge.

A Bayesian spatio-temporal model for forecasting the prevalence of antibodies to Borrelia burgdorferi, causative agent of Lyme disease, in domestic dogs within the contiguous United States

PubMed Central

Watson, Stella C.; Liu, Yan; Lund, Robert B.; Gettings, Jenna R.; Nordone, Shila K.; McMahan, Christopher S.

2017-01-01

This paper models the prevalence of antibodies to Borrelia burgdorferi in domestic dogs in the United States using climate, geographic, and societal factors. We then use this model to forecast the prevalence of antibodies to B. burgdorferi in dogs for 2016. The data available for this study consists of 11,937,925 B. burgdorferi serologic test results collected at the county level within the 48 contiguous United States from 2011-2015. Using the serologic data, a baseline B. burgdorferi antibody prevalence map was constructed through the use of spatial smoothing techniques after temporal aggregation; i.e., head-banging and Kriging. In addition, several covariates purported to be associated with B. burgdorferi prevalence were collected on the same spatio-temporal granularity, and include forestation, elevation, water coverage, temperature, relative humidity, precipitation, population density, and median household income. A Bayesian spatio-temporal conditional autoregressive (CAR) model was used to analyze these data, for the purposes of identifying significant risk factors and for constructing disease forecasts. The fidelity of the forecasting technique was assessed using historical data, and a Lyme disease forecast for dogs in 2016 was constructed. The correlation between the county level model and baseline B. burgdorferi antibody prevalence estimates from 2011 to 2015 is 0.894, illustrating that the Bayesian spatio-temporal CAR model provides a good fit to these data. The fidelity of the forecasting technique was assessed in the usual fashion; i.e., the 2011-2014 data was used to forecast the 2015 county level prevalence, with comparisons between observed and predicted being made. The weighted (to acknowledge sample size) correlation between 2015 county level observed prevalence and 2015 forecasted prevalence is 0.978. A forecast for the prevalence of B. burgdorferi antibodies in domestic dogs in 2016 is also provided. The forecast presented from this model can be used to alert veterinarians in areas likely to see above average B. burgdorferi antibody prevalence in dogs in the upcoming year. In addition, because dogs and humans can be exposed to ticks in similar habitats, these data may ultimately prove useful in predicting areas where human Lyme disease risk may emerge. PMID:28472096

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.

a system approach to the long term forecasting of the climat data in baikal region

NASA Astrophysics Data System (ADS)

Abasov, N.; Berezhnykh, T.

2003-04-01

The Angara river running from Baikal with a cascade of hydropower plants built on it plays a peculiar role in economy of the region. With view of high variability of water inflow into the rivers and lakes (long-term low water periods and catastrophic floods) that is due to climatic peculiarities of the water resource formation, a long-term forecasting is developed and applied for risk decreasing at hydropower plants. Methodology and methods of long-term forecasting of natural-climatic processes employs some ideas of the research schools by Academician I.P.Druzhinin and Prof. A.P.Reznikhov and consists in detailed investigation of cause-effect relations, finding out physical analogs and their application to formalized methods of long-term forecasting. They are divided into qualitative (background method; method of analogs based on solar activity), probabilistic and approximative methods (analog-similarity relations; discrete-continuous model). These forecasting methods have been implemented in the form of analytical aids of the information-forecasting software "GIPSAR" that provides for some elements of artificial intelligence. Background forecasts of the runoff of the Ob, the Yenisei, the Angara Rivers in the south of Siberia are based on space-time regularities that were revealed on taking account of the phase shifts in occurrence of secular maxima and minima on integral-difference curves of many-year hydrological processes in objects compared. Solar activity plays an essential role in investigations of global variations of climatic processes. Its consideration in the method of superimposed epochs has allowed a conclusion to be made on the higher probability of the low-water period in the actual inflow to Lake Baikal that takes place on the increasing branch of solar activity of its 11-year cycle. The higher probability of a high-water period is observed on the decreasing branch of solar activity from the 2nd to the 5th year after its maximum. Probabilistic method of forecasting (with a year in advance) is based on the property of alternation of series of years with increase and decrease in the observed indicators (characteristic indices) of natural processes. Most of the series (98.4-99.6%) are represented by series of one to three years. The problem of forecasting is divided into two parts: 1) qualitative forecast of the probability that the started series will either continue or be replaced by a new series during the next year that is based on the frequency characteristics of series of years with increase or decrease of the forecasted sequence); 2) quantitative estimate of the forecasted value in the form of a curve of conditional frequencies is made on the base of intra-sequence interrelations among hydrometeorological elements by their differentiation with respect to series of years of increase or decrease, by construction of particular curves of conditional frequencies of the runoff for each expected variant of series development and by subsequent construction a generalized curve. Approximative learning methods form forecasted trajectories of the studied process indices for a long-term perspective. The method of analog-similarity relations is based on the fact that long periods of observations reveal some similarities in the character of variability of indices for some fragments of the sequence x (t) by definite criteria. The idea of the method is to estimate similarity of such fragments of the sequence that have been called the analogs. The method applies multistage optimization of both external parameters (e.g. the number of iterations of the sliding averaging needed to decompose the sequence into two components: the smoothed one with isolated periodic oscillations and the residual or random one). The method is applicable to current terms of forecasts and ending with the double solar cycle. Using a special procedure of integration, it separates terms with the best results for the given optimization subsample. Several optimal vectors of parameters obtained are tested on the examination (verifying) subsample. If the procedure is successful, the forecast is immediately made by integration of several best solutions. Peculiarities of forecasting extreme processes. Methods of long-term forecasting allow the sufficiently reliable forecasts to be made within the interval of xmin+Δ_1, xmax - Δ_2 (i.e. in the interval of medium values of indices). Meanwhile, in the intervals close to extreme ones, reliability of forecasts is substantially lower. While for medium values the statistics of the100-year sequence gives acceptable results owing to a sufficiently large number of revealed analogs that correspond to prognostic samples, for extreme values the situation is quite different, first of all by virtue of poverty of statistical data. Decreasing the values of Δ_1,Δ_2: Δ_1,Δ_2 rightarrow 0 (by including them into optimization parameters of the considered forecasting methods) could be one of the ways to improve reliability of forecasts. Partially, such an approach has been realized in the method of analog-similarity relations, giving the possibility to form a range of possible forecasted trajectories in two variants - from the minimum possible trajectory to the maximum possible one. Reliability of long-term forecasts. Both the methodology and the methods considered above have been realized as the information-forecasting system "GIPSAR". The system includes some tools implementing several methods of forecasting, analysis of initial and forecasted information, a developed database, a set of tools for verification of algorithms, additional information on the algorithms of statistical processing of sequences (sliding averaging, integral-difference curves, etc.), aids to organize input of initial information (in its various forms) as well as aids to draw up output prognostic documents. Risk management. The normal functioning of the Angara cascade is periodically interrupted by risks of two types that take place in the Baikal, the Bratsk and Ust-Ilimsk reservoirs: long low-water periods and sudden periods of extremely high water levels. For example, low-water periods, observed in the reservoirs of the Angara cascade can be classified under four risk categories : 1 - acceptable (negligible reduction of electric power generation by hydropower plants; certain difficulty in meeting environmental and navigation requirements); 2 - significant (substantial reduction of electric power generation by hydropower plants; certain restriction on water releases for navigation; violation of environmental requirements in some years); 3 - emergency (big losses in electric power generation; limited electricity supply to large consumers; significant restriction of water releases for navigation; threat of exposure of drinkable water intake works; violation of environmental requirements for a number of years); 4 - catastrophic (energy crisis; social crisis exposure of drinkable water intake works; termination of navigation; environmental catastrophe). Management of energy systems consists in operative, many-year regulation and perspective planning and has to take into account the analysis of operative data (water reserves in reservoirs), long-term statistics and relations among natural processes and also forecasts - short-term (for a day, week, decade), long-term and/or super-long-term (from a month to several decades). Such natural processes as water inflow to reservoirs, air temperatures during heating periods depend in turn on external factors: prevailing types of atmospheric circulation, intensity of the 11- and 22-year cycles of solar activity, volcanic activity, interaction between the ocean and atmosphere, etc. Until recently despite the formed scientific schools on long-term forecasting (I.P.Druzhinin, A.P.Reznikhov) the energy system management has been based on specially drawn dispatching schedules and long-term hydrometeorological forecasts only without attraction of perspective forecasted indices. Insertion of a parallel block of forecast (based on the analysis of data on natural processes and special methods of forecasting) into the scheme can largely smooth unfavorable consequences from the impact of natural processes on sustainable development of energy systems and especially on its safe operation. However, the requirements to reliability and accuracy of long-term forecasts significantly increase. The considered approach to long term forecasting can be used for prediction: mean winter and summer air temperatures, droughts and wood fires.

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.

Flood-inundation maps for the White River at Newberry, Indiana

USGS Publications Warehouse

Fowler, Kathleen K.; Kim, Moon H.; Menke, Chad D.

2012-01-01

Digital flood-inundation maps for a 4.9-mile reach of the White River at Newberry, Indiana (Ind.), were created by the U.S. Geological Survey (USGS) in cooperation with the Indiana Office of Community and Rural Affairs. The inundation maps, which can be accessed through the USGS Flood Inundation Mapping Science Web site at http://water.usgs.gov/osw/flood_inundation, depict estimates of the areal extent of flooding corresponding to selected water levels (stages) at USGS streamgage 03360500, White River at Newberry, Ind. Current conditions at the USGS streamgage may be obtained on the Internet (http://waterdata.usgs.gov/in/nwis/uv?site_no=03360500). The National Weather Service (NWS) forecasts flood hydrographs at the Newberry streamgage. That forecasted peak-stage information, also available on the Internet, may be used in conjunction with the maps developed in this study to show predicted areas of flood inundation. For this study, flood profiles were computed for the White River reach by means of a one-dimensional step-backwater model developed by the U.S. Army Corps of Engineers. The hydraulic model was calibrated by using the most current stage-discharge relation at USGS streamgage 03360500, White River at Newberry, Ind., and high-water marks from a flood in June 2008.The calibrated hydraulic model was then used to determine 22 water-surface profiles for flood stages a1-foot intervals referenced to the streamgage datum and ranging from bankfull to approximately the highest recorded water level at the streamgage. The simulated water-surface profiles were then combined with a geographic information system digital elevation model (derived from Light Detection and Ranging (LiDAR) data) in order to delineate the area flooded at each water level. The availability of these maps, along with Internet information regarding current stage from the USGS streamgage at Newberry, Ind., and forecasted stream stages from the NWS, provide emergency management personnel and residents with information that is critical for flood response activities such as evacuations and road closures as well as for post-flood recovery efforts.

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.

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.

High Resolution Modeling in Mountainous Terrain for Water Resource Management: AN Extreme Precipitation Event Case Study

NASA Astrophysics Data System (ADS)

Masarik, M. T.; Watson, K. A.; Flores, A. N.; Anderson, K.; Tangen, S.

2016-12-01

The water resources infrastructure of the Western US is designed to deliver reliable water supply to users and provide recreational opportunities for the public, as well as afford flood control for communities by buffering variability in precipitation and snow storage. Thus water resource management is a balancing act of meeting multiple objectives while trying to anticipate and mitigate natural variability of water supply. Currently, the forecast guidance available to personnel managing resources in mountainous terrain is lacking in two ways: the spatial resolution is too coarse, and there is a gap in the intermediate time range (10-30 days). To address this need we examine the effectiveness of using the Weather Research and Forecasting (WRF) model, a state of the art, regional, numerical weather prediction model, as a means to generate high-resolution weather guidance in the intermediate time range. This presentation will focus on a reanalysis and hindcasting case study of the extreme precipitation and flooding event in the Payette River Basin of Idaho during the period of June 2nd-4th, 2010. For the reanalysis exercise we use NCEP's Climate Forecast System Reanalysis (CFSR) and the North American Regional Reanalysis (NARR) data sets as input boundary conditions to WRF. The model configuration includes a horizontal spatial resolution of 3km in the outer nest, and 1 km in the inner nest, with output temporal resolution of 3 hrs and 1 hr, respectively. The hindcast simulations, which are currently underway, will make use of the NCEP Climate Forecast System Reforecast (CFSRR) data. The current state of these runs will be discussed. Preparations for the second of two components in this project, weekly WRF forecasts during the intense portion of the water year, will be briefly described. These forecasts will use the NCEP Climate Forecast System version 2 (CFSv2) operational forecast data as boundary conditions to provide forecast guidance geared towards water resource managers out to a lead time of 30 days. We are particularly interested in the degree to which there is forecast skill in basinwide precipitation occurrence, departure from climatology, timing, and amount in the intermediate time range.

A flexible hydrological warning system in Denmark for real-time surface water and groundwater simulations

NASA Astrophysics Data System (ADS)

He, Xin; Stisen, Simon; Wiese, Marianne B.; Jørgen Henriksen, Hans

2015-04-01

In Denmark, increasing focus on extreme weather events has created considerable demand for short term forecasts and early warnings in relation to groundwater and surface water flooding. The Geological Survey of Denmark and Greenland (GEUS) has setup, calibrated and applied a nationwide water resources model, the DK-Model, primarily for simulating groundwater and surface water flows and groundwater levels during the past 20 years. So far, the DK-model has only been used in offline historical and future scenario simulations. Therefore, challenges arise in operating such a model for online forecasts and early warnings, which requires access to continuously updated observed climate input data and forecast data of precipitation, temperature and global radiation for the next 48 hours or longer. GEUS has a close collaboration with the Danish Meteorological Institute in order to test and enable this data input for the DK model. Due to the comprehensive physical descriptions of the DK-Model, the simulation results can potentially be any component of the hydrological cycle within the models domain. Therefore, it is important to identify which results need to be updated and saved in the real-time mode, since it is not computationally economical to save every result considering the heavy load of data. GEUS have worked closely with the end-users and interest groups such as water planners and emergency managers from the municipalities, water supply and waste water companies, consulting companies and farmer organizations, in order to understand their possible needs for real time simulation and monitoring of the nationwide water cycle. This participatory process has been supported by a web based questionnaire survey, and a workshop that connected the model developers and the users. For qualifying the stakeholder engagement, GEUS has selected a representative catchment area (Skjern River) for testing and demonstrating a prototype of the web based hydrological warning system at the workshop, and illustrated simulated groundwater levels, streamflow and water content in the root zone. The webpages can be tailor-made to meet the requirements of the end-users and also enable flexibility to extend while the users' demand changes. The active involvement of stakeholders in the workshop provided very valuable insights and feedbacks for GEUS, relevant for the future development of the nationwide real-time modeling and water cycle monitoring system for Denmark, including possible linking to early warning and real-time forecasting systems operating at the local scale.

Integrated Drought Monitoring and Forecasts for Decision Making in Water and Agricultural Sectors over the Southeastern US under Changing Climate

NASA Astrophysics Data System (ADS)

Arumugam, S.; Mazrooei, A.; Ward, R.

2017-12-01

Changing climate arising from structured oscillations such as ENSO and rising temperature poses challenging issues in meeting the increasing water demand (due to population growth) for public supply and agriculture over the Southeast US. This together with infrastructural (e.g., most reservoirs being within-year systems) and operational (e.g., static rule curves) constraints requires an integrated approach that seamlessly monitors and forecasts water and soil moisture conditions to support adaptive decision making in water and agricultural sectors. In this talk, we discuss the utility of an integrated drought management portal that both monitors and forecasts streamflow and soil moisture over the southeast US. The forecasts are continuously developed and updated by forcing monthly-to-seasonal climate forecasts with a land surface model for various target basins. The portal also houses a reservoir allocation model that allows water managers to explore different release policies in meeting the system constraints and target storages conditioned on the forecasts. The talk will also demonstrate how past events (e.g., 2007-2008 drought) could be proactively monitored and managed to improve decision making in water and agricultural sectors over the Southeast US. Challenges in utilizing the portal information from institutional and operational perspectives will also be presented.

Method for Assessing Impacts of Global Sea Level Rise on Navigation Gate Operations

NASA Astrophysics Data System (ADS)

Obrien, P. S.; White, K. D.; Friedman, D.

2015-12-01

Coastal navigation infrastructure may be highly vulnerable to changing climate, including increasing sea levels and altered frequency and intensity of coastal storms. Future gate operations impacted by global sea level rise will pose unique challenges, especially for structures 50 years and older. Our approach is to estimate future changes in gate operational frequency based on a bootstrapping method to forecast future water levels. A case study will be presented to determine future changes in frequency of operations over the next 100 years. A statistical model in the R programming language was developed to apply future sea level rise projections using the three sea level rise scenarios prescribed by USACE Engineer Regulation ER 1100-2-8162. Information derived from the case study will help forecast changes in operational costs caused by increased gate operations and inform timing of decisions on adaptation measures.

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.

Performance of stochastic approaches for forecasting river water quality.

PubMed

Ahmad, S; Khan, I H; Parida, B P

2001-12-01

This study analysed water quality data collected from the river Ganges in India from 1981 to 1990 for forecasting using stochastic models. Initially the box and whisker plots and Kendall's tau test were used to identify the trends during the study period. For detecting the possible intervention in the data the time series plots and cusum charts were used. The three approaches of stochastic modelling which account for the effect of seasonality in different ways. i.e. multiplicative autoregressive integrated moving average (ARIMA) model. deseasonalised model and Thomas-Fiering model were used to model the observed pattern in water quality. The multiplicative ARIMA model having both nonseasonal and seasonal components were, in general, identified as appropriate models. In the deseasonalised modelling approach, the lower order ARIMA models were found appropriate for the stochastic component. The set of Thomas-Fiering models were formed for each month for all water quality parameters. These models were then used to forecast the future values. The error estimates of forecasts from the three approaches were compared to identify the most suitable approach for the reliable forecast. The deseasonalised modelling approach was recommended for forecasting of water quality parameters of a river.

Financial Risk Reduction and Management of Water Reservoirs Using Forecasts: A Case for Pernambuco, Brazil

NASA Astrophysics Data System (ADS)

Kumar, I.; Josset, L.; e Silva, E. C.; Possas, J. M. C.; Asfora, M. C.; Lall, U.

2017-12-01

The financial health and sustainability, ensuring adequate supply, and adapting to climate are fundamental challenges faced by water managers. These challenges are worsened in semi-arid regions with socio-economic pressures, seasonal supply of water, and projected increase in intensity and frequency of droughts. Over time, probabilistic rainfall forecasts are improving and for water managers, it could be key in addressing the above challenges. Using forecasts can also help make informed decisions about future infrastructure. The study proposes a model to minimize cost of water supply (including cost of deficit) given ensemble forecasts. The model can be applied to seasonal to annual ensemble forecasts, to determine the least cost solution. The objective of the model is to evaluate the resiliency and cost associated to supplying water. A case study is conducted in one of the largest reservoirs (Jucazinho) in Pernambuco state, Brazil, and four other reservoirs, which provide water to nineteen municipalities in the Jucazinho system. The state has been in drought since 2011, and the Jucazinho reservoir, has been empty since January 2017. The importance of climate adaptation along with risk management and financial sustainability are important to the state as it is extremely vulnerable to droughts, and has seasonal streamflow. The objectives of the case study are first, to check if streamflow forecasts help reduce future supply costs by comparing k-nearest neighbor ensemble forecasts with a fixed release policy. Second, to determine the value of future infrastructure, a new source of supply from Rio São Francisco, considered to mitigate drought conditions. The study concludes that using forecasts improve the supply and financial sustainability of water, by reducing cost of failure. It also concludes that additional infrastructure can help reduce the risks of failure significantly, but does not guarantee supply during prolonged droughts like the one experienced currently.

Improving the Resilience of Major Ports and Critical Supply Chains to Extreme Coastal Flooding: a Combined Artificial Neural Network and Hydrodynamic Simulation Approach to Predicting Tidal Surge Inundation of Port Infrastructure and Impact on Operations.

NASA Astrophysics Data System (ADS)

French, J.

2015-12-01

Ports are vital to the global economy, but assessments of global exposure to flood risk have generally focused on major concentrations of population or asset values. Few studies have examined the impact of extreme inundation events on port operation and critical supply chains. Extreme water levels and recurrence intervals have conventionally been estimated via analysis of historic water level maxima, and these vary widely depending on the statistical assumptions made. This information is supplemented by near-term forecasts from operational surge-tide models, which give continuous water levels but at considerable computational cost. As part of a NERC Infrastructure and Risk project, we have investigated the impact of North Sea tidal surges on the Port of Immingham, eastern, UK. This handles the largest volume of bulk cargo in the UK and flows of coal and biomass that are critically important for national energy security. The port was partly flooded during a major tidal surge in 2013. This event highlighted the need for improved local forecasts of surge timing in relation to high water, with a better indication of flood depth and duration. We address this problem using a combination of data-driven and numerical hydrodynamic models. An Artificial Neural Network (ANN) is first used to predict the surge component of water level from meteorological data. The input vector comprises time-series of local wind (easterly and northerly wind stress) and pressure, as well as regional pressure and pressure gradients from stations between the Shetland Islands and the Humber estuary. The ANN achieves rms errors of around 0.1 m and can generate short-range (~ 3 to 12 hour) forecasts given real-time input data feeds. It can also synthesize water level events for a wider range of tidal and meteorological forcing combinations than contained in the observational records. These are used to force Telemac2D numerical floodplain simulations using a LiDAR digital elevation model of the port. Functional relationships between peak water level and surge 'shape' allow estimation of flood depths and durations for any location. Supplementing existing surge warning systems, our approach predicts the location and duration of flooding in detail, and allows port managers to take steps to minimize its impact on the most critical aspects of port operation.

ECMWF Extreme Forecast Index for water vapor transport: A forecast tool for atmospheric rivers and extreme precipitation

NASA Astrophysics Data System (ADS)

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

2016-11-01

In winter, heavy precipitation and floods along the west coasts of midlatitude continents are largely caused by intense water vapor transport (integrated vapor transport (IVT)) within the atmospheric river of extratropical cyclones. This study builds on previous findings that showed that forecasts of IVT have higher predictability than precipitation, by applying and evaluating the European Centre for Medium-Range Weather Forecasts Extreme Forecast Index (EFI) for IVT in ensemble forecasts during three winters across Europe. We show that the IVT EFI is more able (than the precipitation EFI) to capture extreme precipitation in forecast week 2 during forecasts initialized in a positive North Atlantic Oscillation (NAO) phase; conversely, the precipitation EFI is better during the negative NAO phase and at shorter leads. An IVT EFI example for storm Desmond in December 2015 highlights its potential to identify upcoming hydrometeorological extremes, which may prove useful to the user and forecasting communities.

A Bayesian spatio-temporal model for forecasting Anaplasma species seroprevalence in domestic dogs within the contiguous United States.

PubMed

Liu, Yan; Watson, Stella C; Gettings, Jenna R; Lund, Robert B; Nordone, Shila K; Yabsley, Michael J; McMahan, Christopher S

2017-01-01

This paper forecasts the 2016 canine Anaplasma spp. seroprevalence in the United States from eight climate, geographic and societal factors. The forecast's construction and an assessment of its performance are described. The forecast is based on a spatial-temporal conditional autoregressive model fitted to over 11 million Anaplasma spp. seroprevalence test results for dogs conducted in the 48 contiguous United States during 2011-2015. The forecast uses county-level data on eight predictive factors, including annual temperature, precipitation, relative humidity, county elevation, forestation coverage, surface water coverage, population density and median household income. Non-static factors are extrapolated into the forthcoming year with various statistical methods. The fitted model and factor extrapolations are used to estimate next year's regional prevalence. The correlation between the observed and model-estimated county-by-county Anaplasma spp. seroprevalence for the five-year period 2011-2015 is 0.902, demonstrating reasonable model accuracy. The weighted correlation (accounting for different sample sizes) between 2015 observed and forecasted county-by-county Anaplasma spp. seroprevalence is 0.987, exhibiting that the proposed approach can be used to accurately forecast Anaplasma spp. seroprevalence. The forecast presented herein can a priori alert veterinarians to areas expected to see Anaplasma spp. seroprevalence beyond the accepted endemic range. The proposed methods may prove useful for forecasting other diseases.

DEVELOPING SITE-SPECIFIC MODELS FOR FORECASTING BACTERIA LEVELS AT COASTAL BEACHES

EPA Science Inventory

The U.S.Beaches Environmental Assessment and Coastal Health Act of 2000 authorizes studies of pathogen indicators in coastal recreation waters that develop appropriate, accurate, expeditious, and cost-effective methods (including predictive models) for quantifying pathogens in co...

Balancing Flood Risk and Water Supply in California: Policy Search Combining Short-Term Forecast Ensembles and Groundwater Recharge

NASA Astrophysics Data System (ADS)

Herman, J. D.; Steinschneider, S.; Nayak, M. A.

2017-12-01

Short-term weather forecasts are not codified into the operating policies of federal, multi-purpose reservoirs, despite their potential to improve service provision. This is particularly true for facilities that provide flood protection and water supply, since the potential flood damages are often too severe to accept the risk of inaccurate forecasts. Instead, operators must maintain empty storage capacity to mitigate flood risk, even if the system is currently in drought, as occurred in California from 2012-2016. This study investigates the potential for forecast-informed operating rules to improve water supply efficiency while maintaining flood protection, combining state-of-the-art weather hindcasts with a novel tree-based policy optimization framework. We hypothesize that forecasts need only accurately predict the occurrence of a storm, rather than its intensity, to be effective in regions like California where wintertime, synoptic-scale storms dominate the flood regime. We also investigate the potential for downstream groundwater injection to improve the utility of forecasts. These hypotheses are tested in a case study of Folsom Reservoir on the American River. Because available weather hindcasts are relatively short (10-20 years), we propose a new statistical framework to develop synthetic forecasts to assess the risk associated with inaccurate forecasts. The efficiency of operating policies is tested across a range of scenarios that include varying forecast skill and additional groundwater pumping capacity. Results suggest that the combined use of groundwater storage and short-term weather forecasts can substantially improve the tradeoff between water supply and flood control objectives in large, multi-purpose reservoirs in California.

The capability of radial basis function to forecast the volume fractions of the annular three-phase flow of gas-oil-water.

PubMed

Roshani, G H; Karami, A; Salehizadeh, A; Nazemi, E

2017-11-01

The problem of how to precisely measure the volume fractions of oil-gas-water mixtures in a pipeline remains as one of the main challenges in the petroleum industry. This paper reports the capability of Radial Basis Function (RBF) in forecasting the volume fractions in a gas-oil-water multiphase system. Indeed, in the present research, the volume fractions in the annular three-phase flow are measured based on a dual energy metering system including the 152 Eu and 137 Cs and one NaI detector, and then modeled by a RBF model. Since the summation of volume fractions are constant (equal to 100%), therefore it is enough for the RBF model to forecast only two volume fractions. In this investigation, three RBF models are employed. The first model is used to forecast the oil and water volume fractions. The next one is utilized to forecast the water and gas volume fractions, and the last one to forecast the gas and oil volume fractions. In the next stage, the numerical data obtained from MCNP-X code must be introduced to the RBF models. Then, the average errors of these three models are calculated and compared. The model which has the least error is picked up as the best predictive model. Based on the results, the best RBF model, forecasts the oil and water volume fractions with the mean relative error of less than 0.5%, which indicates that the RBF model introduced in this study ensures an effective enough mechanism to forecast the results. Copyright © 2017 Elsevier Ltd. All rights reserved.

Ensemble machine learning and forecasting can achieve 99% uptime for rural handpumps

PubMed Central

Thomas, Evan A.

2017-01-01

Broken water pumps continue to impede efforts to deliver clean and economically-viable water to the global poor. The literature has demonstrated that customers’ health benefits and willingness to pay for clean water are best realized when clean water infrastructure performs extremely well (>99% uptime). In this paper, we used sensor data from 42 Afridev-brand handpumps observed for 14 months in western Kenya to demonstrate how sensors and supervised ensemble machine learning could be used to increase total fleet uptime from a best-practices baseline of about 70% to >99%. We accomplish this increase in uptime by forecasting pump failures and identifying existing failures very quickly. Comparing the costs of operating the pump per functional year over a lifetime of 10 years, we estimate that implementing this algorithm would save 7% on the levelized cost of water relative to a sensor-less scheduled maintenance program. Combined with a rigorous system for dispatching maintenance personnel, implementing this algorithm in a real-world program could significantly improve health outcomes and customers’ willingness to pay for water services. PMID:29182673

Assimilation of lightning data by nudging tropospheric water vapor and applications to numerical forecasts of convective events

NASA Astrophysics Data System (ADS)

Dixon, Kenneth

A lightning data assimilation technique is developed for use with observations from the World Wide Lightning Location Network (WWLLN). The technique nudges the water vapor mixing ratio toward saturation within 10 km of a lightning observation. This technique is applied to deterministic forecasts of convective events on 29 June 2012, 17 November 2013, and 19 April 2011 as well as an ensemble forecast of the 29 June 2012 event using the Weather Research and Forecasting (WRF) model. Lightning data are assimilated over the first 3 hours of the forecasts, and the subsequent impact on forecast quality is evaluated. The nudged deterministic simulations for all events produce composite reflectivity fields that are closer to observations. For the ensemble forecasts of the 29 June 2012 event, the improvement in forecast quality from lightning assimilation is more subtle than for the deterministic forecasts, suggesting that the lightning assimilation may improve ensemble convective forecasts where conventional observations (e.g., aircraft, surface, radiosonde, satellite) are less dense or unavailable.

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.

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.

Post-processing of a low-flow forecasting system in the Thur basin (Switzerland)

NASA Astrophysics Data System (ADS)

Bogner, Konrad; Joerg-Hess, Stefanie; Bernhard, Luzi; Zappa, Massimiliano

2015-04-01

Low-flows and droughts are natural hazards with potentially severe impacts and economic loss or damage in a number of environmental and socio-economic sectors. As droughts develop slowly there is time to prepare and pre-empt some of these impacts. Real-time information and forecasting of a drought situation can therefore be an effective component of drought management. Although Switzerland has traditionally been more concerned with problems related to floods, in recent years some unprecedented low-flow situations have been experienced. Driven by the climate change debate a drought information platform has been developed to guide water resources management during situations where water resources drop below critical low-flow levels characterised by the indices duration (time between onset and offset), severity (cumulative water deficit) and magnitude (severity/duration). However to gain maximum benefit from such an information system it is essential to remove the bias from the meteorological forecast, to derive optimal estimates of the initial conditions, and to post-process the stream-flow forecasts. Quantile mapping methods for pre-processing the meteorological forecasts and improved data assimilation methods of snow measurements, which accounts for much of the seasonal stream-flow predictability for the majority of the basins in Switzerland, have been tested previously. The objective of this study is the testing of post-processing methods in order to remove bias and dispersion errors and to derive the predictive uncertainty of a calibrated low-flow forecast system. Therefore various stream-flow error correction methods with different degrees of complexity have been applied and combined with the Hydrological Uncertainty Processor (HUP) in order to minimise the differences between the observations and model predictions and to derive posterior probabilities. The complexity of the analysed error correction methods ranges from simple AR(1) models to methods including wavelet transformations and support vector machines. These methods have been combined with forecasts driven by Numerical Weather Prediction (NWP) systems with different temporal and spatial resolutions, lead-times and different numbers of ensembles covering short to medium to extended range forecasts (COSMO-LEPS, 10-15 days, monthly and seasonal ENS) as well as climatological forecasts. Additionally the suitability of various skill scores and efficiency measures regarding low-flow predictions will be tested. Amongst others the novel 2afc (2 alternatives forced choices) score and the quantile skill score and its decompositions will be applied to evaluate the probabilistic forecasts and the effects of post-processing. First results of the performance of the low-flow predictions of the hydrological model PREVAH initialised with different NWP's will be shown.

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.

Forecast model for a water table control system in cranberry production

NASA Astrophysics Data System (ADS)

Racine, Cintia; José Gumiere, Silvio; Paniconi, Claudio; Dupuis, Christian; Lafond, Jonathan; Scudeler, Carlotta; Camporese, Matteo

2017-04-01

Water table control is gaining popularity in cranberry production. Cranberry plants require specific soil moisture conditions to enhance crop yields. In fact, water table control systems installed in the fields allow the plants to respond efficiently to the daily demand for evapotranspiration by capillarity rise and also regulate the soil water excess in drainage conditions. The scope of this study is to develop a forecast hydrological model at the field scale, able to simulate water level for water table control operations. In this work, the finite element CATHY (CATchment Hydrology) model associated with sequential data assimilation with an ensemble Kalman filter (EnKF) method will be used to simulated the soil water dynamics and perform model calibration in real-time. The study is conducted in cranberry fields located in Québec, Canada. During the last five years, these fields were extensive characterized regarding hydrological, pedological, and geological processes. Data collected from LIDAR and Ground Penetrating Radar (GPR) surveys and in-situ soil sampling have been used to define the domain geometry and initial soil properties. First results are promising and in agreement the in-situ water table measurements.

Adaptive Regulation of the Northern California Reservoir System for Water, Energy, and Environmental Management

NASA Astrophysics Data System (ADS)

Georgakakos, A. P.; Kistenmacher, M.; Yao, H.; Georgakakos, K. P.

2014-12-01

The 2014 National Climate Assessment of the US Global Change Research Program emphasizes that water resources managers and planners in most US regions will have to cope with new risks, vulnerabilities, and opportunities, and recommends the development of adaptive capacity to effectively respond to the new water resources planning and management challenges. In the face of these challenges, adaptive reservoir regulation is becoming all the more ncessary. Water resources management in Northern California relies on the coordinated operation of several multi-objective reservoirs on the Trinity, Sacramento, American, Feather, and San Joaquin Rivers. To be effective, reservoir regulation must be able to (a) account for forecast uncertainty; (b) assess changing tradeoffs among water uses and regions; and (c) adjust management policies as conditions change; and (d) evaluate the socio-economic and environmental benefits and risks of forecasts and policies for each region and for the system as a whole. The Integrated Forecast and Reservoir Management (INFORM) prototype demonstration project operated in Northern California through the collaboration of several forecast and management agencies has shown that decision support systems (DSS) with these attributes add value to stakeholder decision processes compared to current, less flexible management practices. Key features of the INFORM DSS include: (a) dynamically downscaled operational forecasts and climate projections that maintain the spatio-temporal coherence of the downscaled land surface forcing fields within synoptic scales; (b) use of ensemble forecast methodologies for reservoir inflows; (c) assessment of relevant tradeoffs among water uses on regional and local scales; (d) development and evaluation of dynamic reservoir policies with explicit consideration of hydro-climatic forecast uncertainties; and (e) focus on stakeholder information needs.This article discusses the INFORM integrated design concept, underlying methodologies, and selected applications with the California water resources system.

Advancing Data assimilation for Baltic Monitoring and Forecasting Center: implementation and evaluation of HBP-PDAF system

NASA Astrophysics Data System (ADS)

Korabel, Vasily; She, Jun; Huess, Vibeke; Woge Nielsen, Jacob; Murawsky, Jens; Nerger, Lars

2017-04-01

The potential of an efficient data assimilation (DA) scheme to improve model forecast skill was successfully demonstrated by many operational centres around the world. The Baltic-North Sea region is one of the most heavily monitored seas. Ferryboxes, buoys, ADCP moorings, shallow water Argo floats, and research vessels are providing more and more near-real time observations. Coastal altimetry has now providing increasing amount of high resolution sea level observations, which will be significantly expanded by the launch of SWOT satellite in next years. This will turn operational DA into a valuable tool for improving forecast quality in the region. This motivated us to focus on advancing DA for the Baltic Monitoring and Forecasting Centre (BAL MFC) in order to create a common framework for operational data assimilation in the Baltic Sea. We have implemented HBM-PDAF system based on the Parallel Data Assimilation Framework (PDAF), a highly versatile and optimised parallel suit with a choice of sequential schemes originally developed at AWI, and a hydrodynamic HIROMB-BOOS Model (HBM). At initial phase, only the satellite Sea Surface Temperature (SST) Level 3 data has been assimilated. Several related aspects are discussed, including improvements of the forecast quality for both surface and subsurface fields, the estimation of ensemble-based forecast error covariance, as well as possibilities of assimilating new types of observations, such as in-situ salinity and temperature profiles, coastal altimetry, and ice concentration.

Scenario approach for the seasonal forecast of Kharif flows from the Upper Indus Basin

NASA Astrophysics Data System (ADS)

Fraz Ismail, Muhammad; Bogacki, Wolfgang

2018-02-01

Snow and glacial melt runoff are the major sources of water contribution from the high mountainous terrain of the Indus River upstream of the Tarbela reservoir. A reliable forecast of seasonal water availability for the Kharif cropping season (April-September) can pave the way towards better water management and a subsequent boost in the agro-economy of Pakistan. The use of degree-day models in conjunction with satellite-based remote-sensing data for the forecasting of seasonal snow and ice melt runoff has proved to be a suitable approach for data-scarce regions. In the present research, the Snowmelt Runoff Model (SRM) has not only been enhanced by incorporating the glacier (G) component but also applied for the forecast of seasonal water availability from the Upper Indus Basin (UIB). Excel-based SRM+G takes account of separate degree-day factors for snow and glacier melt processes. All-year simulation runs with SRM+G for the period 2003-2014 result in an average flow component distribution of 53, 21, and 26 % for snow, glacier, and rain, respectively. The UIB has been divided into Upper and Lower parts because of the different climatic conditions in the Tibetan Plateau. The scenario approach for seasonal forecasting, which like the Ensemble Streamflow Prediction method uses historic meteorology as model forcings, has proven to be adequate for long-term water availability forecasts. The accuracy of the forecast with a mean absolute percentage error (MAPE) of 9.5 % could be slightly improved compared to two existing operational forecasts for the UIB, and the bias could be reduced to -2.0 %. However, the association between forecasts and observations as well as the skill in predicting extreme conditions is rather weak for all three models, which motivates further research on the selection of a subset of ensemble members according to forecasted seasonal anomalies.

A Sub-seasonal to Seasonal Western Forecasting Rodeo: Time to Giddy-up!

NASA Astrophysics Data System (ADS)

Nowak, K.; Cifelli, R.; Brekke, L. D.; Webb, R. S.; Hennig, C.; Pulwarty, R. S.

2016-12-01

The Bureau of Reclamation, as the largest water wholesaler and the second largest producer of hydropower in the United States, exhibits an intrinsic need for skillful forecasts of future water availability. Researchers, water managers from local, regional, and federal agencies, and groups such as the Western States Water Council agree that improved precipitation and temperature forecast information at the sub-seasonal to seasonal (S2S) timescale is a recognized need with significant potential benefit to water management. In response, and recognizing NOAA's leadership in forecasting, Reclamation has partnered with NOAA to develop and implement a year-long, real-time forecasting prize competition where solvers will submit S2S forecasts of temperature and precipitation every two weeks. Prize competitions enable federal and other agencies to spur innovation on mission relevant topics by reaching a broad and diverse community of thinkers. Solvers will compete in real-time against each other and with current operational and experimental forecast products as well as climatology and persistence. The competition domain will focus on the 17 western states where Reclamation operates. Forecasts will be evaluated once observational data become available and performance/skill will be posted on a competition leaderboard hosted by the National Integrated Drought Information System (NIDIS). Prize categories include performance overall, regionally, and for any extreme events that may occur during the course of the competition. Potential Reclamation prizes total over $500,000. Although some researchers or entities may not be eligible for monetary prizes, the competition is open to anyone who wishes to participate and jockey for a spot atop the leader board. This is a unique opportunity to solicit innovation and for novel forecast approaches, experimental products, and established models to all compete for both prestige and monetary incentives. The competition is expected to raise awareness on the S2S forecast need and the potential benefits- which extend beyond water management - to drought preparedness, public health, and others, while also yielding actionable advances for the state of the science in S2S prediction.

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.

Experiments with Seasonal Forecasts of ocean conditions for the Northern region of the California Current upwelling system

PubMed Central

Siedlecki, Samantha A.; Kaplan, Isaac C.; Hermann, Albert J.; Nguyen, Thanh Tam; Bond, Nicholas A.; Newton, Jan A.; Williams, Gregory D.; Peterson, William T.; Alin, Simone R.; Feely, Richard A.

2016-01-01

Resource managers at the state, federal, and tribal levels make decisions on a weekly to quarterly basis, and fishers operate on a similar timeframe. To determine the potential of a support tool for these efforts, a seasonal forecast system is experimented with here. JISAO’s Seasonal Coastal Ocean Prediction of the Ecosystem (J-SCOPE) features dynamical downscaling of regional ocean conditions in Washington and Oregon waters using a combination of a high-resolution regional model with biogeochemistry and forecasts from NOAA’s Climate Forecast System (CFS). Model performance and predictability were examined for sea surface temperature (SST), bottom temperature, bottom oxygen, pH, and aragonite saturation state through model hindcasts, reforecast, and forecast comparisons with observations. Results indicate J-SCOPE forecasts have measurable skill on seasonal timescales. Experiments suggest that seasonal forecasting of ocean conditions important for fisheries is possible with the right combination of components. Those components include regional predictability on seasonal timescales of the physical environment from a large-scale model, a high-resolution regional model with biogeochemistry that simulates seasonal conditions in hindcasts, a relationship with local stakeholders, and a real-time observational network. Multiple efforts and approaches in different regions would advance knowledge to provide additional tools to fishers and other stakeholders. PMID:27273473

Experiments with Seasonal Forecasts of ocean conditions for the Northern region of the California Current upwelling system

NASA Astrophysics Data System (ADS)

Siedlecki, Samantha A.; Kaplan, Isaac C.; Hermann, Albert J.; Nguyen, Thanh Tam; Bond, Nicholas A.; Newton, Jan A.; Williams, Gregory D.; Peterson, William T.; Alin, Simone R.; Feely, Richard A.

2016-06-01

Resource managers at the state, federal, and tribal levels make decisions on a weekly to quarterly basis, and fishers operate on a similar timeframe. To determine the potential of a support tool for these efforts, a seasonal forecast system is experimented with here. JISAO’s Seasonal Coastal Ocean Prediction of the Ecosystem (J-SCOPE) features dynamical downscaling of regional ocean conditions in Washington and Oregon waters using a combination of a high-resolution regional model with biogeochemistry and forecasts from NOAA’s Climate Forecast System (CFS). Model performance and predictability were examined for sea surface temperature (SST), bottom temperature, bottom oxygen, pH, and aragonite saturation state through model hindcasts, reforecast, and forecast comparisons with observations. Results indicate J-SCOPE forecasts have measurable skill on seasonal timescales. Experiments suggest that seasonal forecasting of ocean conditions important for fisheries is possible with the right combination of components. Those components include regional predictability on seasonal timescales of the physical environment from a large-scale model, a high-resolution regional model with biogeochemistry that simulates seasonal conditions in hindcasts, a relationship with local stakeholders, and a real-time observational network. Multiple efforts and approaches in different regions would advance knowledge to provide additional tools to fishers and other stakeholders.

Experiments with Seasonal Forecasts of ocean conditions for the Northern region of the California Current upwelling system.

PubMed

Siedlecki, Samantha A; Kaplan, Isaac C; Hermann, Albert J; Nguyen, Thanh Tam; Bond, Nicholas A; Newton, Jan A; Williams, Gregory D; Peterson, William T; Alin, Simone R; Feely, Richard A

2016-06-07

Resource managers at the state, federal, and tribal levels make decisions on a weekly to quarterly basis, and fishers operate on a similar timeframe. To determine the potential of a support tool for these efforts, a seasonal forecast system is experimented with here. JISAO's Seasonal Coastal Ocean Prediction of the Ecosystem (J-SCOPE) features dynamical downscaling of regional ocean conditions in Washington and Oregon waters using a combination of a high-resolution regional model with biogeochemistry and forecasts from NOAA's Climate Forecast System (CFS). Model performance and predictability were examined for sea surface temperature (SST), bottom temperature, bottom oxygen, pH, and aragonite saturation state through model hindcasts, reforecast, and forecast comparisons with observations. Results indicate J-SCOPE forecasts have measurable skill on seasonal timescales. Experiments suggest that seasonal forecasting of ocean conditions important for fisheries is possible with the right combination of components. Those components include regional predictability on seasonal timescales of the physical environment from a large-scale model, a high-resolution regional model with biogeochemistry that simulates seasonal conditions in hindcasts, a relationship with local stakeholders, and a real-time observational network. Multiple efforts and approaches in different regions would advance knowledge to provide additional tools to fishers and other stakeholders.

The Lower Sevier River Basin Crop Monitor and Forecast Decision Support System: Exploiting Landsat Imagery to Provide Continuous Information to Farmers and Water Managers

NASA Astrophysics Data System (ADS)

Torres-Rua, A. F.; Walker, W. R.; McKee, M.

2013-12-01

The last century has seen a large number of innovations in agriculture such as better policies for water control and management, upgraded water conveyance, irrigation, distribution, and monitoring systems, and better weather forecasting products. In spite of this, irrigation management and irrigation water deliveries by farmers/water managers is still based on factors like water share amounts, tradition, and past experience on irrigation. These factors are not necessarily related to the actual crop water use; they are followed because of the absence of related information provided in a timely manner at an affordable cost. Thus, it is necessary to develop means to deliver continuous and personalized information about crop water requirements to water users/managers at the field and irrigation system levels so managers at these levels can better quantify the required versus available water for irrigation during the irrigation season. This study presents a new decision support system (DSS) platform that addresses the absence of information on actual crop water requirements and crop performance by providing continuous updated farm-based crop water use along with other farm performance indicators such as crop yield and farm management to irrigators and water managers. This DSS exploits the periodicity of the Landsat Satellite Mission (8 to 16 days, depending on the period of interest) to provide remote monitoring at the individual field and irrigation system levels. The Landsat satellite images are converted into information about crop water use, yield performance and field management through application of state-of-the-art semi-physical and statistical algorithms that provide this information at a pixel basis that are ultimately aggregated to field and irrigation system levels. A version of the DSS has been implemented for the agricultural lands in the Lower Sevier River, Utah, and has been operational since the beginning of the 2013 irrigation season. The main goal of this DSS implementation is to provide continuous and personalized information to farmers and water managers regarding crops in fields and the irrigation delivery system throughout the irrigation season so that decisions related to agricultural water use can result in water savings while not diminishing crop yields.

Impacts of the Midwestern Drought Forecasts of 2000.

NASA Astrophysics Data System (ADS)

Changnon, Stanley A.

2002-10-01

In March of 2000 (and again in April and May) NOAA issued long-range forecasts indicating that an existing Midwestern drought would continue and intensify through the upcoming summer. These forecasts received extensive media coverage and wide public attention. If the drought persisted and intensified during the summer of 2000, significant agricultural and water supply problems would occur. However, in late May, June, and July heavy rains fell throughout most of the Midwest, ending the drought in most areas and revealing that the forecast was incorrect for most of the Midwest. Significant media coverage was devoted to the `failed' forecast, with considerable speculation that major economic hardship had resulted from the forecast. This study assesses the effects of the failed drought forecast on agricultural and water agency actions in the Midwest. Assessment of the agricultural and water management sectors revealed notable commonalities. Most people surveyed were aware of the drought forecasts, and the information sources were diverse. One-third of those surveyed indicated they did nothing as a result of the forecasts. The decisions and actions taken by others as a result of the forecasts provided mixed impacts. The water resource actions such as conserving water, seeking new sources, and convening state drought groups resulted in little cost and were considered to be beneficial. However, in the three areas of agricultural impacts (crop production shifts, crop insurance purchases, and grain market choices), mainly negative outcomes occurred. The 13 March issuance of the forecast was too late for producers to make sizable changes in production practices or to alter insurance coverage greatly, and most forecast-based actions taken in these two areas were considered to be negative but financially minor losses. However, 48% of the 1017 producers sampled altered their normal crop marketing practices, which in 84% of the cases led to sizable losses in revenue. This loss can be extrapolated as $1.1 billion for the entire Midwest if the sample statistics are representative of the region. A common result of the failed drought forecast among its users was a loss of credibility in climate predictions and a reluctance to use them in the future. Credibility is a fragile commodity that is difficult to obtain and is easy to lose.

Ohio River backwater flood-inundation maps for the Saline and Wabash Rivers in southern Illinois

USGS Publications Warehouse

Murphy, Elizabeth A.; Sharpe, Jennifer B.; Soong, David T.

2012-01-01

Digital flood-inundation maps for the Saline and Wabash Rivers referenced to elevations on the Ohio River in southern Illinois were created by the U.S. Geological Survey (USGS). The inundation maps, accessible through the USGS Flood Inundation Mapping Science Web site at http://water.usgs.gov/osw/flood_inundation/, depict estimates of the areal extent of flooding corresponding to selected water levels (gage heights) at the USGS streamgage at Ohio River at Old Shawneetown, Illinois-Kentucky (station number 03381700). Current gage height and flow conditions at this USGS streamgage may be obtained on the Internet at http://waterdata.usgs.gov/usa/nwis/uv?03381700. In addition, this streamgage is incorporated into the Advanced Hydrologic Prediction Service (AHPS) flood warning system (http://water.weather.gov/ahps/) by the National Weather Service (NWS). The NWS forecasts flood hydrographs at many places that are often co-located at USGS streamgages. That NWS forecasted peak-stage information, also shown on the Ohio River at Old Shawneetown inundation Web site, may be used in conjunction with the maps developed in this study to show predicted areas of flood inundation. In this study, eight water-surface elevations were mapped at 5-foot (ft) intervals referenced to the streamgage datum ranging from just above the NWS Action Stage (31 ft) to above the maximum historical gage height (66 ft). The elevations of the water surfaces were compared to a Digital Elevation Model (DEM) by using a Geographic Information System (GIS) in order to delineate the area flooded at each water level. These maps, along with information on the Internet regarding current gage heights from USGS streamgages and forecasted stream stages from the NWS, provide emergency management personnel and residents with information that is critical for flood response activities such as evacuations and road closures, as well as for post-flood recovery efforts.

Long-term ensemble forecast of snowmelt inflow into the Cheboksary Reservoir under two different weather scenarios

NASA Astrophysics Data System (ADS)

Gelfan, Alexander; Moreydo, Vsevolod; Motovilov, Yury; Solomatine, Dimitri P.

2018-04-01

A long-term forecasting ensemble methodology, applied to water inflows into the Cheboksary Reservoir (Russia), is presented. The methodology is based on a version of the semi-distributed hydrological model ECOMAG (ECOlogical Model for Applied Geophysics) that allows for the calculation of an ensemble of inflow hydrographs using two different sets of weather ensembles for the lead time period: observed weather data, constructed on the basis of the Ensemble Streamflow Prediction methodology (ESP-based forecast), and synthetic weather data, simulated by a multi-site weather generator (WG-based forecast). We have studied the following: (1) whether there is any advantage of the developed ensemble forecasts in comparison with the currently issued operational forecasts of water inflow into the Cheboksary Reservoir, and (2) whether there is any noticeable improvement in probabilistic forecasts when using the WG-simulated ensemble compared to the ESP-based ensemble. We have found that for a 35-year period beginning from the reservoir filling in 1982, both continuous and binary model-based ensemble forecasts (issued in the deterministic form) outperform the operational forecasts of the April-June inflow volume actually used and, additionally, provide acceptable forecasts of additional water regime characteristics besides the inflow volume. We have also demonstrated that the model performance measures (in the verification period) obtained from the WG-based probabilistic forecasts, which are based on a large number of possible weather scenarios, appeared to be more statistically reliable than the corresponding measures calculated from the ESP-based forecasts based on the observed weather scenarios.

Short-term streamflow forecasting with global climate change implications A comparative study between genetic programming and neural network models

NASA Astrophysics Data System (ADS)

Makkeasorn, A.; Chang, N. B.; Zhou, X.

2008-05-01

SummarySustainable water resources management is a critically important priority across the globe. While water scarcity limits the uses of water in many ways, floods may also result in property damages and the loss of life. To more efficiently use the limited amount of water under the changing world or to resourcefully provide adequate time for flood warning, the issues have led us to seek advanced techniques for improving streamflow forecasting on a short-term basis. This study emphasizes the inclusion of sea surface temperature (SST) in addition to the spatio-temporal rainfall distribution via the Next Generation Radar (NEXRAD), meteorological data via local weather stations, and historical stream data via USGS gage stations to collectively forecast discharges in a semi-arid watershed in south Texas. Two types of artificial intelligence models, including genetic programming (GP) and neural network (NN) models, were employed comparatively. Four numerical evaluators were used to evaluate the validity of a suite of forecasting models. Research findings indicate that GP-derived streamflow forecasting models were generally favored in the assessment in which both SST and meteorological data significantly improve the accuracy of forecasting. Among several scenarios, NEXRAD rainfall data were proven its most effectiveness for a 3-day forecast, and SST Gulf-to-Atlantic index shows larger impacts than the SST Gulf-to-Pacific index on the streamflow forecasts. The most forward looking GP-derived models can even perform a 30-day streamflow forecast ahead of time with an r-square of 0.84 and RMS error 5.4 in our study.

A Bayesian spatio-temporal model for forecasting Anaplasma species seroprevalence in domestic dogs within the contiguous United States

PubMed Central

Liu, Yan; Watson, Stella C.; Gettings, Jenna R.; Lund, Robert B.; Nordone, Shila K.; McMahan, Christopher S.

2017-01-01

This paper forecasts the 2016 canine Anaplasma spp. seroprevalence in the United States from eight climate, geographic and societal factors. The forecast’s construction and an assessment of its performance are described. The forecast is based on a spatial-temporal conditional autoregressive model fitted to over 11 million Anaplasma spp. seroprevalence test results for dogs conducted in the 48 contiguous United States during 2011–2015. The forecast uses county-level data on eight predictive factors, including annual temperature, precipitation, relative humidity, county elevation, forestation coverage, surface water coverage, population density and median household income. Non-static factors are extrapolated into the forthcoming year with various statistical methods. The fitted model and factor extrapolations are used to estimate next year’s regional prevalence. The correlation between the observed and model-estimated county-by-county Anaplasma spp. seroprevalence for the five-year period 2011–2015 is 0.902, demonstrating reasonable model accuracy. The weighted correlation (accounting for different sample sizes) between 2015 observed and forecasted county-by-county Anaplasma spp. seroprevalence is 0.987, exhibiting that the proposed approach can be used to accurately forecast Anaplasma spp. seroprevalence. The forecast presented herein can a priori alert veterinarians to areas expected to see Anaplasma spp. seroprevalence beyond the accepted endemic range. The proposed methods may prove useful for forecasting other diseases. PMID:28738085

Uncertainties in Forecasting Streamflow using Entropy Theory

NASA Astrophysics Data System (ADS)

Cui, H.; Singh, V. P.

2017-12-01

Streamflow forecasting is essential in river restoration, reservoir operation, power generation, irrigation, navigation, and water management. However, there is always uncertainties accompanied in forecast, which may affect the forecasting results and lead to large variations. Therefore, uncertainties must be considered and be assessed properly when forecasting streamflow for water management. The aim of our work is to quantify the uncertainties involved in forecasting streamflow and provide reliable streamflow forecast. Despite that streamflow time series are stochastic, they exhibit seasonal and periodic patterns. Therefore, streamflow forecasting entails modeling seasonality, periodicity, and its correlation structure, and assessing uncertainties. This study applies entropy theory to forecast streamflow and measure uncertainties during the forecasting process. To apply entropy theory for streamflow forecasting, spectral analysis is combined to time series analysis, as spectral analysis can be employed to characterize patterns of streamflow variation and identify the periodicity of streamflow. That is, it permits to extract significant information for understanding the streamflow process and prediction thereof. Application of entropy theory for streamflow forecasting involves determination of spectral density, determination of parameters, and extension of autocorrelation function. The uncertainties brought by precipitation input, forecasting model and forecasted results are measured separately using entropy. With information theory, how these uncertainties transported and aggregated during these processes will be described.

Added Value of Assimilating Himawari-8 AHI Water Vapor Radiances on Analyses and Forecasts for "7.19" Severe Storm Over North China

NASA Astrophysics Data System (ADS)

Wang, Yuanbing; Liu, Zhiquan; Yang, Sen; Min, Jinzhong; Chen, Liqiang; Chen, Yaodeng; Zhang, Tao

2018-04-01

Himawari-8 is the first launched and operational new-generation geostationary meteorological satellite. The Advanced Himawari Imager (AHI) on board Himawari-8 provides continuous high-resolution observations of severe weather phenomena in space and time. In this study, the capability to assimilate AHI radiances has been developed within the Weather Research and Forecasting (WRF) model's data assimilation system. As the first attempt to assimilate AHI using WRF data assimilation at convective scales, the added value of hourly AHI clear-sky radiances from three water vapor channels on convection-permitting (3 km) analyses and forecasts of the "7.19" severe rainstorm that occurred over north China during 18-21 July 2016 was investigated. Analyses were produced hourly, and 24 h forecasts were produced every 6 h. The results showed that improved wind and humidity fields were obtained in analyses and forecasts verified against conventional observations after assimilating AHI water vapor radiances when compared to the control experiment which assimilated only conventional observations. It was also found that the assimilation of AHI water vapor radiances had a clearly positive impact on the rainfall forecast for the first 6 h lead time, especially for heavy rainfall exceeding 100 mm when verified against the observed rainfall. Furthermore, the horizontal and vertical distribution of features in the moisture fields were improved after assimilating AHI water vapor radiances, eventually contributing to a better forecast of the severe rainstorm.

Comparison of Groundwater Level Models Based on Artificial Neural Networks and ANFIS

PubMed Central

Domazet, Milka; Stricevic, Ruzica; Pocuca, Vesna; Spalevic, Velibor; Pivic, Radmila; Gregoric, Enika; Domazet, Uros

2015-01-01

Water table forecasting plays an important role in the management of groundwater resources in agricultural regions where there are drainage systems in river valleys. The results presented in this paper pertain to an area along the left bank of the Danube River, in the Province of Vojvodina, which is the northern part of Serbia. Two soft computing techniques were used in this research: an adaptive neurofuzzy inference system (ANFIS) and an artificial neural network (ANN) model for one-month water table forecasts at several wells located at different distances from the river. The results suggest that both these techniques represent useful tools for modeling hydrological processes in agriculture, with similar computing and memory capabilities, such that they constitute an exceptionally good numerical framework for generating high-quality models. PMID:26759830

Comparison of Groundwater Level Models Based on Artificial Neural Networks and ANFIS.

PubMed

Djurovic, Nevenka; Domazet, Milka; Stricevic, Ruzica; Pocuca, Vesna; Spalevic, Velibor; Pivic, Radmila; Gregoric, Enika; Domazet, Uros

2015-01-01

Water table forecasting plays an important role in the management of groundwater resources in agricultural regions where there are drainage systems in river valleys. The results presented in this paper pertain to an area along the left bank of the Danube River, in the Province of Vojvodina, which is the northern part of Serbia. Two soft computing techniques were used in this research: an adaptive neurofuzzy inference system (ANFIS) and an artificial neural network (ANN) model for one-month water table forecasts at several wells located at different distances from the river. The results suggest that both these techniques represent useful tools for modeling hydrological processes in agriculture, with similar computing and memory capabilities, such that they constitute an exceptionally good numerical framework for generating high-quality models.

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.

Development of Ensemble Model Based Water Demand Forecasting Model

NASA Astrophysics Data System (ADS)

Kwon, Hyun-Han; So, Byung-Jin; Kim, Seong-Hyeon; Kim, Byung-Seop

2014-05-01

In recent years, Smart Water Grid (SWG) concept has globally emerged over the last decade and also gained significant recognition in South Korea. Especially, there has been growing interest in water demand forecast and optimal pump operation and this has led to various studies regarding energy saving and improvement of water supply reliability. Existing water demand forecasting models are categorized into two groups in view of modeling and predicting their behavior in time series. One is to consider embedded patterns such as seasonality, periodicity and trends, and the other one is an autoregressive model that is using short memory Markovian processes (Emmanuel et al., 2012). The main disadvantage of the abovementioned model is that there is a limit to predictability of water demands of about sub-daily scale because the system is nonlinear. In this regard, this study aims to develop a nonlinear ensemble model for hourly water demand forecasting which allow us to estimate uncertainties across different model classes. The proposed model is consist of two parts. One is a multi-model scheme that is based on combination of independent prediction model. The other one is a cross validation scheme named Bagging approach introduced by Brieman (1996) to derive weighting factors corresponding to individual models. Individual forecasting models that used in this study are linear regression analysis model, polynomial regression, multivariate adaptive regression splines(MARS), SVM(support vector machine). The concepts are demonstrated through application to observed from water plant at several locations in the South Korea. Keywords: water demand, non-linear model, the ensemble forecasting model, uncertainty. Acknowledgements This subject is supported by Korea Ministry of Environment as "Projects for Developing Eco-Innovation Technologies (GT-11-G-02-001-6)

Developing Multi-model Ensemble for Precipitation and Temperature Seasonal Forecasts: Implications for Karkheh River Basin in Iran

NASA Astrophysics Data System (ADS)

Najafi, Husain; Massah Bavani, Ali Reza; Wanders, Niko; Wood, Eric; Irannejad, Parviz; Robertson, Andrew

2017-04-01

Water resource managers can utilize reliable seasonal forecasts for allocating water between different users within a water year. In the west of Iran where a decline of renewable water resources has been observed, basin-wide water management has been the subject of many inter-provincial conflicts in recent years. The problem is exacerbated when the environmental water requirements is not provided leaving the Hoor-al-Azim marshland in the downstream dry. It has been argued that information on total seasonal rainfall can support the Iranian Ministry of Energy within the water year. This study explores the skill of the North America Multi Model Ensemble for Karkheh River Basin in the of west Iran. NMME seasonal precipitation and temperature forecasts from eight models are evaluated against PERSIANN-CDR and Climate Research Unit (CRU) datasets. Analysis suggests that anomaly correlation for both precipitation and temperature is greater than 0.4 for all individual models. Lead time-dependent seasonal forecasts are improved when a multi-model ensemble is developed for the river basin using stepwise linear regression model. MME R-squared exceeds 0.6 for temperature for almost all initializations suggesting high skill of NMME in Karkheh river basin. The skill of MME for rainfall forecasts is high for 1-month lead time for October, February, March and October initializations. However, for months when the amount of rainfall accounts for a significant proportion of total annual rainfall, the skill of NMME is limited a month in advance. It is proposed that operational regional water companies incorporate NMME seasonal forecasts into water resource planning and management, especially during growing seasons that are essential for agricultural risk management.

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.

Flood-inundation maps for the St. Marys River at Fort Wayne, Indiana

USGS Publications Warehouse

Menke, Chad D.; Kim, Moon H.; Fowler, Kathleen K.

2012-01-01

Digital flood-inundation maps for a 9-mile reach of the St. Marys River that extends from South Anthony Boulevard to Main Street at Fort Wayne, Indiana, were created by the U.S. Geological Survey (USGS) in cooperation with the City of Fort Wayne. The inundation maps, which can be accessed through the USGS Flood Inundation Mapping Science Web site, depict estimates of the areal extent of flooding corresponding to selected water levels (stages) at the USGS streamgage 04182000 St. Marys River near Fort Wayne, Ind. Current conditions at the USGS streamgages in Indiana may be obtained from the National Water Information System: Web Interface. In addition, the information has been provided to the National Weather Service (NWS) for incorporation into their Advanced Hydrologic Prediction Service (AHPS) flood warning system. The NWS forecasts flood hydrographs at many places that are often collocated at USGS streamgages. That forecasted peak-stage information, also available on the Internet, may be used in conjunction with the maps developed in this study to show predicted areas of flood inundation. In this study, water-surface profiles were simulated for the stream reach by means of a hydraulic one-dimensional step-backwater model. The model was calibrated using the most current stage-discharge relation at the USGS streamgage 04182000 St. Marys River near Fort Wayne, Ind. The hydraulic model was then used to simulate 11 water-surface profiles for flood stages at 1-ft intervals referenced to the streamgage datum and ranging from bankfull to approximately the highest recorded water level at the streamgage. The simulated water-surface profiles were then combined with a geographic information system digital elevation model (derived from Light Detection and Ranging (LiDAR) data) in order to delineate the area flooded at each water level. A flood inundation map was generated for each water-surface profile stage (11 maps in all) so that for any given flood stage users will be able to view the estimated area of inundation. The availability of these maps along with current stage from USGS streamgages and forecasted stream stages from the NWS provide emergency management personnel and residents with information that is critical for flood response activities such as evacuations and road closures as well as for post flood recovery efforts.

Development of a method for comprehensive water quality forecasting and its application in Miyun reservoir of Beijing, China.

PubMed

Zhang, Lei; Zou, Zhihong; Shan, Wei

2017-06-01

Water quality forecasting is an essential part of water resource management. Spatiotemporal variations of water quality and their inherent constraints make it very complex. This study explored a data-based method for short-term water quality forecasting. Prediction of water quality indicators including dissolved oxygen, chemical oxygen demand by KMnO 4 and ammonia nitrogen using support vector machine was taken as inputs of the particle swarm algorithm based optimal wavelet neural network to forecast the whole status index of water quality. Gubeikou monitoring section of Miyun reservoir in Beijing, China was taken as the study case to examine effectiveness of this approach. The experiment results also revealed that the proposed model has advantages of stability and time reduction in comparison with other data-driven models including traditional BP neural network model, wavelet neural network model and Gradient Boosting Decision Tree model. It can be used as an effective approach to perform short-term comprehensive water quality prediction. Copyright © 2016. Published by Elsevier B.V.

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

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.

Cyber Surveillance for Flood Disasters

PubMed Central

Lo, Shi-Wei; Wu, Jyh-Horng; Lin, Fang-Pang; Hsu, Ching-Han

2015-01-01

Regional heavy rainfall is usually caused by the influence of extreme weather conditions. Instant heavy rainfall often results in the flooding of rivers and the neighboring low-lying areas, which is responsible for a large number of casualties and considerable property loss. The existing precipitation forecast systems mostly focus on the analysis and forecast of large-scale areas but do not provide precise instant automatic monitoring and alert feedback for individual river areas and sections. Therefore, in this paper, we propose an easy method to automatically monitor the flood object of a specific area, based on the currently widely used remote cyber surveillance systems and image processing methods, in order to obtain instant flooding and waterlogging event feedback. The intrusion detection mode of these surveillance systems is used in this study, wherein a flood is considered a possible invasion object. Through the detection and verification of flood objects, automatic flood risk-level monitoring of specific individual river segments, as well as the automatic urban inundation detection, has become possible. The proposed method can better meet the practical needs of disaster prevention than the method of large-area forecasting. It also has several other advantages, such as flexibility in location selection, no requirement of a standard water-level ruler, and a relatively large field of view, when compared with the traditional water-level measurements using video screens. The results can offer prompt reference for appropriate disaster warning actions in small areas, making them more accurate and effective. PMID:25621609

A Pro-active Real-time Forecasting and Decision Support System for Daily Management of Marine Works

NASA Astrophysics Data System (ADS)

Bollen, Mark; Leyssen, Gert; Smets, Steven; De Wachter, Tom

2016-04-01

Marine Works involving turbidity generating activities (eg. dredging, dredge spoil placement) can generate environmental stress in and around a project area in the form of sediment plumes causing light reduction and sedimentation. If these works are situated near sensitive habitats like sea-grass beds, coral reefs or sensitive human activities eg. aquaculture farms or water intakes, or if contaminants are present in the water soil environmental scrutiny is advised. Environmental Regulations can impose limitations to these activities in the form of turbidity thresholds, spill budgets, contaminant levels. Breaching environmental regulations can result in increased monitoring, adaptation of the works planning and production rates and ultimately in a (temporary) stop of activities all of which entail time and cost impacts for a contractor and/or client. Sediment plume behaviour is governed by the dredging process, soil properties and ambient conditions (currents, water depth) and can be modelled. Usually this is done during the preparatory EIA phase of a project, for estimation of environmental impact based on climatic scenarios. An operational forecasting tool is developed to adapt marine work schedules to the real-time circumstances and thus evade exceedance of critical threshold levels at sensitive areas. The forecasting system is based on a Python-based workflow manager with a MySQL database and a Django frontend web tool for user interaction and visualisation of the model results. The core consists of a numerical hydrodynamic model with sediment transport module (Mike21 from DHI). This model is driven by space and time varying wind fields and wave boundary conditions, and turbidity inputs (suspended sediment source terms) based on marine works production rates and soil properties. The resulting threshold analysis allows the operator to indicate potential impact at the sensitive areas and instigate an adaption of the marine work schedule if needed. In order to use this toolbox in real-time situations and facilitate forecasting of impacts of planned dredge works, the following operational online functionalities are implemented: • Automated fetch and preparation of the input data, including 7 day forecast wind and wave fields and real-time measurements, and user defined the turbidity inputs based on scheduled marine works. • Generate automated forecasts and running user configurable scenarios at the same time in parallel. • Export and convert the model results, time series and maps, into a standardized format (netcdf). • Automatic analysis and processing of model results, including the calculation of indicator turbidity values and the exceedance analysis of threshold levels at the different sensitive areas. Data assimilation with the real time on site turbidity measurements is implemented in this threshold analysis. • Pre-programmed generation of animated sediment plumes, specific charts and pdf reports to allow a rapid interpretation of the model results by the operators and facilitating decision making in the operational planning. The performed marine works, resulting from the marine work schedule proposed by the forecasting system, are evaluated by a threshold analysis on the validated turbidity measurements on the sensitive sites. This machine learning loop allows a check of the system in order to evaluate forecast and model uncertainties.

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.

National Water Model assessment for water management needs over the Western United States.

NASA Astrophysics Data System (ADS)

Viterbo, F.; Thorstensen, A.; Cifelli, R.; Hughes, M.; Johnson, L.; Gochis, D.; Wood, A.; Nowak, K.; Dahm, K.

2017-12-01

The NOAA National Water Model (NWM) became operational in August 2016, providing the first ever, real-time distributed high-resolution forecasts for the continental United States. Since the model predictions occur at the CONUS scale, there is a need to evaluate the NWM in different regions to assess the wide variety and heterogeneity of hydrological processes that are included (e.g., snow melting, ice freezing, flash flooding events). In particular, to address water management needs in the western U.S., a collaborative project between the Bureau of Reclamation, NOAA, and NCAR is ongoing to assess the NWM performance for reservoir inflow forecasting needs and water management operations. In this work, the NWM is evaluated using different forecast ranges (short to medium) and retrospective historical runs forced by North American Land Data Assimilation System (NLDAS) analysis to assess the NWM skills over key headwaters watersheds in the western U.S. that are of interest to the Bureau of Reclamation. The streamflow results are analyzed and compared with the available observations at the gauge sites, evaluating different NWM operational versions together with the already existing local River Forecast Center forecasts. The NWM uncertainty is also considered, evaluating the propagation of the precipitation forcing uncertainties in the resulting hydrograph. In addition, the possible advantages of high-resolution distributed output variables (such as soil moisture, evapotranspiration fluxes) are investigated, to determine the utility of such information for water managers in terms of watershed characteristics in areas that traditionally have not had any forecast information. The results highlight the NWM's ability to provide high-resolution forecast information in space and time. As anticipated, the performance is best in regions that are dominated by natural flows and where the model has benefited from efforts toward parameter calibration. In highly regulated basins, the water management operations result in NWM overestimation of the peak flows and too fast recession curves. As a future project goal, some reforecasts will be run on target locations, ingesting water management information into the NWM and comparing the new results with the actual operational forecast.

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.

Data Assimilation and Regional Forecasts Using Atmospheric InfraRed Sounder (AIRS) Profiles

NASA Technical Reports Server (NTRS)

Chou, Shih-Hung; Zavodsky, Bradley; Jedlovec, Gary

2009-01-01

In data sparse regions, remotely-sensed observations can be used to improve analyses, which in turn should lead to better forecasts. One such source comes from the Atmospheric Infrared Sounder (AIRS), which together with the Advanced Microwave Sounding Unit (AMSU), provides temperature and moisture profiles with an accuracy comparable to that of radiosondes. The purpose of this paper is to describe a procedure to optimally assimilate AIRS thermodynamic profiles--obtained from the version 5.0 Earth Observing System (EOS) science team retrieval algorithm-into a regional configuration of the Weather Research and Forecasting (WRF) model using WRF-Var. The paper focuses on development of background error covariances for the regional domain and background field type, a methodology for ingesting AIRS profiles as separate over-land and over-water retrievals with different error characteristics, and utilization of level-by-level quality indicators to select only the highest quality data. The assessment of the impact of the AIRS profiles on WRF-Var analyses will focus on intelligent use of the quality indicators, optimized tuning of the WRF-Var, and comparison of analysis soundings to radiosondes. The analyses will be used to conduct a month-long series of regional forecasts over the continental U.S. The long-tern1 impact of AIRS profiles on forecast will be assessed against verifying radiosonde and stage IV precipitation data.

Data Assimilation and Regional Forecasts using Atmospheric InfraRed Sounder (AIRS) Profiles

NASA Technical Reports Server (NTRS)

Zabodsky, Brad; Chou, Shih-Hung; Jedlovec, Gary J.

2009-01-01

In data sparse regions, remotely-sensed observations can be used to improve analyses, which in turn should lead to better forecasts. One such source comes from the Atmospheric Infrared Sounder (AIRS), which, together with the Advanced Microwave Sounding Unit (AMSU), provides temperature and moisture profiles with an accuracy comparable to that of radionsondes. The purpose of this poster is to describe a procedure to optimally assimilate AIRS thermodynamic profiles, obtained from the version 5.0 Earth Observing System (EOS) science team retrieval algorithm, into a regional configuration of the Weather Research and Forecasting (WRF) model using WRF-Var. The poster focuses on development of background error covariances for the regional domain and background field type, a methodology for ingesting AIRS profiles as separate over-land and over-water retrievals with different error characteristics, and utilization of level-by-level quality indicators to select only the highest quality data. The assessment of the impact of the AIRS profiles on WRF-Var analyses will focus on intelligent use of the quality indicators, optimized tuning of the WRF-Var, and comparison of analysis soundings to radiosondes. The analyses are used to conduct a month-long series of regional forecasts over the continental U.S. The long-term impact of AIRS profiles on forecast will be assessed against NAM analyses and stage IV precipitation data.

Forecasting daily lake levels using artificial intelligence approaches

NASA Astrophysics Data System (ADS)

Kisi, Ozgur; Shiri, Jalal; Nikoofar, Bagher

2012-04-01

Accurate prediction of lake-level variations is important for planning, design, construction, and operation of lakeshore structures and also in the management of freshwater lakes for water supply purposes. In the present paper, three artificial intelligence approaches, namely artificial neural networks (ANNs), adaptive-neuro-fuzzy inference system (ANFIS), and gene expression programming (GEP), were applied to forecast daily lake-level variations up to 3-day ahead time intervals. The measurements at the Lake Iznik in Western Turkey, for the period of January 1961-December 1982, were used for training, testing, and validating the employed models. The results obtained by the GEP approach indicated that it performs better than ANFIS and ANNs in predicting lake-level variations. A comparison was also made between these artificial intelligence approaches and convenient autoregressive moving average (ARMA) models, which demonstrated the superiority of GEP, ANFIS, and ANN models over ARMA models.

Combining empirical approaches and error modelling to enhance predictive uncertainty estimation in extrapolation for operational flood forecasting. Tests on flood events on the Loire basin, France.

NASA Astrophysics Data System (ADS)

Berthet, Lionel; Marty, Renaud; Bourgin, François; Viatgé, Julie; Piotte, Olivier; Perrin, Charles

2017-04-01

An increasing number of operational flood forecasting centres assess the predictive uncertainty associated with their forecasts and communicate it to the end users. This information can match the end-users needs (i.e. prove to be useful for an efficient crisis management) only if it is reliable: reliability is therefore a key quality for operational flood forecasts. In 2015, the French flood forecasting national and regional services (Vigicrues network; www.vigicrues.gouv.fr) implemented a framework to compute quantitative discharge and water level forecasts and to assess the predictive uncertainty. Among the possible technical options to achieve this goal, a statistical analysis of past forecasting errors of deterministic models has been selected (QUOIQUE method, Bourgin, 2014). It is a data-based and non-parametric approach based on as few assumptions as possible about the forecasting error mathematical structure. In particular, a very simple assumption is made regarding the predictive uncertainty distributions for large events outside the range of the calibration data: the multiplicative error distribution is assumed to be constant, whatever the magnitude of the flood. Indeed, the predictive distributions may not be reliable in extrapolation. However, estimating the predictive uncertainty for these rare events is crucial when major floods are of concern. In order to improve the forecasts reliability for major floods, an attempt at combining the operational strength of the empirical statistical analysis and a simple error modelling is done. Since the heteroscedasticity of forecast errors can considerably weaken the predictive reliability for large floods, this error modelling is based on the log-sinh transformation which proved to reduce significantly the heteroscedasticity of the transformed error in a simulation context, even for flood peaks (Wang et al., 2012). Exploratory tests on some operational forecasts issued during the recent floods experienced in France (major spring floods in June 2016 on the Loire river tributaries and flash floods in fall 2016) will be shown and discussed. References Bourgin, F. (2014). How to assess the predictive uncertainty in hydrological modelling? An exploratory work on a large sample of watersheds, AgroParisTech Wang, Q. J., Shrestha, D. L., Robertson, D. E. and Pokhrel, P (2012). A log-sinh transformation for data normalization and variance stabilization. Water Resources Research, , W05514, doi:10.1029/2011WR010973

a 24/7 High Resolution Storm Surge, Inundation and Circulation Forecasting System for Florida Coast

NASA Astrophysics Data System (ADS)

Paramygin, V.; Davis, J. R.; Sheng, Y.

2012-12-01

A 24/7 forecasting system for Florida is needed because of the high risk of tropical storm surge-induced coastal inundation and damage, and the need to support operational management of water resources, utility infrastructures, and fishery resources. With the anticipated climate change impacts, including sea level rise, coastal areas are facing the challenges of increasing inundation risk and increasing population. Accurate 24/7 forecasting of water level, inundation, and circulation will significantly enhance the sustainability of coastal communities and environments. Supported by the Southeast Coastal Ocean Observing Regional Association (SECOORA) through NOAA IOOS, a 24/7 high-resolution forecasting system for storm surge, coastal inundation, and baroclinic circulation is being developed for Florida using CH3D Storm Surge Modeling System (CH3D-SSMS). CH3D-SSMS is based on the CH3D hydrodynamic model coupled to a coastal wave model SWAN and basin scale surge and wave models. CH3D-SSMS has been verified with surge, wave, and circulation data from several recent hurricanes in the U.S.: Isabel (2003); Charley, Dennis and Ivan (2004); Katrina and Wilma (2005); Ike and Fay (2008); and Irene (2011), as well as typhoons in the Pacific: Fanapi (2010) and Nanmadol (2011). The effects of tropical cyclones on flow and salinity distribution in estuarine and coastal waters has been simulated for Apalachicola Bay as well as Guana-Tolomato-Matanzas Estuary using CH3D-SSMS. The system successfully reproduced different physical phenomena including large waves during Ivan that damaged I-10 Bridges, a large alongshore wave and coastal flooding during Wilma, salinity drop during Fay, and flooding in Taiwan as a result of combined surge and rain effect during Fanapi. The system uses 4 domains that cover entire Florida coastline: West, which covers the Florida panhandle and Tampa Bay; Southwest spans from Florida Keys to Charlotte Harbor; Southeast, covering Biscayne Bay and Miami and East, which continues north to the Florida/Georgia border. The system has a data acquisition and processing module that is used to collect data for model runs (e.g. wind, river flow, precipitation). Depending on the domain, forecasts runs can take ~1-18 hours to complete on a single CPU (8-core) system (1-2 hrs for 2D setup and up to 18 hrs for a 3D setup) with 4 forecasts generated per day. All data is archived / catalogued and model forecast skill is continuously being evaluated. In addition to the baseline forecasts, additional forecasts are being perform using various options for wind forcing (GFS, GFDL, WRF, and parametric hurricane models), model configurations (2D/ 3D), and open boundary conditions by coupling with large scale models (ROMS, NCOM, HYCOM), as well as incorporating real-time and forecast river flow and precipitation data to better understand how to improve model skill. In addition, new forecast products (e.g. more informative inundation maps) are being developed to targeted stakeholders. To support modern data standards, CH3D-SSMS results are available online via a THREDDS server in CF-Compliant NetCDF format as well as other stakeholder-friendly (e.g. GIS) formats. The SECOORA website provides visualization of the model via GODIVA-THREDDS interface.

Ocean modelling and Early-Warning System for the Gulf of Thailand

NASA Astrophysics Data System (ADS)

de Lima Rego, Joao; Yan, Kun; Sisomphon, Piyamarn; Thanathanphon, Watin; Twigt, Daniel; Irazoqui Apecechea, Maialen

2017-04-01

Storm surges associated with severe tropical cyclones are among the most hazardous and damaging natural disasters to coastal areas. The Gulf of Thailand (GoT) has been periodically affected by typhoon induced storm surges in the past (e.g. storm Harriet in 1962, storm Gay in 1989 and storm Linda in 1997). Due to increased touristic / economic development and increased population density in the coastal zone, the combined effect and risk of high water level and increased rainfall / river discharge has dramatically increased and are expected to increase in future due to climate change effects. This presentation describes the development and implementation of the first real-time operational storm surge, wave and wave setup forecasting system in the GoT, a joint applied research initiative by Deltares in The Netherlands and the Hydro and Agro Informatics Institute (HAII) in Thailand. The modelling part includes a new hydrodynamic model to simulate tides and storm surges and two wave models (regional and local). The hydrodynamic model is based on Delft3D Flexible Mesh, capable of simulating water levels and detailed flows. The regional and the recently-developed local wave model are based on the SWAN model, a third-generation wave model. The operational platform is based on Delft-FEWS software, which coordinates all the data inputs, the modelling tasks and the automatic forecast exports including overland inundation in the upper Gulf of Thailand. The main objective of the Gulf of Thailand EWS is to provide daily accurate storm surge, wave and wave setup estimates automatically with various data exports possibilities to support this task. It adds a coastal component to HAII's existing practice of providing daily reports on fluvial flood forecasts, used for decision-support in issuing flood warnings for inland water systems in Thailand. Every day, three-day coastal forecasts are now produced based on the latest regional meteorological predictions. Examples are given to illustrate the system's development and main features, with a focus on decision-support products.

Model simulation of the Manasquan water-supply system in Monmouth County, New Jersey

USGS Publications Warehouse

Chang, Ming; Tasker, Gary D.; Nieswand, Steven

2001-01-01

Model simulation of the Manasquan Water Supply System in Monmouth County, New Jersey, was completed using historic hydrologic data to evaluate the effects of operational and withdrawal alternatives on the Manasquan reservoir and pumping system. Changes in the system operations can be simulated with the model using precipitation forecasts. The Manasquan Reservoir system model operates by using daily streamflow values, which were reconstructed from historical U.S. Geological Survey streamflow-gaging station records. The model is able to run in two modes--General Risk analysis Model (GRAM) and Position Analysis Model (POSA). The GRAM simulation procedure uses reconstructed historical streamflow records to provide probability estimates of certain events, such as reservoir storage levels declining below a specific level, when given an assumed set of operating rules and withdrawal rates. POSA can be used to forecast the likelihood of specified outcomes, such as streamflows falling below statutory passing flows, associated with a specific working plan for the water-supply system over a period of months. The user can manipulate the model and generate graphs and tables of streamflows and storage, for example. This model can be used as a management tool to facilitate the development of drought warning and drought emergency rule curves and safe yield values for the water-supply system.

Sub-seasonal-to-seasonal Reservoir Inflow Forecast using Bayesian Hierarchical Hidden Markov Model

NASA Astrophysics Data System (ADS)

Mukhopadhyay, S.; Arumugam, S.

2017-12-01

Sub-seasonal-to-seasonal (S2S) (15-90 days) streamflow forecasting is an emerging area of research that provides seamless information for reservoir operation from weather time scales to seasonal time scales. From an operational perspective, sub-seasonal inflow forecasts are highly valuable as these enable water managers to decide short-term releases (15-30 days), while holding water for seasonal needs (e.g., irrigation and municipal supply) and to meet end-of-the-season target storage at a desired level. We propose a Bayesian Hierarchical Hidden Markov Model (BHHMM) to develop S2S inflow forecasts for the Tennessee Valley Area (TVA) reservoir system. Here, the hidden states are predicted by relevant indices that influence the inflows at S2S time scale. The hidden Markov model also captures the both spatial and temporal hierarchy in predictors that operate at S2S time scale with model parameters being estimated as a posterior distribution using a Bayesian framework. We present our work in two steps, namely single site model and multi-site model. For proof of concept, we consider inflows to Douglas Dam, Tennessee, in the single site model. For multisite model we consider reservoirs in the upper Tennessee valley. Streamflow forecasts are issued and updated continuously every day at S2S time scale. We considered precipitation forecasts obtained from NOAA Climate Forecast System (CFSv2) GCM as predictors for developing S2S streamflow forecasts along with relevant indices for predicting hidden states. Spatial dependence of the inflow series of reservoirs are also preserved in the multi-site model. To circumvent the non-normality of the data, we consider the HMM in a Generalized Linear Model setting. Skill of the proposed approach is tested using split sample validation against a traditional multi-site canonical correlation model developed using the same set of predictors. From the posterior distribution of the inflow forecasts, we also highlight different system behavior under varied global and local scale climatic influences from the developed BHMM.

Quantifying the Usefulness of Ensemble-Based Precipitation Forecasts with Respect to Water Use and Yield during a Field Trial

NASA Astrophysics Data System (ADS)

Christ, E.; Webster, P. J.; Collins, G.; Byrd, S.

2014-12-01

Recent droughts and the continuing water wars between the states of Georgia, Alabama and Florida have made agricultural producers more aware of the importance of managing their irrigation systems more efficiently. Many southeastern states are beginning to consider laws that will require monitoring and regulation of water used for irrigation. Recently, Georgia suspended issuing irrigation permits in some areas of the southwestern portion of the state to try and limit the amount of water being used in irrigation. However, even in southern Georgia, which receives on average between 23 and 33 inches of rain during the growing season, irrigation can significantly impact crop yields. In fact, studies have shown that when fields do not receive rainfall at the most critical stages in the life of cotton, yield for irrigated fields can be up to twice as much as fields for non-irrigated cotton. This leads to the motivation for this study, which is to produce a forecast tool that will enable producers to make more efficient irrigation management decisions. We will use the ECMWF (European Centre for Medium-Range Weather Forecasts) vars EPS (Ensemble Prediction System) model precipitation forecasts for the grid points included in the 1◦ x 1◦ lat/lon square surrounding the point of interest. We will then apply q-to-q bias corrections to the forecasts. Once we have applied the bias corrections, we will use the check-book method of irrigation scheduling to determine the probability of receiving the required amount of rainfall for each week of the growing season. These forecasts will be used during a field trial conducted at the CM Stripling Irrigation Research Park in Camilla, Georgia. This research will compare differences in yield and water use among the standard checkbook method of irrigation, which uses no precipitation forecast knowledge, the weather.com forecast, a dry land plot, and the ensemble-based forecasts mentioned above.

Forecast model applications of retrieved three dimensional liquid water fields

NASA Technical Reports Server (NTRS)

Raymond, William H.; Olson, William S.

1990-01-01

Forecasts are made for tropical storm Emily using heating rates derived from the SSM/I physical retrievals described in chapters 2 and 3. Average values of the latent heating rates from the convective and stratiform cloud simulations, used in the physical retrieval, are obtained for individual 1.1 km thick vertical layers. Then, the layer-mean latent heating rates are regressed against the slant path-integrated liquid and ice precipitation water contents to determine the best fit two parameter regression coefficients for each layer. The regression formulae and retrieved precipitation water contents are utilized to infer the vertical distribution of heating rates for forecast model applications. In the forecast model, diabatic temperature contributions are calculated and used in a diabatic initialization, or in a diabatic initialization combined with a diabatic forcing procedure. Our forecasts show that the time needed to spin-up precipitation processes in tropical storm Emily is greatly accelerated through the application of the data.

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.

Application of Satellite information (JASON-2) in improvement of Flood Forecasting and Early Warning Service in Bangladesh

NASA Astrophysics Data System (ADS)

Hossain, M. A.; Anderson, E. R.; Bhuiyan, M. A.; Hossain, F.; Shah-Newaz, S. M.

2014-12-01

Bangladesh is the lowest riparian of the huge system of the Ganges, Brahmaputra and Meghna (GBM) basins, second to that of Amazan, with 1.75 million sq-km catchment area, only 7% is inside Bangladesh. High inflow from GBM associated with the intense rainfall is the source of flood in Bangladesh. Flood Forecasting and Early Warning (FFEW) is the mandate and responsibility of Bangladesh Water Development Board (BWDB) and Flood Forecasting and Warning Center (FFWC) under BWDB has been carrying out this responsibility since 1972 and operational on 7-days a week during monsoon (May to October). FFEW system started with few hours lead time has been upgraded up to to 5-days with reasonable accuracy. At FFWC numerical Hydrodynamic model is used for generating water level (WL) forecast upto 5-days at 54 points on 29 rivers based on real-time observed WL of 83 and rainfall of 56 stations with boundary estimationa on daily basis. Main challenge of this system is the boundary estimation is the limited upstream data of the transboundary rivers, obstacle for increasing lead-time for FFEW. The satellite based upper catchment data may overcome this limitation. Recent NASA-French joint Satellite mission JASON-2 records Water Elevation (WE) and it may be used within 24 hours. Using JASON-2 recorded WE data of 4 and 3 virtual stations on the Ganges and Brahmaputra rivers , respectively (upper catchment), a new methodology has been developed for increasing lead time of forecast. Correlation between the JASON-2 recorded WE on the virtual stations at the upper catchment and WL of 2 dominating boundary stations at model boundary on the Ganges and Brahmaputra has been derived for generating WL forecast at those 2 boundary stations, which used as input in model. FFWC has started experimental 8-days lead-time WL forecast at 09 stations (5 in Brahmaputra and 4 in Ganges) using generated boundary data and regularly updating the results in the website. The trend of the forecasted WL using JASON-2 data is similar to those upto 5-days forecast generated in the existing system. This is a new approach in FFEW in Bangladesh where boundary estimation becomes possible using JASON-2 observed WE data of the Transboundary rivers. There is scope of further development of this system along with increase of lead time. Reference: www.ffwc.gov.bd

Coupling Fluvial and Oceanic Drivers in Flooding Forecasts for San Francisco Bay

NASA Astrophysics Data System (ADS)

Herdman, L.; Kim, J.; Cifelli, R.; Barnard, P.; Erikson, L. H.; Johnson, L. E.; Chandrasekar, V.

2016-12-01

San Francisco Bay is a highly urbanized estuary and the surrounding communities are susceptible to flooding along the bay shoreline and inland rivers and 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. This study introduces the state-of-the-art coupling of the USGS Coastal Storm Modeling System (CoSMoS) with the NWS Research Distributed Hydrologic Model (RDHM) for San Francisco Bay. 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. The tributary discharges from RDHM are dynamic, meteorologically driven allowing for operational use of CoSMoS which has previously relied on statistical estimates of river discharge. The flooding extent is determined by overlaying the resulting maximum water levels onto a recently updated 2-m digital elevation model of the study area which best resolves the extensive levee and tidal marsh systems in the region. The results we present here are focused on the interaction of the Bay and the Napa River watershed. This study demonstrates the interoperability of the CoSMoS and RDHM prediction models. We also use this pilot region to examine storm flooding impacts in a series of storm scenarios that simulate 5-100yr return period events in terms of either coastal or fluvial events. These scenarios demonstrate the wide range of possible flooding outcomes considering rainfall recurrence intervals, soil moisture conditions, storm surge, wind speed, and tides (spring and neap). With a simulated set of over 25 storm scenarios we show how the extent, level, and duration of flooding is dependent on these atmospheric and hydrologic parameters and we also determine a range of likely flood events.

An empirical approach to improving tidal predictions using recent real-time tide gauge data

NASA Astrophysics Data System (ADS)

Hibbert, Angela; Royston, Samantha; Horsburgh, Kevin J.; Leach, Harry

2014-05-01

Classical harmonic methods of tidal prediction are often problematic in estuarine environments due to the distortion of tidal fluctuations in shallow water, which results in a disparity between predicted and observed sea levels. This is of particular concern in the Bristol Channel, where the error associated with tidal predictions is potentially greater due to an unusually large tidal range of around 12m. As such predictions are fundamental to the short-term forecasting of High Water (HW) extremes, it is vital that alternative solutions are found. In a pilot study, using a year-long observational sea level record from the Port of Avonmouth in the Bristol Channel, the UK National Tidal and Sea Level Facility (NTSLF) tested the potential for reducing tidal prediction errors, using three alternatives to the Harmonic Method of tidal prediction. The three methods evaluated were (1) the use of Artificial Neural Network (ANN) models, (2) the Species Concordance technique and (3) a simple empirical procedure for correcting Harmonic Method High Water predictions based upon a few recent observations (referred to as the Empirical Correction Method). This latter method was then successfully applied to sea level records from an additional 42 of the 45 tide gauges that comprise the UK Tide Gauge Network. Consequently, it is to be incorporated into the operational systems of the UK Coastal Monitoring and Forecasting Partnership in order to improve short-term sea level predictions for the UK and in particular, the accurate estimation of HW extremes.

Extending to seasonal scales the current usage of short range weather forecasts and climate projections for water management in Spain

NASA Astrophysics Data System (ADS)

Rodriguez-Camino, Ernesto; Voces, José; Sánchez, Eroteida; Navascues, Beatriz; Pouget, Laurent; Roldan, Tamara; Gómez, Manuel; Cabello, Angels; Comas, Pau; Pastor, Fernando; Concepción García-Gómez, M.°; José Gil, Juan; Gil, Delfina; Galván, Rogelio; Solera, Abel

2016-04-01

This presentation, first, briefly describes the current use of weather forecasts and climate projections delivered by AEMET for water management in Spain. The potential use of seasonal climate predictions for water -in particular dams- management is then discussed more in-depth, using a pilot experience carried out by a multidisciplinary group coordinated by AEMET and DG for Water of Spain. This initiative is being developed in the framework of the national implementation of the GFCS and the European project, EUPORIAS. Among the main components of this experience there are meteorological and hydrological observations, and an empirical seasonal forecasting technique that provides an ensemble of water reservoir inflows. These forecasted inflows feed a prediction model for the dam state that has been adapted for this purpose. The full system is being tested retrospectively, over several decades, for selected water reservoirs located in different Spanish river basins. The assessment includes an objective verification of the probabilistic seasonal forecasts using standard metrics, and the evaluation of the potential social and economic benefits, with special attention to drought and flooding conditions. The methodology of implementation of these seasonal predictions in the decision making process is being developed in close collaboration with final users participating in this pilot experience.

7 CFR 612.7 - Forecast user responsibility.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 7 Agriculture 6 2010-01-01 2010-01-01 false Forecast user responsibility. 612.7 Section 612.7 Agriculture Regulations of the Department of Agriculture (Continued) NATURAL RESOURCES CONSERVATION SERVICE, DEPARTMENT OF AGRICULTURE CONSERVATION OPERATIONS SNOW SURVEYS AND WATER SUPPLY FORECASTS § 612.7 Forecast...

The Hydrometeorological Testbed (HMT): Opportunities for Scenario Development in a Framework of Improving Precipitation and Streamflow Science and Prediction

NASA Astrophysics Data System (ADS)

Ralph, F. M.; Kingsmill, D.; Restrepo, P.; Nowlis, J.; White, A.

2006-12-01

The Hydrometeorology Testbed (HMT) is an effort to accelerate the infusion of new technologies, models, and scientific results from the research community into daily hydrometeorological forecast operations of the National Weather Service (NWS) and its River Forecast Centers (RFCs). HMT is a product of NOAA's CALJET and PACJET projects from 1997-2003 on the West Coast and it has been identified in the NWS Hydrology Science and Technology Implementation Plan (STIP) and NOAA's Programming, Planning, Budgeting and Execution System (PPBES) as a key new R&D approach for improving flood and streamflow forecasts. Preliminary, small scale tests of HMT facilities, led by the NOAA Earth System Research Laboratory, were conducted in California's Coast Range in 2004 (HMT-04) and were moved to the western slopes of the Sierra Nevada for the winters of 2005-2006 and 2006-2007. Unlike typical research field projects, the HMT operates as a demonstration with forecasters and researchers joining forces in the operational setting, to addressing key forecast user needs. The current HMT targets California's flood-vulnerable American River Basin with the first full-scale deployment of this highly instrumented facility. Following the California demonstration, HMT facilities will be sequentially deployed to other regions of the Nation to address additional serious hydrometeorology problems that are unique to those locations. The project will run for a few years in each regional demonstration to determine the new tools most useful for improving precipitation and runoff forecasting methods. These successful tools will remain in place and will be duplicated as the HMT moves to the next region. Through NOAA funding, HMT will provide a foundation level of effort and infrastructure each year in the test region. It is expected that this foundation will be augmented by occasional ramping-up to more intensive operations that include additional participants and specialized instrumentation. The HMT activities in the American River Basin can benefit from the development and analysis of management scenarios that evaluate the impacts HMT can provide through improved predictions of water inflow into the Folsom Reservoir. Management models that explore various water management policies and their relative performance at controlling floods, generating energy, presenting recreational opportunities, maintaining healthy downstream ecosystems, and providing water for agricultural, urban, and industrial uses, would be particularly valuable. Use of conventional inputs to estimate reservoir levels could be compared with improved estimates resulting from HMT.. The difference between the model results will illustrate the value of improved predictability of extreme weather, while also providing insight into the strengths and weaknesses of various water management policies.

Flood-inundation maps for the Yellow River at Plymouth, Indiana

USGS Publications Warehouse

Menke, Chad D.; Bunch, Aubrey R.; Kim, Moon H.

2016-11-16

Digital flood-inundation maps for a 4.9-mile reach of the Yellow River at Plymouth, Indiana (Ind.), were created by the U.S. Geological Survey (USGS) in cooperation with the Indiana Office of Community and Rural Affairs. The flood-inundation maps, which can be accessed through the USGS Flood Inundation Mapping Science Web site at http://water.usgs.gov/osw/flood_inundation/, depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at the USGS streamgage 05516500, Yellow River at Plymouth, Ind. Current conditions for estimating near-real-time areas of inundation using USGS streamgage information may be obtained on the Internet at http://waterdata.usgs.gov/in/nwis/uv?site_no=05516500. In addition, information has been provided to the National Weather Service (NWS) for incorporation into their Advanced Hydrologic Prediction Service (AHPS) flood-warning system (http:/water.weather.gov/ahps/). The NWS AHPS forecasts flood hydrographs at many sites that are often collocated with USGS streamgages, including the Yellow River at Plymouth, Ind. NWS AHPS-forecast peak-stage information may be used in conjunction with the maps developed in this study to show predicted areas of flood and forecasts of flood hydrographs at this site.For this study, flood profiles were computed for the Yellow River reach by means of a one-dimensional step-backwater model. The hydraulic model was calibrated by using the current stage-discharge relations at the Yellow River streamgage, in combination with the flood-insurance study for Marshall County (issued in 2011). The calibrated hydraulic model was then used to determine eight water-surface profiles for flood stages at 1-foot intervals referenced to the streamgage datum and ranging from bankfull to the highest stage of the current stage-discharge rating curve. The 1-percent annual exceedance probability flood profile elevation (flood elevation with recurrence intervals within 100 years) is within the calibrated water-surface elevations for comparison. The simulated water-surface profiles were then used with a geographic information system (GIS) digital elevation model (DEM, derived from Light Detection and Ranging [lidar]) in order to delineate the area flooded at each water level.The availability of these maps, along with Internet information regarding current stage from the USGS streamgage 05516500, Yellow River at Plymouth, Ind., and forecast stream stages from the NWS AHPS, provides emergency management personnel and residents with information that is critical for flood response activities such as evacuations and road closures, as well as for postflood recovery efforts.

Flood-inundation maps for the Driftwood River and Sugar Creek near Edinburgh, Indiana

USGS Publications Warehouse

Fowler, Kathleen K.; Kim, Moon H.; Menke, Chad D.

2012-01-01

Digital flood-inundation maps for an 11.2 mile reach of the Driftwood River and a 5.2 mile reach of Sugar Creek, both near Edinburgh, Indiana, were created by the U.S. Geological Survey (USGS) in cooperation with the Camp Atterbury Joint Maneuver Training Center, Edinburgh, Indiana. The inundation maps, which can be accessed through the USGS Flood Inundation Mapping Science Web site at http://water.usgs.gov/osw/flood_inundation/, depict estimates of the areal extent of flooding corresponding to selected water levels (stages) at the USGS streamgage 03363000 Driftwood River near Edinburgh, Ind. Current conditions at the USGS streamgage in Indiana may be obtained on the Internet at http://waterdata.usgs.gov/in/nwis/current/?type=flow. In addition, the information has been provided to the National Weather Service (NWS) for incorporation into their Advanced Hydrologic Prediction Service (AHPS) flood warning system at http://water.weather.gov/ahps/. The NWS forecasts flood hydrographs at many places that are often collocated at USGS streamgages. That forecasted peak-stage information, also available on the Internet, may be used in conjunction with the maps developed in this study to show predicted areas of flood inundation. For this study, flood profiles were computed for the stream reaches by means of a one-dimensional step-backwater model. The model was calibrated using the most current stage-discharge relations at the USGS streamgage 03363000 Driftwood River near Edinburgh, Ind. The hydraulic model was then used to determine elevations throughout the study reaches for nine water-surface profiles for flood stages at 1-ft intervals referenced to the streamgage datum and ranging from bankfull to nearly the highest recorded water level at the USGS streamgage 03363000 Driftwood River near Edinburgh, Ind. The simulated water-surface profiles were then combined with a geospatial digital elevation model (derived from Light Detection and Ranging (LiDAR) data) in order to delineate the area flooded at each water level. The availability of these maps along with real-time information available online regarding current stage from USGS streamgages and forecasted stream stages from the NWS provide emergency management personnel and residents with information that is critical for flood response activities such as evacuations and road closures as well as for post flood recovery efforts.

Flood-inundation maps for the Elkhart River at Goshen, Indiana

USGS Publications Warehouse

Strauch, Kellan R.

2013-01-01

The U.S. Geological Survey (USGS), in cooperation with the Indiana Office of Community and Rural Affairs, created digital flood-inundation maps for an 8.3-mile reach of the Elkhart River at Goshen, Indiana, extending from downstream of the Goshen Dam to downstream from County Road 17. The inundation maps, which can be accessed through the USGS Flood Inundation Mapping Science Web site at http://water.usgs.gov/osw/flood_inundation/, depict estimates of the areal extent and depth of flooding corresponding to nine selected water levels (stages) at the USGS streamgage at Elkhart River at Goshen (station number 04100500). Current conditions for the USGS streamgages in Indiana may be obtained on the Internet at http://waterdata.usgs.gov/. In addition, stream stage data have been provided to the National Weather Service (NWS) for incorporation into their Advanced Hydrologic Prediction Service (AHPS) flood warning system (http://water.weather.gov/ahps/). The NWS forecasts flood hydrographs at many places that are often colocated with USGS streamgages. NWS-forecasted peak-stage information may be used in conjunction with the maps developed in this study to show predicted areas of flood inundation. In this study, flood profiles were computed for the stream reach by means of a one-dimensional step-backwater model. The model was calibrated using the most current stage-discharge relation at the Elkhart River at Goshen streamgage. The hydraulic model was then used to compute nine water-surface profiles for flood stages at 1-foot (ft) intervals referenced to the streamgage datum and ranging from approximately bankfull (5 ft) to greater than the highest recorded water level (13 ft). The simulated water-surface profiles were then combined with a geographic information system (GIS) digital-elevation model (DEM), derived from Light Detection and Ranging (LiDAR) data having a 0.37-ft vertical accuracy and 3.9-ft horizontal resolution in order to delineate the area flooded at each water level. The availability of these maps, along with Internet information regarding current stage from USGS streamgages and forecasted stream stages from the NWS, provide emergency management personnel and residents with information that is critical for flood response activities such as evacuations and road closures as well as for postflood recovery efforts.

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.

Hybrid Stochastic Forecasting Model for Management of Large Open Water Reservoir with Storage Function

NASA Astrophysics Data System (ADS)

Kozel, Tomas; Stary, Milos

2017-12-01

The main advantage of stochastic forecasting is fan of possible value whose deterministic method of forecasting could not give us. Future development of random process is described better by stochastic then deterministic forecasting. Discharge in measurement profile could be categorized as random process. Content of article is construction and application of forecasting model for managed large open water reservoir with supply function. Model is based on neural networks (NS) and zone models, which forecasting values of average monthly flow from inputs values of average monthly flow, learned neural network and random numbers. Part of data was sorted to one moving zone. The zone is created around last measurement average monthly flow. Matrix of correlation was assembled only from data belonging to zone. The model was compiled for forecast of 1 to 12 month with using backward month flows (NS inputs) from 2 to 11 months for model construction. Data was got ridded of asymmetry with help of Box-Cox rule (Box, Cox, 1964), value r was found by optimization. In next step were data transform to standard normal distribution. The data were with monthly step and forecast is not recurring. 90 years long real flow series was used for compile of the model. First 75 years were used for calibration of model (matrix input-output relationship), last 15 years were used only for validation. Outputs of model were compared with real flow series. For comparison between real flow series (100% successfully of forecast) and forecasts, was used application to management of artificially made reservoir. Course of water reservoir management using Genetic algorithm (GE) + real flow series was compared with Fuzzy model (Fuzzy) + forecast made by Moving zone model. During evaluation process was founding the best size of zone. Results show that the highest number of input did not give the best results and ideal size of zone is in interval from 25 to 35, when course of management was almost same for all numbers from interval. Resulted course of management was compared with course, which was obtained from using GE + real flow series. Comparing results showed that fuzzy model with forecasted values has been able to manage main malfunction and artificially disorders made by model were founded essential, after values of water volume during management were evaluated. Forecasting model in combination with fuzzy model provide very good results in management of water reservoir with storage function and can be recommended for this purpose.

Monitoring inland storm tide and flooding from Hurricane Irene along the Atlantic Coast of the United States, August 2011

USGS Publications Warehouse

McCallum, Brian E.; Painter, Jaime A.; Frantz, Eric R.

2012-01-01

The U.S. Geological Survey (USGS) deployed a temporary monitoring network of water-level sensors at 212 locations along the Atlantic coast from South Carolina to Maine during August 2011 to record the timing, areal extent, and magnitude of inland hurricane storm tide and coastal flooding generated by Hurricane Irene. Water-level sensor locations were selected to augment existing tide-gage networks to ensure adequate monitoring in areas forecasted to have substantial storm tide. As defined by the National Oceanic and Atmospheric Administration (NOAA; 2011a,b), storm tide is the water-level rise generated by a coastal storm as a result of the combination of storm surge and astronomical tide.

ALASKA MARINE VHF VOICE

Science.gov Websites

Tsunamis 406 EPIRB's National Weather Service Marine Forecasts ALASKA MARINE VHF VOICE Marine Forecast greater danger near shore or any shallow waters? NATIONAL WEATHER SERVICE PRODUCTS VIA ALASKA MARINE VHF VOICE NOAA broadcasts offshore forecasts, nearshore forecasts and storm warnings on marine VHF channels

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).

Short-range precipitation forecasts using assimilation of simulated satellite water vapor profiles and column cloud liquid water amounts

NASA Technical Reports Server (NTRS)

Wu, Xiaohua; Diak, George R.; Hayden, Cristopher M.; Young, John A.

1995-01-01

These observing system simulation experiments investigate the assimilation of satellite-observed water vapor and cloud liquid water data in the initialization of a limited-area primitive equations model with the goal of improving short-range precipitation forecasts. The assimilation procedure presented includes two aspects: specification of an initial cloud liquid water vertical distribution and diabatic initialization. The satellite data is simulated for the next generation of polar-orbiting satellite instruments, the Advanced Microwave Sounding Unit (AMSU) and the High-Resolution Infrared Sounder (HIRS), which are scheduled to be launched on the NOAA-K satellite in the mid-1990s. Based on cloud-top height and total column cloud liquid water amounts simulated for satellite data a diagnostic method is used to specify an initial cloud water vertical distribution and to modify the initial moisture distribution in cloudy areas. Using a diabatic initialization procedure, the associated latent heating profiles are directly assimilated into the numerical model. The initial heating is estimated by time averaging the latent heat release from convective and large-scale condensation during the early forecast stage after insertion of satellite-observed temperature, water vapor, and cloud water formation. The assimilation of satellite-observed moisture and cloud water, together withy three-mode diabatic initialization, significantly alleviates the model precipitation spinup problem, especially in the first 3 h of the forecast. Experimental forecasts indicate that the impact of satellite-observed temperature and water vapor profiles and cloud water alone in the initialization procedure shortens the spinup time for precipitation rates by 1-2 h and for regeneration of the areal coverage by 3 h. The diabatic initialization further reduces the precipitation spinup time (compared to adiabatic initialization) by 1 h.

Forecast on Water Locking Damage of Low Permeable Reservoir with Quantum Neural Network

NASA Astrophysics Data System (ADS)

Zhao, Jingyuan; Sun, Yuxue; Feng, Fuping; Zhao, Fulei; Sui, Dianjie; Xu, Jianjun

2018-01-01

It is of great importance in oil-gas reservoir protection to timely and correctly forecast the water locking damage, the greatest damage for low permeable reservoir. An analysis is conducted on the production mechanism and various influence factors of water locking damage, based on which a quantum neuron is constructed based on the information processing manner of a biological neuron and the principle of quantum neural algorithm, besides, the quantum neural network model forecasting the water locking of the reservoir is established and related software is also made to forecast the water locking damage of the gas reservoir. This method has overcome the defects of grey correlation analysis that requires evaluation matrix analysis and complicated operation. According to the practice in Longxi Area of Daqing Oilfield, this method is characterized by fast operation, few system parameters and high accuracy rate (the general incidence rate may reach 90%), which can provide reliable support for the protection technique of low permeable reservoir.

Forecasting of Seasonal Rainfall using ENSO and IOD teleconnection with Classification Models

NASA Astrophysics Data System (ADS)

De Silva, T.; Hornberger, G. M.

2017-12-01

Seasonal to annual forecasts of precipitation patterns are very important for water infrastructure management. In particular, such forecasts can be used to inform decisions about the operation of multipurpose reservoir systems in the face of changing climate conditions. Success in making useful forecasts often is achieved by considering climate teleconnections such as the El-Nino-Southern Oscillation (ENSO), Indian Ocean Dipole (IOD) as related to sea surface temperature variations. We present an analysis to explore the utility of using rainfall relationships in Sri Lanka with ENSO and IOD to predict rainfall to the Mahaweli, river basin. Forecasting of rainfall as classes - above normal, normal, and below normal - can be useful for water resource management decision making. Quadratic discrimination analysis (QDA) and random forest models are used to identify the patterns of rainfall classes with respect to ENSO and IOD indices. These models can be used to forecast the likelihood of areal rainfall anomalies using predicted climate indices. Results can be used for decisions regarding allocation of water for agriculture and electricity generation within the Mahaweli project of Sri Lanka.

Forecasting the Water Demand in Chongqing, China Using a Grey Prediction Model and Recommendations for the Sustainable Development of Urban Water Consumption.

PubMed

Wu, Hua'an; Zeng, Bo; Zhou, Meng

2017-11-15

High accuracy in water demand predictions is an important basis for the rational allocation of city water resources and forms the basis for sustainable urban development. The shortage of water resources in Chongqing, the youngest central municipality in Southwest China, has significantly increased with the population growth and rapid economic development. In this paper, a new grey water-forecasting model (GWFM) was built based on the data characteristics of water consumption. The parameter estimation and error checking methods of the GWFM model were investigated. Then, the GWFM model was employed to simulate the water demands of Chongqing from 2009 to 2015 and forecast it in 2016. The simulation and prediction errors of the GWFM model was checked, and the results show the GWFM model exhibits better simulation and prediction precisions than those of the classical Grey Model with one variable and single order equation GM(1,1) for short and the frequently-used Discrete Grey Model with one variable and single order equation, DGM(1,1) for short. Finally, the water demand in Chongqing from 2017 to 2022 was forecasted, and some corresponding control measures and recommendations were provided based on the prediction results to ensure a viable water supply and promote the sustainable development of the Chongqing economy.

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.

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.

Flood-inundation maps for the Iroquois River at Rensselaer, Indiana

USGS Publications Warehouse

Fowler, Kathleen K.; Bunch, Aubrey R.

2013-01-01

Digital flood-inundation maps for a 4.0-mile reach of the Iroquois River at Rensselaer, Indiana (Ind.), were created by the U.S. Geological Survey (USGS) in cooperation with the Indiana Department of Transportation. The inundation maps, which can be accessed through the USGS Flood Inundation Mapping Science Web site at http://water.usgs.gov/osw/flood_inundation/, depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at USGS streamgage 05522500, Iroquois River at Rensselaer, Ind. Current conditions for estimating near-real-time areas of inundation using USGS streamgage information may be obtained on the Internet at (http://waterdata.usgs.gov/in/nwis/uv?site_no=05522500). In addition, the National Weather Service (NWS) forecasts flood hydrographs at the Rensselaer streamgage. That forecasted peak-stage information, also available on the Internet (http://water.weather.gov/ahps/), may be used in conjunction with the maps developed in this study to show predicted areas of flood inundation. For this study, flood profiles were computed for the Iroquois River reach by means of a one-dimensional step-backwater model developed by the U.S. Army Corps of Engineers. The hydraulic model was calibrated by using the most current (June 27, 2012) stage-discharge relations at USGS streamgage 05522500, Iroquois River at Rensselaer, Ind., and high-water marks from the flood of July 2003. The calibrated hydraulic model was then used to determine nine water-surface profiles for flood stages at 1-foot intervals referenced to the streamgage datum and ranging from bankfull to the highest stage of the current stage-discharge rating curve. The simulated water-surface profiles were then combined with a Geographic Information System digital elevation model (derived from Light Detection and Ranging (LiDAR) data) in order to delineate the area flooded at each water level. The availability of these maps, along with Internet information regarding current stage from the USGS streamgage at Rensselaer, Ind., and forecasted stream stages from the NWS, provides emergency management personnel and residents with information that is critical for flood response activities such as evacuations and road closures, as well as for post-flood recovery efforts.

Groundwater-level trends and forecasts, and salinity trends, in the Azraq, Dead Sea, Hammad, Jordan Side Valleys, Yarmouk, and Zarqa groundwater basins, Jordan

USGS Publications Warehouse

Goode, Daniel J.; Senior, Lisa A.; Subah, Ali; Jaber, Ayman

2013-01-01

Changes in groundwater levels and salinity in six groundwater basins in Jordan were characterized by using linear trends fit to well-monitoring data collected from 1960 to early 2011. On the basis of data for 117 wells, groundwater levels in the six basins were declining, on average about -1 meter per year (m/yr), in 2010. The highest average rate of decline, -1.9 m/yr, occurred in the Jordan Side Valleys basin, and on average no decline occurred in the Hammad basin. The highest rate of decline for an individual well was -9 m/yr. Aquifer saturated thickness, a measure of water storage, was forecast for year 2030 by using linear extrapolation of the groundwater-level trend in 2010. From 30 to 40 percent of the saturated thickness, on average, was forecast to be depleted by 2030. Five percent of the wells evaluated were forecast to have zero saturated thickness by 2030. Electrical conductivity was used as a surrogate for salinity (total dissolved solids). Salinity trends in groundwater were much more variable and less linear than groundwater-level trends. The long-term linear salinity trend at most of the 205 wells evaluated was not increasing, although salinity trends are increasing in some areas. The salinity in about 58 percent of the wells in the Amman-Zarqa basin was substantially increasing, and the salinity in Hammad basin showed a long-term increasing trend. Salinity increases were not always observed in areas with groundwater-level declines. The highest rates of salinity increase were observed in regional discharge areas near groundwater pumping centers.

A seasonal agricultural drought forecast system for food-insecure regions of East Africa

USGS Publications Warehouse

Shukla, Shraddhanand; McNally, Amy; Husak, Gregory; Funk, Christopher C.

2014-01-01

 The increasing food and water demands of East Africa's growing population are stressing the region's inconsistent water resources and rain-fed agriculture. More accurate seasonal agricultural drought forecasts for this region can inform better water and agricultural management decisions, support optimal allocation of the region's water resources, and mitigate socio-economic losses incurred by droughts and floods. Here we describe the development and implementation of a seasonal agricultural drought forecast system for East Africa (EA) that provides decision support for the Famine Early Warning Systems Network's science team. We evaluate this forecast system for a region of equatorial EA (2° S to 8° N, and 36° to 46° E) for the March-April-May growing season. This domain encompasses one of the most food insecure, climatically variable and socio-economically vulnerable regions in EA, and potentially the world: this region has experienced famine as recently as 2011. To assess the agricultural outlook for the upcoming season our forecast system simulates soil moisture (SM) scenarios using the Variable Infiltration Capacity (VIC) hydrologic model forced with climate scenarios for the upcoming season. First, to show that the VIC model is appropriate for this application we forced the model with high quality atmospheric observations and found that the resulting SM values were consistent with the Food and Agriculture Organization's (FAO's) Water Requirement Satisfaction Index (WRSI), an index used by FEWS NET to estimate crop yields. Next we tested our forecasting system with hindcast runs (1993–2012). We found that initializing SM forecasts with start-of-season (5 March) SM conditions resulted in useful SM forecast skill (> 0.5 correlation) at 1-month, and in some cases at 3 month lead times. Similarly, when the forecast was initialized with mid-season (i.e. 5 April) SM conditions the skill until the end-of-season improved. This shows that early-season rainfall is critical for end-of-season outcomes. Finally we show that, in terms of forecasting spatial patterns of SM anomalies, the skill of this agricultural drought forecast system is generally greater (> 0.8 correlation) during drought years. This means that this system might be particularity useful for identifying the events that present the greatest risk to the region.

The impact of water vapor assimilation on quantitative precipitation forecast over the Washington, DC metropolitan area

NASA Astrophysics Data System (ADS)

Walford, Segayle Cereta

Forecasting subtle, small-scale convective cases in both winter and summer time is an ongoing challenge in weather forecasting. Recent studies have shown that better structure of moisture within the boundary layer is crucial for improving forecasting skills, particularly quantitative precipitation forecasting (QPF). Lidars, which take high temporal observations of moisture, are able to capture very detailed structures, especially within the boundary layer where convection often begins. This study first investigates the extent to which an aerosol and a water vapor lidar are able to capture key boundary layer processes necessary for the development of convection. The results of this preliminary study show that the water vapor lidar is best able to capture the small scale water vapor variability that is necessary for the development of convection. These results are then used to investigate impacts of assimilating moisture from the Howard University Raman Lidar (HURL) for one mesoscale convective case, July 27-28, 2006. The data for this case is from the Water Vapor Validation Experiment-Satellite and Sondes (WAVES) field campaign located at the Howard University Beltsville Site (HUBS) in Beltsville, MD. Specifically, lidar-based water vapor mixing ratio profiles are assimilated into the Weather Research and Forecasting (WRF) regional model over a 4 km grid resolution over Washington, DC. Model verification is conducted using the Meteorological Evaluation Tool (MET) and the results from the lidar run are then compared to a control (no assimilation) run. The findings indicate that quantitatively conclusions cannot be draw from this one case study. However, qualitatively, the assimilation of the lidar observations improved the equivalent potential temperature, and water vapor distribution of the region. This difference changed location, strength and spatial coverage of the convective system over the HUBS region.

The Norwegian forecasting and warning service for rainfall- and snowmelt-induced landslides

NASA Astrophysics Data System (ADS)

Krøgli, Ingeborg K.; Devoli, Graziella; Colleuille, Hervé; Boje, Søren; Sund, Monica; Engen, Inger Karin

2018-05-01

The Norwegian Water Resources and Energy Directorate (NVE) have run a national flood forecasting and warning service since 1989. In 2009, the directorate was given the responsibility of also initiating a national forecasting service for rainfall-induced landslides. Both services are part of a political effort to improve flood and landslide risk prevention. The Landslide Forecasting and Warning Service was officially launched in 2013 and is developed as a joint initiative across public agencies between NVE, the Norwegian Meteorological Institute (MET), the Norwegian Public Road Administration (NPRA) and the Norwegian Rail Administration (Bane NOR). The main goal of the service is to reduce economic and human losses caused by landslides. The service performs daily a national landslide hazard assessment describing the expected awareness level at a regional level (i.e. for a county and/or group of municipalities). The service is operative 7 days a week throughout the year. Assessments and updates are published at the warning portal http://www.varsom.no/ at least twice a day, for the three coming days. The service delivers continuous updates on the current situation and future development to national and regional stakeholders and to the general public. The service is run in close cooperation with the flood forecasting service. Both services are based on the five pillars: automatic hydrological and meteorological stations, landslide and flood historical database, hydro-meteorological forecasting models, thresholds or return periods, and a trained group of forecasters. The main components of the service are herein described. A recent evaluation, conducted on the 4 years of operation, shows a rate of over 95 % correct daily assessments. In addition positive feedbacks have been received from users through a questionnaire. The capability of the service to forecast landslides by following the hydro-meteorological conditions is illustrated by an example from autumn 2017. The case shows how the landslide service has developed into a well-functioning system providing useful information, effectively and on time.

Prototype methodology for obtaining cloud seeding guidance from HRRR model data

NASA Astrophysics Data System (ADS)

Dawson, N.; Blestrud, D.; Kunkel, M. L.; Waller, B.; Ceratto, J.

2017-12-01

Weather model data, along with real time observations, are critical to determine whether atmospheric conditions are prime for super-cooled liquid water during cloud seeding operations. Cloud seeding groups can either use operational forecast models, or run their own model on a computer cluster. A custom weather model provides the most flexibility, but is also expensive. For programs with smaller budgets, openly-available operational forecasting models are the de facto method for obtaining forecast data. The new High-Resolution Rapid Refresh (HRRR) model (3 x 3 km grid size), developed by the Earth System Research Laboratory (ESRL), provides hourly model runs with 18 forecast hours per run. While the model cannot be fine-tuned for a specific area or edited to provide cloud-seeding-specific output, model output is openly available on a near-real-time basis. This presentation focuses on a prototype methodology for using HRRR model data to create maps which aid in near-real-time cloud seeding decision making. The R programming language is utilized to run a script on a Windows® desktop/laptop computer either on a schedule (such as every half hour) or manually. The latest HRRR model run is downloaded from NOAA's Operational Model Archive and Distribution System (NOMADS). A GRIB-filter service, provided by NOMADS, is used to obtain surface and mandatory pressure level data for a subset domain which greatly cuts down on the amount of data transfer. Then, a set of criteria, identified by the Idaho Power Atmospheric Science Group, is used to create guidance maps. These criteria include atmospheric stability (lapse rates), dew point depression, air temperature, and wet bulb temperature. The maps highlight potential areas where super-cooled liquid water may exist, reasons as to why cloud seeding should not be attempted, and wind speed at flight level.

Coastal ocean forecasting with an unstructured grid model in the southern Adriatic and northern Ionian seas

NASA Astrophysics Data System (ADS)

Federico, Ivan; Pinardi, Nadia; Coppini, Giovanni; Oddo, Paolo; Lecci, Rita; Mossa, Michele

2017-01-01

SANIFS (Southern Adriatic Northern Ionian coastal Forecasting System) is a coastal-ocean operational system based on the unstructured grid finite-element three-dimensional hydrodynamic SHYFEM model, providing short-term forecasts. The operational chain is based on a downscaling approach starting from the large-scale system for the entire Mediterranean Basin (MFS, Mediterranean Forecasting System), which provides initial and boundary condition fields to the nested system. The model is configured to provide hydrodynamics and active tracer forecasts both in open ocean and coastal waters of southeastern Italy using a variable horizontal resolution from the open sea (3-4 km) to coastal areas (50-500 m). Given that the coastal fields are driven by a combination of both local (also known as coastal) and deep-ocean forcings propagating along the shelf, the performance of SANIFS was verified both in forecast and simulation mode, first (i) on the large and shelf-coastal scales by comparing with a large-scale survey CTD (conductivity-temperature-depth) in the Gulf of Taranto and then (ii) on the coastal-harbour scale (Mar Grande of Taranto) by comparison with CTD, ADCP (acoustic doppler current profiler) and tide gauge data. Sensitivity tests were performed on initialization conditions (mainly focused on spin-up procedures) and on surface boundary conditions by assessing the reliability of two alternative datasets at different horizontal resolution (12.5 and 6.5 km). The SANIFS forecasts at a lead time of 1 day were compared with the MFS forecasts, highlighting that SANIFS is able to retain the large-scale dynamics of MFS. The large-scale dynamics of MFS are correctly propagated to the shelf-coastal scale, improving the forecast accuracy (+17 % for temperature and +6 % for salinity compared to MFS). Moreover, the added value of SANIFS was assessed on the coastal-harbour scale, which is not covered by the coarse resolution of MFS, where the fields forecasted by SANIFS reproduced the observations well (temperature RMSE equal to 0.11 °C). Furthermore, SANIFS simulations were compared with hourly time series of temperature, sea level and velocity measured on the coastal-harbour scale, showing a good agreement. Simulations in the Gulf of Taranto described a circulation mainly characterized by an anticyclonic gyre with the presence of cyclonic vortexes in shelf-coastal areas. A surface water inflow from the open sea to Mar Grande characterizes the coastal-harbour scale.

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.

Flood-inundation maps for the Tippecanoe River near Delphi, Indiana

USGS Publications Warehouse

Menke, Chad D.; Bunch, Aubrey R.; Kim, Moon H.

2013-01-01

Digital flood-inundation maps for an 11-mile reach of the Tippecanoe River that extends from County Road W725N to State Road 18 below Oakdale Dam, Indiana (Ind.), were created by the U.S. Geological Survey (USGS) in cooperation with the Indiana Department of Transportation. The inundation maps, which can be accessed through the USGS Flood Inundation Mapping Science Web site at http://water.usgs.gov/osw/flood_inundation/, depict estimates of the areal extent of flooding corresponding to selected water levels (stages) at USGS streamgage 03333050, Tippecanoe River near Delphi, Ind. Current conditions at the USGS streamgages in Indiana may be obtained online at http://waterdata.usgs.gov/in/nwis/current/?type=flow. In addition, the information has been provided to the National Weather Service (NWS) for incorporation into their Advanced Hydrologic Prediction Service (AHPS) flood warning system (http://water.weather.gov/ahps/). The NWS forecasts flood hydrographs at many places that are often co-located at USGS streamgages. That forecasted peak-stage information, also available on the Internet, may be used in conjunction with the maps developed in this study to show predicted areas of flood inundation. In this study, water-surface profiles were simulated for the stream reach by means of a hydraulic one-dimensional step-backwater model. The model was calibrated by using the most current stage-discharge relation at USGS streamgage 03333050, Tippecanoe River near Delphi, Ind., and USGS streamgage 03332605, Tippecanoe River below Oakdale Dam, Ind. The hydraulic model was then used to simulate 13 water-surface profiles for flood stages at 1-foot intervals reference to the streamgage datum and ranging from bankfull to approximately the highest recorded water level at the streamgage. The simulated water-surface profiles were then combined with a geographic information system digital elevation model (derived from Light Detection and Ranging (LiDAR) data) in order to delineate the area flooded at each water level. A flood inundation map was generated for each water-surface profile stage (13 maps in all) so that, for any given flood stage, users will be able to view the estimated area of inundation. The availability of these maps, along with current stage from USGS streamgages and forecasted stream stages from the NWS, provides emergency management personnel and residents with information that is critical for flood response activities such as evacuations and road closures, as well as for post-flood recovery efforts.

Forecasting the Human Pathogen Vibrio Parahaemolyticus in Shellfish Tissue within Long Island Sound

NASA Astrophysics Data System (ADS)

Whitney, M. M.; DeRosia-Banick, K.

2016-02-01

Vibrio parahaemolyticus (Vp) is a marine bacterium that occurs naturally in brackish and saltwater environments and may be found in higher concentrations in the warmest months. Vp is a growing threat to producing safe seafood. Consumption of shellfish with high Vp levels can result in gastrointestinal human illnesses. Management response to Vp-related illness outbreaks includes closure of shellfish growing areas. Water quality observations, Vp measurements, and model forecasts are key components to effective management of shellfish growing areas. There is a clear need for observations within the growing area themselves. These areas are offshore of coastal stations and typically inshore of the observing system moorings. New field observations in Long Island Sound (LIS) shellfish growing areas are described and their agreement with high-resolution satellite sea surface temperature data is discussed. A new dataset of Vp concentrations in shellfish tissue is used to determine the LIS-specific Vp vs. temperature relationship following methods in the FDA pre-harvest Vp risk model. This information is combined with output from a high-resolution hydrodynamic model of LIS to make daily forecasts of Vp levels. The influence of river inflows, the role of heat waves, and predictions for future warmer climates are discussed. The key elements of this observational-modeling approach to pathogen forecasting are extendable to other coastal systems.

Climate Forecasts and Water Resource Management: Applications for a Developing Country

NASA Astrophysics Data System (ADS)

Brown, C.; Rogers, P.

2002-05-01

While the quantity of water on the planet earth is relatively constant, the demand for water is continuously increasing. Population growth leads to linear increases in water demand, and economic growth leads to further demand growth. Strzepek et al. calculate that with a United Nations mean population estimate of 8.5 billion people by 2025 and globally balanced economic growth, water use could increase by 70% over that time (Strzepek et al., 1995). For developing nations especially, supplying water for this growing demand requires the construction of new water supply infrastructure. The prospect of designing and constructing long life-span infrastructure is clouded by the uncertainty of future climate. The availability of future water resources is highly dependent on future climate. With realization of the nonstationarity of climate, responsible design emphasizes resiliency and robustness of water resource systems (IPCC, 1995; Gleick et al., 1999). Resilient systems feature multiple sources and complex transport and distribution systems, and so come at a high economic and environmental price. A less capital-intense alternative to creating resilient and robust water resource systems is the use of seasonal climate forecasts. Such forecasts provide adequate lead time and accuracy to allow water managers and water-based sectors such as agriculture or hydropower to optimize decisions for the expected water supply. This study will assess the use of seasonal climate forecasts from regional climate models as a method to improve water resource management in systems with limited water supply infrastructure

Forecasting the quality of water-suppressed 1 H MR spectra based on a single-shot water scan.

PubMed

Kyathanahally, Sreenath P; Kreis, Roland

2017-08-01

To investigate whether an initial non-water-suppressed acquisition that provides information about the signal-to-noise ratio (SNR) and linewidth is enough to forecast the maximally achievable final spectral quality and thus inform the operator whether the foreseen number of averages and achieved field homogeneity is adequate. A large range of spectra with varying SNR and linewidth was simulated and fitted with popular fitting programs to determine the dependence of fitting errors on linewidth and SNR. A tool to forecast variance based on a single acquisition was developed and its performance evaluated on simulated and in vivo data obtained at 3 Tesla from various brain regions and acquisition settings. A strong correlation to real uncertainties in estimated metabolite contents was found for the forecast values and the Cramer-Rao lower bounds obtained from the water-suppressed spectra. It appears to be possible to forecast the best-case errors associated with specific metabolites to be found in model fits of water-suppressed spectra based on a single water scan. Thus, nonspecialist operators will be able to judge ahead of time whether the planned acquisition can possibly be of sufficient quality to answer the targeted clinical question or whether it needs more averages or improved shimming. Magn Reson Med 78:441-451, 2017. © 2016 International Society for Magnetic Resonance in Medicine. © 2016 International Society for Magnetic Resonance in Medicine.

Snow mass and river flows modelled using GRACE total water storage observations

NASA Astrophysics Data System (ADS)

Wang, S.

2017-12-01

Snow mass and river flow measurements are difficult and less accurate in cold regions due to the hash environment. Floods in cold regions are commonly a result of snowmelt during the spring break-up. Flooding is projected to increase with climate change in many parts of the world. Forecasting floods from snowmelt remains a challenge due to scarce and quality issues in basin-scale snow observations and lack of knowledge for cold region hydrological processes. This study developed a model for estimating basin-level snow mass (snow water equivalent SWE) and river flows using the total water storage (TWS) observations from the Gravity Recovery and Climate Experiment (GRACE) satellite mission. The SWE estimation is based on mass balance approach which is independent of in situ snow gauge observations, thus largely eliminates the limitations and uncertainties with traditional in situ or remote sensing snow estimates. The model forecasts river flows by simulating surface runoff from snowmelt and the corresponding baseflow from groundwater discharge. Snowmelt is predicted using a temperature index model. Baseflow is predicted using a modified linear reservoir model. The model also quantifies the hysteresis between the snowmelt and the streamflow rates, or the lump time for water travel in the basin. The model was applied to the Red River Basin, the Mackenzie River Basin, and the Hudson Bay Lowland Basins in Canada. The predicted river flows were compared with the observed values at downstream hydrometric stations. The results were also compared to that for the Lower Fraser River obtained in a separate study to help better understand the roles of environmental factors in determining flood and their variations with different hydroclimatic conditions. This study advances the applications of space-based time-variable gravity measurements in cold region snow mass estimation, river flow and flood forecasting. It demonstrates a relatively simple method that only needs GRACE TWS and temperature data for river flow or flood forecasting. The model can be particularly useful for regions with spare observation networks, and can be used in combination with other available methods to help improve the accuracy in river flow and flood forecasting over cold regions.

Predicatbility of windstorm Klaus; sensitivity to PV perturbations

NASA Astrophysics Data System (ADS)

Arbogast, P.; Maynard, K.

2010-09-01

It appears that some short-range weather forecast failures may be attributed to initial conditions errors. In some cases it is possible to anticipate the behavior of the model by comparison between observations and model analyses. In the case of extratropical cyclone development one may qualify the representation of the upper-level precursors described in terms of PV in the initial conditions by comparison with either satellite ozone or water-vapor. A step forward has been made in developing a tool based upon manual modifications of dynamical tropopause (i.e. height of 1.5 PV units) and PV inversion. After five years of experimentations it turns out that the forecasters eventually succeed in improving the forecast of some strong cyclone development. However the present approach is subjective per se. To measure the subjectivity of the procedure a set of 15 experiments has been performed provided by 7 different people (senior forecasters and scientists involved in dynamical meteorology) in order to improve an initial state of the global model ARPEGE leading to a poor forecast of the wind storm Klaus (24 January 2009). This experiment reveals that the manually defined corrections present common features but also a large spread.

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.

Diabatic forcing and intialization with assimilation of cloud water and rainwater in a forecast model

NASA Technical Reports Server (NTRS)

Raymond, William H.; Olson, William S.; Callan, Geary

1995-01-01

In this study, diabatic forcing, and liquid water assimilation techniques are tested in a semi-implicit hydrostatic regional forecast model containing explicit representations of grid-scale cloud water and rainwater. Diabatic forcing, in conjunction with diabatic contributions in the initialization, is found to help the forecast retain the diabatic signal found in the liquid water or heating rate data, consequently reducing the spinup time associated with grid-scale precipitation processes. Both observational Special Sensor Microwave/Imager (SSM/I) and model-generated data are used. A physical retrieval method incorporating SSM/I radiance data is utilized to estimate the 3D distribution of precipitating storms. In the retrieval method the relationship between precipitation distributions and upwelling microwave radiances is parameterized, based upon cloud ensemble-radiative model simulations. Regression formulae relating vertically integrated liquid and ice-phase precipitation amounts to latent heating rates are also derived from the cloud ensemble simulations. Thus, retrieved SSM/I precipitation structures can be used in conjunction with the regression-formulas to infer the 3D distribution of latent heating rates. These heating rates are used directly in the forecast model to help initiate Tropical Storm Emily (21 September 1987). The 14-h forecast of Emily's development yields atmospheric precipitation water contents that compare favorably with coincident SSM/I estimates.

Forecasting and prevention of water inrush during the excavation process of a diversion tunnel at the Jinping II Hydropower Station, China.

PubMed

Hou, Tian-Xing; Yang, Xing-Guo; Xing, Hui-Ge; Huang, Kang-Xin; Zhou, Jia-Wen

2016-01-01

Estimating groundwater inflow into a tunnel before and during the excavation process is an important task to ensure the safety and schedule during the underground construction process. Here we report a case of the forecasting and prevention of water inrush at the Jinping II Hydropower Station diversion tunnel groups during the excavation process. The diversion tunnel groups are located in mountains and valleys, and with high water pressure head. Three forecasting methods are used to predict the total water inflow of the #2 diversion tunnel. Furthermore, based on the accurate estimation of the water inrush around the tunnel working area, a theoretical method is presented to forecast the water inflow at the working area during the excavation process. The simulated results show that the total water flow is 1586.9, 1309.4 and 2070.2 m(3)/h using the Qshima method, Kostyakov method and Ochiai method, respectively. The Qshima method is the best one because it most closely matches the monitoring result. According to the huge water inflow into the #2 diversion tunnel, reasonable drainage measures are arranged to prevent the potential disaster of water inrush. The groundwater pressure head can be determined using the water flow velocity from the advancing holes; then, the groundwater pressure head can be used to predict the possible water inflow. The simulated results show that the groundwater pressure head and water inflow re stable and relatively small around the region of the intact rock mass, but there is a sudden change around the fault region with a large water inflow and groundwater pressure head. Different countermeasures are adopted to prevent water inrush disasters during the tunnel excavation process. Reasonable forecasting the characteristic parameters of water inrush is very useful for the formation of prevention and mitigation schemes during the tunnel excavation process.

Electricity forecasting on the individual household level enhanced based on activity patterns

PubMed Central

Gajowniczek, Krzysztof; Ząbkowski, Tomasz

2017-01-01

Leveraging smart metering solutions to support energy efficiency on the individual household level poses novel research challenges in monitoring usage and providing accurate load forecasting. Forecasting electricity usage is an especially important component that can provide intelligence to smart meters. In this paper, we propose an enhanced approach for load forecasting at the household level. The impacts of residents’ daily activities and appliance usages on the power consumption of the entire household are incorporated to improve the accuracy of the forecasting model. The contributions of this paper are threefold: (1) we addressed short-term electricity load forecasting for 24 hours ahead, not on the aggregate but on the individual household level, which fits into the Residential Power Load Forecasting (RPLF) methods; (2) for the forecasting, we utilized a household specific dataset of behaviors that influence power consumption, which was derived using segmentation and sequence mining algorithms; and (3) an extensive load forecasting study using different forecasting algorithms enhanced by the household activity patterns was undertaken. PMID:28423039

Electricity forecasting on the individual household level enhanced based on activity patterns.

PubMed

Gajowniczek, Krzysztof; Ząbkowski, Tomasz

2017-01-01

Leveraging smart metering solutions to support energy efficiency on the individual household level poses novel research challenges in monitoring usage and providing accurate load forecasting. Forecasting electricity usage is an especially important component that can provide intelligence to smart meters. In this paper, we propose an enhanced approach for load forecasting at the household level. The impacts of residents' daily activities and appliance usages on the power consumption of the entire household are incorporated to improve the accuracy of the forecasting model. The contributions of this paper are threefold: (1) we addressed short-term electricity load forecasting for 24 hours ahead, not on the aggregate but on the individual household level, which fits into the Residential Power Load Forecasting (RPLF) methods; (2) for the forecasting, we utilized a household specific dataset of behaviors that influence power consumption, which was derived using segmentation and sequence mining algorithms; and (3) an extensive load forecasting study using different forecasting algorithms enhanced by the household activity patterns was undertaken.

Predictability of horizontal water vapor transport relative to precipitation: Enhancing situational awareness for forecasting western U.S. extreme precipitation and flooding

USGS Publications Warehouse

Lavers, David A.; Waliser, Duane E.; Ralph, F. Martin; Dettinger, Michael

2016-01-01

The western United States is vulnerable to socioeconomic disruption due to extreme winter precipitation and floods. Traditionally, forecasts of precipitation and river discharge provide the basis for preparations. Herein we show that earlier event awareness may be possible through use of horizontal water vapor transport (integrated vapor transport (IVT)) forecasts. Applying the potential predictability concept to the National Centers for Environmental Prediction global ensemble reforecasts, across 31 winters, IVT is found to be more predictable than precipitation. IVT ensemble forecasts with the smallest spreads (least forecast uncertainty) are associated with initiation states with anomalously high geopotential heights south of Alaska, a setup conducive for anticyclonic conditions and weak IVT into the western United States. IVT ensemble forecasts with the greatest spreads (most forecast uncertainty) have initiation states with anomalously low geopotential heights south of Alaska and correspond to atmospheric rivers. The greater IVT predictability could provide warnings of impending storminess with additional lead times for hydrometeorological applications.

Quantifying Uncertainty in Flood Inundation Mapping Using Streamflow Ensembles and Multiple Hydraulic Modeling Techniques

NASA Astrophysics Data System (ADS)

Hosseiny, S. M. H.; Zarzar, C.; Gomez, M.; Siddique, R.; Smith, V.; Mejia, A.; Demir, I.

2016-12-01

The National Water Model (NWM) provides a platform for operationalize nationwide flood inundation forecasting and mapping. The ability to model flood inundation on a national scale will provide invaluable information to decision makers and local emergency officials. Often, forecast products use deterministic model output to provide a visual representation of a single inundation scenario, which is subject to uncertainty from various sources. While this provides a straightforward representation of the potential inundation, the inherent uncertainty associated with the model output should be considered to optimize this tool for decision making support. The goal of this study is to produce ensembles of future flood inundation conditions (i.e. extent, depth, and velocity) to spatially quantify and visually assess uncertainties associated with the predicted flood inundation maps. The setting for this study is located in a highly urbanized watershed along the Darby Creek in Pennsylvania. A forecasting framework coupling the NWM with multiple hydraulic models was developed to produce a suite ensembles of future flood inundation predictions. Time lagged ensembles from the NWM short range forecasts were used to account for uncertainty associated with the hydrologic forecasts. The forecasts from the NWM were input to iRIC and HEC-RAS two-dimensional software packages, from which water extent, depth, and flow velocity were output. Quantifying the agreement between output ensembles for each forecast grid provided the uncertainty metrics for predicted flood water inundation extent, depth, and flow velocity. For visualization, a series of flood maps that display flood extent, water depth, and flow velocity along with the underlying uncertainty associated with each of the forecasted variables were produced. The results from this study demonstrate the potential to incorporate and visualize model uncertainties in flood inundation maps in order to identify the high flood risk zones.

A prototype system for forecasting landslides in the Seattle, Washington, area

USGS Publications Warehouse

Chleborad, Alan F.; Baum, Rex L.; Godt, Jonathan W.; Powers, Philip S.

2008-01-01

Empirical rainfall thresholds and related information form the basis of a prototype system for forecasting landslides in the Seattle area. The forecasts are tied to four alert levels, and a decision tree guides the use of thresholds to determine the appropriate level. From analysis of historical landslide data, we developed a formula for a cumulative rainfall threshold (CT), P3  =  88.9 − 0.67P15, defined by rainfall amounts in millimeters during consecutive 3 d (72 h) periods, P3, and the 15 d (360 h) period before P3, P15. The variable CT captures more than 90% of historical events of three or more landslides in 1 d and 3 d periods recorded from 1978 to 2003. However, the low probability of landslide occurrence on a day when the CT is exceeded at one or more rain gauges (8.4%) justifies a low-level of alert for possible landslide occurrence, but it does trigger more vigilant monitoring of rainfall and soil wetness. Exceedance of a rainfall intensity-duration threshold I  =  82.73D−1.13, for intensity, I (mm/hr), and duration, D (hr), corresponds to a higher probability of landslide occurrence (30%) and forms the basis for issuing warnings of impending, widespread occurrence of landslides. Information about the area of exceedance and soil wetness can be used to increase the certainty of landslide forecasts (probabilities as great as 71%). Automated analysis of real-time rainfall and subsurface water data and digital quantitative precipitation forecasts are needed to fully implement a warning system based on the two thresholds.

New watershed-based climate forecast products for hydrologists and water managers

NASA Astrophysics Data System (ADS)

Baker, S. A.; Wood, A.; Rajagopalan, B.; Lehner, F.; Peng, P.; Ray, A. J.; Barsugli, J. J.; Werner, K.

2017-12-01

Operational sub-seasonal to seasonal (S2S) climate predictions have advanced in skill in recent years but are yet to be broadly utilized by stakeholders in the water management sector. While some of the challenges that relate to fundamental predictability are difficult or impossible to surmount, other hurdles related to forecast product formulation, translation, relevance, and accessibility can be directly addressed. These include products being misaligned with users' space-time needs, products disseminated in formats users cannot easily process, and products based on raw model outputs that are biased relative to user climatologies. In each of these areas, more can be done to bridge the gap by enhancing the usability, quality, and relevance of water-oriented predictions. In addition, water stakeholder impacts can benefit from short-range extremes predictions (such as 2-3 day storms or 1-week heat waves) at S2S time-scales, for which few products exist. We present interim results of a Research to Operations (R2O) effort sponsored by the NOAA MAPP Climate Testbed to (1) formulate climate prediction products so as to reduce hurdles to in water stakeholder adoption, and to (2) explore opportunities for extremes prediction at S2S time scales. The project is currently using CFSv2 and National Multi-­Model Ensemble (NMME) reforecasts and forecasts to develop real-time watershed-based climate forecast products, and to train post-processing approaches to enhance the skill and reliability of raw real-time S2S forecasts. Prototype S2S climate data products (forecasts and associated skill analyses) are now being operationally staged at NCAR on a public website to facilitate further product development through interactions with water managers. Initial demonstration products include CFSv2-based bi-weekly climate forecasts (weeks 1-2, 2-3, and 3-4) for sub-regional scale hydrologic units, and NMME-based monthly and seasonal prediction products. Raw model mean skill at these time-space resolutions for some periods (e.g., weeks 3-4) is unusably low, but for other periods, and for multi-month leads with NMME, precipitation and particularly temperature forecasts exhibit useful skill. Website: http://hydro.rap.ucar.edu/s2s/

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.

33 CFR 208.26 - Altus Dam and Reservoir, North Fork Red River, Okla.

Code of Federal Regulations, 2010 CFR

2010-07-01

... elevation forecast indicates that this operation will result in a reservoir level exceeding elevation 1562... and reservoir from major damage. (j) Any time that the Bureau of Reclamation determines that operation... 33 Navigation and Navigable Waters 3 2010-07-01 2010-07-01 false Altus Dam and Reservoir, North...

33 CFR 208.26 - Altus Dam and Reservoir, North Fork Red River, Okla.

Code of Federal Regulations, 2011 CFR

2011-07-01

... elevation forecast indicates that this operation will result in a reservoir level exceeding elevation 1562... and reservoir from major damage. (j) Any time that the Bureau of Reclamation determines that operation... 33 Navigation and Navigable Waters 3 2011-07-01 2011-07-01 false Altus Dam and Reservoir, North...

Workshop on Cyclostationary Signals

DTIC Science & Technology

1993-06-30

Thompstone (1987) "Combining hydrologic forecasts," Journal of Water Resources Planning and Management, Vol. 113, pp. 29-41, January 1987. Miamee, A. G...was simulated and added at a level of about 7-8 dB below that of the recorded signal. The carrer frequency of the BPSK signal was 44 kHz and its symbol

FP7 GLOWASIS - A new collaborative project aimed at pre-validation of a GMES Global Water Scarcity Information Service

NASA Astrophysics Data System (ADS)

Westerhoff, R.; Levizzani, V.; Pappenberger, F.; de Roo, A.; Lange, R. D.; Wagner, W.; Bierkens, M. F.; Ceran, M.; Weerts, A.; Sinclair, S.; Miguez-Macho, G.; Langius, E.; Glowasis Team

2011-12-01

The main objective of the project GLOWASIS is to pre-validate a GMES Global Service for Water Scarcity Information. It will be set up as a one-stop-shop portal for water scarcity information, in which focus is put on: - monitoring data from satellites and in-situ sensors; - improving forecasting models with improved monitoring data; - linking statistical water data in forecasting; - promotion of GMES Services and European satellites. In European and global pilots on the scale of river catchments it combines hydrological models with in-situ and satellite derived water cycle information, as well as government ruled statistical water demand data. By linking water demand and supply in three pilot studies with existing platforms (European Drought Observatory and PCR-GLOBWB) for medium- and long-term forecasting in Europe, Africa and worldwide, GLOWASIS' information contributes both in near-real time reporting for emerging drought events as well as in provision of climate change time series. By combining complex water cycle variables, governmental issues and economic relations with respect to water demand, GLOWASIS will aim for the needed streamlining of the wide variety of important water scarcity information. More awareness for the complexity of the water scarcity problem will be created and additional capabilities of satellite-measured water cycle parameters can be promoted. The service uses data from GMES Core Services LMCS Geoland2 and Marine Core Service MyOcean (land use, soil moisture, soil sealing, sea level), in-situ data from GEWEX' initiatives (i.e. International Soil Moisture network), agricultural and industrial water use and demand (statistical - AQUASTAT, SEEAW and modelled) and additional water-cycle information from existing global satellite services. In-depth interviews with a.o. EEA and the Australian Bureau of Meteorology are taking place. GLOWASIS will aim for an open source and open-standard information portal on water scarcity and use of modern media (forums, Twitter, etc). Infrastructure of the GLOWASIS portal is set up for dissemination and inclusion of current and future innovative and integrated multi-purpose products for research & operational applications with open standards. The project has started in January 2011 and the duration is 24 months.

Convective Weather Forecast Accuracy Analysis at Center and Sector Levels

NASA Technical Reports Server (NTRS)

Wang, Yao; Sridhar, Banavar

2010-01-01

This paper presents a detailed convective forecast accuracy analysis at center and sector levels. The study is aimed to provide more meaningful forecast verification measures to aviation community, as well as to obtain useful information leading to the improvements in the weather translation capacity models. In general, the vast majority of forecast verification efforts over past decades have been on the calculation of traditional standard verification measure scores over forecast and observation data analyses onto grids. These verification measures based on the binary classification have been applied in quality assurance of weather forecast products at the national level for many years. Our research focuses on the forecast at the center and sector levels. We calculate the standard forecast verification measure scores for en-route air traffic centers and sectors first, followed by conducting the forecast validation analysis and related verification measures for weather intensities and locations at centers and sectors levels. An approach to improve the prediction of sector weather coverage by multiple sector forecasts is then developed. The weather severe intensity assessment was carried out by using the correlations between forecast and actual weather observation airspace coverage. The weather forecast accuracy on horizontal location was assessed by examining the forecast errors. The improvement in prediction of weather coverage was determined by the correlation between actual sector weather coverage and prediction. observed and forecasted Convective Weather Avoidance Model (CWAM) data collected from June to September in 2007. CWAM zero-minute forecast data with aircraft avoidance probability of 60% and 80% are used as the actual weather observation. All forecast measurements are based on 30-minute, 60- minute, 90-minute, and 120-minute forecasts with the same avoidance probabilities. The forecast accuracy analysis for times under one-hour showed that the errors in intensity and location for center forecast are relatively low. For example, 1-hour forecast intensity and horizontal location errors for ZDC center were about 0.12 and 0.13. However, the correlation between sector 1-hour forecast and actual weather coverage was weak, for sector ZDC32, about 32% of the total variation of observation weather intensity was unexplained by forecast; the sector horizontal location error was about 0.10. The paper also introduces an approach to estimate the sector three-dimensional actual weather coverage by using multiple sector forecasts, which turned out to produce better predictions. Using Multiple Linear Regression (MLR) model for this approach, the correlations between actual observation and the multiple sector forecast model prediction improved by several percents at 95% confidence level in comparison with single sector forecast.

Development, Implementation, and Skill Assessment of the NOAA/NOS Great Lakes Operational Forecast System

DTIC Science & Technology

2011-01-01

USA) 2011 Abstract The NOAA Great Lakes Operational Forecast System ( GLOFS ) uses near-real-time atmospheric observa- tions and numerical weather...Operational Oceanographic Products and Services (CO-OPS) in Silver Spring, MD. GLOFS has been making operational nowcasts and forecasts at CO-OPS... GLOFS ) uses near-real-time atmospheric observations and numerical weather prediction forecast guidance to produce three-dimensional forecasts of water

Flood-inundation maps for the Saluda River from Old Easley Bridge Road to Saluda Lake Dam near Greenville, South Carolina

USGS Publications Warehouse

Benedict, Stephen T.; Caldwell, Andral W.; Clark, Jimmy M.

2013-01-01

Digital flood-inundation maps for a 3.95-mile reach of the Saluda River from approximately 815 feet downstream from Old Easley Bridge Road to approximately 150 feet downstream from Saluda Lake Dam near Greenville, South Carolina, were developed by the U.S. Geological Survey (USGS). The inundation maps, which can be accessed through the USGS Flood Inundation Mapping Science Web site at http://water.usgs.gov/osw/flood_inundation/, depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at the USGS streamgage at Saluda River near Greenville, South Carolina (station 02162500). Current conditions at the USGS streamgage may be obtained through the National Water Information System Web site at http://waterdata.usgs.gov/sc/nwis/uv/?site_no=02162500&PARAmeter_cd=00065,00060,00062. The National Weather Service (NWS) forecasts flood hydrographs at many places that are often collocated with USGS streamgages. Forecasted peak-stage information is available on the Internet at the NWS Advanced Hydrologic Prediction Service (AHPS) flood-warning system Web site (http://water.weather.gov/ahps/) and may be used in conjunction with the maps developed in this study to show predicted areas of flood inundation.In this study, flood profiles were computed for the stream reach by means of a one-dimensional step-backwater model. The model was calibrated using the most current stage-streamflow relations at USGS streamgage station 02162500, Saluda River near Greenville, South Carolina. The hydraulic model was then used to determine water-surface profiles for flood stages at 1.0-foot intervals referenced to the streamgage datum and ranging from approximately bankfull to 2 feet higher than the highest recorded water level at the streamgage. The simulated water-surface profiles were then exported to a geographic information system, ArcGIS, and combined with a digital elevation model (derived from Light Detection and Ranging [LiDAR] data with a 0.6-foot vertical Root Mean Square Error [RMSE] and a 3.0-foot horizontal RMSE), using HEC-GeoRAS tools in order to delineate the area flooded at each water level. The availability of these maps, along with real-time stage data from the USGS streamgage station 02162500 and forecasted stream stages from the NWS, can provide emergency management personnel and residents with information that is critical during flood-response and flood-recovery activities, such as evacuations, road closures, and disaster declarations.

An expanded model: flood-inundation maps for the Leaf River at Hattiesburg, Mississippi, 2013

USGS Publications Warehouse

Storm, John B.

2014-01-01

Digital flood-inundation maps for a 6.8-mile reach of the Leaf River at Hattiesburg, Mississippi (Miss.), were created by the U.S. Geological Survey (USGS) in cooperation with the City of Hattiesburg, City of Petal, Forrest County, Mississippi Emergency Management Agency, Mississippi Department of Homeland Security, and the Emergency Management District. The inundation maps, which can be accessed through the USGS Flood Inundation Mapping Science Web site at http://water.usgs.gov/osw/flood_inundation/, depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at the USGS streamgage at Leaf River at Hattiesburg, Miss. (station no. 02473000). Current conditions for estimating near-real-time areas of inundation by use of USGS streamgage information may be obtained on the Internet at http://waterdata.usgs.gov/. In addition, the information has been provided to the National Weather Service (NWS) for incorporation into their Advanced Hydrologic Prediction Service (AHPS) flood warning system (http://water.weather.gov/ahps/). The NWS forecasts flood hydrographs at many places that are often colocated with USGS streamgages. NWS-forecasted peak-stage information may be used in conjunction with the maps developed in this study to show predicted areas of flood inundation. In this study, flood profiles were computed for the stream reach by means of a one-dimensional step-backwater model. The model was calibrated by using the most current stage-discharge relations at the Leaf River at Hattiesburg, Miss. streamgage (02473000) and documented high-water marks from recent and historical floods. The hydraulic model was then used to determine 13 water-surface profiles for flood stages at 1.0-foot intervals referenced to the streamgage datum and ranging from bankfull to approximately the highest recorded water level at the streamgage. The simulated water-surface profiles were then combined with a geographic information system (GIS) digital elevation model (DEM, derived from light detection and ranging (lidar) data having a 0.6-foot vertical and 9.84-foot horizontal resolution) in order to delineate the area flooded at each water level. Development of the estimated flood inundation maps as described in this report update previously published inundation estimates by including reaches of the Bouie and Leaf Rivers above their confluence. The availability of these maps along with Internet information regarding current stage from USGS streamgages and forecasted stream stages from the NWS provide emergency management personnel and residents with information that is critical for flood response activities such as evacuations and road closures as well as for post flood recovery efforts.

Interpretation of snowcover from satellite imagery for use in water supply forecasts in the Sierra Nevada

NASA Technical Reports Server (NTRS)

Brown, A. J.; Hannaford, J. F.

1975-01-01

The California ASVT test area is composed of two study areas; one in Northern California covering the Upper Sacramento and Feather River Basins, and the other covering the Southern Sierra Basins of the San Joaquin, Kings, Kaweah, Tule, and Kern Rivers. Experiences of reducing snowcover from satellite imagery; the accuracy of present water supply forecast schemes; and the potential advantages of introducing snowcover into the forecast procedures are described.

Forecasting the Water Demand in Chongqing, China Using a Grey Prediction Model and Recommendations for the Sustainable Development of Urban Water Consumption

PubMed Central

Wu, Hua’an; Zhou, Meng

2017-01-01

High accuracy in water demand predictions is an important basis for the rational allocation of city water resources and forms the basis for sustainable urban development. The shortage of water resources in Chongqing, the youngest central municipality in Southwest China, has significantly increased with the population growth and rapid economic development. In this paper, a new grey water-forecasting model (GWFM) was built based on the data characteristics of water consumption. The parameter estimation and error checking methods of the GWFM model were investigated. Then, the GWFM model was employed to simulate the water demands of Chongqing from 2009 to 2015 and forecast it in 2016. The simulation and prediction errors of the GWFM model was checked, and the results show the GWFM model exhibits better simulation and prediction precisions than those of the classical Grey Model with one variable and single order equation GM(1,1) for short and the frequently-used Discrete Grey Model with one variable and single order equation, DGM(1,1) for short. Finally, the water demand in Chongqing from 2017 to 2022 was forecasted, and some corresponding control measures and recommendations were provided based on the prediction results to ensure a viable water supply and promote the sustainable development of the Chongqing economy. PMID:29140266

Bias Correction for Assimilation of Retrieved AIRS Profiles of Temperature and Humidity

NASA Technical Reports Server (NTRS)

Blankenship, Clay; Zavodsky, Brad; Blackwell, William

2014-01-01

Atmospheric Infrared Sounder (AIRS) is a hyperspectral radiometer aboard NASA's Aqua satellite designed to measure atmospheric profiles of temperature and humidity. AIRS retrievals are assimilated into the Weather Research and Forecasting (WRF) model over the North Pacific for some cases involving "atmospheric rivers". These events bring a large flux of water vapor to the west coast of North America and often lead to extreme precipitation in the coastal mountain ranges. An advantage of assimilating retrievals rather than radiances is that information in partly cloudy fields of view can be used. Two different Level 2 AIRS retrieval products are compared: the Version 6 AIRS Science Team standard retrievals and a neural net retrieval from MIT. Before assimilation, a bias correction is applied to adjust each layer of retrieved temperature and humidity so the layer mean values agree with a short-term model climatology. WRF runs assimilating each of the products are compared against each other and against a control run with no assimilation. This paper will describe the bias correction technique and results from forecasts evaluated by validation against a Total Precipitable Water (TPW) product from CIRA and against Global Forecast System (GFS) analyses.

Visualizing Coastal Erosion, Overwash and Coastal Flooding in New England

NASA Astrophysics Data System (ADS)

Young Morse, R.; Shyka, T.

2017-12-01

Powerful East Coast storms and their associated storm tides and large, battering waves can lead to severe coastal change through erosion and re-deposition of beach sediment. The United States Geological Survey (USGS) has modeled such potential for geological response using a storm-impact scale that compares predicted elevations of hurricane-induced water levels and associated wave action to known elevations of coastal topography. The resulting storm surge and wave run-up hindcasts calculate dynamic surf zone collisions with dune structures using discrete regime categories of; "collision" (dune erosion), "overwash" and "inundation". The National Weather Service (NWS) recently began prototyping this empirical technique under the auspices of the North Atlantic Regional Team (NART). Real-time erosion and inundation forecasts were expanded to include both tropical and extra-tropical cyclones along vulnerable beaches (hotspots) on the New England coast. Preliminary results showed successful predictions of impact during hurricane Sandy and several intense Nor'easters. The forecasts were verified using observational datasets, including "ground truth" reports from Emergency Managers and storm-based, dune profile measurements organized through a Maine Sea Grant partnership. In an effort to produce real-time visualizations of this forecast output, the Northeastern Regional Association of Coastal Ocean Observing Systems (NERACOOS) and the Gulf of Maine Research Institute (GMRI) partnered with NART to create graphical products of wave run-up levels for each New England "hotspot". The resulting prototype system updates the forecasts twice daily and allows users the ability to adjust atmospheric and sea state input into the calculations to account for model errors and forecast uncertainty. This talk will provide an overview of the empirical wave run-up calculations, the system used to produce forecast output and a demonstration of the new web based tool.

[Real-time irrigation forecast of cotton mulched with plastic film under drip irrigation based on meteorological date].

PubMed

Shen, Xiao-jun; Sun, Jing-sheng; Li, Ming-si; Zhang, Ji-yang; Wang, Jing-lei; Li, Dong-wei

2015-02-01

It is important to improve the real-time irrigation forecasting precision by predicting real-time water consumption of cotton mulched with plastic film under drip irrigation based on meteorological data and cotton growth status. The model parameters for calculating ET0 based on Hargreaves formula were determined using historical meteorological data from 1953 to 2008 in Shihezi reclamation area. According to the field experimental data of growing season in 2009-2010, the model of computing crop coefficient Kc was established based on accumulated temperature. On the basis of crop water requirement (ET0) and Kc, a real-time irrigation forecast model was finally constructed, and it was verified by the field experimental data in 2011. The results showed that the forecast model had high forecasting precision, and the average absolute values of relative error between the predicted value and measured value were about 3.7%, 2.4% and 1.6% during seedling, squaring and blossom-boll forming stages, respectively. The forecast model could be used to modify the predicted values in time according to the real-time meteorological data and to guide the water management in local film-mulched cotton field under drip irrigation.

Flood-inundation maps for a nine-mile reach of the Des Plaines River from Riverwoods to Mettawa, Illinois

USGS Publications Warehouse

Murphy, Elizabeth A.; Soong, David T.; Sharpe, Jennifer B.

2012-01-01

Digital flood-inundation maps for a 9-mile reach of the Des Plaines River from Riverwoods to Mettawa, Illinois, were created by the U.S. Geological Survey (USGS) in cooperation with the Lake County Stormwater Management Commission and the Villages of Lincolnshire and Riverwoods. The inundation maps, which can be accessed through the USGS Flood Inundation Mapping Science Web site at http://water.usgs.gov/osw/flood_inundation/, depict estimates of the areal extent of flooding corresponding to selected water levels (gage heights) at the USGS streamgage at Des Plaines River at Lincolnshire, Illinois (station no. 05528100). Current conditions at the USGS streamgage may be obtained on the Internet at http://waterdata.usgs.gov/usa/nwis/uv?05528100. In addition, this streamgage is incorporated into the Advanced Hydrologic Prediction Service (AHPS) flood warning system (http://water.weather.gov/ahps/) by the National Weather Service (NWS). The NWS forecasts flood hydrographs at many places that are often co-located at USGS streamgages. The NWS forecasted peak-stage information, also shown on the Des Plaines River at Lincolnshire inundation Web site, may be used in conjunction with the maps developed in this study to show predicted areas of flood inundation. In this study, flood profiles were computed for the stream reach by means of a one-dimensional step-backwater model. The hydraulic model was then used to determine seven water-surface profiles for flood stages at roughly 1-ft intervals referenced to the streamgage datum and ranging from the 50- to 0.2-percent annual exceedance probability flows. The simulated water-surface profiles were then combined with a Geographic Information System (GIS) Digital Elevation Model (DEM) (derived from Light Detection And Ranging (LiDAR) data) in order to delineate the area flooded at each water level. These maps, along with information on the Internet regarding current gage height from USGS streamgages and forecasted stream stages from the NWS, provide emergency management personnel and residents with information that is critical for flood response activities such as evacuations and road closures, as well as for post-flood recovery efforts.

Flood-inundation maps for the DuPage River from Plainfield to Shorewood, Illinois, 2013

USGS Publications Warehouse

Murphy, Elizabeth A.; Sharpe, Jennifer B.

2013-01-01

Digital flood-inundation maps for a 15.5-mi reach of the DuPage River from Plainfield to Shorewood, Illinois, were created by the U.S. Geological Survey (USGS) in cooperation with the Will County Stormwater Management Planning Committee. The inundation maps, which can be accessed through the USGS Flood Inundation Mapping Science Web site at http://water.usgs.gov/osw/flood_inundation/ depict estimates of the areal extent of flooding corresponding to selected water levels (gage heights or stages) at the USGS streamgage at DuPage River at Shorewood, Illinois (sta. no. 05540500). Current conditions at the USGS streamgage may be obtained on the Internet at http://waterdata.usgs.gov/usa/nwis/uv?05540500. In addition, the information has been provided to the National Weather Service (NWS) for incorporation into their Advanced Hydrologic Prediction Service (AHPS) flood warning system (http://water.weather.gov/ahps/). The NWS forecasts flood hydrographs at many places that are often colocated with USGS streamgages. The NWS-forecasted peak-stage information, also shown on the DuPage River at Shorewood inundation Web site, may be used in conjunction with the maps developed in this study to show predicted areas of flood inundation. In this study, flood profiles were computed for the stream reach by means of a one-dimensional step-backwater model. The hydraulic model was then used to determine nine water-surface profiles for flood stages at 1-ft intervals referenced to the streamgage datum and ranging from NWS Action stage of 6 ft to the historic crest of 14.0 ft. The simulated water-surface profiles were then combined with a Digital Elevation Model (DEM) (derived from Light Detection And Ranging (LiDAR) data) by using a Geographic Information System (GIS) in order to delineate the area flooded at each water level. These maps, along with information on the Internet regarding current gage height from USGS streamgages and forecasted stream stages from the NWS, provide emergency management personnel and residents with information that is critical for flood response activities such as evacuations and road closures, as well as for postflood recovery efforts.

Improving a stage forecasting Muskingum model by relating local stage and remote discharge

NASA Astrophysics Data System (ADS)

Barbetta, S.; Moramarco, T.; Melone, F.; Brocca, L.

2009-04-01

Following the parsimonious concept of parameters, simplified models for flood forecasting based only on flood routing have been developed for flood-prone sites located downstream of a gauged station and at a distance allowing an appropriate forecasting lead-time. In this context, the Muskingum model can be a useful tool. However, critical points in hydrological routing are the representation of lateral inflows contribution and the knowledge of stage-discharge relationships. As regards the former, O'Donnell (O'Donnell, T., 1985. A direct three-parameter Muskingum procedure incorporating lateral inflow, Hydrol. Sci. J., 30[4/12], 479-496) proposed a three-parameter Muskingum procedure assuming the lateral inflows proportional to the contribution entering upstream. Using this approach, Franchini and Lamberti (Franchini, M. & Lamberti, P., 1994. A flood routing Muskingum type simulation and forecasting model based on level data alone, Water Resour. Res., 30[7], 2183-2196) presented a simple model Muskingum type to provide forecast water levels at the downstream end by selecting a routing time interval and, hence, a forecasting lead-time allowing to express the forecast stage as a function of only observed quantities. Moramarco et al. (Moramarco, T., Barbetta, S., Melone, F. & Singh, V.P., 2006. A real-time stage Muskingum forecasting model for a site without rating curve, Hydrol. Sci. J., 51[1], 66-82) enhanced the modeling scheme incorporating a procedure for adapting the parameter linked to lateral inflows. This last model, called STAFOM (STAge FOrecasting Model), was also extended to a two connected river branches schematization in order to improve significantly the forecasting lead-time. The STAFOM model provided satisfactory results for most of the analysed flood events observed in different river reaches in the Upper-Middle Tiber River basin in Central Italy. However, the analysis highlighted that the stage forecast should be enhanced when sudden modifications occur in the upstream and downstream hydrographs recorded in real-time. Moramarco et al. (Moramarco, T., Barbetta, S., F. Melone, F. & Singh, V.P., 2005. Relating local stage and remote discharge with significant lateral inflow, J. Hydrol. Engng ASCE, 10[1], 58-69) showed that for any flood condition at ends of a river reach, a direct proportionality between the upstream and downstream mean velocity can be found. This insight was the basis for developing the Rating Curve Model (RCM) that allows to also accommodate significant lateral inflow contributions, permitting, without using a flood routing procedure and without the need of a rating curve at a local site, to relate the local hydraulic conditions with those at a remote gauged section. Therefore, to improve the STAFOM performance mainly for highly varying flood conditions, the model has been here modified by coupling it with a procedure based on the RCM approach. Several flood events occurred along different equipped river reaches of the Upper Tiber River basin have been used as case study. Results showed that the new model, named STAFOM-RCM, apart from to improve the stage forecast accuracy in terms of error on peak stage, Nash-Sutcliffe efficiency coefficient and the coefficient of persistence, allowed to use a larger lead time thus avoiding the two-river branches cascade schematization where fluctuations in stage forecasting occur more frequently.

Decision Support Model for Optimal Management of Coastal Gate

NASA Astrophysics Data System (ADS)

Ditthakit, Pakorn; Chittaladakorn, Suwatana

2010-05-01

The coastal areas are intensely settled by human beings owing to their fertility of natural resources. However, at present those areas are facing with water scarcity problems: inadequate water and poor water quality as a result of saltwater intrusion and inappropriate land-use management. To solve these problems, several measures have been exploited. The coastal gate construction is a structural measure widely performed in several countries. This manner requires the plan for suitably operating coastal gates. Coastal gate operation is a complicated task and usually concerns with the management of multiple purposes, which are generally conflicted one another. This paper delineates the methodology and used theories for developing decision support modeling for coastal gate operation scheduling. The developed model was based on coupling simulation and optimization model. The weighting optimization technique based on Differential Evolution (DE) was selected herein for solving multiple objective problems. The hydrodynamic and water quality models were repeatedly invoked during searching the optimal gate operations. In addition, two forecasting models:- Auto Regressive model (AR model) and Harmonic Analysis model (HA model) were applied for forecasting water levels and tide levels, respectively. To demonstrate the applicability of the developed model, it was applied to plan the operations for hypothetical system of Pak Phanang coastal gate system, located in Nakhon Si Thammarat province, southern part of Thailand. It was found that the proposed model could satisfyingly assist decision-makers for operating coastal gates under various environmental, ecological and hydraulic conditions.

The Early-Warning System for incoming storm surge and tide in the Republic of Mauritius

NASA Astrophysics Data System (ADS)

Bogaard, Tom; de Lima Rego, Joao; Vatvani, Deepak; Virasami, Renganaden; Verlaan, Martin

2016-04-01

The Republic of Mauritius (ROM) is a group of islands in the South West of the Indian Ocean, consisting of the main islands of Mauritius, Rodrigues and Agalega and the archipelago of Saint Brandon. The ROM is particularly vulnerable to the adverse effects of climate change, especially in the coastal zone, where a convergence of accelerating sea level rise and increasing intensity of tropical cyclones is expected to result in considerable economic loss, humanitarian stresses, and environmental degradation. Storm surges and swell waves are expected to be aggravated through sea level rise and climate change effects on weather patterns. Adaptation to increased vulnerability requires a re-evaluation of existing preparedness measures. The focus of this project is on more effective preparedness and issuing of alerts developing a fully-automated Early-Warning System for incoming storm surge and tide, together with the Mauritius Meteorological Services and the National Disaster Risk Reduction and Management Centre (NDRRMC), such that coastal communities in Mauritius, Rodrigues and Agalega Islands are able to evacuate timely and safely in case of predicted extreme water levels. The Mauritius Early-Warning System for storm surge and tide was implemented using software from Deltares' Open-Source and free software Community. A set of five depth-averaged Delft3D-FLOW hydrodynamic models are run every six-hours with a forecast horizon of three days, simulating water levels along the coast of the three main islands. Two regional models of horizontal resolution 5km force the three detailed models of 500m resolution; all models are forced at the surface by the 0.25° NOAA/GFS meteorological forecasts. In addition, our Wind-Enhancement Scheme is used to blend detailed cyclone track bulletin's info with the larger-scale Numerical Weather Predictions. Measured data is retrieved near real-time from available Automatic Weather Stations. All these workflows are managed by the operational platform software, Delft-FEWS. The presently operational Mauritius Early-Warning System produces a set of intuitive tables for each island, containing time- and space-varying information on threshold crossings by predicted water levels. At multiple locations for each island of the ROM, the operator is informed in one glance about the recommended preparedness level, from "Safe" to "Watch", "Alert" or "Warning" based on water level forecasts. The HTML page was designed together with the MMS and the NDRRMC, in order to be easy to interpret and disseminate by local authorities.

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.

Ecological Forecasting of Vibrio sp. in U.S. Coastal Waters Using an Operational Platform, a Pilot Project of the NOAA Ecological Forecasting Roadmap. Development of Web based Tools and Forecasts to Help the Public Avoid Exposure to Vibrio vulnificus and Shell Fish Harvesters Avoid Dangerous Concentrations of Vibrio parahaemolyticus.

NASA Astrophysics Data System (ADS)

Daniels, R. M.; Jacobs, J. M.; Paranjpye, R.; Lanerolle, L. W.

2016-02-01

The Pathogens group of the NOAA Ecological Forecasting Roadmap has begun a range of efforts to monitor and predict potential pathogen occurrences in shellfish and in U.S. Coastal waters. NOAA/NCOSS along with NMFS/NWFSC have led the Pathogens group and the development of web based tools and forecasts for both Vibrio vulnificus and Vibrio parahaemolyticus. A strong relationship with FDA has allowed the team to develop forecasts that will serve U.S. shellfish harvesters and consumers. NOAA/NOS/CSDL has provided modeling expertise to help the group use the hydrodynamic models and their forecasts of physical variables that drive the ecological predictions. The NOAA/NWS/Ocean Prediction Center has enabled these ecological forecasting efforts by providing the infrastructure, computing knowledge and experience in an operational culture. Daily forecasts have been demonstrated and are available from the web for the Chesapeake Bay, Delaware Bay, Northern Gulf of Mexico, Tampa Bay, Puget Sound and Long Island Sound. The forecast systems run on a daily basis being fed by NOS model data from the NWS/NCEP super computers. New forecast tools including V. parahaemolyticus post harvest growth and doubling time in ambient air temperature will be described.

Combining Envisat type and CryoSat-2 altimetry to inform hydrodynamic models

NASA Astrophysics Data System (ADS)

Schneider, Raphael; Nygaard Godiksen, Peter; Villadsen, Heidi; Madsen, Henrik; Bauer-Gottwein, Peter

2015-04-01

Hydrological models are developed and used for flood forecasting and water resources management. Such models rely on a variety of input and calibration data. In general, and especially in data scarce areas, remote sensing provides valuable data for the parameterization and updating of such models. Satellite radar altimeters provide water level measurements of inland water bodies. So far, many studies making use of satellite altimeters have been based on data from repeat-orbit missions such as Envisat, ERS or Jason or on synthetic wide-swath altimetry data as expected from the SWOT mission. This work represents one of the first hydrologic applications of altimetry data from a drifting orbit satellite mission, using data from CryoSat-2. We present an application where CryoSat-2 data is used to improve a hydrodynamic model of the Ganges and Brahmaputra river basins in South Asia set up in the DHI MIKE 11 software. The model's parameterization and forcing is mainly based on remote sensing data, for example the TRMM 3B42 precipitation product and the SRTM DEM for river and subcatchment delineation. CryoSat-2 water levels were extracted over a river mask derived from Landsat 7 and 8 imagery. After calibrating the hydrological-hydrodynamic model against observed discharge, simulated water levels were fitted to the CryoSat-2 data, with a focus on the Brahmaputra river in the Assam valley: The average simulated water level in the hydrodynamic model was fitted to the average water level along the river's course as observed by CryoSat-2 over the years 2011-2013 by adjusting the river bed elevation. In a second step, the cross section shapes were adjusted so that the simulated water level dynamics matched those obtained from Envisat virtual station time series. The discharge calibration resulted in Nash-Sutcliffe coefficients of 0.86 and 0.94 for the Ganges and Brahmaputra. Using the Landsat river mask, the CryoSat-2 water levels show consistency along the river and are in good accordance with other products, such as the SRTM DEM. The adjusted hydrodynamic model reproduced the average water level profile along the river channel with a higher accuracy than a model based on the SRTM DEM. Furthermore, the amplitudes as observed in Envisat virtual station time series could be reproduced fitting simple triangular cross section shapes. A hydrodynamic model prepared in such a way provides water levels at any point along the river and any point in time, which are consistent with the multi-mission altimetric dataset. This means it can for example be updated by assimilation of near real-time water level measurements from CryoSat-2 improving its flood forecasting capability.

Flood monitoring for ungauged rivers: the power of combining space-based monitoring and global forecasting models

NASA Astrophysics Data System (ADS)

Revilla-Romero, Beatriz; Netgeka, Victor; Raynaud, Damien; Thielen, Jutta

2013-04-01

Flood warning systems typically rely on forecasts from national meteorological services and in-situ observations from hydrological gauging stations. This capacity is not equally developed in flood-prone developing countries. Low-cost satellite monitoring systems and global flood forecasting systems can be an alternative source of information for national flood authorities. The Global Flood Awareness System (GloFAS) has been develop jointly with the European Centre for Medium-Range Weather Forecast (ECMWF) and the Joint Research Centre, and it is running quasi operational now since June 2011. The system couples state-of-the art weather forecasts with a hydrological model driven at a continental scale. The system provides downstream countries with information on upstream river conditions as well as continental and global overviews. In its test phase, this global forecast system provides probabilities for large transnational river flooding at the global scale up to 30 days in advance. It has shown its real-life potential for the first time during the flood in Southeast Asia in 2011, and more recently during the floods in Australia in March 2012, India (Assam, September-October 2012) and Chad Floods (August-October 2012).The Joint Research Centre is working on further research and development, rigorous testing and adaptations of the system to create an operational tool for decision makers, including national and regional water authorities, water resource managers, hydropower companies, civil protection and first line responders, and international humanitarian aid organizations. Currently efforts are being made to link GloFAS to the Global Flood Detection System (GFDS). GFDS is a Space-based river gauging and flood monitoring system using passive microwave remote sensing which was developed by a collaboration between the JRC and Dartmouth Flood Observatory. GFDS provides flood alerts based on daily water surface change measurements from space. Alerts are shown on a world map, with detailed reports for individual gauging sites. A comparison of discharge estimates from the Global Flood Detection System (GFDS) and the Global Flood Awareness System (GloFAS) with observations for representative climatic zones is presented. Both systems have demonstrated strong potential in forecasting and detecting recent catastrophic floods. The usefulness of their combined information on global scale for decision makers at different levels is discussed. Combining space-based monitoring and global forecasting models is an innovative approach and has significant benefits for international river commissions as well as international aid organisations. This is in line with the objectives of the Hyogo and the Post-2015 Framework that aim at the development of systems which involve trans-boundary collaboration, space-based earth observation, flood forecasting and early warning.

Forecasting Hourly Water Demands With Seasonal Autoregressive Models for Real-Time Application

NASA Astrophysics Data System (ADS)

Chen, Jinduan; Boccelli, Dominic L.

2018-02-01

Consumer water demands are not typically measured at temporal or spatial scales adequate to support real-time decision making, and recent approaches for estimating unobserved demands using observed hydraulic measurements are generally not capable of forecasting demands and uncertainty information. While time series modeling has shown promise for representing total system demands, these models have generally not been evaluated at spatial scales appropriate for representative real-time modeling. This study investigates the use of a double-seasonal time series model to capture daily and weekly autocorrelations to both total system demands and regional aggregated demands at a scale that would capture demand variability across a distribution system. Emphasis was placed on the ability to forecast demands and quantify uncertainties with results compared to traditional time series pattern-based demand models as well as nonseasonal and single-seasonal time series models. Additional research included the implementation of an adaptive-parameter estimation scheme to update the time series model when unobserved changes occurred in the system. For two case studies, results showed that (1) for the smaller-scale aggregated water demands, the log-transformed time series model resulted in improved forecasts, (2) the double-seasonal model outperformed other models in terms of forecasting errors, and (3) the adaptive adjustment of parameters during forecasting improved the accuracy of the generated prediction intervals. These results illustrate the capabilities of time series modeling to forecast both water demands and uncertainty estimates at spatial scales commensurate for real-time modeling applications and provide a foundation for developing a real-time integrated demand-hydraulic model.

Flood-inundation maps for the Suncook River in Epsom, Pembroke, Allenstown, and Chichester, New Hampshire

USGS Publications Warehouse

Flynn, Robert H.; Johnston, Craig M.; Hays, Laura

2012-01-01

Digital flood-inundation maps for a 16.5-mile reach of the Suncook River in Epsom, Pembroke, Allenstown, and Chichester, N.H., from the confluence with the Merrimack River to U.S. Geological Survey (USGS) Suncook River streamgage 01089500 at Depot Road in North Chichester, N.H., were created by the USGS in cooperation with the New Hampshire Department of Homeland Security and Emergency Management. The inundation maps presented in this report depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at the USGS streamgage at Suncook River at North Chichester, N.H. (station 01089500). The current conditions at the USGS streamgage may be obtained on the Internet (http://waterdata.usgs.gov/nh/nwis/uv/?site_no=01089500&PARAmeter_cd=00065,00060). The National Weather Service forecasts flood hydrographs at many places that are often collocated with USGS streamgages. Forecasted peak-stage information is available on the Internet at the National Weather Service (NWS) Advanced Hydrologic Prediction Service (AHPS) flood-warning system site (http://water.weather.gov/ahps/) and may be used in conjunction with the maps developed in this study to show predicted areas of flood inundation. These maps along with real-time stream stage data from the USGS Suncook River streamgage (station 01089500) and forecasted stream stage from the NWS will provide emergency management personnel and residents with information that is critical for flood-response activities, such as evacuations, road closures, disaster declarations, and post-flood recovery. The maps, along with current stream-stage data from the USGS Suncook River streamgage and forecasted stream-stage data from the NWS, can be accessed at the USGS Flood Inundation Mapping Science Web site http://water.usgs.gov/osw/flood_inundation/.

Forecasting the effects of land-use and climate change on wildlife communities and habitats in the lower Mississippi Valley

USGS Publications Warehouse

Faulkner, Stephen P.

2010-01-01

Landscape patterns and processes reflect both natural ecosystem attributes and the policy and management decisions of individual Federal, State, county, and private organizations. Land-use regulation, water management, and habitat conservation and restoration efforts increasingly rely on landscape-level approaches that incorporate scientific information into the decision-making process. Since management actions are implemented to affect future conditions, decision-support models are necessary to forecast potential future conditions resulting from these decisions. Spatially explicit modeling approaches enable testing of different scenarios and help evaluate potential outcomes of management actions in conjunction with natural processes such as climate change. The ability to forecast the effects of changing land use and climate is critically important to land and resource managers since their work is inherently site specific, yet conservation strategies and practices are expressed at higher spatial and temporal scales that must be considered in the decisionmaking process.

Natural and human drivers of salinity in reservoirs and their implications in water supply operation through a Decision Support System

NASA Astrophysics Data System (ADS)

Contreras, Eva; Gómez-Beas, Raquel; Linares-Sáez, Antonio

2016-04-01

Salt can be a problem when is originally in aquifers or when it dissolves in groundwater and comes to the ground surface or flows into streams. The problem increases in lakes hydraulically connected with aquifers affecting water quality. This issue is even more alarming when water resources are used for urban and irrigation supply and water quantity and quality restrict that water demand. This work shows a data based and physical modeling approach in the Guadalhorce reservoir, located in southern Spain. This water body receives salt contribution from mainly groundwater flow, getting salinity values in the reservoir from 3500 to 5500 μScm-1. Moreover, Guadalhorce reservoir is part of a complex system of reservoirs fed from the Guadalhorce River that supplies all urban, irrigation, tourism, energy and ecology water uses, which makes that implementation and validation of methods and tools for smart water management is required. Meteorological, hydrological and water quality data from several monitoring networks and data sources, with both historical and real time data during a 40-years period, were used to analyze the impact salinity. On the other hand, variables that mainly depend on the dam operation, such as reservoir water level and water outflow, were also analyzed to understand how they affect to salinity in depth and time. Finally surface and groundwater inflows to the reservoir were evaluated through a physically based hydrological model to forecast when the major contributions take place. Reservoir water level and surface and groundwater inflows were found to be the main drivers of salinity in the reservoir. When reservoir water level is high, daily water inflow around 0.4 hm3 causes changes in salinity (both drop and rise) up to 500 μScm-1, but no significant changes are found when water level falls 2-3 m. However the gradual water outflows due to dam operation and consequent decrease in reservoir water levels makes that, after dry periods, salinity changes from 3800 to 5100 μScm-1 in the deepest layers are found with a similar daily water inflow. On the other hand, when reservoir water level is low, salinity increases around 1000 μScm-1 are found with a 2 m water level falling. In view of the influence of water level in the reservoir dynamics, this factor should be considered when dam operation decisions are taken by managers in terms of satisfying the water demand. The results will be implemented in a Decision Support System that is being displayed in the Guadalhorce River and which includes prediction of water quantity and quality in the reservoir in terms of salinity, involving water level and water inflow forecasting as the main factors to control the state of the reservoir and therefore with implications in water management. This methodology could be implemented in other reservoirs with high salinity and be adapted to other substances (such as nutrients and heavy metals) associated to water inflow in water bodies where water quality and quantity are driven by human decisions factors besides natural factors such as floods and dynamics of flows in the reservoir.

Integrated Water Vapour Retrieval From Irish GPS Network: Results From Validation With Radiosondes And Microwave Profiler And Assimilation Into HIRLAM 7.2 Operational Forecasting Model

NASA Astrophysics Data System (ADS)

Hanafin, J. A.; Whelan, E.; McGrath, R.; Jennings, S. G.; O'Dowd, C.

2009-12-01

Retrieval of atmospheric integrated water vapour (IWV) from ground-based GPS receivers and provision of this data product for meteorological applications is the focus of the European EUMETNET GPS water vapour programme. The results presented here are the first from a project to provide such information about the state of the atmosphere around Ireland for climate monitoring and improved numerical weather prediction. Two geodetic reference GPS receivers have been deployed at Valentia Observatory in Co. Kerry and Mace Head Atmospheric Research Station in Co. Galway, Ireland. A system to retrieve column-integrated atmospheric water vapour from the data they provide has been developed. Data quality has been assessed using co-located radiosondes at Valentia and observations from a microwave profiling radiometer at Mace Head. Results from the data processing and comparisons with independent observations will be presented. Water vapour retrievals from such sensors can provide good quality observations at hourly intervals of this essential climate variable for assimilation into numerical nowcast and forecast systems. Previous studies have shown that using these data to constrain initial model conditions can improve the accuracy of precipitation forecasts, particularly for heavy rainfall. The current operational forecast model in use at Met Éireann for the region is the new version 7.2 HIRLAM (High-Resolution Limited Area Model). The effects on the forecast for Ireland have been evaluated by assimilating the data into 48-hour forecast runs of this model and results of this study will also be presented.

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.

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.

SONARC: A Sea Ice Monitoring and Forecasting System to Support Safe Operations and Navigation in Arctic Seas

NASA Astrophysics Data System (ADS)

Stephenson, S. R.; Babiker, M.; Sandven, S.; Muckenhuber, S.; Korosov, A.; Bobylev, L.; Vesman, A.; Mushta, A.; Demchev, D.; Volkov, V.; Smirnov, K.; Hamre, T.

2015-12-01

Sea ice monitoring and forecasting systems are important tools for minimizing accident risk and environmental impacts of Arctic maritime operations. Satellite data such as synthetic aperture radar (SAR), combined with atmosphere-ice-ocean forecasting models, navigation models and automatic identification system (AIS) transponder data from ships are essential components of such systems. Here we present first results from the SONARC project (project term: 2015-2017), an international multidisciplinary effort to develop novel and complementary ice monitoring and forecasting systems for vessels and offshore platforms in the Arctic. Automated classification methods (Zakhvatkina et al., 2012) are applied to Sentinel-1 dual-polarization SAR images from the Barents and Kara Sea region to identify ice types (e.g. multi-year ice, level first-year ice, deformed first-year ice, new/young ice, open water) and ridges. Short-term (1-3 days) ice drift forecasts are computed from SAR images using feature tracking and pattern tracking methods (Berg & Eriksson, 2014). Ice classification and drift forecast products are combined with ship positions based on AIS data from a selected period of 3-4 weeks to determine optimal vessel speed and routing in ice. Results illustrate the potential of high-resolution SAR data for near-real-time monitoring and forecasting of Arctic ice conditions. Over the next 3 years, SONARC findings will contribute new knowledge about sea ice in the Arctic while promoting safe and cost-effective shipping, domain awareness, resource management, and environmental protection.

Applications of remote sensing to water resources

NASA Technical Reports Server (NTRS)

1977-01-01

Analyses were made of selected long-term (1985 and beyond) objectives, with the intent of determining if significant data-related problems would be encountered and to develop alternative solutions to any potential problems. One long-term objective selected for analysis was Water Availability Forecasting. A brief overview was scheduled in FY-77 of the objective -- primarily a fact-finding study to allow Data Management personnel to gain adequate background information to perform subsequent data system analyses. This report, includes discussions on some of the larger problems currently encountered in water measurement, the potential users of water availability forecasts, projected demands of users, current sensing accuracies, required parameter monitoring, status of forecasting modeling, and some measurement accuracies likely to be achievable by 1980 and 1990.

A framework for improving a seasonal hydrological forecasting system using sensitivity analysis

NASA Astrophysics Data System (ADS)

Arnal, Louise; Pappenberger, Florian; Smith, Paul; Cloke, Hannah

2017-04-01

Seasonal streamflow forecasts are of great value for the socio-economic sector, for applications such as navigation, flood and drought mitigation and reservoir management for hydropower generation and water allocation to agriculture and drinking water. However, as we speak, the performance of dynamical seasonal hydrological forecasting systems (systems based on running seasonal meteorological forecasts through a hydrological model to produce seasonal hydrological forecasts) is still limited in space and time. In this context, the ESP (Ensemble Streamflow Prediction) remains an attractive forecasting method for seasonal streamflow forecasting as it relies on forcing a hydrological model (starting from the latest observed or simulated initial hydrological conditions) with historical meteorological observations. This makes it cheaper to run than a standard dynamical seasonal hydrological forecasting system, for which the seasonal meteorological forecasts will first have to be produced, while still producing skilful forecasts. There is thus the need to focus resources and time towards improvements in dynamical seasonal hydrological forecasting systems which will eventually lead to significant improvements in the skill of the streamflow forecasts generated. Sensitivity analyses are a powerful tool that can be used to disentangle the relative contributions of the two main sources of errors in seasonal streamflow forecasts, namely the initial hydrological conditions (IHC; e.g., soil moisture, snow cover, initial streamflow, among others) and the meteorological forcing (MF; i.e., seasonal meteorological forecasts of precipitation and temperature, input to the hydrological model). Sensitivity analyses are however most useful if they inform and change current operational practices. To this end, we propose a method to improve the design of a seasonal hydrological forecasting system. This method is based on sensitivity analyses, informing the forecasters as to which element of the forecasting chain (i.e., IHC or MF) could potentially lead to the highest increase in seasonal hydrological forecasting performance, after each forecast update.

Operational forecasting with the subgrid technique on the Elbe Estuary

NASA Astrophysics Data System (ADS)

Sehili, Aissa

2017-04-01

Modern remote sensing technologies can deliver very detailed land surface height data that should be considered for more accurate simulations. In that case, and even if some compromise is made with regard to grid resolution of an unstructured grid, simulations still will require large grids which can be computationally very demanding. The subgrid technique, first published by Casulli (2009), is based on the idea of making use of the available detailed subgrid bathymetric information while performing computations on relatively coarse grids permitting large time steps. Consequently, accuracy and efficiency are drastically enhanced if compared to the classical linear method, where the underlying bathymetry is solely discretized by the computational grid. The algorithm guarantees rigorous mass conservation and nonnegative water depths for any time step size. Computational grid-cells are permitted to be wet, partially wet or dry and no drying threshold is needed. The subgrid technique is used in an operational forecast model for water level, current velocity, salinity and temperature of the Elbe estuary in Germany. Comparison is performed with the comparatively highly resolved classical unstructured grid model UnTRIM. The daily meteorological forcing data are delivered by the German Weather Service (DWD) using the ICON-EU model. Open boundary data are delivered by the coastal model BSHcmod of the German Federal Maritime and Hydrographic Agency (BSH). Comparison of predicted water levels between classical and subgrid model shows a very good agreement. The speedup in computational performance due to the use of the subgrid technique is about a factor of 20. A typical daily forecast can be carried out within less than 10 minutes on standard PC-like hardware. The model is capable of permanently delivering highly resolved temporal and spatial information on water level, current velocity, salinity and temperature for the whole estuary. The model offers also the possibility to recalculate any previous situation. This can be helpful to figure out for instance the context in which a certain event occurred like an accident. In addition to measurement, the model can be used to improve navigability by adjusting the tidal transit-schedule for container vessels that are depending on the tide to approach or leave the port of Hamburg.

A temporal-spatial postprocessing model for probabilistic run-off forecast. With a case study from Ulla-Førre with five catchments and ten lead times

NASA Astrophysics Data System (ADS)

Engeland, K.; Steinsland, I.

2012-04-01

This work is driven by the needs of next generation short term optimization methodology for hydro power production. Stochastic optimization are about to be introduced; i.e. optimizing when available resources (water) and utility (prices) are uncertain. In this paper we focus on the available resources, i.e. water, where uncertainty mainly comes from uncertainty in future runoff. When optimizing a water system all catchments and several lead times have to be considered simultaneously. Depending on the system of hydropower reservoirs, it might be a set of headwater catchments, a system of upstream /downstream reservoirs where water used from one catchment /dam arrives in a lower catchment maybe days later, or a combination of both. The aim of this paper is therefore to construct a simultaneous probabilistic forecast for several catchments and lead times, i.e. to provide a predictive distribution for the forecasts. Stochastic optimization methods need samples/ensembles of run-off forecasts as input. Hence, it should also be possible to sample from our probabilistic forecast. A post-processing approach is taken, and an error model based on Box- Cox transformation, power transform and a temporal-spatial copula model is used. It accounts for both between catchment and between lead time dependencies. In operational use it is strait forward to sample run-off ensembles from this models that inherits the catchment and lead time dependencies. The methodology is tested and demonstrated in the Ulla-Førre river system, and simultaneous probabilistic forecasts for five catchments and ten lead times are constructed. The methodology has enough flexibility to model operationally important features in this case study such as hetroscadasety, lead-time varying temporal dependency and lead-time varying inter-catchment dependency. Our model is evaluated using CRPS for marginal predictive distributions and energy score for joint predictive distribution. It is tested against deterministic run-off forecast, climatology forecast and a persistent forecast, and is found to be the better probabilistic forecast for lead time grater then two. From an operational point of view the results are interesting as the between catchment dependency gets stronger with longer lead-times.

The implementation of reverse Kessler warm rain scheme for radar reflectivity assimilation using a nudging approach in New Zealand

NASA Astrophysics Data System (ADS)

Zhang, Sijin; Austin, Geoff; Sutherland-Stacey, Luke

2014-05-01

Reverse Kessler warm rain processes were implemented within the Weather Research and Forecasting Model (WRF) and coupled with a Newtonian relaxation, or nudging technique designed to improve quantitative precipitation forecasting (QPF) in New Zealand by making use of observed radar reflectivity and modest computing facilities. One of the reasons for developing such a scheme, rather than using 4D-Var for example, is that radar VAR scheme in general, and 4D-Var in particular, requires computational resources beyond the capability of most university groups and indeed some national forecasting centres of small countries like New Zealand. The new scheme adjusts the model water vapor mixing ratio profiles based on observed reflectivity at each time step within an assimilation time window. The whole scheme can be divided into following steps: (i) The radar reflectivity is firstly converted to rain water, and (ii) then the rain water is used to derive cloud water content according to the reverse Kessler scheme; (iii) The cloud water content associated water vapor mixing ratio is then calculated based on the saturation adjustment processes; (iv) Finally the adjusted water vapor is nudged into the model and the model background is updated. 13 rainfall cases which occurred in the summer of 2011/2012 in New Zealand were used to evaluate the new scheme, different forecast scores were calculated and showed that the new scheme was able to improve precipitation forecasts on average up to around 7 hours ahead depending on different verification thresholds.

Statistical forecast of seasonal discharge in Central Asia using observational records: development of a generic linear modelling tool for operational water resource management

NASA Astrophysics Data System (ADS)

Apel, Heiko; Abdykerimova, Zharkinay; Agalhanova, Marina; Baimaganbetov, Azamat; Gavrilenko, Nadejda; Gerlitz, Lars; Kalashnikova, Olga; Unger-Shayesteh, Katy; Vorogushyn, Sergiy; Gafurov, Abror

2018-04-01

The semi-arid regions of Central Asia crucially depend on the water resources supplied by the mountainous areas of the Tien Shan and Pamir and Altai mountains. During the summer months the snow-melt- and glacier-melt-dominated river discharge originating in the mountains provides the main water resource available for agricultural production, but also for storage in reservoirs for energy generation during the winter months. Thus a reliable seasonal forecast of the water resources is crucial for sustainable management and planning of water resources. In fact, seasonal forecasts are mandatory tasks of all national hydro-meteorological services in the region. In order to support the operational seasonal forecast procedures of hydro-meteorological services, this study aims to develop a generic tool for deriving statistical forecast models of seasonal river discharge based solely on observational records. The generic model structure is kept as simple as possible in order to be driven by meteorological and hydrological data readily available at the hydro-meteorological services, and to be applicable for all catchments in the region. As snow melt dominates summer runoff, the main meteorological predictors for the forecast models are monthly values of winter precipitation and temperature, satellite-based snow cover data, and antecedent discharge. This basic predictor set was further extended by multi-monthly means of the individual predictors, as well as composites of the predictors. Forecast models are derived based on these predictors as linear combinations of up to four predictors. A user-selectable number of the best models is extracted automatically by the developed model fitting algorithm, which includes a test for robustness by a leave-one-out cross-validation. Based on the cross-validation the predictive uncertainty was quantified for every prediction model. Forecasts of the mean seasonal discharge of the period April to September are derived every month from January until June. The application of the model for several catchments in Central Asia - ranging from small to the largest rivers (240 to 290 000 km2 catchment area) - for the period 2000-2015 provided skilful forecasts for most catchments already in January, with adjusted R2 values of the best model in the range of 0.6-0.8 for most of the catchments. The skill of the prediction increased every following month, i.e. with reduced lead time, with adjusted R2 values usually in the range 0.8-0.9 for the best and 0.7-0.8 on average for the set of models in April just before the prediction period. The later forecasts in May and June improve further due to the high predictive power of the discharge in the first 2 months of the snow melt period. The improved skill of the set of forecast models with decreasing lead time resulted in narrow predictive uncertainty bands at the beginning of the snow melt period. In summary, the proposed generic automatic forecast model development tool provides robust predictions for seasonal water availability in Central Asia, which will be tested against the official forecasts in the upcoming years, with the vision of operational implementation.

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.

Value of Adaptive Drought Forecasting and Management for the ACF River Basin in the Southeast U.S.

NASA Astrophysics Data System (ADS)

Georgakakos, A. P.; Kistenmacher, M.

2016-12-01

In recent times, severe droughts in the southeast U.S. occur every 6 to 10 years and last for up to 4 years. During such drought episodes, the ACF River Basin supplies decline by up to 50 % of their normal levels, and water stresses increase rather markedly, exacerbating stakeholder anxiety and conflicts. As part of the ACF Stakeholder planning process, GWRI has developed new tools and carried out comprehensive assessments to provide quantitative answers to several important questions related to drought prediction and management: (i) Can dry and wet climatic periods be reliably anticipated with sufficiently long lead times? What drought indices can support reliable, skillful, and long-lead forecasts? (ii) What management objectives can seasonal climate forecasts benefit? How should benefits/impacts be shared? (iii) What operational adjustments are likely to mitigate stakeholder impacts or increase benefits consistent with stakeholder expectations? Regarding drought prediction, a large number of indices were defined and tested at different basin locations and lag times. These included local/cumulative unimpaired flows (UIFs) at 10 river nodes; Mean Areal Precipitation (MAP); Standard Precipitation Index (SPI); Palmer Drought Severity Index; Palmer Modified Drought Index; Palmer Z-Index; Palmer Hydrologic Drought Severity Index; and Soil Moisture—GWRI watershed model. Our findings show that all ACF sub-basins exhibit good forecast skill throughout the year and with sufficient lead time. Index variables with high explanatory value include: previous UIFs, soil moisture states (generated by the GWRI watershed model), and PDSI. Regarding drought management, assessments with coupled forecast-management schemes demonstrate that the use of adaptive forecast-management procedures improves reservoir operations and meets basin demands more reliably. Such improvements can support better management of lake levels, higher environmental and navigation flows, higher dependable power generation hours, and better management of consumptive uses without adverse impacts on other stakeholder interests. However, realizing these improvements requires (1) usage of adaptive reservoir management procedures (incorporating forecasts), and (2) stakeholder agreement on equitable benefit sharing.

A seasonal hydrologic ensemble prediction system for water resource management

NASA Astrophysics Data System (ADS)

Luo, L.; Wood, E. F.

2006-12-01

A seasonal hydrologic ensemble prediction system, developed for the Ohio River basin, has been improved and expanded to several other regions including the Eastern U.S., Africa and East Asia. The prediction system adopts the traditional Extended Streamflow Prediction (ESP) approach, utilizing the VIC (Variable Infiltration Capacity) hydrological model as the central tool for producing ensemble prediction of soil moisture, snow and streamflow with lead times up to 6-month. VIC is forced by observed meteorology to estimate the hydrological initial condition prior to the forecast, but during the forecast period the atmospheric forcing comes from statistically downscaled, seasonal forecast from dynamic climate models. The seasonal hydrologic ensemble prediction system is currently producing realtime seasonal hydrologic forecast for these regions on a monthly basis. Using hindcasts from a 19-year period (1981-1999), during which seasonal hindcasts from NCEP Climate Forecast System (CFS) and European Union DEMETER project are available, we evaluate the performance of the forecast system over our forecast regions. The evaluation shows that the prediction system using the current forecast approach is able to produce reliable and accurate precipitation, soil moisture and streamflow predictions. The overall skill is much higher then the traditional ESP. In particular, forecasts based on multiple climate model forecast are more skillful than single model-based forecast. This emphasizes the significant need for producing seasonal climate forecast with multiple climate models for hydrologic applications. Forecast from this system is expected to provide very valuable information about future hydrologic states and associated risks for end users, including water resource management and financial sectors.

Inductive reasoning and forecasting of population dynamics of Cylindrospermopsis raciborskii in three sub-tropical reservoirs by evolutionary computation.

PubMed

Recknagel, Friedrich; Orr, Philip T; Cao, Hongqing

2014-01-01

Seven-day-ahead forecasting models of Cylindrospermopsis raciborskii in three warm-monomictic and mesotrophic reservoirs in south-east Queensland have been developed by means of water quality data from 1999 to 2010 and the hybrid evolutionary algorithm HEA. Resulting models using all measured variables as inputs as well as models using electronically measurable variables only as inputs forecasted accurately timing of overgrowth of C. raciborskii and matched well high and low magnitudes of observed bloom events with 0.45≤r 2 >0.61 and 0.4≤r 2 >0.57, respectively. The models also revealed relationships and thresholds triggering bloom events that provide valuable information on synergism between water quality conditions and population dynamics of C. raciborskii. Best performing models based on using all measured variables as inputs indicated electrical conductivity (EC) within the range of 206-280mSm -1 as threshold above which fast growth and high abundances of C. raciborskii have been observed for the three lakes. Best models based on electronically measurable variables for the Lakes Wivenhoe and Somerset indicated a water temperature (WT) range of 25.5-32.7°C within which fast growth and high abundances of C. raciborskii can be expected. By contrast the model for Lake Samsonvale highlighted a turbidity (TURB) level of 4.8 NTU as indicator for mass developments of C. raciborskii. Experiments with online measured water quality data of the Lake Wivenhoe from 2007 to 2010 resulted in predictive models with 0.61≤r 2 >0.65 whereby again similar levels of EC and WT have been discovered as thresholds for outgrowth of C. raciborskii. The highest validity of r 2 =0.75 for an in situ data-based model has been achieved after considering time lags for EC by 7 days and dissolved oxygen by 1 day. These time lags have been discovered by a systematic screening of all possible combinations of time lags between 0 and 10 days for all electronically measurable variables. The so-developed model performs seven-day-ahead forecasts and is currently implemented and tested for early warning of C. raciborskii blooms in the Wivenhoe reservoir. Copyright © 2013 Elsevier B.V. All rights reserved.

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.

A water management decision support system contributing to sustainability

NASA Astrophysics Data System (ADS)

Horváth, Klaudia; van Esch, Bart; Baayen, Jorn; Pothof, Ivo; Talsma, Jan; van Heeringen, Klaas-Jan

2017-04-01

Deltares and Eindhoven University of Technology are developing a new decision support system (DSS) for regional water authorities. In order to maintain water levels in the Dutch polder system, water should be drained and pumped out from the polders to the sea. The time and amount of pumping depends on the current sea level, the water level in the polder, the weather forecast and the electricity price forecast and possibly local renewable power production. This is a multivariable optimisation problem, where the goal is to keep the water level in the polder within certain bounds. By optimizing the operation of the pumps the energy usage and costs can be reduced, hence the operation of the regional water authorities can be more sustainable, while also anticipating on increasing share of renewables in the energy mix in a cost-effective way. The decision support system, based on Delft-FEWS as operational data-integration platform, is running an optimization model built in RTC-Tools 2, which is performing real-time optimization in order to calculate the pumping strategy. It is taking into account the present and future circumstances. As being the core of the real time decision support system, RTC-Tools 2 fulfils the key requirements to a DSS: it is fast, robust and always finds the optimal solution. These properties are associated with convex optimization. In such problems the global optimum can always be found. The challenge in the development is to maintain the convex formulation of all the non-linear components in the system, i.e. open channels, hydraulic structures, and pumps. The system is introduced through 4 pilot projects, one of which is a pilot of the Dutch Water Authority Rivierenland. This is a typical Dutch polder system: several polders are drained to the main water system, the Linge. The water from the Linge can be released to the main rivers that are subject to tidal fluctuations. In case of low tide, water can be released via the gates. In case of high tide, water should be pumped. The goal of the pilot is to make the operation of the regional water authority more sustainable and cost-efficient. Sustainability can be achieved by minimizing the CO2 production trough minimizing the energy used for pumping. This work is showing the functionalities of the new decision support system, using RTC-Tools 2, through the example of a pilot project.

Using JPSS Retrievals to Implement a Multisensor, Synoptic, Layered Water Vapor Product for Forecasters

NASA Astrophysics Data System (ADS)

Forsythe, J. M.; Jones, A. S.; Kidder, S. Q.; Fuell, K.; LeRoy, A.; Bikos, D.; Szoke, E.

2015-12-01

Forecasters have been using the NOAA operational blended total precipitable water (TPW) product, developed by the Cooperative Institute for Research in the Atmosphere (CIRA), since 2009. Blended TPW has a wide variety of uses related to heavy precipitation and flooding, such as measuring the amount of moisture in an atmospheric river originating in the tropics. But blended TPW conveys no information on the vertical distribution of moisture, which is relevant to a variety of forecast concerns. Vertical profile information is particularly lacking over the oceans for landfalling storms. A blended six-satellite, four-layer, layered water vapor product demonstrated by CIRA and the NASA Short-term Prediction Research and Transition Center (SPoRT) in allows forecasters to see the vertical distribution of water vapor in near real-time. National Weather Service (NWS) forecaster feedback indicated that this new, vertically-resolved view of water vapor has a substantial impact on forecasts. This product uses NOAA investments in polar orbiting satellite sounding retrievals from passive microwave radiances, in particular, the Microwave Integrated Retrieval System (MIRS). The product currently utilizes data from the NOAA-18 and -19 spacecraft, Metop-A and -B, and the Defense Meteorological Program (DMSP) F18 spacecraft. The sounding instruments onboard the Suomi-NPP and JPSS spacecraft will be cornerstone instruments in the future evolution of this product. Applications of the product to heavy rain cases will be presented and compared to commonly used data such as radiosondes and Geostationary Operational Environmental Satellite (GOES) water vapor channel imagery. Research is currently beginning to implement advective blending, where model winds are used to move the water vapor profiles to a common time. Interactions with the NOAA Satellite Analysis Branch (SAB), National Center for Environmental Prediction (NCEP) centers including the Ocean Prediction Center (OPC) and Weather Prediction Center (WPC) will be discussed.

Development of Hydrometeorological Monitoring and Forecasting as AN Essential Component of the Early Flood Warning System:

NASA Astrophysics Data System (ADS)

Manukalo, V.

2012-12-01

Defining issue The river inundations are the most common and destructive natural hazards in Ukraine. Among non-structural flood management and protection measures a creation of the Early Flood Warning System is extremely important to be able to timely recognize dangerous situations in the flood-prone areas. Hydrometeorological information and forecasts are a core importance in this system. The primary factors affecting reliability and a lead - time of forecasts include: accuracy, speed and reliability with which real - time data are collected. The existing individual conception of monitoring and forecasting resulted in a need in reconsideration of the concept of integrated monitoring and forecasting approach - from "sensors to database and forecasters". Result presentation The Project: "Development of Flood Monitoring and Forecasting in the Ukrainian part of the Dniester River Basin" is presented. The project is developed by the Ukrainian Hydrometeorological Service in a conjunction with the Water Management Agency and the Energy Company "Ukrhydroenergo". The implementation of the Project is funded by the Ukrainian Government and the World Bank. The author is nominated as the responsible person for coordination of activity of organizations involved in the Project. The term of the Project implementation: 2012 - 2014. The principal objectives of the Project are: a) designing integrated automatic hydrometeorological measurement network (including using remote sensing technologies); b) hydrometeorological GIS database construction and coupling with electronic maps for flood risk assessment; c) interface-construction classic numerical database -GIS and with satellite images, and radar data collection; d) providing the real-time data dissemination from observation points to forecasting centers; e) developing hydrometeoroogical forecasting methods; f) providing a flood hazards risk assessment for different temporal and spatial scales; g) providing a dissemination of current information, forecasts and warnings to consumers automatically. Besides scientific and technical issues the implementation of these objectives requires solution of a number of organizational issues. Thus, as a result of the increased complexity of types of hydrometeorological data and in order to develop forecasting methods, a reconsideration of meteorological and hydrological measurement networks should be carried out. The "optimal density of measuring networks" is proposed taking into account principal terms: a) minimizing an uncertainty in characterizing the spacial distribution of hydrometeorological parameters; b) minimizing the Total Life Cycle Cost of creation and maintenance of measurement networks. Much attention will be given to training Ukrainian disaster management authorities from the Ministry of Emergencies and the Water Management Agency to identify the flood hazard risk level and to indicate the best protection measures on the basis of continuous monitoring and forecasts of evolution of meteorological and hydrological conditions in the river basin.

Application of snowcovered area to runoff forecasting in selected basins of the Sierra Nevada, California. [Kings, Kern and Kaweah River Basins

NASA Technical Reports Server (NTRS)

Brown, A. J.; Hannaford, J. F. (Principal Investigator)

1980-01-01

The author has identified the following significant results. Direct overlay onto 1:1,000,000 prints takes about one third the time of 1:500,000 zone transfer scope analysis using transparencies, but the consistency of the transparencies reduce the time for data analysis. LANDSAT data received on transparencies is better and more easily interpreted than the near real-time data from Quick Look, or imagery from other sources such as NOAA. The greatest potential for water supply forecasting is probably in improving forecast accuracy and in expanding forecast services during the period of snowmelt. Problems of transient snow line and uncertainties in future weather are the main reasons that snow cover area appears to offer little in water supply forecast accuracy improvement during the peroid snowpack accumulation.

Sub-seasonal Predictability of Heavy Precipitation Events: Implication for Real-time Flood Management in Iran

NASA Astrophysics Data System (ADS)

Najafi, H.; Shahbazi, A.; Zohrabi, N.; Robertson, A. W.; Mofidi, A.; Massah Bavani, A. R.

2016-12-01

Each year, a number of high impact weather events occur worldwide. Since any level of predictability at sub-seasonal to seasonal timescale is highly beneficial to society, international efforts is now on progress to promote reliable Ensemble Prediction Systems for monthly forecasts within the WWRP/WCRP initiative (S2S) project and North American Multi Model Ensemble (NMME). For water resources managers in the face of extreme events, not only can reliable forecasts of high impact weather events prevent catastrophic losses caused by floods but also contribute to benefits gained from hydropower generation and water markets. The aim of this paper is to analyze the predictability of recent severe weather events over Iran. Two recent heavy precipitations are considered as an illustration to examine whether S2S forecasts can be used for developing flood alert systems especially where large cascade of dams are in operation. Both events have caused major damages to cities and infrastructures. The first severe precipitation was is in the early November 2015 when heavy precipitation (more than 50 mm) occurred in 2 days. More recently, up to 300 mm of precipitation is observed within less than a week in April 2016 causing a consequent flash flood. Over some stations, the observed precipitation was even more than the total annual mean precipitation. To analyze the predictive capability, ensemble forecasts from several operational centers including (European Centre for Medium-Range Weather Forecasts (ECMWF) system, Climate Forecast System Version 2 (CFSv2) and Chinese Meteorological Center (CMA) are evaluated. It has been observed that significant changes in precipitation anomalies were likely to be predicted days in advance. The next step will be to conduct thorough analysis based on comparing multi-model outputs over the full hindcast dataset developing real-time high impact weather prediction systems.

Mesoscale Predictability and Error Growth in Short Range Ensemble Forecasts

NASA Astrophysics Data System (ADS)

Gingrich, Mark

Although it was originally suggested that small-scale, unresolved errors corrupt forecasts at all scales through an inverse error cascade, some authors have proposed that those mesoscale circulations resulting from stationary forcing on the larger scale may inherit the predictability of the large-scale motions. Further, the relative contributions of large- and small-scale uncertainties in producing error growth in the mesoscales remain largely unknown. Here, 100 member ensemble forecasts are initialized from an ensemble Kalman filter (EnKF) to simulate two winter storms impacting the East Coast of the United States in 2010. Four verification metrics are considered: the local snow water equivalence, total liquid water, and 850 hPa temperatures representing mesoscale features; and the sea level pressure field representing a synoptic feature. It is found that while the predictability of the mesoscale features can be tied to the synoptic forecast, significant uncertainty existed on the synoptic scale at lead times as short as 18 hours. Therefore, mesoscale details remained uncertain in both storms due to uncertainties at the large scale. Additionally, the ensemble perturbation kinetic energy did not show an appreciable upscale propagation of error for either case. Instead, the initial condition perturbations from the cycling EnKF were maximized at large scales and immediately amplified at all scales without requiring initial upscale propagation. This suggests that relatively small errors in the synoptic-scale initialization may have more importance in limiting predictability than errors in the unresolved, small-scale initial conditions.

DEVELOPMENT OF NEAR-SHORE HYDRODYNAMIC MODELS FOR BEACH CLOSURE FORECASTING IN THE GREAT LAKES

EPA Science Inventory

Water quality managers and other planning and decision entities are increasingly calling for up-to-the-minute data on present water quality conditions or forecasts of these data that can be used to adjust or respond to quickly developing activities with environmental implications...

Impact of single-point GPS integrated water vapor estimates on short-range WRF model forecasts over southern India

NASA Astrophysics Data System (ADS)

Kumar, Prashant; Gopalan, Kaushik; Shukla, Bipasha Paul; Shyam, Abhineet

2017-11-01

Specifying physically consistent and accurate initial conditions is one of the major challenges of numerical weather prediction (NWP) models. In this study, ground-based global positioning system (GPS) integrated water vapor (IWV) measurements available from the International Global Navigation Satellite Systems (GNSS) Service (IGS) station in Bangalore, India, are used to assess the impact of GPS data on NWP model forecasts over southern India. Two experiments are performed with and without assimilation of GPS-retrieved IWV observations during the Indian winter monsoon period (November-December, 2012) using a four-dimensional variational (4D-Var) data assimilation method. Assimilation of GPS data improved the model IWV analysis as well as the subsequent forecasts. There is a positive impact of ˜10 % over Bangalore and nearby regions. The Weather Research and Forecasting (WRF) model-predicted 24-h surface temperature forecasts have also improved when compared with observations. Small but significant improvements were found in the rainfall forecasts compared to control experiments.

Distribution and transport of water vapor in the UTLS over the Tibetan Plateau as inferred from the MLS satellite data and WRF model simulations

NASA Astrophysics Data System (ADS)

Jain, S.; Kar, S. C.

2016-12-01

Water vapor is an important minor constituent in the lower stratosphere as it influences the stratospheric chemistry and total radiation budget. The spatial distribution of water vapor mixing ratio (WVMR) obtained from Aura Microwave Limb Sounder (MLS) satellite at 100 hPa level shows prominent maxima over the Tibetan Plateau during August 2015. The Asian monsoon upper level anticyclone is also known to occur over this region during this period. The Indian Meteorological Department (IMD) and National Centre of Medium Range Weather Forecasting (NCMRWF) observed daily gridded rainfall data shows moderate to heavy rainfall over the Tibetan Plateau, suggesting active convection from 26 July to 10 August 2015. The atmospheric conditions are simulated over the Asian region for the 15-day period using the Weather Research Forecasting (WRF) model. The simulations are carried out using two nested domains with resolution of 12 km and 4 km. The initial and boundary conditions are taken from the NGFS (up-graded version of the NCEP GFS) data. The WRF WVMR profiles are observed to be comparatively moist than the MLS profiles in the UTLS region over the Tibetan Plateau. This may be due to the relatively higher temperatures (1-2 K) simulated in the WRF model near 100 hPa level. It is noted that the WRF model has a drying tendency at all the levels. The UTLS WVMR and temperatures show poor sensitivity to the convective schemes. The parent domain and the explicit convective scheme simulate almost same moisture over time in the inner domain. The cloud micro-physics is observed to play a rather important role in controlling the UTLS water vapor content. The WSM-6 convective scheme is observed to simulate the UTLS moisture comparatively well and therefore the processes associated with the formation of ice, snow and graupel formation may be of much more importance in controlling the UTLS WVMR in the WRF model. The 24 hr, 48 hr and 72 hr forecast averaged for the 15-day period shows that over the Tibetan Plateau, high WVMR in the UTLS is not centered within the anticyclone, contrary to what has been shown by earlier studies. Similar simulations are also being carried out using the Era-interim initial and boundary conditions to confirm the above findings.

Development of a Probabilistic Decision-Support Model to Forecast Coastal Resilience

NASA Astrophysics Data System (ADS)

Wilson, K.; Safak, I.; Brenner, O.; Lentz, E. E.; Hapke, C. J.

2016-02-01

Site-specific forecasts of coastal change are a valuable management tool in preparing for and assessing storm-driven impacts in coastal areas. More specifically, understanding the likelihood of storm impacts, recovery following events, and the alongshore variability of both is central in evaluating vulnerability and resiliency of barrier islands. We introduce a probabilistic modeling framework that integrates hydrodynamic, anthropogenic, and morphologic components of the barrier system to evaluate coastal change at Fire Island, New York. The model is structured on a Bayesian network (BN), which utilizes observations to learn statistical relationships between system variables. In addition to predictive ability, probabilistic models convey the level of confidence associated with a prediction, an important consideration for coastal managers. Our model predicts the likelihood of morphologic change on the upper beach based on several decades of beach monitoring data. A coupled hydrodynamic BN combines probabilistic and deterministic modeling approaches; by querying nearly two decades of nested-grid wave simulations that account for both distant swells and local seas, we produce scenarios of event and seasonal wave climates. The wave scenarios of total water level - a sum of run up, surge and tide - and anthropogenic modification are the primary drivers of morphologic change in our model structure. Preliminary results show the hydrodynamic BN is able to reproduce time series of total water levels, a critical validation process before generating scenarios, and forecasts of geomorphic change over three month intervals are up to 70% accurate. Predictions of storm-induced change and recovery are linked to evaluate zones of persistent vulnerability or resilience and will help managers target restoration efforts, identify areas most vulnerable to habitat degradation, and highlight resilient zones that may best support relocation of critical infrastructure.

Impact of Synoptic-Scale Factors on Rainfall Forecast in Different Stages of a Persistent Heavy Rainfall Event in South China

NASA Astrophysics Data System (ADS)

Zhang, Murong; Meng, Zhiyong

2018-04-01

This study investigates the stage-dependent rainfall forecast skills and the associated synoptic-scale features in a persistent heavy rainfall event in south China, Guangdong Province, during 29-31 March 2014, using operational global ensemble forecasts from the European Centre for Medium-Range Weather Forecasts. This persistent rainfall was divided into two stages with a better precipitation forecast skill in Stage 2 (S2) than Stage 1 (S1) although S2 had a longer lead time. Using ensemble-based sensitivity analysis, key synoptic-scale factors that affected the rainfall were diagnosed by correlating the accumulated precipitation of each stage to atmospheric state variables in the middle of respective stage. The precipitation in both stages was found to be significantly correlated with midlevel trough, low-level vortex, and particularly the low-level jet on the southeast flank of the vortex and its associated moisture transport. The rainfall forecast skill was mainly determined by the forecast accuracy in the location of the low-level jet, which was possibly related to the different juxtapositions between the direction of the movement of the low-level vortex and the orientation of the low-level jet. The uncertainty in rainfall forecast in S1 was mainly from the location uncertainty of the low-level jet, while the uncertainty in rainfall forecast in S2 was mainly from the width uncertainty of the low-level jet with the relatively accurate location of the low-level jet.

Real-time Ensemble Flow Forecasts for a 2017 Mock Operation Test Trial of Forecast Informed Reservoir Operations for Lake Mendocino in Mendocino County, California

NASA Astrophysics Data System (ADS)

Delaney, C.; Mendoza, J.; Jasperse, J.; Hartman, R. K.; Whitin, B.; Kalansky, J.

2017-12-01

Forecast informed reservoir operations (FIRO) is a methodology that incorporates short to mid-range precipitation and flow forecasts to inform the flood operations of reservoirs. The Ensemble Forecast Operations (EFO) alternative is a probabilistic approach of FIRO that incorporates 15-day ensemble streamflow predictions (ESPs) made by NOAA's California-Nevada River Forecast Center (CNRFC). With the EFO approach, release decisions are made to manage forecasted risk of reaching critical operational thresholds. A water management model was developed for Lake Mendocino, a 111,000 acre-foot reservoir located near Ukiah, California, to conduct a mock operation test trial of the EFO alternative for 2017. Lake Mendocino is a dual use reservoir, which is owned and operated for flood control by the United States Army Corps of Engineers and is operated for water supply by the Sonoma County Water Agency. Due to recent changes in the operations of an upstream hydroelectric facility, this reservoir has suffered from water supply reliability issues since 2007. The operational trial utilized real-time ESPs prepared by the CNRFC and observed flow information to simulate hydrologic conditions in Lake Mendocino and a 50-mile downstream reach of the Russian River to the City of Healdsburg. Results of the EFO trial demonstrate a 6% increase in reservoir storage at the end of trial period (May 10) relative to observed conditions. Additionally, model results show no increase in flows above flood stage for points downstream of Lake Mendocino. Results of this investigation and other studies demonstrate that the EFO alternative may be a viable flood control operations approach for Lake Mendocino and warrants further investigation through additional modeling and analysis.

Navigating the "Research-to-Operations" Bridge of Death: Collaborative Transition of Remotely-Sensed Snow Data from Research into Operational Water Resources Forecasting

NASA Astrophysics Data System (ADS)

Miller, W. P.; Bender, S.; Painter, T. H.; Bernard, B.

2016-12-01

Water and resource management agencies can benefit from hydrologic forecasts during both flood and drought conditions. Improved predictions of seasonal snowmelt-driven runoff volume and timing can assist operational water managers with decision support and efficient resource management within the spring runoff season. Using operational models and forecasting systems, NOAA's Colorado Basin River Forecast Center (CBRFC) produces hydrologic forecasts for stakeholders and water management groups in the western United States. Collaborative incorporation of research-oriented remote sensing data into CBRFC operational models and systems is one route by which CBRFC forecasts can be improved, ultimately for the benefit of water managers. Successful navigation of research-oriented remote sensing products across the "research-to-operations"/R2O gap (also known as the "valley of death") to operational destinations requires dedicated personnel on both the research and operations sides, working in a highly collaborative environment. Since 2012, the operational CBRFC has collaborated with the research-oriented Jet Propulsion Laboratory (JPL) under funding from NASA to transition remotely-sensed snow data into CBRFC's operational models and forecasting systems. Two specific datasets from JPL, the MODIS Dust Radiative Forcing in Snow (MODDRFS) and the MODIS Snow Covered-Area and Grain size (MODSCAG) products, are used in CBRFC operations as of 2016. Over the past several years, JPL and CBRFC have worked together to analyze patterns in JPL's remote sensing snow datasets from the operational perspective of the CBRFC and to develop techniques to bridge the R2O gap. Retrospective and real-time analyses have yielded valuable insight into the remotely-sensed snow datasets themselves, CBRFC's operational systems, and the collaborative R2O process. Examples of research-oriented JPL snow data, as used in CBRFC operations, are described. A timeline of the collaboration, challenges encountered during the journey across the R2O gap, or "valley of death", and solutions to those challenges are also illustrated.

Incorporating probabilistic seasonal climate forecasts into river management using a risk-based framework

USGS Publications Warehouse

Sojda, Richard S.; Towler, Erin; Roberts, Mike; Rajagopalan, Balaji

2013-01-01

[1] Despite the influence of hydroclimate on river ecosystems, most efforts to date have focused on using climate information to predict streamflow for water supply. However, as water demands intensify and river systems are increasingly stressed, research is needed to explicitly integrate climate into streamflow forecasts that are relevant to river ecosystem management. To this end, we present a five step risk-based framework: (1) define risk tolerance, (2) develop a streamflow forecast model, (3) generate climate forecast ensembles, (4) estimate streamflow ensembles and associated risk, and (5) manage for climate risk. The framework is successfully demonstrated for an unregulated watershed in southwest Montana, where the combination of recent drought and water withdrawals has made it challenging to maintain flows needed for healthy fisheries. We put forth a generalized linear modeling (GLM) approach to develop a suite of tools that skillfully model decision-relevant low flow characteristics in terms of climate predictors. Probabilistic precipitation forecasts are used in conjunction with the GLMs, resulting in season-ahead prediction ensembles that provide the full risk profile. These tools are embedded in an end-to-end risk management framework that directly supports proactive fish conservation efforts. Results show that the use of forecasts can be beneficial to planning, especially in wet years, but historical precipitation forecasts are quite conservative (i.e., not very “sharp”). Synthetic forecasts show that a modest “sharpening” can strongly impact risk and improve skill. We emphasize that use in management depends on defining relevant environmental flows and risk tolerance, requiring local stakeholder involvement.

Introducing seasonal hydro-meteorological forecasts in local water management. First reflections from the Messara site, Crete, Greece.

NASA Astrophysics Data System (ADS)

Koutroulis, Aristeidis; Grillakis, Manolis; Tsanis, Ioannis

2017-04-01

Seasonal prediction is recently at the center of the forecasting research efforts, especially for regions that are projected to be severely affected by global warming. The value of skillful seasonal forecasts can be considerable for many sectors and especially for the agricultural in which water users and managers can benefit to better anticipate against drought conditions. Here we present the first reflections from the user/stakeholder interactions and the design of a tailored drought decision support system in an attempt to bring seasonal predictions into local practice for the Messara valley located in the central-south area of Crete, Greece. Findings from interactions with the users and stakeholders reveal that although long range and seasonal predictions are not used, there is a strong interest for this type of information. The increase in the skill of short range weather predictions is also of great interest. The drought monitoring and prediction tool under development that support local water and agricultural management will include (a) sources of skillful short to medium term forecast information, (b) tailored drought monitoring and forecasting indices for the local groundwater aquifer and rain-fed agriculture, and (c) seasonal inflow forecasts for the local dam through hydrologic simulation to support management of freshwater resources and drought impacts on irrigated agriculture.

Operational implications of a cloud model simulation of space shuttle exhaust clouds in different atmospheric conditions

NASA Technical Reports Server (NTRS)

Zak, J. A.

1989-01-01

A three-dimensional cloud model was used to characterize the dominant influence of the environment on the Space Shuttle exhaust cloud. The model was modified to accept the actual heat and moisture from rocket exhausts and deluge water as initial conditions. An upper-air sounding determined the ambient atmosphere in which the cloud would grow. The model was validated by comparing simulated clouds with observed clouds from four actual Shuttle launches. Results are discussed with operational weather forecasters in mind. The model successfully produced clouds with dimensions, rise, decay, liquid water contents, and vertical motion fields very similar to observed clouds whose dimensions were calculated from 16 mm film frames. Once validated, the model was used in a number of different atmospheric conditions ranging from very unstable to very stable. Wind shear strongly affected the appearance of both the ground cloud and vertical column cloud. The ambient low-level atmospheric moisture governed the amount of cloud water in model clouds. Some dry atmospheres produced little or no cloud water. An empirical forecast technique for Shuttle cloud rise is presented and differences between natural atmospheric convection and exhaust clouds are discussed.

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.

Precipitable water vapour forecasting: a tool for optimizing IR observations at Roque de los Muchachos Observatory

NASA Astrophysics Data System (ADS)

Pérez-Jordán, wG; Castro-Almazán, J. A.; Muñoz-Tuñón, C.

2018-07-01

We validate the Weather Research and Forecasting (WRF) model for precipitable water vapour (PWV) forecasting as a fully operational tool for optimizing astronomical infrared observations at Roque de los Muchachos Observatory (ORM). For the model validation, we used GNSS-based (Global Navigation Satellite System) data from the PWV monitor located at the ORM. We have run WRF every 24 h for near two months, with a horizon of 48 h (hourly forecasts), from 2016 January 11 to March 04. These runs represent 1296 hourly forecast points. The validation is carried out using different approaches: performance as a function of the forecast range, time horizon accuracy, performance as a function of the PWV value, and performance of the operational WRF time series with 24- and 48-h horizons. Excellent agreement was found between the model forecasts and observations, with R = 0.951 and 0.904 for the 24- and 48-h forecast time series, respectively. The 48-h forecast was further improved by correcting a time lag of 2 h found in the predictions. The final errors, taking into account all the uncertainties involved, are 1.75 mm for the 24-h forecasts and 1.99 mm for 48 h. We found linear trends in both the correlation and root-mean-square error of the residuals (measurements - forecasts) as a function of the forecast range within the horizons analysed (up to 48 h). In summary, the WRF performance is excellent and accurate, thus allowing it to be implemented as an operational tool at the ORM.

Precipitable water vapour forecasting: a tool for optimizing IR observations at Roque de los Muchachos Observatory.

NASA Astrophysics Data System (ADS)

Pérez-Jordán, G.; Castro-Almazán, J. A.; Muñoz-Tuñón, C.

2018-04-01

We validate the Weather Research and Forecasting (WRF) model for precipitable water vapour (PWV) forecasting as a fully operational tool for optimizing astronomical infrared (IR) observations at Roque de los Muchachos Observatory (ORM). For the model validation we used GNSS-based (Global Navigation Satellite System) data from the PWV monitor located at the ORM. We have run WRF every 24 h for near two months, with a horizon of 48 hours (hourly forecasts), from 2016 January 11 to 2016 March 4. These runs represent 1296 hourly forecast points. The validation is carried out using different approaches: performance as a function of the forecast range, time horizon accuracy, performance as a function of the PWV value, and performance of the operational WRF time series with 24- and 48-hour horizons. Excellent agreement was found between the model forecasts and observations, with R =0.951 and R =0.904 for the 24- and 48-h forecast time series respectively. The 48-h forecast was further improved by correcting a time lag of 2 h found in the predictions. The final errors, taking into account all the uncertainties involved, are 1.75 mm for the 24-h forecasts and 1.99 mm for 48 h. We found linear trends in both the correlation and RMSE of the residuals (measurements - forecasts) as a function of the forecast range within the horizons analysed (up to 48 h). In summary, the WRF performance is excellent and accurate, thus allowing it to be implemented as an operational tool at the ORM.

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.

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.

Water survey of Canada: Application for use of ERTS-A for retransmission of water resources data

NASA Technical Reports Server (NTRS)

Halliday, R. A. (Principal Investigator)

1973-01-01

The author has identified the following significant results. Nine sites in isolated regions in Canada have been selected for installation of ERTS data collection platforms. Seven platforms were installed in 1972, one of which did not operate. The six operating platforms transmitted over 7000 water level readings from stream gauging stations. This data is available on a near real time basis through the Canada Center for Remote Sensing and is used for river flow forecasting. The practicability of using satellite retransmission as a means of obtaining data from remote areas has been demonstrated.

Seasonal forecasting of groundwater levels in natural aquifers in the United Kingdom

NASA Astrophysics Data System (ADS)

Mackay, Jonathan; Jackson, Christopher; Pachocka, Magdalena; Brookshaw, Anca; Scaife, Adam

2014-05-01

Groundwater aquifers comprise the world's largest freshwater resource and provide resilience to climate extremes which could become more frequent under future climate changes. Prolonged dry conditions can induce groundwater drought, often characterised by significantly low groundwater levels which may persist for months to years. In contrast, lasting wet conditions can result in anomalously high groundwater levels which result in flooding, potentially at large economic cost. Using computational models to produce groundwater level forecasts allows appropriate management strategies to be considered in advance of extreme events. The majority of groundwater level forecasting studies to date use data-based models, which exploit the long response time of groundwater levels to meteorological drivers and make forecasts based only on the current state of the system. Instead, seasonal meteorological forecasts can be used to drive hydrological models and simulate groundwater levels months into the future. Such approaches have not been used in the past due to a lack of skill in these long-range forecast products. However systems such as the latest version of the Met Office Global Seasonal Forecast System (GloSea5) are now showing increased skill up to a 3-month lead time. We demonstrate the first groundwater level ensemble forecasting system using a multi-member ensemble of hindcasts from GloSea5 between 1996 and 2009 to force 21 simple lumped conceptual groundwater models covering most of the UK's major aquifers. We present the results from this hindcasting study and demonstrate that the system can be used to forecast groundwater levels with some skill up to three months into the future.

Investigation of water vapor motion winds from geostationary satellites

NASA Technical Reports Server (NTRS)

Velden, Christopher S.; Nieman, Steven J.; Wanzong, Steven

1994-01-01

Water vapor imagery from geostationary satellites has been available for over a decade. These data are used extensively by operational analysts and forecasters, mainly in a qualitative mode (Weldon and Holmes 1991). In addition to qualitative applications, motions deduced in animated water vapor imagery can be used to infer wind fields in cloudless regimes, thereby augmenting the information provided by cloud-drift wind vectors. Early attempts at quantifying the data by tracking features in water vapor imagery met with modest success (Stewart et al. 1985; Hayden and Stewart 1987). More recently, automated techniques have been developed and refined, and have resulted in upper-level wind observations comparable in quality to current operational cloud-tracked winds (Laurent 1993). In a recent study by Velden et al. (1993) it was demonstrated that wind sets derived from Meteosat-3 (M-3) water vapor imagery can provide important environmental wind information in data void areas surrounding tropical cyclones, and can positively impact objective track forecasts. M-3 was repositioned to 75W by the European Space Agency in 1992 in order to provide complete coverage of the Atlantic Ocean. Data from this satellite are being transmitted to the U.S. for operational use. Compared with the current GOES-7 (G-7) satellite (positioned near 112W), the M-3 water vapor channel contains a superior horizontal resolution (5 km vs. 16 km ). In this paper, we examine wind sets derived using automated procedures from both GOES-7 and Meteosat-3 full disk water vapor imagery in order to assess this data as a potentially important source of large-scale wind information. As part of a product demonstration wind sets were produced twice a day at CIMSS during a six-week period in March and April (1994). These data sets are assessed in terms of geographic coverage, statistical accuracy, and meteorological impact through preliminary results of numerical model forecast studies.

Development of a hydraulic model and flood-inundation maps for the Wabash River near the Interstate 64 Bridge near Grayville, Illinois

USGS Publications Warehouse

Boldt, Justin A.

2018-01-16

A two-dimensional hydraulic model and digital flood‑inundation maps were developed for a 30-mile reach of the Wabash River near the Interstate 64 Bridge near Grayville, Illinois. The flood-inundation maps, which can be accessed through the U.S. Geological Survey (USGS) Flood Inundation Mapping Science web site at http://water.usgs.gov/osw/flood_inundation/, depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at the USGS streamgage on the Wabash River at Mount Carmel, Ill (USGS station number 03377500). Near-real-time stages at this streamgage may be obtained on the internet from the USGS National Water Information System at http://waterdata.usgs.gov/ or the National Weather Service (NWS) Advanced Hydrologic Prediction Service (AHPS) at http://water.weather.gov/ahps/, which also forecasts flood hydrographs at this site (NWS AHPS site MCRI2). The NWS AHPS forecasts peak stage information that may be used with the maps developed in this study to show predicted areas of flood inundation.Flood elevations were computed for the Wabash River reach by means of a two-dimensional, finite-volume numerical modeling application for river hydraulics. The hydraulic model was calibrated by using global positioning system measurements of water-surface elevation and the current stage-discharge relation at both USGS streamgage 03377500, Wabash River at Mount Carmel, Ill., and USGS streamgage 03378500, Wabash River at New Harmony, Indiana. The calibrated hydraulic model was then used to compute 27 water-surface elevations for flood stages at 1-foot (ft) intervals referenced to the streamgage datum and ranging from less than the action stage (9 ft) to the highest stage (35 ft) of the current stage-discharge rating curve. The simulated water‑surface elevations were then combined with a geographic information system digital elevation model, derived from light detection and ranging data, to delineate the area flooded at each water level.The availability of these maps, along with information on the internet regarding current stage from the USGS streamgage at Mount Carmel, Ill., and forecasted stream stages from the NWS AHPS, provides emergency management personnel and residents with information that is critical for flood-response activities such as evacuations and road closures, as well as for postflood recovery efforts.

Flood-inundation maps for the Wabash River at Lafayette, Indiana

USGS Publications Warehouse

Kim, Moon H.

2018-05-10

Digital flood-inundation maps for an approximately 4.8-mile reach of the Wabash River at Lafayette, Indiana (Ind.) were created by the U.S. Geological Survey (USGS) in cooperation with the Indiana Office of Community and Rural Affairs. The inundation maps, which can be accessed through the USGS Flood Inundation Mapping Science web site at https://water.usgs.gov/osw/flood_inundation/, depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at USGS streamgage 03335500, Wabash River at Lafayette, Ind. Current streamflow conditions for estimating near-real-time areas of inundation using USGS streamgage information may be obtained on the internet at https://waterdata.usgs.gov/in/nwis/uv?site_no=03335500. In addition, information has been provided to the National Weather Service (NWS) for incorporation into their Advanced Hydrologic Prediction Service (AHPS) flood-warning system (https://water.weather.gov/ahps/). The NWS AHPS forecasts flood hydrographs at many places that are often colocated with USGS streamgages, including the Wabash River at Lafayette, Ind. NWS AHPS-forecast peak-stage information may be used with the maps developed in this study to show predicted areas of flood inundation.For this study, flood profiles were computed for the Wabash River reach by means of a one-dimensional step-backwater model. The hydraulic model was calibrated by using the most current stage-discharge relations at USGS streamgage 03335500, Wabash River at Lafayette, Ind., and high-water marks from the flood of July 2003 (U.S. Army Corps of Engineers [USACE], 2007). The calibrated hydraulic model was then used to determine 23 water-surface profiles for flood stages at 1-foot intervals referenced to the streamgage datum and ranging from bankfull to the highest stage of the current stage-discharge rating curve. The simulated water-surface profiles were then combined with a geographic information system digital elevation model derived from light detection and ranging to delineate the area flooded at each water level. The availability of these maps, along with internet information regarding current stage from the USGS streamgage 03335500, Wabash River at Lafayette, Ind., and forecasted high-flow stages from the NWS AHPS, will provide emergency management personnel and residents with information that is critical for flood-response activities such as evacuations and road closures, and for postflood recovery efforts.

Flood-inundation maps for the North Branch Elkhart River at Cosperville, Indiana

USGS Publications Warehouse

Kim, Moon H.; Johnson, Esther M.

2014-01-01

Digital flood-inundation maps for a reach of the North Branch Elkhart River at Cosperville, Indiana (Ind.), were created by the U.S. Geological Survey (USGS) in cooperation with the U.S. Army Corps of Engineers, Detroit District. The inundation maps, which can be accessed through the USGS Flood Inundation Mapping Science Web site at http://water.usgs.gov/osw/flood_inundation/ depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at USGS streamgage 04100222, North Branch Elkhart River at Cosperville, Ind. Current conditions for estimating near-real-time areas of inundation using USGS streamgage information may be obtained on the Internet at http://waterdata.usgs.gov/in/nwis/uv?site_no=04100222. In addition, information has been provided to the National Weather Service (NWS) for incorporation into their Advanced Hydrologic Prediction Service (AHPS) flood warning system (http:/water.weather.gov/ahps/). The NWS AHPS forecasts flood hydrographs at many places that are often colocated with USGS streamgages, including the North Branch Elkhart River at Cosperville, Ind. NWS AHPS-forecast peak-stage information may be used in conjunction with the maps developed in this study to show predicted areas of flood inundation. For this study, flood profiles were computed for the North Branch Elkhart River reach by means of a one-dimensional step-backwater model. The hydraulic model was calibrated by using the most current stage-discharge relations at USGS streamgage 04100222, North Branch Elkhart River at Cosperville, Ind., and preliminary high-water marks from the flood of March 1982. The calibrated hydraulic model was then used to determine four water-surface profiles for flood stages at 1-foot intervals referenced to the streamgage datum and ranging from bankfull to the highest stage of the current stage-discharge rating curve. The simulated water-surface profiles were then combined with a geographic information system (GIS) digital elevation model (DEM, derived from Light Detection and Ranging [LiDAR]) in order to delineate the area flooded at each water level. The availability of these maps, along with Internet information regarding current stage from the USGS streamgage 04100222, North Branch Elkhart River at Cosperville, Ind., and forecast stream stages from the NWS AHPS, provides emergency management personnel and residents with information that is critical for flood response activities such as evacuations and road closures, as well as for post-flood recovery efforts.

Rough Precipitation Forecasts based on Analogue Method: an Operational System

NASA Astrophysics Data System (ADS)

Raffa, Mario; Mercogliano, Paola; Lacressonnière, Gwendoline; Guillaume, Bruno; Deandreis, Céline; Castanier, Pierre

2017-04-01

In the framework of the Climate KIC partnership, has been funded the project Wat-Ener-Cast (WEC), coordinated by ARIA Technologies, having the goal to adapt, through tailored weather-related forecast, the water and energy operations to the increased weather fluctuation and to climate change. The WEC products allow providing high quality forecast suited in risk and opportunities assessment dashboard for water and energy operational decisions and addressing the needs of sewage/water distribution operators, energy transport & distribution system operators, energy manager and wind energy producers. A common "energy water" web platform, able to interface with newest smart water-energy IT network have been developed. The main benefit by sharing resources through the "WEC platform" is the possibility to optimize the cost and the procedures of safety and maintenance team, in case of alerts and, finally to reduce overflows. Among the different services implemented on the WEC platform, ARIA have developed a product having the goal to support sewage/water distribution operators, based on a gradual forecast information system ( at 48hrs/24hrs/12hrs horizons) of heavy precipitation. For each fixed deadline different type of operation are implemented: 1) 48hour horizon, organisation of "on call team", 2) 24 hour horizon, update and confirm the "on call team", 3) 12 hour horizon, secure human resources and equipment (emptying storage basins, pipes manipulations …). More specifically CMCC have provided a statistical downscaling method in order to provide a "rough" daily local precipitation at 24 hours, especially when high precipitation values are expected. This statistical technique consists of an adaptation of analogue method based on ECMWF data (analysis and forecast at 24 hours). One of the most advantages of this technique concerns a lower computational burden and budget compared to running a Numerical Weather Prediction (NWP) model, also if, of course it provides only this specific atmospheric variable without a complete description of the weather situation. In the first phase, the method considers a selection of analogous situations in terms of mean sea level pressure, specific humidity and total precipitation. In the second one, a subset of observations data is extracted according to the analogues found. The research of analogues consists of cascading filters designed to find the most similar weather situation in a historical archive of ECMWF analysis. The method has been calibrated in the period between 2008 and 2011, over different France weather stations (Paris, Meaux, La Londe Les Maures etc) in order to forecast extreme rainfall events. The results of the operational demonstrator, which has been running since September 2016 over the same France weather stations, show good performances in terms of prediction of extreme events at 24hrs horizon, meant as daily quantitative precipitation greater than 93th percentile of distribution, with a relative low false alarm rate.

Design and development of surface rainfall forecast products on GRAPES_MESO model

NASA Astrophysics Data System (ADS)

Zhili, Liu

2016-04-01

In this paper, we designed and developed the surface rainfall forecast products using medium scale GRAPES_MESO model precipitation forecast products. The horizontal resolution of GRAPES_MESO model is 10km*10km, the number of Grids points is 751*501, vertical levels is 26, the range is 70°E－145.15°E, 15°N－64.35 °N. We divided the basin into 7 major watersheds. Each watersheds was divided into a number of sub regions. There were 95 sub regions in all. Tyson polygon method is adopted in the calculation of surface rainfall. We used 24 hours forecast precipitation data of GRAPES_MESO model to calculate the surface rainfall. According to the site of information and boundary information of the 95 sub regions, the forecast surface rainfall of each sub regions was calculated. We can provide real-time surface rainfall forecast products every day. We used the method of fuzzy evaluation to carry out a preliminary test and verify about the surface rainfall forecast product. Results shows that the fuzzy score of heavy rain, rainstorm and downpour level forecast rainfall were higher, the fuzzy score of light rain level was lower. The forecast effect of heavy rain, rainstorm and downpour level surface rainfall were better. The rate of missing and empty forecast of light rainfall level surface rainfall were higher, so it's fuzzy score were lower.

Water Stage Forecasting in Tidal streams during High Water Using EEMD

NASA Astrophysics Data System (ADS)

Chen, Yen-Chang; Kao, Su-Pai; Su, Pei-Yi

2017-04-01

There are so many factors may affect the water stages in tidal streams. Not only the ocean wave but also the stream flow affects the water stage in a tidal stream. During high water, two of the most important factors affecting water stages in tidal streams are flood and tide. However the hydrological processes in tidal streams during high water are nonlinear and nonstationary. Generally the conventional methods used for forecasting water stages in tidal streams are very complicated. It explains the accurately forecasting water stages, especially during high water, in tidal streams is always a difficult task. The study makes used of Ensemble Empirical Model Decomposition (EEMD) to analyze the water stages in tidal streams. One of the advantages of the EEMD is it can be used to analyze the nonlinear and nonstationary data. The EEMD divides the water stage into several intrinsic mode functions (IMFs) and a residual; meanwhile, the physical meaning still remains during the process. By comparing the IMF frequency with tidal frequency, it is possible to identify if the IMF is affected by tides. Then the IMFs is separated into two groups, affected by tide or not by tide. The IMFs in each group are assembled to become a factor. Therefore the water stages in tidal streams are only affected by two factors, tidal factor and flood factor. Finally the regression analysis is used to establish the relationship between the factors of the gaging stations in the tidal stream. The available data during 15 typhoon periods of the Tanshui River whose downstream reach is in estuary area is used to illustrate the accuracy and reliability of the proposed method. The results show that the simple but reliable method is capable of forecasting water stages in tidal streams.

An Improved Ocean Observing System for Coastal Louisiana: WAVCIS (WAVE-CURRENT-SURGE Information System )

NASA Astrophysics Data System (ADS)

Zhang, X.; Stone, G. W.; Gibson, W. J.; Braud, D.

2005-05-01

WAVCIS is a regional ocean observing and forecasting system. It was designed to measure, process, forecast, and distribute oceanographic and meteorological information. WAVCIS was developed and is maintained by the Coastal Studies Institute at Louisiana State University. The in-situ observing stations are distributed along the central Louisiana and Mississippi coast. The forecast region covers the entire Gulf of Mexico with emphasis on offshore Louisiana. By using state-of-the-art instrumentation, WAVCIS measures directional waves, currents, temperature, water level, conductivity, turbidity, salinity, dissolved oxygen, chlorophyll, Meteorological parameters include wind speed and direction, air pressure and temperature visibility and humidity. Through satellite communication links, the measured data are transmitted to the WAVCIS laboratory. After processing, they are available to the public via the internet on a near real-time basis. WAVCIS also includes a forecasting capability. Waves, tides, currents, and winds are forecast daily for up to 80 hours in advance. There are a number of numerical wave and surge models that can be used for forecasts. WAM and SWAN are used for operational purposes to forecast sea state. Tides at each station are predicted based on the harmonic constants calculated from past in-situ observations at respective sites. Interpolated winds from the ETA model are used as input forcing for waves. Both in-situ and forecast information are available online to the users through WWW. Interactive GIS web mapping is implemented on the WAVCIS webpage to visualize the model output and in-situ observational data. WAVCIS data can be queried, retrieved, downloaded, and analyzed through the web page. Near real-time numerical model skill assessment can also be performed by using the data from in-situ observing stations.

Signature-forecasting and early outbreak detection system

PubMed Central

Naumova, Elena N.; MacNeill, Ian B.

2008-01-01

SUMMARY Daily disease monitoring via a public health surveillance system provides valuable information on population risks. Efficient statistical tools for early detection of rapid changes in the disease incidence are a must for modern surveillance. The need for statistical tools for early detection of outbreaks that are not based on historical information is apparent. A system is discussed for monitoring cases of infections with a view to early detection of outbreaks and to forecasting the extent of detected outbreaks. We propose a set of adaptive algorithms for early outbreak detection that does not rely on extensive historical recording. We also include knowledge of infection disease epidemiology into forecasts. To demonstrate this system we use data from the largest water-borne outbreak of cryptosporidiosis, which occurred in Milwaukee in 1993. Historical data are smoothed using a loess-type smoother. Upon receipt of a new datum, the smoothing is updated and estimates are made of the first two derivatives of the smooth curve, and these are used for near-term forecasting. Recent data and the near-term forecasts are used to compute a color-coded warning index, which quantify the level of concern. The algorithms for computing the warning index have been designed to balance Type I errors (false prediction of an epidemic) and Type II errors (failure to correctly predict an epidemic). If the warning index signals a sufficiently high probability of an epidemic, then a forecast of the possible size of the outbreak is made. This longer term forecast is made by fitting a ‘signature’ curve to the available data. The effectiveness of the forecast depends upon the extent to which the signature curve captures the shape of outbreaks of the infection under consideration. PMID:18716671

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.

California Data Exchange Center

Science.gov Websites

Historical Strong El Nino Years (PDF): 8-Station | 5-Station | 6-Station River Forecast Delta Tide Forecast year has been monitoring water quality in the Sacramento-San Joaquin Delta and upper San Francisco Delta and San Francisco Bay. http://www.water.ca.gov/news/newsreleases/2016/121916.pdf 12/12/2016

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.

A Kalman filter for a two-dimensional shallow-water model

NASA Technical Reports Server (NTRS)

Parrish, D. F.; Cohn, S. E.

1985-01-01

A two-dimensional Kalman filter is described for data assimilation for making weather forecasts. The filter is regarded as superior to the optimal interpolation method because the filter determines the forecast error covariance matrix exactly instead of using an approximation. A generalized time step is defined which includes expressions for one time step of the forecast model, the error covariance matrix, the gain matrix, and the evolution of the covariance matrix. Subsequent time steps are achieved by quantifying the forecast variables or employing a linear extrapolation from a current variable set, assuming the forecast dynamics are linear. Calculations for the evolution of the error covariance matrix are banded, i.e., are performed only with the elements significantly different from zero. Experimental results are provided from an application of the filter to a shallow-water simulation covering a 6000 x 6000 km grid.

Mitigating the Impacts of Climate Nonstationarity on Seasonal Streamflow Predictability in the U.S. Southwest

NASA Astrophysics Data System (ADS)

Lehner, Flavio; Wood, Andrew W.; Llewellyn, Dagmar; Blatchford, Douglas B.; Goodbody, Angus G.; Pappenberger, Florian

2017-12-01

Seasonal streamflow predictions provide a critical management tool for water managers in the American Southwest. In recent decades, persistent prediction errors for spring and summer runoff volumes have been observed in a number of watersheds in the American Southwest. While mostly driven by decadal precipitation trends, these errors also relate to the influence of increasing temperature on streamflow in these basins. Here we show that incorporating seasonal temperature forecasts from operational global climate prediction models into streamflow forecasting models adds prediction skill for watersheds in the headwaters of the Colorado and Rio Grande River basins. Current dynamical seasonal temperature forecasts now show sufficient skill to reduce streamflow forecast errors in snowmelt-driven regions. Such predictions can increase the resilience of streamflow forecasting and water management systems in the face of continuing warming as well as decadal-scale temperature variability and thus help to mitigate the impacts of climate nonstationarity on streamflow predictability.

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.

Electric energy demand and supply prospects for California

NASA Technical Reports Server (NTRS)

Jones, H. G. M.

1978-01-01

A recent history of electricity forecasting in California is given. Dealing with forecasts and regulatory uncertainty is discussed. Graphs are presented for: (1) Los Angeles Department of Water and Power and Pacific Gas and Electric present and projected reserve margins; (2) California electricity peak demand forecast; and (3) California electricity production.

National Weather Service Marine Forecasts

Science.gov Websites

discontinued **NEW** Experimental Offshore Waters Forecasts for the Pacific Ocean Near Mexico Atlantic and . Use Marine Cell Pages Termination Notice (Non Government website) US Coast Guard Mobile App...your safe boating needs in one app Going to the Beach? Check out the Experimental Beach Forecast Page

COMMERCIAL SERVICES PROVIDING MARINE FORECASTS VIA SATELLITE

Science.gov Websites

Tsunamis 406 EPIRB's National Weather Service Marine Forecasts COMMERCIAL SERVICES PROVIDING MARINE forecast seas? And may present an even greater danger near shore or any shallow waters? COMMERCIAL SERVICES commercial product or service does not imply any endorsement by the National Weather Service as to function

Modification of land-atmosphere interactions by CO2 effects

NASA Astrophysics Data System (ADS)

Lemordant, Leo; Gentine, Pierre

2017-04-01

Plant stomata couple the energy, water and carbon cycles. Increased CO2 modifies the seasonality of the water cycle through stomatal regulation and increased leaf area. As a result, the water saved during the growing season through higher water use efficiency mitigates summer dryness and the impact of potential heat waves. Land-atmosphere interactions and CO2 fertilization together synergistically contribute to increased summer transpiration. This, in turn, alters the surface energy budget and decreases sensible heat flux, mitigating air temperature rise. Accurate representation of the response to higher CO2 levels, and of the coupling between the carbon and water cycles are therefore critical to forecasting seasonal climate, water cycle dynamics and to enhance the accuracy of extreme event prediction under future climate.

Atmospheric radiation measurement program facilities newsletter, March 2002.

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

Holdridge, D. J.

2002-04-18

International H2O Project (IHOP-2002)--The International H2O Project (IHOP-2002) will take place in west-central Oklahoma over 44 days, May 13-June 25, 2002. The main focus will be water vapor and its role in storm development and rainfall production, information needed to improve rainfall forecasting. Forecasting the amount and location of rainfall is difficult, particularly in the warm months, and improvements are urgently needed. Accurate prediction of floods would be very beneficial to society, because flooding is costly in terms of loss of life and property damage. Deaths resulting from flash flooding outnumber those from hurricanes, tornadoes, windstorms, or lightning, and damagemore » due to flooding exceeds $5 billion annually. One measure of weather forecasting success is the accuracy of the Quantitative Precipitation Forecast (QPF), which predicts the amount of precipitation to be received at a certain location. One of the research goals of IHOP-2002 is to determine whether more accurate, detailed measurement of humidity will improve a computer model's ability to forecast rainfall amounts accurately. Current water vapor measurements are inadequate. The weather balloons (radiosondes) that gather most of the water vapor data used in today's weather and global climate models have three problems. First, the radiosonde stations are located too far apart, generating a grid of data that is too coarse to show the needed details in water vapor variability. Second, the radiosonde launches occur only every 12 hours, again providing too few data points for a highly variable parameter. Third, the radiosonde instrument has biases and inaccuracies in its measurements. Questionable data quality and data sets too coarse in both time and space make accurate forecasting difficult. The key to better, more accurate, higher-resolution water vapor measurements is dependable, ground-based sensors that operate continually and accurately. Such sensors will decrease dependence on sparsely spaced, costly weather balloon releases. IHOP-2002 will give researchers an active platform for testing and evaluating the capabilities and limitations of several water vapor measurement instruments. For example, the National Oceanic and Atmospheric Administration (NOAA) Environmental Technology Laboratory will be bringing a mini-DIAL (differential absorption lidar) to the SGP central facility for comparison with the SGP Raman lidar. Lidars send beams of laser light skyward and measure scattered light not absorbed by water molecules. The collection of IHOP-2002 instruments includes 2 fixed radars, 6 mobile radars, 2 airborne radars, 8 lidars (6 of which can sample water vapor), 1 advanced wind profiler, 2 sodars, 3 interferometers, 18 special surface stations, 800 radiosondes, 400 dropsondes, 1 tethersonde system, 52 global positioning system receivers, 3 profiling radiometers, 1 mobile profiling radiometer and wind profiler, and 5 water vapor radiometers. Six research aircraft will be deployed during the course of the field campaign. The aircraft will occasionally fly low-level tracks and will deploy dropsondes. A dropsonde resembles a radiosonde, an instrument package attached to a helium-filled balloon that rises into the atmosphere, but the dropsonde is released from an airplane and collects data on its way down to the ground. Finders of dropsondes are asked to follow the instructions on the package for returning the device to the researcher. Funding for IHOP-2002 is from many sources, including NOAA, the National Science Foundation, the National Center for Atmospheric Research, and the U.S. Department of Energy. Participation is worldwide, including researchers from Australia, Canada, France, Germany, the Netherlands, the United Kingdom, and the United States.« less

Statistical prediction of seasonal discharge in the Naryn basin for water resources planning in Central Asia

NASA Astrophysics Data System (ADS)

Apel, Heiko; Gafurov, Abror; Gerlitz, Lars; Unger-Shayesteh, Katy; Vorogushyn, Sergiy; Merkushkin, Aleksandr; Merz, Bruno

2016-04-01

The semi-arid regions of Central Asia crucially depend on the water resources supplied by the mountainous areas of the Tien-Shan and Pamirs. During the summer months the snow and glacier melt water of the rivers originating in the mountains provides the only water resource available for agricultural production but also for water collection in reservoirs for energy production in winter months. Thus a reliable seasonal forecast of the water resources is crucial for a sustainable management and planning of water resources.. In fact, seasonal forecasts are mandatory tasks of national hydro-meteorological services in the region. Thus this study aims at a statistical forecast of the seasonal water availability, whereas the focus is put on the usage of freely available data in order to facilitate an operational use without data access limitations. The study takes the Naryn basin as a test case, at which outlet the Toktogul reservoir stores the discharge of the Naryn River. As most of the water originates form snow and glacier melt, a statistical forecast model should use data sets that can serve as proxy data for the snow masses and snow water equivalent in late spring, which essentially determines the bulk of the seasonal discharge. CRU climate data describing the precipitation and temperature in the basin during winter and spring was used as base information, which was complemented by MODIS snow cover data processed through ModSnow tool, discharge during the spring and also GRACE gravimetry anomalies. For the construction of linear forecast models monthly as well as multi-monthly means over the period January to April were used to predict the seasonal mean discharge of May-September at the station Uchterek. An automatic model selection was performed in multiple steps, whereas the best models were selected according to several performance measures and their robustness in a leave-one-out cross validation. It could be shown that the seasonal discharge can be predicted with exceptionally high skill reaching explained variances of 86% in the cross validation using ModSnow processed snow cover data and CRU temperature and precipitation data, i.e. freely available data only. Using antecedent discharge information from the Uchterek station over the period January to April the skill can be improved even further. Also the addition of latest EGSIEM GRACE products can improve this skill to > 90% explained variance by replacing the CRU temperature data in the forecast model. From all variables the ModSnow processed MODIS snow cover data proved to be the most important predictor. However, although the prediction models proved to be robust in the cross validation, it has to be mentioned that the models are based on a limited time spanning the period 2000-2012 only. Nevertheless it is believed that the models are reliable, as this time period shows a high variability in seasonal water availability spanning from exceptionally dry to wet years. In summary, the developed forecast model may be a valuable complementary tool for the seasonal discharge prediction in Central Asia for water resources planning, that does not suffer from limited data access required for other forecast methods.

Improving governance action by an advanced water modelling system applied to the Po river basin in Italy

NASA Astrophysics Data System (ADS)

Alessandrini, Cinzia; Del Longo, Mauro; Pecora, Silvano; Puma, Francesco; Vezzani, Claudia

2013-04-01

In spite of the historical abundance of water due to rains and to huge storage capacity provided by alpine lakes, Po river basin, the most important Italian water district experienced in the past ten years five drought/water scarcity events respectively in 2003, 2006, 2007 and 2012 summers and in the 2011-2012 winter season. The basic approach to these crises was the observation and the post-event evaluation; from 2007 an advanced numerical modelling system, called Drought Early Warning System for the Po River (DEWS-Po) was developed, providing advanced tools to simulate the hydrological and anthropic processes that affect river flows and allowing to follow events with real-time evaluations. In early 2012 the same system enabled also forecasts. Dews-Po system gives a real-time representation of water distribution across the basin, characterized by high anthropogenic pressure, optimizing with specific tools water allocation in competing situations. The system represents an innovative approach in drought forecast and in water resource management in the Po basin, giving deterministic and probabilistic meteorological forecasts as input to a chain for numerical distributed modelling of hydrological and hydraulic simulations. The system architecture is designed to receive in input hydro-meteorological actually observed and forecasted variables: deterministic meteorological forecasts with a fifteen days lead time, withdrawals data for different uses, natural an artificial reservoirs storage and release data. The model details are very sharp, simulating also the interaction between Adriatic sea and Po river in the delta area in terms of salt intrusion forecasting. Calculation of return period through run-method and of drought stochastic-indicators are enabled to assess the characteristics of the on-going and forecasted event. An Inter-institutional Technical Board is constituted within the Po River Basin Authority since 2008 and meets regularly during water crises to act decisions regarding water management in order to prevent major impacts. The Board is made of experts from public administrations with a strong involvement of stakeholders representative of different uses. The Dews- Po was intensively used by the Technical Board as decision support system during the 2012 summer event, providing tools to understand the on-going situation of water availability and use across the basin, helping to evaluate water management choices in an objective way, through what-if scenarios considering withdrawals reduction and increased releases from regulated Alpine lakes. A description of the use of Dews- Po system within the Technical Board is given, especially focusing on those elements, prone to be considered "good management indicators", which proved to be most useful in ensuring the success of governance action. Strength and improvement needs of the system are then described

Artificial Neural Network forecasting of storm surge water levels at major estuarine ports to supplement national tide-surge models and improve port resilience planning

NASA Astrophysics Data System (ADS)

French, Jon; Mawdsley, Robert; Fujiyama, Taku; Achuthan, Kamal

2017-04-01

Effective prediction of tidal storm surge is of considerable importance for operators of major ports, since much of their infrastructure is necessarily located close to sea level. Storm surge inundation can damage critical elements of this infrastructure and significantly disrupt port operations and downstream supply chains. The risk of surge inundation is typically approached using extreme value analysis, while short-term forecasting generally relies on coastal shelf-scale tide and surge models. However, extreme value analysis does not provide information on the duration of a surge event and can be sensitive to the assumptions made and the historic data available. Also, whilst regional tide and surge models perform well along open coasts, their fairly coarse spatial resolution means that they do not always provide accurate predictions for estuarine ports. As part of a NERC Environmental Risks to Infrastructure Innovation Programme project, we have developed a tool that is specifically designed to forecast the North Sea storm surges on major ports along the east coast of the UK. Of particular interest is the Port of Immingham, Humber estuary, which handles the largest volume of bulk cargo in the UK including major flows of coal and biomass for power generation. A tidal surge in December 2013, with an estimated return period of 760 years, partly flooded the port, damaged infrastructure and disrupted operations for several weeks. This and other recent surge events highlight the need for additional tools to supplement the national UK Storm Tide Warning Service. Port operators are also keen to have access to less computationally expensive forecasting tools for scenario planning and to improve their resilience to actual events. In this paper, we demonstrate the potential of machine learning methods based on Artificial Neural Networks (ANNs) to generate accurate short-term forecasts of extreme water levels at estuarine North Sea ports such as Immingham. An ANN is configured to take advantage of far-field information on developing tidal surges provided by tide gauges in NW Scotland (the 'external surge'), supported by observations of wind and atmospheric pressure and the predicted astronomical tide at Immingham. Missing data can cause problems with ANN models and a novel aspect of our implementation is the use of multiple redundant inputs (nearby tide gauges that experience a high degree of surge coherence) to synthesise a single external surge input. A similar approach is taken with meteorological forcings, creating an ANN that is resilient against data drop-outs within its input vector. The ANN generates 6 to 24 hour surge forecasts at Immingham with accuracy better than the present UK Storm Tide Warning Service. These can be used to cross-check national forecasts, generate more accurate estimates of likely flood depths, timings and durations and trigger planned responses to severe forecasts. Crucially, this capability can be 'owned' by the port operator, which encourages the development of a shared understanding of storm surge hazards and the challenges of port resilience planning between scientist and stakeholder.

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.

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.

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.

The impact of satellite temperature soundings on the forecasts of a small national meteorological service

NASA Technical Reports Server (NTRS)

Wolfson, N.; Thomasell, A.; Alperson, Z.; Brodrick, H.; Chang, J. T.; Gruber, A.; Ohring, G.

1984-01-01

The impact of introducing satellite temperature sounding data on a numerical weather prediction model of a national weather service is evaluated. A dry five level, primitive equation model which covers most of the Northern Hemisphere, is used for these experiments. Series of parallel forecast runs out to 48 hours are made with three different sets of initial conditions: (1) NOSAT runs, only conventional surface and upper air observations are used; (2) SAT runs, satellite soundings are added to the conventional data over oceanic regions and North Africa; and (3) ALLSAT runs, the conventional upper air observations are replaced by satellite soundings over the entire model domain. The impact on the forecasts is evaluated by three verification methods: the RMS errors in sea level pressure forecasts, systematic errors in sea level pressure forecasts, and errors in subjective forecasts of significant weather elements for a selected portion of the model domain. For the relatively short range of the present forecasts, the major beneficial impacts on the sea level pressure forecasts are found precisely in those areas where the satellite sounding are inserted and where conventional upper air observations are sparse. The RMS and systematic errors are reduced in these regions. The subjective forecasts of significant weather elements are improved with the use of the satellite data. It is found that the ALLSAT forecasts are of a quality comparable to the SAR forecasts.

Real-time demonstration and evaluation of over-the-loop short to medium-range ensemble streamflow forecasting

NASA Astrophysics Data System (ADS)

Wood, A. W.; Clark, E.; Newman, A. J.; Nijssen, B.; Clark, M. P.; Gangopadhyay, S.; Arnold, J. R.

2015-12-01

The US National Weather Service River Forecasting Centers are beginning to operationalize short range to medium range ensemble predictions that have been in development for several years. This practice contrasts with the traditional single-value forecast practice at these lead times not only because the ensemble forecasts offer a basis for quantifying forecast uncertainty, but also because the use of ensembles requires a greater degree of automation in the forecast workflow than is currently used. For instance, individual ensemble member forcings cannot (practically) be manually adjusted, a step not uncommon with the current single-value paradigm, thus the forecaster is required to adopt a more 'over-the-loop' role than before. The relative lack of experience among operational forecasters and forecast users (eg, water managers) in the US with over-the-loop approaches motivates the creation of a real-time demonstration and evaluation platform for exploring the potential of over-the-loop workflows to produce usable ensemble short-to-medium range forecasts, as well as long range predictions. We describe the development and early results of such an effort by a collaboration between NCAR and the two water agencies, the US Army Corps of Engineers and the US Bureau of Reclamation. Focusing on small to medium sized headwater basins around the US, and using multi-decade series of ensemble streamflow hindcasts, we also describe early results, assessing the skill of daily-updating, over-the-loop forecasts driven by a set of ensemble atmospheric outputs from the NCEP GEFS for lead times from 1-15 days.

How seasonal forecast could help a decision maker: an example of climate service for water resource management

NASA Astrophysics Data System (ADS)

Viel, Christian; Beaulant, Anne-Lise; Soubeyroux, Jean-Michel; Céron, Jean-Pierre

2016-04-01

The FP7 project EUPORIAS was a great opportunity for the climate community to co-design with stakeholders some original and innovative climate services at seasonal time scales. In this framework, Météo-France proposed a prototype that aimed to provide to water resource managers some tailored information to better anticipate the coming season. It is based on a forecasting system, built on a refined hydrological suite, forced by a coupled seasonal forecast model. It particularly delivers probabilistic river flow prediction on river basins all over the French territory. This paper presents the work we have done with "EPTB Seine Grands Lacs" (EPTB SGL), an institutional stakeholder in charge of the management of 4 great reservoirs on the upper Seine Basin. First, we present the co-design phase, which means the translation of classical climate outputs into several indices, relevant to influence the stakeholder's decision making process (DMP). And second, we detail the evaluation of the impact of the forecast on the DMP. This evaluation is based on an experiment realised in collaboration with the stakeholder. Concretely EPTB SGL has replayed some past decisions, in three different contexts: without any forecast, with a forecast A and with a forecast B. One of forecast A and B really contained seasonal forecast, the other only contained random forecasts taken from past climate. This placebo experiment, realised in a blind test, allowed us to calculate promising skill scores of the DMP based on seasonal forecast in comparison to a classical approach based on climatology, and to EPTG SGL current practice.

Evaluation of a Real-Time Monitoring System for River Quality-A Trade-off between Risk Attitudes, Costs, and Uncertainly.

ERIC Educational Resources Information Center

Varis, Olli; And Others

1993-01-01

Presents one approach to handling the trade-off between reducing uncertainty in environmental assessment and management and additional expenses. Uses the approach in the evaluation of three alternatives for a real time river water quality forecasting system. Analysis of risk attitudes, costs and uncertainty indicated the levels of socioeconomic…

Real-time reservoir operation considering non-stationary inflow prediction

NASA Astrophysics Data System (ADS)

Zhao, J.; Xu, W.; Cai, X.; Wang, Z.

2011-12-01

Stationarity of inflow has been a basic assumption for reservoir operation rule design, which is now facing challenges due to climate change and human interferences. This paper proposes a modeling framework to incorporate non-stationary inflow prediction for optimizing the hedging operation rule of large reservoirs with multiple-year flow regulation capacity. A multi-stage optimization model is formulated and a solution algorithm based on the optimality conditions is developed to incorporate non-stationary annual inflow prediction through a rolling, dynamic framework that updates the prediction from period to period and adopt the updated prediction in reservoir operation decision. The prediction model is ARIMA(4,1,0), in which parameter 4 stands for the order of autoregressive, 1 represents a linear trend, and 0 is the order of moving average. The modeling framework and solution algorithm is applied to the Miyun reservoir in China, determining a yearly operating schedule during the period from 1996 to 2009, during which there was a significant declining trend of reservoir inflow. Different operation policy scenarios are modeled, including standard operation policy (SOP, matching the current demand as much as possible), hedging rule (i.e., leaving a certain amount of water for future to avoid large risk of water deficit) with forecast from ARIMA (HR-1), hedging (HR) with perfect forecast (HR-2 ). Compared to the results of these scenarios to that of the actual reservoir operation (AO), the utility of the reservoir operation under HR-1 is 3.0% lower than HR-2, but 3.7% higher than the AO and 14.4% higher than SOP. Note that the utility under AO is 10.3% higher than that under SOP, which shows that a certain level of hedging under some inflow prediction or forecast was used in the real-world operation. Moreover, the impacts of discount rate and forecast uncertainty level on the operation will be discussed.

Improving Forecasts for Water Management

NASA Astrophysics Data System (ADS)

Arumugam, Sankar; Wood, Andy; Rajagopalan, Balaji; Schaake, John

2014-01-01

Recent advances in seasonal to interannual hydroclimate predictions provide an opportunity for developing a proactive approach toward water management. This motivated a recent AGU Chapman Conference (see program details at http://chapman.agu.org/watermanagement/). Approximately 85 participants from the United States, Oceania, Asia, Europe, and South America presented and discussed the current state of successes, challenges, and opportunities in seasonal to interannual hydroclimate forecasts and water management, and a number of key messages emerged.

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.

A Real-time Irrigation Forecasting System in Jiefangzha Irrigation District, China

NASA Astrophysics Data System (ADS)

Cong, Z.

2015-12-01

In order to improve the irrigation efficiency, we need to know when and how much to irrigate in real time. If we know the soil moisture content at this time, we can forecast the soil moisture content in the next days based on the rainfall forecasting and the crop evapotranspiration forecasting. Then the irrigation should be considered when the forecasting soil moisture content reaches to a threshold. Jiefangzha Irrigation District, a part of Hetao Irrigation District, is located in Inner Mongolia, China. The irrigated area of this irrigation district is about 140,000 ha mainly planting wheat, maize and sunflower. The annual precipitation is below 200mm, so the irrigation is necessary and the irrigation water comes from the Yellow river. We set up 10 sites with 4 TDR sensors at each site (20cm, 40cm, 60cm and 80cm depth) to monitor the soil moisture content. The weather forecasting data are downloaded from the website of European Centre for Medium-Range Weather Forecasts (ECMWF). The reference evapotranspiration is estimated based on FAO-Blaney-Criddle equation with only the air temperature from ECMWF. Then the crop water requirement is forecasted by the crop coefficient multiplying the reference evapotranspiration. Finally, the soil moisture content is forecasted based on soil water balance with the initial condition is set as the monitoring soil moisture content. When the soil moisture content reaches to a threshold, the irrigation warning will be announced. The irrigation mount can be estimated through three ways: (1) making the soil moisture content be equal to the field capacity; (2) making the soil moisture saturated; or (3) according to the irrigation quota. The forecasting period is 10 days. The system is developed according to B2C model with Java language. All the databases and the data analysis are carried out in the server. The customers can log in the website with their own username and password then get the information about the irrigation forecasting and other information about the irrigation. This system can be expanded in other irrigation districts. In future, it is even possible to upgrade the system for the mobile user.

New Employment Forecasts. Hotel and Catering Industry 1988-1993.

ERIC Educational Resources Information Center

Measurement for Management Decision, Ltd., London (England).

Econometric forecasting models were used to forecast employment levels in the hotel and catering industry in Great Britain through 1993 under several different forecasting scenarios. The growth in employment in the hotel and catering industry over the next 5 years is likely to be broadly based, both across income levels of domestic consumers,…

Optimizing Tsunami Forecast Model Accuracy

NASA Astrophysics Data System (ADS)

Whitmore, P.; Nyland, D. L.; Huang, P. Y.

2015-12-01

Recent tsunamis provide a means to determine the accuracy that can be expected of real-time tsunami forecast models. Forecast accuracy using two different tsunami forecast models are compared for seven events since 2006 based on both real-time application and optimized, after-the-fact "forecasts". Lessons learned by comparing the forecast accuracy determined during an event to modified applications of the models after-the-fact provide improved methods for real-time forecasting for future events. Variables such as source definition, data assimilation, and model scaling factors are examined to optimize forecast accuracy. Forecast accuracy is also compared for direct forward modeling based on earthquake source parameters versus accuracy obtained by assimilating sea level data into the forecast model. Results show that including assimilated sea level data into the models increases accuracy by approximately 15% for the events examined.

Development of seasonal flow outlook model for Ganges-Brahmaputra Basins in Bangladesh

NASA Astrophysics Data System (ADS)

Hossain, Sazzad; Haque Khan, Raihanul; Gautum, Dilip Kumar; Karmaker, Ripon; Hossain, Amirul

2016-10-01

Bangladesh is crisscrossed by the branches and tributaries of three main river systems, the Ganges, Bramaputra and Meghna (GBM). The temporal variation of water availability of those rivers has an impact on the different water usages such as irrigation, urban water supply, hydropower generation, navigation etc. Thus, seasonal flow outlook can play important role in various aspects of water management. The Flood Forecasting and Warning Center (FFWC) in Bangladesh provides short term and medium term flood forecast, and there is a wide demand from end-users about seasonal flow outlook for agricultural purposes. The objective of this study is to develop a seasonal flow outlook model in Bangladesh based on rainfall forecast. It uses European Centre for Medium-Range Weather Forecasts (ECMWF) seasonal precipitation, temperature forecast to simulate HYDROMAD hydrological model. Present study is limited for Ganges and Brahmaputra River Basins. ARIMA correction is applied to correct the model error. The performance of the model is evaluated using coefficient of determination (R2) and Nash-Sutcliffe Efficiency (NSE). The model result shows good performance with R2 value of 0.78 and NSE of 0.61 for the Brahmaputra River Basin, and R2 value of 0.72 and NSE of 0.59 for the Ganges River Basin for the period of May to July 2015. The result of the study indicates strong potential to make seasonal outlook to be operationalized.

Bayesian analyses of seasonal runoff forecasts

NASA Astrophysics Data System (ADS)

Krzysztofowicz, R.; Reese, S.

1991-12-01

Forecasts of seasonal snowmelt runoff volume provide indispensable information for rational decision making by water project operators, irrigation district managers, and farmers in the western United States. Bayesian statistical models and communication frames have been researched in order to enhance the forecast information disseminated to the users, and to characterize forecast skill from the decision maker's point of view. Four products are presented: (i) a Bayesian Processor of Forecasts, which provides a statistical filter for calibrating the forecasts, and a procedure for estimating the posterior probability distribution of the seasonal runoff; (ii) the Bayesian Correlation Score, a new measure of forecast skill, which is related monotonically to the ex ante economic value of forecasts for decision making; (iii) a statistical predictor of monthly cumulative runoffs within the snowmelt season, conditional on the total seasonal runoff forecast; and (iv) a framing of the forecast message that conveys the uncertainty associated with the forecast estimates to the users. All analyses are illustrated with numerical examples of forecasts for six gauging stations from the period 1971 1988.

Towards uncertainty estimation for operational forecast products - a multi-model-ensemble approach for the North Sea and the Baltic Sea

NASA Astrophysics Data System (ADS)

Golbeck, Inga; Li, Xin; Janssen, Frank

2014-05-01

Several independent operational ocean models provide forecasts of the ocean state (e.g. sea level, temperature, salinity and ice cover) in the North Sea and the Baltic Sea on a daily basis. These forecasts are the primary source of information for a variety of information and emergency response systems used e.g. to issue sea level warnings or carry out oil drift forecast. The forecasts are of course highly valuable as such, but often suffer from a lack of information on their uncertainty. With the aim of augmenting the existing operational ocean forecasts in the North Sea and the Baltic Sea by a measure of uncertainty a multi-model-ensemble (MME) system for sea surface temperature (SST), sea surface salinity (SSS) and water transports has been set up in the framework of the MyOcean-2 project. Members of MyOcean-2, the NOOS² and HIROMB/BOOS³ communities provide 48h-forecasts serving as inputs. Different variables are processed separately due to their different physical characteristics. Based on the so far collected daily MME products of SST and SSS, a statistical method, Empirical Orthogonal Function (EOF) analysis is applied to assess their spatial and temporal variability. For sea surface currents, progressive vector diagrams at specific points are consulted to estimate the performance of the circulation models especially in hydrodynamic important areas, e.g. inflow/outflow of the Baltic Sea, Norwegian trench and English Channel. For further versions of the MME system, it is planned to extend the MME to other variables like e.g. sea level, ocean currents or ice cover based on the needs of the model providers and their customers. It is also planned to include in-situ data to augment the uncertainty information and for validation purposes. Additionally, weighting methods will be implemented into the MME system to develop more complex uncertainty measures. The methodology used to create the MME will be outlined and different ensemble products will be presented. In addition, some preliminary results based on the statistical analysis of the uncertainty measures provide first estimates of the regional and temporal performance of the ocean models for each parameter. ²Northwest European Shelf Operational Oceanography System ³High-resolution Operational Model of the Baltic / Baltic Operational Oceanographic System

A Quick Response Forecasting Model of Pathogen Transport and Inactivation in Near-shore Regions

NASA Astrophysics Data System (ADS)

Liu, L.; Fu, X.

2011-12-01

Modeling methods supporting water quality assessments play a critical role by facilitating people to understand and promptly predict the potential threat of waterborne bacterial pathogens pose to human health. A mathematical model to describe and predict bacterial levels can provide foundation for water managers in making decisions on whether a water system is safe to open to the public. The inactivation (decay or die-off) rate of bacteria is critical in a bacterial model by controlling bacterial concentration in waters and depends on numerous factors of hydrodynamics, meteorology, geology, chemistry and biology. Transport and fate of waterborne pathogens in fresh water systems is an essentially three-dimensional problem, which requires a coupling of hydrodynamic equations and transport equations that describe the pathogen and suspended sediment dynamics. However, such an approach could be very demanding and time consuming from a practical point of view due to excess computational efforts. Long computation time may lead people unintentionally drinking or swimming in the contaminated water during the period before the predictive results of water quality come out. Therefore, it is very necessary to find a quick-response model to forecast bacterial concentration instantly to protect human health without any delay. Nearshore regions are the most commonly and directly used area for people in a huge water system. The prior multi-dimensional investigations of E. Coli and Enterococci inactivation in literature indicate that along-shore current predominated the nearshore region. Consequently, the complex dynamic conditions may be potentially simplified to one-dimensional scenario. In this research, a one-dimensional model system coupling both hydrodynamic and bacterial transport modules is constructed considering different complex processes to simulate the transport and fate of pathogens in nearshore regions. The quick-response model mainly focuses on promptly forecasting purpose and will be verified and calibrated with the available data collected from southern Lake Michigan. The modeling results will be compared with those from prior multi-dimensional models. This model is specifically effective for the outfall-controlled waters, where pathogens are primarily predominated by loadings from nearby tributaries and tend to show wide variations in concentrations.

Applying downscaled global climate model data to a hydrodynamic surface-water and groundwater model

USGS Publications Warehouse

Swain, Eric; Stefanova, Lydia; Smith, Thomas

2014-01-01

Precipitation data from Global Climate Models have been downscaled to smaller regions. Adapting this downscaled precipitation data to a coupled hydrodynamic surface-water/groundwater model of southern Florida allows an examination of future conditions and their effect on groundwater levels, inundation patterns, surface-water stage and flows, and salinity. The downscaled rainfall data include the 1996-2001 time series from the European Center for Medium-Range Weather Forecasting ERA-40 simulation and both the 1996-1999 and 2038-2057 time series from two global climate models: the Community Climate System Model (CCSM) and the Geophysical Fluid Dynamic Laboratory (GFDL). Synthesized surface-water inflow datasets were developed for the 2038-2057 simulations. The resulting hydrologic simulations, with and without a 30-cm sea-level rise, were compared with each other and field data to analyze a range of projected conditions. Simulations predicted generally higher future stage and groundwater levels and surface-water flows, with sea-level rise inducing higher coastal salinities. A coincident rise in sea level, precipitation and surface-water flows resulted in a narrower inland saline/fresh transition zone. The inland areas were affected more by the rainfall difference than the sea-level rise, and the rainfall differences make little difference in coastal inundation, but a larger difference in coastal salinities.

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.

Summary of the Ground-Water-Level Hydrologic Conditions in New Jersey 2006

USGS Publications Warehouse

Jones, Walter; Pope, Daryll

2007-01-01

Ground water is one of the Nation's most important natural resources. It provides about 40 percent of our Nation's public water supply. Currently, nearly one-half of New Jersey's drinking-water is supplied by over 300,000 wells that serve more than 4.3 million people (John P. Nawyn, U.S. Geological Survey, written commun., 2007). New Jersey's population is projected to grow by more than a million people by 2030 (U.S. Census Bureau, accessed March 2, 2006, at http://www.census.gov). As demand for water increases, managing the development and use of the ground-water resource so that the supply can be maintained for an indefinite time without causing unacceptable environmental, economic, or social consequences is of paramount importance. This report describes the U.S. Geological Survey (USGS) New Jersey Water Science Center Observation Well Networks. Record low ground-water levels during water year 2006 (October 1, 2005 to September 30, 2006) are listed, and water levels in six selected water-table observation wells and three selected confined wells are shown in hydrographs. The report describes the trends in water levels in various confined aquifers in southern New Jersey and in water-table and fracture rock aquifers throughout the State. Web site addresses to access the data also are included. The USGS has operated a network of observation wells in New Jersey since 1923 for the purpose of monitoring ground-water-level changes throughout the State. Long-term systematic measurement of water levels in observation wells provides the data needed to evaluate changes in the ground-water resource over time. Records of ground-water levels are used to evaluate the effects of climate changes and water-supply development, to develop ground-water models, and to forecast trends.

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.

Statistical prediction of seasonal discharge in Central Asia for water resources management: development of a generic (pre-)operational modeling tool

NASA Astrophysics Data System (ADS)

Apel, Heiko; Baimaganbetov, Azamat; Kalashnikova, Olga; Gavrilenko, Nadejda; Abdykerimova, Zharkinay; Agalhanova, Marina; Gerlitz, Lars; Unger-Shayesteh, Katy; Vorogushyn, Sergiy; Gafurov, Abror

2017-04-01

The semi-arid regions of Central Asia crucially depend on the water resources supplied by the mountainous areas of the Tien-Shan and Pamirs. During the summer months the snow and glacier melt dominated river discharge originating in the mountains provides the main water resource available for agricultural production, but also for storage in reservoirs for energy generation during the winter months. Thus a reliable seasonal forecast of the water resources is crucial for a sustainable management and planning of water resources. In fact, seasonal forecasts are mandatory tasks of all national hydro-meteorological services in the region. In order to support the operational seasonal forecast procedures of hydromet services, this study aims at the development of a generic tool for deriving statistical forecast models of seasonal river discharge. The generic model is kept as simple as possible in order to be driven by available hydrological and meteorological data, and be applicable for all catchments with their often limited data availability in the region. As snowmelt dominates summer runoff, the main meteorological predictors for the forecast models are monthly values of winter precipitation and temperature as recorded by climatological stations in the catchments. These data sets are accompanied by snow cover predictors derived from the operational ModSnow tool, which provides cloud free snow cover data for the selected catchments based on MODIS satellite images. In addition to the meteorological data antecedent streamflow is used as a predictor variable. This basic predictor set was further extended by multi-monthly means of the individual predictors, as well as composites of the predictors. Forecast models are derived based on these predictors as linear combinations of up to 3 or 4 predictors. A user selectable number of best models according to pre-defined performance criteria is extracted automatically by the developed model fitting algorithm, which includes a test for robustness by a leave-one-out cross validation. Based on the cross validation the predictive uncertainty was quantified for every prediction model. According to the official procedures of the hydromet services forecasts of the mean seasonal discharge of the period April to September are derived every month starting from January until June. The application of the model for several catchments in Central Asia - ranging from small to the largest rivers - for the period 2000-2015 provided skillful forecasts for most catchments already in January. The skill of the prediction increased every month, with R2 values often in the range 0.8 - 0.9 in April just before the prediction period. The forecasts further improve in the following months, most likely due to the integration of spring precipitation, which is not included in the predictors before May, or spring discharge, which contains indicative information for the overall seasonal discharge. In summary, the proposed generic automatic forecast model development tool provides robust predictions for seasonal water availability in Central Asia, which will be tested against the official forecasts in the upcoming years, with the vision of eventual operational implementation.

A comparison of water vapor quantities from model short-range forecasts and ARM observations

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

Hnilo, J J

2006-03-17

Model evolution and improvement is complicated by the lack of high quality observational data. To address a major limitation of these measurements the Atmospheric Radiation Measurement (ARM) program was formed. For the second quarter ARM metric we will make use of new water vapor data that has become available, and called the 'Merged-sounding' value added product (referred to as OBS, within the text) at three sites: the North Slope of Alaska (NSA), Darwin Australia (DAR) and the Southern Great Plains (SGP) and compare these observations to model forecast data. Two time periods will be analyzed March 2000 for the SGPmore » and October 2004 for both DAR and NSA. The merged-sounding data have been interpolated to 37 pressure levels (e.g., from 1000hPa to 100hPa at 25hPa increments) and time averaged to 3 hourly data for direct comparison to our model output.« less

A Comparison of Water Vapor Quantities from Model Short-Range Forecasts and ARM Observations (in English; Croatian)

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

Hnilo, J.

2006-03-17

Model evolution and improvement is complicated by the lack of high quality observational data. To address a major limitation of these measurements the Atmospheric Radiation Measurement (ARM) program was formed. For the second quarter ARM metric we will make use of new water vapor data that has become available, and called the “Mergedsounding” value added product (referred to as OBS, within the text) at three sites: the North Slope of Alaska (NSA), Darwin Australia (DAR) and the Southern Great Plains (SGP) and compare these observations to model forecast data. Two time periods will be analyzed March 2000 for the SGPmore » and October 2004 for both DAR and NSA. The merged-sounding data have been interpolated to 37 pressure levels (e.g., from 1000hPa to 100hPa at 25hPa increments) and time averaged to 3 hourly data for direct comparison to our model output.« less

Operational wave now- and forecast in the German Bight as a basis for the assessment of wave-induced hydrodynamic loads on coastal dikes

NASA Astrophysics Data System (ADS)

Dreier, Norman; Fröhle, Peter

2017-12-01

The knowledge of the wave-induced hydrodynamic loads on coastal dikes including their temporal and spatial resolution on the dike in combination with actual water levels is of crucial importance of any risk-based early warning system. As a basis for the assessment of the wave-induced hydrodynamic loads, an operational wave now- and forecast system is set up that consists of i) available field measurements from the federal and local authorities and ii) data from numerical simulation of waves in the German Bight using the SWAN wave model. In this study, results of the hindcast of deep water wave conditions during the winter storm on 5-6 December, 2013 (German name `Xaver') are shown and compared with available measurements. Moreover field measurements of wave run-up from the local authorities at a sea dike on the German North Sea Island of Pellworm are presented and compared against calculated wave run-up using the EurOtop (2016) approach.

Techniques for water demand analysis and forecasting: Puerto Rico, a case study

USGS Publications Warehouse

Attanasi, E.D.; Close, E.R.; Lopez, M.A.

1975-01-01

The rapid economic growth of the Commonwealth-of Puerto Rico since 1947 has brought public pressure on Government agencies for rapid development of public water supply and waste treatment facilities. Since 1945 the Puerto Rico Aqueduct and Sewer Authority has had the responsibility for planning, developing and operating water supply and waste treatment facilities on a municipal basis. The purpose of this study was to develop operational techniques whereby a planning agency, such as the Puerto Rico Aqueduct and Sewer Authority, could project the temporal and spatial distribution of .future water demands. This report is part of a 2-year cooperative study between the U.S. Geological Survey and the Environmental Quality Board of the Commonwealth of Puerto Rico, for the development of systems analysis techniques for use in water resources planning. While the Commonwealth was assisted in the development of techniques to facilitate ongoing planning, the U.S. Geological Survey attempted to gain insights in order to better interface its data collection efforts with the planning process. The report reviews the institutional structure associated with water resources planning for the Commonwealth. A brief description of alternative water demand forecasting procedures is presented and specific techniques and analyses of Puerto Rico demand data are discussed. Water demand models for a specific area of Puerto Rico are then developed. These models provide a framework for making several sets of water demand forecasts based on alternative economic and demographic assumptions. In the second part of this report, the historical impact of water resources investment on regional economic development is analyzed and related to water demand .forecasting. Conclusions and future data needs are in the last section.

Effective Presentation of Metabolic Rate Information for Lunar Extravehicular Activity (EVA)

NASA Technical Reports Server (NTRS)

Mackin, Michael A.; Gonia, Philip; Lombay-Gonzalez, Jose

2010-01-01

During human exploration of the lunar surface, a suited crewmember needs effective and accurate information about consumable levels remaining in their life support system. The information must be presented in a manner that supports real-time consumable monitoring and route planning. Since consumable usage is closely tied to metabolic rate, the lunar suit must estimate metabolic rate from life support sensors, such as oxygen tank pressures, carbon dioxide partial pressure, and cooling water inlet and outlet temperatures. To provide adequate warnings that account for traverse time for a crewmember to return to a safe haven, accurate forecasts of consumable depletion rates are required. The forecasts must be presented to the crewmember in a straightforward, effective manner. In order to evaluate methods for displaying consumable forecasts, a desktop-based simulation of a lunar Extravehicular Activity (EVA) has been developed for the Constellation lunar suite s life-support system. The program was used to compare the effectiveness of several different data presentation methods.

Comparing Vertical Distributions of Water Vapor Flux within Two Landfalling Atmospheric Rivers

NASA Astrophysics Data System (ADS)

Rutz, J. J.; Lavers, D. A.

2015-12-01

The West Coast of North America is frequently impacted by atmospheric rivers (ARs), regions of intense horizontal water vapor transport that often produce heavy rain, flooding, and landslides when they interact with near-coastal mountains. Recently, studies have shown that ARs penetrate farther inland on many occasions, with indications that the vertical distribution of vapor transport within the ARs may play a key role in this penetration (Alexander et al. 2015; Rutz et al. 2015). We hypothesize that the amount of near-coastal precipitation and the likelihood of AR penetration farther inland may be inversely linked by vertical distributions of vapor fluxes before, during, and after landfall. To explore whether differing vertical distributions of transport explain differing precipitation and penetration outcomes, we compare two landfalling ARs that had very similar spatial extents and rates of vertically integrated (total) vapor transport, but which nonetheless produced very different amounts of precipitation over northern California. The vertical distribution of water vapor flux, specific humidity, and wind speed during these two ARs are examined along several transects using cross-sectional analyses of the Climate Forecast System Reanalysis with a horizontal resolution of ~0.5° (~63 km) and a sigma-pressure hybrid coordinate at 64 vertical levels. In addition, we pursue similar analyses of forecasts from the NCEP Global Ensemble Forecast System GEFS to assess whether numerical weather prediction models accurately represent these distributions. Finally, we calculate backward trajectories from within each AR to examine whether or not the origins of their respective air parcels play a role in the resulting vertical distribution of water vapor flux. The results have major implications for two problems in weather prediction: (1) the near-coastal precipitation associated with landfalling ARs and (2) the likelihood of AR penetration farther inland.

Assessing the skill of seasonal meteorological forecast products for predicting droughts and water scarcity in highly regulated basins

NASA Astrophysics Data System (ADS)

Squeri, Marika; Giuliani, Matteo; Castelletti, Andrea; Pulido-Velazquez, Manuel; Marcos-Garcia, Patricia; Macian-Sorribes, Hector

2017-04-01

Drought and water scarcity are important issues in Southern Europe and many predictions suggest that their frequency and severity will increase over the next years, potentially leading to negative environmental and socio-economic impacts. This work focuses on the Jucar river basin, located in the hinterland of Valencia (Eastern Spain), which is historically affected by long and severe dry periods that negatively impact several economic sectors, with irrigated agriculture representing the main consumptive demand in the basin (79%). Monitoring drought and water scarcity is crucial to activate timely drought management strategies in the basin. However, most traditional drought indexes fail in detecting critical events due to the large presence of human regulation supporting the irrigated agriculture. Over the last 20 years, a sophisticated drought monitoring system has been set up to properly capture the status of the catchment by means of the state index, a weighted linear combination of twelve indicators that depends on observations of precipitation, streamflow, reservoirs' storages and groundwater levels in representative locations at the basin. In this work, we explore the possibility of predicting the state index, which is currently used only as a monitoring tool, in order to prompt anticipatory actions before the drought/water scarcity event starts. In particular, we test the forecasting skill of retrospective seasonal meteorological predictions from the European Centre for Medium-range Weather Forecasts (ECMWF) System 4. The 7-months lead time of these products allows predicting in February the values of the state index until September, thus covering the entire agricultural season. Preliminary results suggest that the Sys4-ECMWF products are skillful in predicting the state index, potentially supporting the design of anticipatory drought management actions.

Flood-inundation maps for the East Fork White River near Bedford, Indiana

USGS Publications Warehouse

Fowler, Kathleen K.

2014-01-01

Digital flood-inundation maps for an 1.8-mile reach of the East Fork White River near Bedford, Indiana (Ind.) were created by the U.S. Geological Survey (USGS) in cooperation with the Indiana Department of Transportation. The inundation maps, which can be accessed through the USGS Flood Inundation Mapping Science Web site at http://water.usgs.gov/osw/flood_inundation/ depict estimates of the areal extent and depth of flooding corresponding to selectedwater levels (stages) at USGS streamgage 03371500, East Fork White River near Bedford, Ind. Current conditions for estimating near-real-time areas of inundation using USGS streamgage information may be obtained on the Internet at http://waterdata.usgs.gov/in/nwis/uv?site_no=03371500. In addition, information has been provided to the National Weather Service (NWS) for incorporation into their Advanced Hydrologic Prediction Service (AHPS) flood warning system (http://water.weather.gov/ahps/). The NWS forecasts flood hydrographs at many places that are often colocated with USGS streamgages, including the East Fork White River near Bedford, Ind. NWS-forecasted peak-stage information may be used in conjunction with the maps developed in this study to show predicted areas of flood inundation. For this study, flood profiles were computed for the East Fork White River reach by means of a one-dimensional step-backwater model. The hydraulic model was calibrated by using the most current stage-discharge relations at USGS streamgage 03371500, East Fork White River near Bedford, Ind., and documented high-water marks from the flood of June 2008. The calibrated hydraulic model was then used to determine 20 water-surface profiles for flood stages at 1-foot intervals referenced to the streamgage datum and ranging from bankfull to the highest stage of the current stage-discharge rating curve. The simulated water-surface profiles were then combined with a geographic information system (GIS) digital elevation model (DEM, derived from Light Detection and Ranging (LiDAR) data having a 0.593-foot vertical accuracy) in order to delineate the area flooded at each water level. The availability of these maps, along with Internet information regarding current stage from the USGS streamgage near Bedford, Ind., and forecasted stream stages from the NWS, provides emergency management personnel and residents with information that is critical for flood response activities such as evacuations and road closures, as well as for postflood recovery eforts.

Detection and forecasting of oyster norovirus outbreaks: recent advances and future perspectives.

PubMed

Wang, Jiao; Deng, Zhiqiang

2012-09-01

Norovirus is a highly infectious pathogen that is commonly found in oysters growing in fecally contaminated waters. Norovirus outbreaks can cause the closure of oyster harvesting waters and acute gastroenteritis in humans associated with consumption of contaminated raw oysters. Extensive efforts and progresses have been made in detection and forecasting of oyster norovirus outbreaks over the past decades. The main objective of this paper is to provide a literature review of methods and techniques for detecting and forecasting oyster norovirus outbreaks and thereby to identify the future directions for improving the detection and forecasting of norovirus outbreaks. It is found that (1) norovirus outbreaks display strong seasonality with the outbreak peak occurring commonly in December-March in the U.S. and April-May in the Europe; (2) norovirus outbreaks are affected by multiple environmental factors, including but not limited to precipitation, temperature, solar radiation, wind, and salinity; (3) various modeling approaches may be employed to forecast norovirus outbreaks, including Bayesian models, regression models, Artificial Neural Networks, and process-based models; and (4) diverse techniques are available for near real-time detection of norovirus outbreaks, including multiplex PCR, seminested PCR, real-time PCR, quantitative PCR, and satellite remote sensing. The findings are important to the management of oyster growing waters and to future investigations into norovirus outbreaks. It is recommended that a combined approach of sensor-assisted real time monitoring and modeling-based forecasting should be utilized for an efficient and effective detection and forecasting of norovirus outbreaks caused by consumption of contaminated oysters. Copyright © 2012 Elsevier Ltd. All rights reserved.

Using FRET for Drought Mitigation

NASA Astrophysics Data System (ADS)

Osborne, H. D.; Palmer, C. K.; Hobbins, M.

2016-12-01

With the ongoing drought plaguing California and much of the Western United States, water agencies and the general public have a heightened need for short term forecasts of evapotranspiration. The National Weather Service's (NWS) Forecast Reference Evapotranspiration (FRET) product suite can fill this need. The FRET product suite uses the Penman - Monteith Reference Evapotranspiration (ETrc) equation for a short canopy (12 cm grasses), adopted by the Environmental Water Resources Institute of the American Society of Civil Engineers. FRET is calculated across the contiguous U.S. using temperatures, humidity, winds, and sky cover from Numerical Weather Prediction (NPW) models and adjusted by NWS forecasters with local expertise of terrain and weather patterns. The Weekly ETrc product is easily incorporated into drought-planning strategies, allowing water managers, the agricultural community, and the public to make better informed water-use decisions. FRET can assist with the decision making process for scheduling irrigation (e.g., farms, golf courses, vineyards) and timing of fertilizers. The California Department of Water Resources (CA DWR) also ingests the FRET into their soil moisture models, and uses FRET to assist in determining the reservoir releases for the Feather River. The United States Bureau of Reclamation (USBR) also uses FRET in determining reservoir releases and assessing water temperature along the Sacramento and American Rivers. FRET is now operational on the National Digital Forecast Database (NDFD), permitting other agencies easy access to this nationwide data for all drought mitigation and planning purposes.

Season-ahead streamflow forecast informed tax strategies for semi-arid water rights markets

NASA Astrophysics Data System (ADS)

Delorit, J. D.; Block, P. J.

2016-12-01

In many semi-arid regions multisectoral demands stress available water supplies. The Elqui River valley of north central Chile, which draws on limited capacity reservoirs supplied largely by annually variable snowmelt, is one of these cases. This variability forces water managers to develop demand-based allocation strategies which have typically resulted in water right volume reductions, applied equally per right. Compounding this issue is often deferred or delayed infrastructure investments, which has been linked Chile's Coasian approach to water markets, under which rights holders do not pay direct procurement costs, non-use fees, nor taxes. Here we build upon our previous research using forecasts of likely water rights reductions, informed by season-ahead prediction models of October-January (austral growing season) streamflow, to construct annual, forecast-sensitive, per right tax. We believe this tax, to be borne by right holders, will improve the beneficial use of water resources by stimulating water rights trading and improving system efficiency by generating funds for infrastructure investment, thereby reducing free-ridership and conflict between rights holders. Research outputs will include sectoral per right tax assessments, tax revenue generation, Elqui River valley economic output, and water rights trading activity.

Projecting the Water and Electric Consumption of Polytechnic University of the Philippines

NASA Astrophysics Data System (ADS)

Urrutia, Jackie D.; Mercado, Joseph; Bautista, Lincoln A.; Baccay, Edcon B.

2017-03-01

This study investigates water and electric consumption in Polytechnic University of the Philippines - Sta. Mesa using a time series analysis. The researchers analyzed the water and electric usage separately. Electric consumption was examined in terms of pesos and kilowatt-hour, while water consumption was analyzed in pesos and cubic meter. The data are gathered from the university limited only from January 2009 to July 2015 in a monthly based record. The aim is to forecast the water and electric usage of the university for the years 2016 and 2017. There are two main statistical treatments that the researchers conducted to be able to formulate mathematical models that can estimate the water and electric consumption of the said school. Using Seasonal Autoregressive Integrated Moving Average (SARIMA), electric usage was forecasted in peso and kilowatt-hour, and water usage in peso and cubic meter. Moreover, the predicted values of the consumptions are compared to the actual values using Paired T-test to examine whether there is a significant difference. Forecasting accurately the water and electric consumption would be helpful to manage the budget allotted for the water and electric consumption of PUP - Sta. Mesa for the next two years.

Do we need demographic data to forecast plant population dynamics?

USGS Publications Warehouse

Tredennick, Andrew T.; Hooten, Mevin B.; Adler, Peter B.

2017-01-01

Rapid environmental change has generated growing interest in forecasts of future population trajectories. Traditional population models built with detailed demographic observations from one study site can address the impacts of environmental change at particular locations, but are difficult to scale up to the landscape and regional scales relevant to management decisions. An alternative is to build models using population-level data that are much easier to collect over broad spatial scales than individual-level data. However, it is unknown whether models built using population-level data adequately capture the effects of density-dependence and environmental forcing that are necessary to generate skillful forecasts.Here, we test the consequences of aggregating individual responses when forecasting the population states (percent cover) and trajectories of four perennial grass species in a semi-arid grassland in Montana, USA. We parameterized two population models for each species, one based on individual-level data (survival, growth and recruitment) and one on population-level data (percent cover), and compared their forecasting accuracy and forecast horizons with and without the inclusion of climate covariates. For both models, we used Bayesian ridge regression to weight the influence of climate covariates for optimal prediction.In the absence of climate effects, we found no significant difference between the forecast accuracy of models based on individual-level data and models based on population-level data. Climate effects were weak, but increased forecast accuracy for two species. Increases in accuracy with climate covariates were similar between model types.In our case study, percent cover models generated forecasts as accurate as those from a demographic model. For the goal of forecasting, models based on aggregated individual-level data may offer a practical alternative to data-intensive demographic models. Long time series of percent cover data already exist for many plant species. Modelers should exploit these data to predict the impacts of environmental change.

How accurate are the weather forecasts for Bierun (southern Poland)?

NASA Astrophysics Data System (ADS)

Gawor, J.

2012-04-01

Weather forecast accuracy has increased in recent times mainly thanks to significant development of numerical weather prediction models. Despite the improvements, the forecasts should be verified to control their quality. The evaluation of forecast accuracy can also be an interesting learning activity for students. It joins natural curiosity about everyday weather and scientific process skills: problem solving, database technologies, graph construction and graphical analysis. The examination of the weather forecasts has been taken by a group of 14-year-old students from Bierun (southern Poland). They participate in the GLOBE program to develop inquiry-based investigations of the local environment. For the atmospheric research the automatic weather station is used. The observed data were compared with corresponding forecasts produced by two numerical weather prediction models, i.e. COAMPS (Coupled Ocean/Atmosphere Mesoscale Prediction System) developed by Naval Research Laboratory Monterey, USA; it runs operationally at the Interdisciplinary Centre for Mathematical and Computational Modelling in Warsaw, Poland and COSMO (The Consortium for Small-scale Modelling) used by the Polish Institute of Meteorology and Water Management. The analysed data included air temperature, precipitation, wind speed, wind chill and sea level pressure. The prediction periods from 0 to 24 hours (Day 1) and from 24 to 48 hours (Day 2) were considered. The verification statistics that are commonly used in meteorology have been applied: mean error, also known as bias, for continuous data and a 2x2 contingency table to get the hit rate and false alarm ratio for a few precipitation thresholds. The results of the aforementioned activity became an interesting basis for discussion. The most important topics are: 1) to what extent can we rely on the weather forecasts? 2) How accurate are the forecasts for two considered time ranges? 3) Which precipitation threshold is the most predictable? 4) Why are some weather elements easier to verify than others? 5) What factors may contribute to the quality of the weather forecast?

An Operational System for Surveillance and Ecological Forecasting of West Nile Virus Outbreaks

NASA Astrophysics Data System (ADS)

Wimberly, M. C.; Davis, J. K.; Vincent, G.; Hess, A.; Hildreth, M. B.

2017-12-01

Mosquito-borne disease surveillance has traditionally focused on tracking human cases along with the abundance and infection status of mosquito vectors. For many of these diseases, vector and host population dynamics are also sensitive to climatic factors, including temperature fluctuations and the availability of surface water for mosquito breeding. Thus, there is a potential to strengthen surveillance and predict future outbreaks by monitoring environmental risk factors using broad-scale sensor networks that include earth-observing satellites. The South Dakota Mosquito Information System (SDMIS) project combines entomological surveillance with gridded meteorological data from NASA's North American Land Data Assimilation System (NLDAS) to generate weekly risk maps for West Nile virus (WNV) in the north-central United States. Critical components include a mosquito infection model that smooths the noisy infection rate and compensates for unbalanced sampling, and a human infection model that combines the entomological risk estimates with lagged effects of meteorological variables from the North American Land Data Assimilation System (NLDAS). Two types of forecasts are generated: long-term forecasts of statewide risk extending through the entire WNV season, and short-term forecasts of the geographic pattern of WNV risk in the upcoming week. Model forecasts are connected to public health actions through decision support matrices that link predicted risk levels to a set of phased responses. In 2016, the SDMIS successfully forecast an early start to the WNV season and a large outbreak of WNV cases following several years of low transmission. An evaluation of the 2017 forecasts will also be presented. Our experiences with the SDMIS highlight several important lessons that can inform future efforts at disease early warning. These include the value of integrating climatic models with recent observations of infection, the critical role of automated workflows to facilitate the timely integration of multiple data streams, the need for effective synthesis and visualization of forecasts, and the importance of linking forecasts to specific public health responses.

Visualizing water

NASA Astrophysics Data System (ADS)

Baart, F.; van Gils, A.; Hagenaars, G.; Donchyts, G.; Eisemann, E.; van Velzen, J. W.

2016-12-01

A compelling visualization is captivating, beautiful and narrative. Here we show how melding the skills of computer graphics, art, statistics, and environmental modeling can be used to generate innovative, attractive and very informative visualizations. We focus on the topic of visualizing forecasts and measurements of water (water level, waves, currents, density, and salinity). For the field of computer graphics and arts, water is an important topic because it occurs in many natural scenes. For environmental modeling and statistics, water is an important topic because the water is essential for transport, a healthy environment, fruitful agriculture, and a safe environment.The different disciplines take different approaches to visualizing water. In computer graphics, one focusses on creating water as realistic looking as possible. The focus on realistic perception (versus the focus on the physical balance pursued by environmental scientists) resulted in fascinating renderings, as seen in recent games and movies. Visualization techniques for statistical results have benefited from the advancement in design and journalism, resulting in enthralling infographics. The field of environmental modeling has absorbed advances in contemporary cartography as seen in the latest interactive data-driven maps. We systematically review the design emerging types of water visualizations. The examples that we analyze range from dynamically animated forecasts, interactive paintings, infographics, modern cartography to web-based photorealistic rendering. By characterizing the intended audience, the design choices, the scales (e.g. time, space), and the explorability we provide a set of guidelines and genres. The unique contributions of the different fields show how the innovations in the current state of the art of water visualization have benefited from inter-disciplinary collaborations.

On using scatterometer and altimeter data to improve storm surge forecasting in the Adriatic Sea

NASA Astrophysics Data System (ADS)

Bajo, Marco; Umgiesser, Georg; De Biasio, Francesco; Vignudelli, Stefano; Zecchetto, Stefano

2017-04-01

Satellite data are seldom used in storm surge forecasting. Among the most important issues related to the storm surge forecasting are the quality of the model wind forcing and the initial condition of the sea surface elevation. In this work, focused on storm surge forecasting in the Adriatic Sea, satellite scatterometer wind data are used to correct the wind speed and direction biases of the ECMWF global atmospheric model by tuning the spatial fields, as an alternative to data assimilation. The capability of such an unbiased wind is tested against that of a high resolution wind, produced by a regional non-hydrostatic model. On the other hand, altimeter Total Water Level Envelope (TWLE) data, which provide the sea level elevation, are used to improve the accuracy of the initial state of the model simulations. This is done by assimilating into a storm surge model the TWLE obtained by the altimeter observations along ground tracks, after subtraction of the tidal components. In order to test the methodology, eleven storm surge events recorded in Venice, from 2008 to 2012, have been simulated using different configurations of forcing wind and altimeter data assimilation. Results show that the relative error on the estimation of the maximum surge peak, averaged over the cases considered, decreases from 13% to 7% using both the unbiased wind and the altimeter data assimilation, while forcing the hydrodynamic model with the high resolution wind (no tuning), the altimeter data assimilation reduces the error from 9% to 6%.

A comparison of model short-range forecasts and the ARM Microbase data

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

Hnilo, J J

2006-09-22

For the fourth quarter ARM metric we will make use of new liquid water data that has become available, and called the 'Microbase' value added product (referred to as OBS, within the text) at three sites: the North Slope of Alaska (NSA), Tropical West Pacific (TWP) and the Southern Great Plains (SGP) and compare these observations to model forecast data. Two time periods will be analyzed March 2000 for the SGP and October 2004 for both TWP and NSA. The Microbase data have been averaged to 35 pressure levels (e.g., from 1000hPa to 100hPa at 25hPa increments) and time averagedmore » to 3hourly data for direct comparison to our model output.« less

USA Nutrient managment forecasting via the "Fertilizer Forecaster": linking surface runnof, nutrient application and ecohydrology.

NASA Astrophysics Data System (ADS)

Drohan, Patrick; Buda, Anthony; Kleinman, Peter; Miller, Douglas; Lin, Henry; Beegle, Douglas; Knight, Paul

2017-04-01

USA and state nutrient management planning offers strategic guidance that strives to educate farmers and those involved in nutrient management to make wise management decisions. A goal of such programs is to manage hotspots of water quality degradation that threaten human and ecosystem health, water and food security. The guidance provided by nutrient management plans does not provide the day-to-day support necessary to make operational decisions, particularly when and where to apply nutrients over the short term. These short-term decisions on when and where to apply nutrients often make the difference between whether the nutrients impact water quality or are efficiently utilized by crops. Infiltrating rainfall events occurring shortly after broadcast nutrient applications are beneficial, given they will wash soluble nutrients into the soil where they are used by crops. Rainfall events that generate runoff shortly after nutrients are broadcast may wash off applied nutrients, and produce substantial nutrient losses from that site. We are developing a model and data based support tool for nutrient management, the Fertilizer Forecaster, which identifies the relative probability of runoff or infiltrating events in Pennsylvania (PA) landscapes in order to improve water quality. This tool will support field specific decisions by farmers and land managers on when and where to apply fertilizers and manures over 24, 48 and 72 hour periods. Our objectives are to: (1) monitor agricultural hillslopes in watersheds representing four of the five Physiographic Provinces of the Chesapeake Bay basin; (2) validate a high resolution mapping model that identifies soils prone to runoff; (3) develop an empirically based approach to relate state-of-the-art weather forecast variables to site-specific rainfall infiltration or runoff occurrence; (4) test the empirical forecasting model against alternative approaches to forecasting runoff occurrence; and (5) recruit farmers from the four watersheds to use web-based forecast maps in daily manure and fertilizer application decisions. Data from on-farm trials is being used to assess farmer fertilizer, manure, and tillage management decisions before and after use of the Fertilizer Forecaster. This data will help us understand not only the effectiveness of the tool, but also characteristics of farmers with the greatest potential to benefit from such a tool. Feedback from on-farm trials will be used to refine a final tool for field deployment. We hope that the Fertilizer Forecaster will serve as the basis for state (USA-PA), regional (Chesapeake Bay), and national changes in nutrient management planning. This Fertilizer Forecaster is an innovative management practice that is designed to enhance the services of aquatic ecosystems by improving water quality and enhance the services of terrestrial ecosystems by increasing the efficiency of nutrient use by targeted crops.

California's transition from conventional snowpack measurements to a developing remote sensing capability for water supply forecasting

NASA Technical Reports Server (NTRS)

Brown, A. J.; Peterson, N.

1980-01-01

California's Snow Survey Program and water supply forecasting procedures are described. A review is made of current activities and program direction on such matters as: the growing statewide network of automatic snow sensors; restrictions on the gathering hydrometeorological data in areas designated as wilderness; the use of satellite communications, which both provides a flexible network without mountaintop repeaters and satisfies the need for unobtrusiveness in wilderness areas; and the increasing operational use of snow covered area (SCA) obtained from satellite imagery, which, combined with water equivalent from snow sensors, provides a high correlation to the volumes and rates of snowmelt runoff. Also examined are the advantages of remote sensing; the anticipated effects of a new input of basin wide index of water equivalent, such as the obtained through microwave techniques, on future forecasting opportunities; and the future direction and goals of the California Snow Surveys Program.

State updating and calibration period selection to improve dynamic monthly streamflow forecasts for an environmental flow management application

NASA Astrophysics Data System (ADS)

Gibbs, Matthew S.; McInerney, David; Humphrey, Greer; Thyer, Mark A.; Maier, Holger R.; Dandy, Graeme C.; Kavetski, Dmitri

2018-02-01

Monthly to seasonal streamflow forecasts provide useful information for a range of water resource management and planning applications. This work focuses on improving such forecasts by considering the following two aspects: (1) state updating to force the models to match observations from the start of the forecast period, and (2) selection of a shorter calibration period that is more representative of the forecast period, compared to a longer calibration period traditionally used. The analysis is undertaken in the context of using streamflow forecasts for environmental flow water management of an open channel drainage network in southern Australia. Forecasts of monthly streamflow are obtained using a conceptual rainfall-runoff model combined with a post-processor error model for uncertainty analysis. This model set-up is applied to two catchments, one with stronger evidence of non-stationarity than the other. A range of metrics are used to assess different aspects of predictive performance, including reliability, sharpness, bias and accuracy. The results indicate that, for most scenarios and metrics, state updating improves predictive performance for both observed rainfall and forecast rainfall sources. Using the shorter calibration period also improves predictive performance, particularly for the catchment with stronger evidence of non-stationarity. The results highlight that a traditional approach of using a long calibration period can degrade predictive performance when there is evidence of non-stationarity. The techniques presented can form the basis for operational monthly streamflow forecasting systems and provide support for environmental decision-making.

Modification of land-atmosphere interactions by CO2 effects: Implications for summer dryness and heat wave amplitude

NASA Astrophysics Data System (ADS)

Lemordant, Léo.; Gentine, Pierre; Stéfanon, Marc; Drobinski, Philippe; Fatichi, Simone

2016-10-01

Plant stomata couple the energy, water, and carbon cycles. We use the framework of Regional Climate Modeling to simulate the 2003 European heat wave and assess how higher levels of surface CO2 may affect such an extreme event through land-atmosphere interactions. Increased CO2 modifies the seasonality of the water cycle through stomatal regulation and increased leaf area. As a result, the water saved during the growing season through higher water use efficiency mitigates summer dryness and the heat wave impact. Land-atmosphere interactions and CO2 fertilization together synergistically contribute to increased summer transpiration. This, in turn, alters the surface energy budget and decreases sensible heat flux, mitigating air temperature rise. Accurate representation of the response to higher CO2 levels and of the coupling between the carbon and water cycles is therefore critical to forecasting seasonal climate, water cycle dynamics, and to enhance the accuracy of extreme event prediction under future climate.

Retransmission of water resources data using the ERTS-1 data collection system

NASA Technical Reports Server (NTRS)

Halliday, R. A.; Reid, I. A.; Chapman, E. F.

1974-01-01

The Water Survey of Canada operates a network of approximately 2400 gauging stations at which water level data are collected. Nine DCPs were installed in isolated areas of northern and western Canada. It was felt that DCPs in these locations would be exposed to climatic conditions severe enough to provide a check on system reliability. Water level data are transmitted from all sites and, also, some of the DCPs are used to transmit ice break-up, water velocity, precipitation, air temperature, water stage recorder clock operation or DCP battery voltage. Consideration is being given to transmitting other parameters that would be of value in flood or flow forecasting. Results of the project have been excellent. There has been no data loss that can be attributed to failure of a DCP although data were lost because of sensor malfunctions. The quality of data collected compares favourably with that of the hard record obtained at the remote sites. Costs of using the ERTS Data Collection System are reasonable.

Time to death and the forecasting of macro-level health care expenditures: some further considerations.

PubMed

van Baal, Pieter H; Wong, Albert

2012-12-01

Although the effect of time to death (TTD) on health care expenditures (HCE) has been investigated using individual level data, the most profound implications of TTD have been for the forecasting of macro-level HCE. Here we estimate the TTD model using macro-level data from the Netherlands consisting of mortality rates and age- and gender-specific per capita health expenditures for the years 1981-2007. Forecasts for the years 2008-2020 of this macro-level TTD model were compared to forecasts that excluded TTD. Results revealed that the effect of TTD on HCE in our macro model was similar to those found in micro-econometric studies. As the inclusion of TTD pushed growth rate estimates from unidentified causes upwards, however, the two models' forecasts of HCE for the 2008-2020 were similar. We argue that including TTD, if modeled correctly, does not lower forecasts of HCE. Copyright © 2012 Elsevier B.V. All rights reserved.

Transitioning a Chesapeake Bay Ecological Prediction System to Operations

NASA Astrophysics Data System (ADS)

Brown, C.; Green, D. S.; Eco Forecasters

2011-12-01

Ecological prediction of the impacts of physical, chemical, biological, and human-induced change on ecosystems and their components, encompass a wide range of space and time scales, and subject matter. They vary from predicting the occurrence and/or transport of certain species, such harmful algal blooms, or biogeochemical constituents, such as dissolved oxygen concentrations, to large-scale ecosystem responses and higher trophic levels. The timescales of ecological prediction, including guidance and forecasts, range from nowcasts and short-term forecasts (days), to intraseasonal and interannual outlooks (weeks to months), to decadal and century projections in climate change scenarios. The spatial scales range from small coastal inlets to basin and global scale biogeochemical and ecological forecasts. The types of models that have been used include conceptual, empirical, mechanistic, and hybrid approaches. This presentation will identify the challenges and progress toward transitioning experimental model-based ecological prediction into operational guidance and forecasting. Recent efforts are targeting integration of regional ocean, hydrodynamic and hydrological models and leveraging weather and water service infrastructure to enable the prototyping of an operational ecological forecast capability for the Chesapeake Bay and its tidal tributaries. A path finder demonstration predicts the probability of encountering sea nettles (Chrysaora quinquecirrha), a stinging jellyfish. These jellyfish can negatively impact safety and economic activities in the bay and an impact-based forecast that predicts where and when this biotic nuisance occurs may help management effects. The issuance of bay-wide nowcasts and three-day forecasts of sea nettle probability are generated daily by forcing an empirical habitat model (that predicts the probability of sea nettles) with real-time and 3-day forecasts of sea-surface temperature (SST) and salinity (SSS). In the first demonstration phase, the sea surface temperature (SST) and sea surface salinity (SSS) fields are generated by the Chesapeake Bay Operational Forecast System (CBOFS2), a 3-dimensional hydrodynamic model developed and operated by NOAA's National Ocean Service and run operationally at the National Weather Service National Centers for Environmental Prediction (NCEP). Importantly, this system is readily modified to predict the probability of other important target organisms, such as harmful algal blooms, biogeochemical constituents, such as dissolved oxygen concentration, and water-borne pathogens. Extending this initial effort includes advancement of a regional coastal ocean modeling testbed and proving ground. Such formal collaboration is intended to accelerate transition to operations and increase confidence and use of forecast guidance. The outcome will be improved decision making by emergency and resource managers, scientific researchers and the general public. The presentation will describe partnership plans for this testbed as well as the potential implications for the services and research community.

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.

Tsunami Forecasting in the Atlantic Basin

NASA Astrophysics Data System (ADS)

Knight, W. R.; Whitmore, P.; Sterling, K.; Hale, D. A.; Bahng, B.

2012-12-01

The mission of the West Coast and Alaska Tsunami Warning Center (WCATWC) is to provide advance tsunami warning and guidance to coastal communities within its Area-of-Responsibility (AOR). Predictive tsunami models, based on the shallow water wave equations, are an important part of the Center's guidance support. An Atlantic-based counterpart to the long-standing forecasting ability in the Pacific known as the Alaska Tsunami Forecast Model (ATFM) is now developed. The Atlantic forecasting method is based on ATFM version 2 which contains advanced capabilities over the original model; including better handling of the dynamic interactions between grids, inundation over dry land, new forecast model products, an optional non-hydrostatic approach, and the ability to pre-compute larger and more finely gridded regions using parallel computational techniques. The wide and nearly continuous Atlantic shelf region presents a challenge for forecast models. Our solution to this problem has been to develop a single unbroken high resolution sub-mesh (currently 30 arc-seconds), trimmed to the shelf break. This allows for edge wave propagation and for kilometer scale bathymetric feature resolution. Terminating the fine mesh at the 2000m isobath keeps the number of grid points manageable while allowing for a coarse (4 minute) mesh to adequately resolve deep water tsunami dynamics. Higher resolution sub-meshes are then included around coastal forecast points of interest. The WCATWC Atlantic AOR includes eastern U.S. and Canada, the U.S. Gulf of Mexico, Puerto Rico, and the Virgin Islands. Puerto Rico and the Virgin Islands are in very close proximity to well-known tsunami sources. Because travel times are under an hour and response must be immediate, our focus is on pre-computing many tsunami source "scenarios" and compiling those results into a database accessible and calibrated with observations during an event. Seismic source evaluation determines the order of model pre-computation - starting with those sources that carry the highest risk. Model computation zones are confined to regions at risk to save computation time. For example, Atlantic sources have been shown to not propagate into the Gulf of Mexico. Therefore, fine grid computations are not performed in the Gulf for Atlantic sources. Outputs from the Atlantic model include forecast marigrams at selected sites, maximum amplitudes, drawdowns, and currents for all coastal points. The maximum amplitude maps will be supplemented with contoured energy flux maps which show more clearly the effects of bathymetric features on tsunami wave propagation. During an event, forecast marigrams will be compared to observations to adjust the model results. The modified forecasts will then be used to set alert levels between coastal breakpoints, and provided to emergency management.

GREAT LAKES FAX-BACK SERVICE

Science.gov Websites

available to ships participating in the Voluntary Observing Ships (VOS) program. To register as a Fax-Back Tsunamis 406 EPIRB's National Weather Service Marine Forecasts GREAT LAKES FAX-BACK SERVICE Marine Forecast months. Did you know your body can cool 25 times faster in water than in air? That water does not need to

Hydroclimate Forecasts in Ethiopia: Benefits, Impediments, and Ways Forward

NASA Astrophysics Data System (ADS)

Block, P. J.

2014-12-01

Numerous hydroclimate forecast models, tools, and guidance exist for application across Ethiopia and East Africa in the agricultural, water, energy, disasters, and economic sectors. This has resulted from concerted local and international interdisciplinary efforts, yet little evidence exists of rapid forecast uptake and use. We will review projected benefits and gains of seasonal forecast application, impediments, and options for the way forward. Specific case studies regarding floods, agricultural-economic links, and hydropower will be reviewed.

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.

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.

Enhancing Seasonal Water Outlooks: Needs and Opportunities in the Critical Runoff Season

NASA Astrophysics Data System (ADS)

Ray, A. J.; Barsugli, J. J.; Yocum, H.; Stokes, M.; Miskus, D.

2017-12-01

The runoff season is a critical period for the management of water supply in the western U.S., where in many places over 70% of the annual runoff occurs in the snowmelt period. Managing not only the volume, but the intra-seasonal timing of the runoff is important for optimizing storage, as well as achieving other goals such as mitigating flood risk, and providing peak flows for riparian habitat management, for example, for endangered species. Western river forecast centers produce volume forecasts for western reservoirs that are key input into many water supply decisions, and also short term river forecasts out to 10 days. The early volume forecasts each year typically begin in December, and are updated throughout the winter and into the runoff season (April-July for many areas, but varies). This presentation will discuss opportunities for enhancing this existing suite of RFC water outlooks, including the needs for and potential use for "intraseasonal" products beyond those provided by the Ensemble Streamflow Prediction system and the volume forecasts. While precipitation outlooks have little skill for many areas and seasons, and may not contribute significantly to the outlook, late winter and spring temperature forecasts have meaningful skill in certain areas and sub-seasonal to seasonal time scales. This current skill in CPC temperature outlooks is an opportunity to translate these products into information about the snowpack and potential runoff timing, even where the skill in precipitation is low. Temperature is important for whether precipitation falls as snow or rain, which is critical for streamflow forecasts, especially in the melt season in snowpack-dependent watersheds. There is a need for better outlooks of the evolution of snowpack, conditions influencing the April-July runoff, and the timing of spring peak or shape of the spring hydrograph. The presentation will also discuss a our work with stakeholders of the River Forecast Centers and the NIDIS Drought Early Warning Systems to refine stakeholder needs and create a refined decision calendar for upper Colorado River reservoirs that details decisions in the runoff period.

Total probabilities of ensemble runoff forecasts

NASA Astrophysics Data System (ADS)

Olav Skøien, Jon; Bogner, Konrad; Salamon, Peter; Smith, Paul; Pappenberger, Florian

2016-04-01

Ensemble forecasting has for a long time been used as a method in meteorological modelling to indicate the uncertainty of the forecasts. However, as the ensembles often exhibit both bias and dispersion errors, it is necessary to calibrate and post-process them. Two of the most common 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). Engeland and Steinsland Engeland and Steinsland (2014) developed a framework which can estimate post-processing parameters which are different in space and time, but still can give a spatially and temporally consistent output. However, their method is computationally complex for our larger number of stations, and cannot directly be regionalized in the way we would like, so we suggest a different path below. The target of our work is to create a mean forecast with uncertainty bounds for a large number of locations in the framework of the European Flood Awareness System (EFAS - http://www.efas.eu) We are therefore more interested in improving the forecast skill for high-flows rather than the forecast skill of lower runoff levels. EFAS uses a combination of ensemble forecasts and deterministic forecasts from different forecasters to force a distributed hydrologic model and to compute runoff ensembles for each river pixel within the model domain. Instead of showing the mean and the variability of each forecast ensemble individually, we will now post-process all model outputs to find a total probability, the post-processed mean and uncertainty of all ensembles. The post-processing parameters are first calibrated for each calibration location, but assuring that they have some spatial correlation, by adding a spatial penalty in the calibration process. This can in some cases have a slight negative impact on the calibration error, but makes it easier to interpolate the post-processing parameters to uncalibrated locations. We also look into different methods for handling the non-normal distributions of runoff data and the effect of different data transformations 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. Engeland, K. and Steinsland, I.: Probabilistic postprocessing models for flow forecasts for a system of catchments and several lead times, Water Resour. Res., 50(1), 182-197, doi:10.1002/2012WR012757, 2014. 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.

Semi-arid vegetation response to antecedent climate and water balance windows

USGS Publications Warehouse

Thoma, David P.; Munson, Seth M.; Irvine, Kathryn M.; Witwicki, Dana L.; Bunting, Erin

2016-01-01

Questions Can we improve understanding of vegetation response to water availability on monthly time scales in semi-arid environments using remote sensing methods? What climatic or water balance variables and antecedent windows of time associated with these variables best relate to the condition of vegetation? Can we develop credible near-term forecasts from climate data that can be used to prepare for future climate change effects on vegetation? Location Semi-arid grasslands in Capitol Reef National Park, Utah, USA. Methods We built vegetation response models by relating the normalized difference vegetation index (NDVI) from MODIS imagery in Mar–Nov 2000–2013 to antecedent climate and water balance variables preceding the monthly NDVI observations. We compared how climate and water balance variables explained vegetation greenness and then used a multi-model ensemble of climate and water balance models to forecast monthly NDVI for three holdout years. Results Water balance variables explained vegetation greenness to a greater degree than climate variables for most growing season months. Seasonally important variables included measures of antecedent water input and storage in spring, switching to indicators of drought, input or use in summer, followed by antecedent moisture availability in autumn. In spite of similar climates, there was evidence the grazed grassland showed a response to drying conditions 1 mo sooner than the ungrazed grassland. Lead times were generally short early in the growing season and antecedent window durations increased from 3 mo early in the growing season to 1 yr or more as the growing season progressed. Forecast accuracy for three holdout years using a multi-model ensemble of climate and water balance variables outperformed forecasts made with a naïve NDVI climatology. Conclusions We determined the influence of climate and water balance on vegetation at a fine temporal scale, which presents an opportunity to forecast vegetation response with short lead times. This understanding was obtained through high-frequency vegetation monitoring using remote sensing, which reduces the costs and time necessary for field measurements and can lead to more rapid detection of vegetation changes that could help managers take appropriate actions.

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.

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.

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.

Hybrid vs Adaptive Ensemble Kalman Filtering for Storm Surge Forecasting

NASA Astrophysics Data System (ADS)

Altaf, M. U.; Raboudi, N.; Gharamti, M. E.; Dawson, C.; McCabe, M. F.; Hoteit, I.

2014-12-01

Recent storm surge events due to Hurricanes in the Gulf of Mexico have motivated the efforts to accurately forecast water levels. Toward this goal, a parallel architecture has been implemented based on a high resolution storm surge model, ADCIRC. However the accuracy of the model notably depends on the quality and the recentness of the input data (mainly winds and bathymetry), model parameters (e.g. wind and bottom drag coefficients), and the resolution of the model grid. Given all these uncertainties in the system, the challenge is to build an efficient prediction system capable of providing accurate forecasts enough ahead of time for the authorities to evacuate the areas at risk. We have developed an ensemble-based data assimilation system to frequently assimilate available data into the ADCIRC model in order to improve the accuracy of the model. In this contribution we study and analyze the performances of different ensemble Kalman filter methodologies for efficient short-range storm surge forecasting, the aim being to produce the most accurate forecasts at the lowest possible computing time. Using Hurricane Ike meteorological data to force the ADCIRC model over a domain including the Gulf of Mexico coastline, we implement and compare the forecasts of the standard EnKF, the hybrid EnKF and an adaptive EnKF. The last two schemes have been introduced as efficient tools for enhancing the behavior of the EnKF when implemented with small ensembles by exploiting information from a static background covariance matrix. Covariance inflation and localization are implemented in all these filters. Our results suggest that both the hybrid and the adaptive approach provide significantly better forecasts than those resulting from the standard EnKF, even when implemented with much smaller ensembles.

Flood-inundation maps for the Saddle River from Upper Saddle River Borough to Saddle River Borough, New Jersey, 2013

USGS Publications Warehouse

Watson, Kara M.; Hoppe, Heidi L.

2013-01-01

Digital flood-inundation maps for a 4.1-mile reach of the Saddle River from 0.6 miles downstream from the New Jersey-New York State boundary in Upper Saddle River Borough to 0.2 miles downstream from the East Allendale Road bridge in Saddle River Borough, New Jersey, were created by the U.S. Geological Survey (USGS) in cooperation with the New Jersey Department of Environmental Protection (NJDEP). The inundation maps, which can be accessed through the USGS Flood Inundation Mapping Science Web site at http://water.usgs.gov/osw/flood_inundation/, depict estimates of the areal extent and depth of flooding corresponding to select water levels (stages) at the USGS streamgage 01390450, Saddle River at Upper Saddle River, New Jersey. Current conditions for estimating near real-time areas of inundation using USGS streamgage information may be obtained on the Internet at http://waterdata.usgs.gov/nwis/uv?site_no=01390450. The National Weather Service (NWS) forecasts flood hydrographs at many places that are often collocated with USGS streamgages. NWS-forecasted peak-stage information may be used in conjunction with the maps developed in this study to show predicted areas of flood inundation. In this study, flood profiles were computed for the stream reach by means of a one-dimensional step-backwater model. The model was calibrated by using the most current stage-discharge relations (in effect March 2013) at USGS streamgage 01390450, Saddle River at Upper Saddle River, New Jersey, and documented high-water marks from recent floods. The hydraulic model was then used to determine eight water-surface profiles for flood stages at 0.5-foot (ft) intervals referenced to the streamgage datum, North American Vertical Datum of 1988 (NAVD 88), and ranging from bankfull, 0.5 ft below NWS Action Stage, to the upper extent of the stage-discharge rating which is approximately 1 ft higher than the highest recorded water level at the streamgage. Action Stage is the stage which when reached by a rising stream the NWS or a partner needs to take some type of mitigation action in preparation for possible significant hydrologic activity. The simulated water-surface profiles were then combined with a geographic information system 3-meter (9.84 ft) digital elevation model (derived from Light Detection and Ranging (LiDAR) data) in order to delineate the area flooded at each water level. The availability of these maps along with real-time streamflow data and information regarding current stage from USGS streamgages and forecasted stream stages from the NWS provide emergency management personnel and residents with information that is critical for flood response activities, such as evacuations and road closures, as well as for post-flood recovery efforts.

Flood-inundation maps for the Saddle River from Rochelle Park to Lodi, New Jersey, 2012

USGS Publications Warehouse

Hoppe, Heidi L.; Watson, Kara M.

2012-01-01

Digital flood-inundation maps for a 2.75-mile reach of the Saddle River from 0.2 mile upstream from the Interstate 80 bridge in Rochelle Park to 1.5 miles downstream from the U.S. Route 46 bridge in Lodi, New Jersey, were created by the U.S. Geological Survey (USGS) in cooperation with the New Jersey Department of Environmental Protection (NJDEP). The inundation maps, which can be accessed through the USGS Flood Inundation Mapping Science Web site at http://water.usgs.gov/osw/flood_inundation, depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at the USGS streamgage at Saddle River at Lodi, New Jersey (station 01391500). Current conditions for estimating near real-time areas of inundation using USGS streamgage information may be obtained on the Internet at http://waterdata.usgs.gov/nwis/uv?site_no=01391500. The National Weather Service (NWS) forecasts flood hydrographs at many places that are often collocated with USGS streamgages. NWS-forecasted peak-stage information may be used in conjunction with the maps developed in this study to show predicted areas of flood inundation. In this study, flood profiles were computed for the stream reach by means of a one-dimensional step-backwater model. The model was calibrated using the most current stage-discharge relations at the Saddle River at Lodi, New Jersey streamgage and documented high-water marks from recent floods. The hydraulic model was then used to determine 11 water-surface profiles for flood stages at the Saddle River streamgage at 1-ft intervals referenced to the streamgage datum, North American Vertical Datum of 1988 (NAVD 88), and ranging from bankfull, 0.5 ft below NWS Action Stage, to the extent of the stage-discharge rating, which is approximately 1 ft higher than the highest recorded water level at the streamgage. Action Stage is the stage which when reached by a rising stream the NWS or a partner needs to take some type of mitigation action in preparation for possible significant hydrologic activity. The simulated water-surface profiles were then combined with a geographic information system 3-meter (9.84-ft) digital elevation model (derived from Light Detection and Ranging (LiDAR) data) in order to delineate the area flooded at each water level. The availability of these maps, along with Internet information regarding current stage from USGS streamgages and forecasted stream stages from the NWS, provide emergency management personnel and residents with information that is critical for flood response activities, such as evacuations and road closures, as well as for post-flood recovery efforts.

Potential influences of neglecting aerosol effects on the NCEP GFS precipitation forecast

NASA Astrophysics Data System (ADS)

Jiang, Mengjiao; Feng, Jinqin; Li, Zhanqing; Sun, Ruiyu; Hou, Yu-Tai; Zhu, Yuejian; Wan, Bingcheng; Guo, Jianping; Cribb, Maureen

2017-11-01

Aerosol-cloud interactions (ACIs) have been widely recognized as a factor affecting precipitation. However, they have not been considered in the operational National Centers for Environmental Predictions Global Forecast System model. We evaluated the potential impact of neglecting ACI on the operational rainfall forecast using ground-based and satellite observations and model reanalysis. The Climate Prediction Center unified gauge-based precipitation analysis and the Modern-Era Retrospective analysis for Research and Applications Version 2 aerosol reanalysis were used to evaluate the forecast in three countries for the year 2015. The overestimation of light rain (47.84 %) and underestimation of heavier rain (31.83, 52.94, and 65.74 % for moderate rain, heavy rain, and very heavy rain, respectively) from the model are qualitatively consistent with the potential errors arising from not accounting for ACI, although other factors cannot be totally ruled out. The standard deviation of the forecast bias was significantly correlated with aerosol optical depth in Australia, the US, and China. To gain further insight, we chose the province of Fujian in China to pursue a more insightful investigation using a suite of variables from gauge-based observations of precipitation, visibility, water vapor, convective available potential energy (CAPE), and satellite datasets. Similar forecast biases were found: over-forecasted light rain and under-forecasted heavy rain. Long-term analyses revealed an increasing trend in heavy rain in summer and a decreasing trend in light rain in other seasons, accompanied by a decreasing trend in visibility, no trend in water vapor, and a slight increasing trend in summertime CAPE. More aerosols decreased cloud effective radii for cases where the liquid water path was greater than 100 g m-2. All findings are consistent with the effects of ACI, i.e., where aerosols inhibit the development of shallow liquid clouds and invigorate warm-base mixed-phase clouds (especially in summertime), which in turn affects precipitation. While we cannot establish rigorous causal relations based on the analyses presented in this study, the significant rainfall forecast bias seen in operational weather forecast model simulations warrants consideration in future model improvements.

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.

Sea level forecasts for Pacific Islands based on Satellite Altimetry

NASA Astrophysics Data System (ADS)

Yoon, H.; Merrifield, M. A.; Thompson, P. R.; Widlansky, M. J.; Marra, J. J.

2017-12-01

Coastal flooding at tropical Pacific Islands often occurs when positive sea level anomalies coincide with high tides. To help mitigate this risk, a forecast tool for daily-averaged sea level anomalies is developed that can be added to predicted tides at tropical Pacific Island sites. The forecast takes advantage of the observed westward propagation that sea level anomalies exhibit over a range of time scales. The daily near-real time altimetry gridded data from Archiving, Validation, and Interpretation of Satellite Oceanographic (AVISO) is used to specify upstream sea level at each site, with lead times computed based on mode-one baroclinic Rossby wave speeds. To validate the forecast, hindcasts are compared to tide gauge and nearby AVISO gridded time series. The forecast skills exceed persistence at most stations out to a month or more lead time. The skill is highest at stations where eddy variability is relatively weak. The impacts on the forecasts due to varying propagation speed, decay time, and smoothing of the AVISO data are examined. In addition, the inclusion of forecast winds in a forced wave equation is compared to the freely propagating results. Case studies are presented for seasonally high tide events throughout the Pacific Island region.

Developing flood-inundation maps for Johnson Creek, Portland, Oregon

USGS Publications Warehouse

Stonewall, Adam J.; Beal, Benjamin A.

2017-04-14

Digital flood-inundation maps were created for a 12.9‑mile reach of Johnson Creek by the U.S. Geological Survey (USGS). The flood-inundation maps depict estimates of water depth and areal extent of flooding from the mouth of Johnson Creek to just upstream of Southeast 174th Avenue in Portland, Oregon. Each flood-inundation map is based on a specific water level and associated streamflow at the USGS streamgage, Johnson Creek at Sycamore, Oregon (14211500), which is located near the upstream boundary of the maps. The maps produced by the USGS, and the forecasted flood hydrographs produced by National Weather Service River Forecast Center can be accessed through the USGS Flood Inundation Mapper Web site (http://wimcloud.usgs.gov/apps/FIM/FloodInundationMapper.html).Water-surface elevations were computed for Johnson Creek using a combined one-dimensional and two‑dimensional unsteady hydraulic flow model. The model was calibrated using data collected from the flood of December 2015 (including the calculated streamflows at two USGS streamgages on Johnson Creek) and validated with data from the flood of January 2009. Results were typically within 0.6 foot (ft) of recorded or measured water-surface elevations from the December 2015 flood, and within 0.8 ft from the January 2009 flood. Output from the hydraulic model was used to create eight flood inundation maps ranging in stage from 9 to 16 ft. Boundary condition hydrographs were identical in shape to those from the December 2015 flood event, but were scaled up or down to produce the amount of streamflow corresponding to a specific water-surface elevation at the Sycamore streamgage (14211500). Sensitivity analyses using other hydrograph shapes, and a version of the model in which the peak flow is maintained for an extended period of time, showed minimal variation, except for overbank areas near the Foster Floodplain Natural Area.Simulated water-surface profiles were combined with light detection and ranging (lidar) data collected in 2014 to delineate water-surface extents for each of the eight modeled stages. The availability of flood-inundation maps in conjunction with real-time data from the USGS streamgages along Johnson Creek and forecasted hydrographs from the National Weather Service Northwest River Forecast Center will provide residents of the watershed and emergency management personnel with valuable information that may aid in flood response, including potential evacuations, road closures, and mitigation efforts. In addition, these maps may be used for post-flood recovery efforts.

Real-Time System for Water Modeling and Management

NASA Astrophysics Data System (ADS)

Lee, J.; Zhao, T.; David, C. H.; Minsker, B.

2012-12-01

Working closely with the Texas Commission on Environmental Quality (TCEQ) and the University of Texas at Austin (UT-Austin), we are developing a real-time system for water modeling and management using advanced cyberinfrastructure, data integration and geospatial visualization, and numerical modeling. The state of Texas suffered a severe drought in 2011 that cost the state $7.62 billion in agricultural losses (crops and livestock). Devastating situations such as this could potentially be avoided with better water modeling and management strategies that incorporate state of the art simulation and digital data integration. The goal of the project is to prototype a near-real-time decision support system for river modeling and management in Texas that can serve as a national and international model to promote more sustainable and resilient water systems. The system uses National Weather Service current and predicted precipitation data as input to the Noah-MP Land Surface model, which forecasts runoff, soil moisture, evapotranspiration, and water table levels given land surface features. These results are then used by a river model called RAPID, along with an error model currently under development at UT-Austin, to forecast stream flows in the rivers. Model forecasts are visualized as a Web application for TCEQ decision makers, who issue water diversion (withdrawal) permits and any needed drought restrictions; permit holders; and reservoir operation managers. Users will be able to adjust model parameters to predict the impacts of alternative curtailment scenarios or weather forecasts. A real-time optimization system under development will help TCEQ to identify optimal curtailment strategies to minimize impacts on permit holders and protect health and safety. To develop the system we have implemented RAPID as a remotely-executed modeling service using the Cyberintegrator workflow system with input data downloaded from the North American Land Data Assimilation System. The Cyberintegrator workflow system provides RESTful web services for users to provide inputs, execute workflows, and retrieve outputs. Along with REST endpoints, PAW (Publishable Active Workflows) provides the web user interface toolkit for us to develop web applications with scientific workflows. The prototype web application is built on top of workflows with PAW, so that users will have a user-friendly web environment to provide input parameters, execute the model, and visualize/retrieve the results using geospatial mapping tools. In future work the optimization model will be developed and integrated into the workflow.; Real-Time System for Water Modeling and Management

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.

Modeling and forecasting the distribution of Vibrio vulnificus in Chesapeake Bay.

PubMed

Jacobs, J M; Rhodes, M; Brown, C W; Hood, R R; Leight, A; Long, W; Wood, R

2014-11-01

To construct statistical models to predict the presence, abundance and potential virulence of Vibrio vulnificus in surface waters of Chesapeake Bay for implementation in ecological forecasting systems. We evaluated and applied previously published qPCR assays to water samples (n = 1636) collected from Chesapeake Bay from 2007-2010 in conjunction with State water quality monitoring programmes. A variety of statistical techniques were used in concert to identify water quality parameters associated with V. vulnificus presence, abundance and virulence markers in the interest of developing strong predictive models for use in regional oceanographic modeling systems. A suite of models are provided to represent the best model fit and alternatives using environmental variables that allow them to be put to immediate use in current ecological forecasting efforts. Environmental parameters such as temperature, salinity and turbidity are capable of accurately predicting abundance and distribution of V. vulnificus in Chesapeake Bay. Forcing these empirical models with output from ocean modeling systems allows for spatially explicit forecasts for up to 48 h in the future. This study uses one of the largest data sets compiled to model Vibrio in an estuary, enhances our understanding of environmental correlates with abundance, distribution and presence of potentially virulent strains and offers a method to forecast these pathogens that may be replicated in other regions. This article has been contributed to by US Government employees and their work is in the public domain in the USA.

The use of snowcovered area in runoff forecasts

NASA Technical Reports Server (NTRS)

Rango, A.; Hannaford, J. F.; Hall, R. L.; Rosenzweig, M.; Brown, A. J.

1977-01-01

Long-term snowcovered area data from aircraft and satellite observations have proven useful in reducing seasonal runoff forecast error on the Kern river watershed. Similar use of snowcovered area on the Kings river watershed produced results that were about equivalent to methods based solely on conventional data. Snowcovered area will be most effective in reducing forecast procedural error on watersheds with: (1) a substantial amount of area within a limited elevation range; (2) an erratic precipitation and/or snowpack accumulation pattern not strongly related to elevation; and (3) poor coverage by precipitation stations or snow courses restricting adequate indexing of water supply conditions. When satellite data acquisition and delivery problems are resolved, the derived snowcover information should provide a means for enhancing operational streamflow forecasts for areas that depend primarily on snowmelt for their water supply.

GPS Estimates of Integrated Precipitable Water Aid Weather Forecasters

NASA Technical Reports Server (NTRS)

Moore, Angelyn W.; Gutman, Seth I.; Holub, Kirk; Bock, Yehuda; Danielson, David; Laber, Jayme; Small, Ivory

2013-01-01

Global Positioning System (GPS) meteorology provides enhanced density, low-latency (30-min resolution), integrated precipitable water (IPW) estimates to NOAA NWS (National Oceanic and Atmospheric Adminis tration Nat ional Weather Service) Weather Forecast Offices (WFOs) to provide improved model and satellite data verification capability and more accurate forecasts of extreme weather such as flooding. An early activity of this project was to increase the number of stations contributing to the NOAA Earth System Research Laboratory (ESRL) GPS meteorology observing network in Southern California by about 27 stations. Following this, the Los Angeles/Oxnard and San Diego WFOs began using the enhanced GPS-based IPW measurements provided by ESRL in the 2012 and 2013 monsoon seasons. Forecasters found GPS IPW to be an effective tool in evaluating model performance, and in monitoring monsoon development between weather model runs for improved flood forecasting. GPS stations are multi-purpose, and routine processing for position solutions also yields estimates of tropospheric zenith delays, which can be converted into mm-accuracy PWV (precipitable water vapor) using in situ pressure and temperature measurements, the basis for GPS meteorology. NOAA ESRL has implemented this concept with a nationwide distribution of more than 300 "GPSMet" stations providing IPW estimates at sub-hourly resolution currently used in operational weather models in the U.S.

A 3-month long operational implementation of an ensemble prediction system of storm surge for the city of Venice

NASA Astrophysics Data System (ADS)

Mel, Riccardo; Lionello, Piero

2014-05-01

Advantages of an ensemble prediction forecast (EPF) technique that has been used for sea level (SL) prediction at the Northern Adriatic coast are investigated. The aims is to explore whether EPF is more precise than the traditional Deterministic Forecast (DF) and the value of the added information, mainly on forecast uncertainty. Improving the SL forecast for the city of Venice is of paramount importance for the management and maintenance of this historical city and for operating the movable barriers that are presently being built for its protection. The operational practice is simulated for three months from 1st October to 31st December 2010. The EPF is based on the HYPSE model, which is a standard single-layer nonlinear shallow water model, whose equations are derived from the depth averaged momentum equations and predicts the SL. A description of the model is available in the scientific literature. Forcing of HYPSE are provided by three different sets of 3-hourly ECMWF 10m-wind and MSLP fields: the high resolution meteorological forecast (which is used for the deterministic SL forecast, DF), the control run forecast (CRF, that differs from the DF forecast only for it lower meteorological fields resolution) and the 50 ensemble members of the ECMWF EPS (which are used for the SL-EPS. The resolution of DF fields is T1279 and resolution of both CRF and ECMWF EPS fields is T639 resolution. The 10m wind and MSLP fields have been downloaded at 0.125degs (DF) and 0.25degs(CRF and EPS) and linearly interpolated to the HYPSE grid (which is the same for all simulations). The version of HYPSE used in the SR EPS uses a rectangular mesh grid of variable size, which has the minimum grid step (0.03 degrees) in the northern part of the Adriatic Sea, from where grid step increases with a 1.01 factor in both latitude and longitude (In practice, resolution varies in the range from 3.3 to 7km). Results are analyzed considering the EPS spread, the rms of the simulations, the Brier Skill Score and are compared to observations at tide gauges distributed along the Croatian and Italian coast of the Adriatic Sea. It is shown that the ensemble spread is indeed a reliable indicator of the uncertainty of the storm surge prediction. Further, results show how uncertainty depends on the predicted value of sea level and how it increases with the forecast time range. The accuracy of the ensemble mean forecast is actually larger than that of the deterministic forecast, though the latter is produced by meteorological forcings at higher resolution

Development of a System to Generate Near Real Time Tropospheric Delay and Precipitable Water Vapor in situ at Geodetic GPS Stations, to Improve Forecasting of Severe Weather Events

NASA Astrophysics Data System (ADS)

Moore, A. W.; Bock, Y.; Geng, J.; Gutman, S. I.; Laber, J. L.; Morris, T.; Offield, D. G.; Small, I.; Squibb, M. B.

2012-12-01

We describe a system under development for generating ultra-low latency tropospheric delay and precipitable water vapor (PWV) estimates in situ at a prototype network of geodetic GPS sites in southern California, and demonstrating their utility in forecasting severe storms commonly associated with flooding and debris flow events along the west coast of North America through infusion of this meteorological data at NOAA National Weather Service (NWS) Forecast Offices and the NOAA Earth System Research Laboratory (ESRL). The first continuous geodetic GPS network was established in southern California in the early 1990s and much of it was converted to real-time (latency <1s) high-rate (1Hz) mode over the following decades. GPS stations are multi-purpose and can also provide estimates of tropospheric zenith delays, which can be converted into mm-accuracy PWV using collocated pressure and temperature measurements, the basis for GPS meteorology (Bevis et al. 1992, 1994; Duan et al. 1996) as implemented by NOAA with a nationwide distribution of about 300 GPS-Met stations providing PW estimates at subhourly resolution currently used in operational weather forecasting in the U.S. We improve upon the current paradigm of transmitting large quantities of raw data back to a central facility for processing into higher-order products. By operating semi-autonomously, each station will provide low-latency, high-fidelity and compact data products within the constraints of the narrow communications bandwidth that often occurs in the aftermath of natural disasters. The onsite ambiguity-resolved precise point positioning solutions are enabled by a power-efficient, low-cost, plug-in Geodetic Module for fusion of data from in situ sensors including GPS and a low-cost MEMS meteorological sensor package. The decreased latency (~5 minutes) PW estimates will provide the detailed knowledge of the distribution and magnitude of PW that NWS forecasters require to monitor and predict severe winter storms, landfalling atmospheric rivers, and summer thunderstorms associated with the North American monsoon. On the national level, the ESRL will evaluate the utility of ultra-low resolution GNSS observations to improve NOAA's warning and forecast capabilities. The overall objective is to better forecast, assess, and mitigate natural hazards through the flow of information from multiple geodetic stations to scientists, mission planners, decision makers, and first responders.

Many atolls may be uninhabitable within decades due to climate change

USGS Publications Warehouse

Storlazzi, Curt; Elias, Edwin P.L.; Berkowitz, Paul

2015-01-01

Observations show global sea level is rising due to climate change, with the highest rates in the tropical Pacific Ocean where many of the world’s low-lying atolls are located. Sea-level rise is particularly critical for low-lying carbonate reef-lined atoll islands; these islands have limited land and water available for human habitation, water and food sources, and ecosystems that are vulnerable to inundation from sea-level rise. Here we demonstrate that sea-level rise will result in larger waves and higher wave-driven water levels along atoll islands’ shorelines than at present. Numerical model results reveal waves will synergistically interact with sea-level rise, causing twice as much land forecast to be flooded for a given value of sea-level rise than currently predicted by current models that do not take wave-driven water levels into account. Atolls with islands close to the shallow reef crest are more likely to be subjected to greater wave-induced run-up and flooding due to sea-level rise than those with deeper reef crests farther from the islands’ shorelines. It appears that many atoll islands will be flooded annually, salinizing the limited freshwater resources and thus likely forcing inhabitants to abandon their islands in decades, not centuries, as previously thought.

Droughts in the US: Modeling and Forecasting for Agriculture-Water Management and Adaptation

NASA Astrophysics Data System (ADS)

Perveen, S.; Devineni, N.; Lall, U.

2012-12-01

More than half of all US counties are currently mired in a drought that is considered the worst in decades. A persistent drought can not only lead to widespread impacts on water access with interstate implications (as has been shown in the Southeast US and Texas), chronic scarcity can emerge as a risk in regions where fossil aquifers have become the primary source of supply and are being depleted at rates much faster than recharge (e.g., Midwestern US). The standardized drought indices on which the drought declarations are made in the US so far consider only the static decision frameworks—where only the supply is the control variable and not the water consumption. If a location has low demands, drought as manifest in the usual indices does not really have "proportionate" social impact. Conversely, a modest drought as indicated by the traditional measures may have significant impacts where demand is close to the climatological mean value of precipitation. This may also lead to drought being declared too late or too soon. Against this fact, the importance of improved drought forecasting and preparedness for different sectors of the economy becomes increasingly important. The central issue we propose to address through this paper is the construction and testing of a drought index that considers regional water demands for specific purposes (e.g., crops, municipal use) and their temporal distribution over the year for continental US. Here, we have highlighted the use of the proposed index for three main sectors: (i) water management organizations, (ii) optimizing agricultural water use, and (iii) supply chain water risk. The drought index will consider day-to-day climate variability and sectoral demands to develop aggregate regional conditions or disaggregated indices for water users. For the daily temperature and precipitation data, we are using NLDAS dataset that is available from 1949 onwards. The national agricultural statistics services (NASS) online database has been accessed for the agricultural data at the county level. Preliminary analyses show that large parts of Midwest and Southern parts of Florida and California are prone to multiyear droughts. This can primarily be attributed to high agricultural and/or urban water demands coupled with high interannual variability in supply. We propose to develop season-ahead and monthly updated forecasts of the drought index for informing the drought management plans. Given the already customized (sector specific) nature of the proposed drought index and its ability to represent the variability in both supply and demand, the early warning or forecasting of the index would not only complement the drought early warning systems in place by the national integrated drought information system (NIDIS) but also help in prescribing the ameliorative measures for adaptation.

Post LANDSAT D Advanced Concept Evaluation (PLACE). [with emphasis on mission planning, technological forecasting, and user requirements

NASA Technical Reports Server (NTRS)

1977-01-01

An outline is given of the mission objectives and requirements, system elements, system concepts, technology requirements and forecasting, and priority analysis for LANDSAT D. User requirements and mission analysis and technological forecasting are emphasized. Mission areas considered include agriculture, range management, forestry, geology, land use, water resources, environmental quality, and disaster assessment.

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 .

Daily rainfall forecasting for one year in a single run using Singular Spectrum Analysis

NASA Astrophysics Data System (ADS)

Unnikrishnan, Poornima; Jothiprakash, V.

2018-06-01

Effective modelling and prediction of smaller time step rainfall is reported to be very difficult owing to its highly erratic nature. Accurate forecast of daily rainfall for longer duration (multi time step) may be exceptionally helpful in the efficient planning and management of water resources systems. Identification of inherent patterns in a rainfall time series is also important for an effective water resources planning and management system. In the present study, Singular Spectrum Analysis (SSA) is utilized to forecast the daily rainfall time series pertaining to Koyna watershed in Maharashtra, India, for 365 days after extracting various components of the rainfall time series such as trend, periodic component, noise and cyclic component. In order to forecast the time series for longer time step (365 days-one window length), the signal and noise components of the time series are forecasted separately and then added together. The results of the study show that the method of SSA could extract the various components of the time series effectively and could also forecast the daily rainfall time series for longer duration such as one year in a single run with reasonable accuracy.

ENSURF: multi-model sea level forecast - implementation and validation results for the IBIROOS and Western Mediterranean regions

NASA Astrophysics Data System (ADS)

Pérez, B.; Brouwer, R.; Beckers, J.; Paradis, D.; Balseiro, C.; Lyons, K.; Cure, M.; Sotillo, M. G.; Hackett, B.; Verlaan, M.; Fanjul, E. A.

2012-03-01

ENSURF (Ensemble SURge Forecast) is a multi-model application for sea level forecast that makes use of several storm surge or circulation models and near-real time tide gauge data in the region, with the following main goals: 1. providing easy access to existing forecasts, as well as to its performance and model validation, by means of an adequate visualization tool; 2. generation of better forecasts of sea level, including confidence intervals, by means of the Bayesian Model Average technique (BMA). The Bayesian Model Average technique generates an overall forecast probability density function (PDF) by making a weighted average of the individual forecasts PDF's; the weights represent the Bayesian likelihood that a model will give the correct forecast and are continuously updated based on the performance of the models during a recent training period. This implies the technique needs the availability of sea level data from tide gauges in near-real time. The system was implemented for the European Atlantic facade (IBIROOS region) and Western Mediterranean coast based on the MATROOS visualization tool developed by Deltares. Results of validation of the different models and BMA implementation for the main harbours are presented for these regions where this kind of activity is performed for the first time. The system is currently operational at Puertos del Estado and has proved to be useful in the detection of calibration problems in some of the circulation models, in the identification of the systematic differences between baroclinic and barotropic models for sea level forecasts and to demonstrate the feasibility of providing an overall probabilistic forecast, based on the BMA method.

Advancing Data Assimilation in Operational Hydrologic Forecasting: Progresses, Challenges, and Emerging Opportunities

NASA Technical Reports Server (NTRS)

Liu, Yuqiong; Weerts, A.; Clark, M.; Hendricks Franssen, H.-J; Kumar, S.; Moradkhani, H.; Seo, D.-J.; Schwanenberg, D.; Smith, P.; van Dijk, A. I. J. M.;

ENSURF: multi-model sea level forecast - implementation and validation results for the IBIROOS and Western Mediterranean regions

NASA Astrophysics Data System (ADS)

Pérez, B.; Brower, R.; Beckers, J.; Paradis, D.; Balseiro, C.; Lyons, K.; Cure, M.; Sotillo, M. G.; Hacket, B.; Verlaan, M.; Alvarez Fanjul, E.

2011-04-01

ENSURF (Ensemble SURge Forecast) is a multi-model application for sea level forecast that makes use of existing storm surge or circulation models today operational in Europe, as well as near-real time tide gauge data in the region, with the following main goals: - providing an easy access to existing forecasts, as well as to its performance and model validation, by means of an adequate visualization tool - generation of better forecasts of sea level, including confidence intervals, by means of the Bayesian Model Average Technique (BMA) The system was developed and implemented within ECOOP (C.No. 036355) European Project for the NOOS and the IBIROOS regions, based on MATROOS visualization tool developed by Deltares. Both systems are today operational at Deltares and Puertos del Estado respectively. The Bayesian Modelling Average technique generates an overall forecast probability density function (PDF) by making a weighted average of the individual forecasts PDF's; the weights represent the probability that a model will give the correct forecast PDF and are determined and updated operationally based on the performance of the models during a recent training period. This implies the technique needs the availability of sea level data from tide gauges in near-real time. Results of validation of the different models and BMA implementation for the main harbours will be presented for the IBIROOS and Western Mediterranean regions, where this kind of activity is performed for the first time. The work has proved to be useful to detect problems in some of the circulation models not previously well calibrated with sea level data, to identify the differences on baroclinic and barotropic models for sea level applications and to confirm the general improvement of the BMA forecasts.

Research on Coupling Method of Watershed Initial Water Rights Allocation in Daling River

NASA Astrophysics Data System (ADS)

Liu, J.; Fengping, W.

2016-12-01

Water scarcity is now a common occurrence in many countries. The situation of watershed initial water rights allocation has caused many benefit conflicts among regions and regional water sectors of domestic and ecology environment and industries in China. This study aims to investigate the method of watershed initial water rights allocation in the perspective of coupling in Daling River Watershed taking provincial initial water rights and watershed-level governmental reserved water as objects. First of all, regarding the allocation subsystem of initial water rights among provinces, this research calculates initial water rights of different provinces by establishing the coupling model of water quantity and quality on the principle of "rewarding efficiency and penalizing inefficiency" based on the two control objectives of water quantity and quality. Secondly, regarding the allocation subsystem of watershed-level governmental reserved water rights, the study forecasts the demand of watershed-level governmental reserved water rights by the combination of case-based reasoning and water supply quotas. Then, the bilaterally coupled allocation model on water supply and demand is designed after supply analysis to get watershed-level governmental reserved water rights. The results of research method applied to Daling River Watershed reveal the recommended scheme of watershed initial water rights allocation based on coordinated degree criterion. It's found that the feasibility of the iteration coupling model and put forward related policies and suggestions. This study owns the advantages of complying with watershed initial water rights allocation mechanism and meeting the control requirements of water quantity, water quality and water utilization efficiency, which help to achieve the effective allocation of water resources.

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.

Performance assessment of deterministic and probabilistic weather predictions for the short-term optimization of a tropical hydropower reservoir

NASA Astrophysics Data System (ADS)

Mainardi Fan, Fernando; Schwanenberg, Dirk; Alvarado, Rodolfo; Assis dos Reis, Alberto; Naumann, Steffi; Collischonn, Walter

2016-04-01

Hydropower is the most important electricity source in Brazil. During recent years, it accounted for 60% to 70% of the total electric power supply. Marginal costs of hydropower are lower than for thermal power plants, therefore, there is a strong economic motivation to maximize its share. On the other hand, hydropower depends on the availability of water, which has a natural variability. Its extremes lead to the risks of power production deficits during droughts and safety issues in the reservoir and downstream river reaches during flood events. One building block of the proper management of hydropower assets is the short-term forecast of reservoir inflows as input for an online, event-based optimization of its release strategy. While deterministic forecasts and optimization schemes are the established techniques for the short-term reservoir management, the use of probabilistic ensemble forecasts and stochastic optimization techniques receives growing attention and a number of researches have shown its benefit. The present work shows one of the first hindcasting and closed-loop control experiments for a multi-purpose hydropower reservoir in a tropical region in Brazil. The case study is the hydropower project (HPP) Três Marias, located in southeast Brazil. The HPP reservoir is operated with two main objectives: (i) hydroelectricity generation and (ii) flood control at Pirapora City located 120 km downstream of the dam. In the experiments, precipitation forecasts based on observed data, deterministic and probabilistic forecasts with 50 ensemble members of the ECMWF are used as forcing of the MGB-IPH hydrological model to generate streamflow forecasts over a period of 2 years. The online optimization depends on a deterministic and multi-stage stochastic version of a model predictive control scheme. Results for the perfect forecasts show the potential benefit of the online optimization and indicate a desired forecast lead time of 30 days. In comparison, the use of actual forecasts with shorter lead times of up to 15 days shows the practical benefit of actual operational data. It appears that the use of stochastic optimization combined with ensemble forecasts leads to a significant higher level of flood protection without compromising the HPP's energy production.

Sources of information for tsunami forecasting in New Zealand

NASA Astrophysics Data System (ADS)

Barberopoulou, A.; Ristau, J. P.; D'Anastasio, E.; Wang, X.

2013-12-01

Tsunami science has evolved considerably in the last two decades due to technological advancements which also helped push for better numerical modelling of the tsunami phases (generation to inundation). The deployment of DART buoys has also been a considerable milestone in tsunami forecasting. Tsunami forecasting is one of the parts that tsunami modelling feeds into and is related to response, preparedness and planning. Usually tsunami forecasting refers to short-term forecasting that takes place in real-time after a tsunami has or appears to have been generated. In this report we refer to all types of forecasting (short-term or long-term) related to work in advance of a tsunami impacting a coastline that would help in response, planning or preparedness. We look at the standard types of data (seismic, GPS, water level) that are available in New Zealand for tsunami forecasting, how they are currently being used, other ways to use these data and provide recommendations for better utilisation. The main findings are: -Current investigations of the use of seismic parameters quickly obtained after an earthquake, have potential to provide critical information about the tsunamigenic potential of earthquakes. Further analysis of the most promising methods should be undertaken to determine a path to full implementation. -Network communication of the largest part of the GPS network is not currently at a stage that can provide sufficient data early enough for tsunami warning. It is believed that it has potential, but changes including data transmission improvements may have to happen before real-time processing oriented to tsunami early warning is implemented on the data that is currently provided. -Tide gauge data is currently under-utilised for tsunami forecasting. Spectral analysis, modal analysis based on identified modes and arrival times extracted from the records can be useful in forecasting. -The current study is by no means exhaustive of the ways the different types of data can be used. We are only presenting an overview of what can be done. More extensive studies with each one of the types of data collected by GeoNet and other relevant networks will help improve tsunami forecasting in New Zealand.

Monitoring Lake and Reservoir Level: Satellite Observations, Modeling and Prediction

NASA Astrophysics Data System (ADS)

Ricko, M.; Birkett, C. M.; Adler, R. F.; Carton, J.

2013-12-01

Satellite measurements of lake and reservoir water levels complement in situ observations by providing stage information for un-gauged basins and by filling data gaps in gauge records. However, different satellite radar altimeter-derived continental water level products may differ significantly owing to choice of satellites and data processing methods. To explore the impacts of these differences, a direct comparison between three different altimeter-based surface water level estimates (USDA/NASA GRLM, LEGOS and ESA-DMU) will be presented and products validated with lake level gauge time series for lakes and reservoirs of a variety of sizes and conditions. The availability of satellite-based rainfall (i.e., TRMM and GPCP) and satellite-based lake/reservoir levels offers exciting opportunities to estimate and monitor the hydrologic properties of the lake systems. Here, a simple water balance model is utilized to relate net freshwater flux on a catchment basin to lake/reservoir level. Focused on tropical lakes and reservoirs it allows a comparison of the flux to altimetric lake level estimates. The combined use of model, satellite-based rainfall, evaporation information and reanalysis products, can be used to output water-level hindcasts and seasonal future forecasts. Such a tool is fundamental for understanding present-day and future variations in lake/reservoir levels and enabling a better understand of climatic variations on inter-annual to inter-decadal time-scales. New model-derived water level estimates of lakes and reservoirs, on regional to global scales, would assist communities with interests in climate studies focusing on extreme events, such as floods and droughts, and be important for water resources management.

The development and evaluation of a hydrological seasonal forecast system prototype for predicting spring flood volumes in Swedish rivers

NASA Astrophysics Data System (ADS)

Foster, Kean; Bertacchi Uvo, Cintia; Olsson, Jonas

2018-05-01

Hydropower makes up nearly half of Sweden's electrical energy production. However, the distribution of the water resources is not aligned with demand, as most of the inflows to the reservoirs occur during the spring flood period. This means that carefully planned reservoir management is required to help redistribute water resources to ensure optimal production and accurate forecasts of the spring flood volume (SFV) is essential for this. The current operational SFV forecasts use a historical ensemble approach where the HBV model is forced with historical observations of precipitation and temperature. In this work we develop and test a multi-model prototype, building on previous work, and evaluate its ability to forecast the SFV in 84 sub-basins in northern Sweden. The hypothesis explored in this work is that a multi-model seasonal forecast system incorporating different modelling approaches is generally more skilful at forecasting the SFV in snow dominated regions than a forecast system that utilises only one approach. The testing is done using cross-validated hindcasts for the period 1981-2015 and the results are evaluated against both climatology and the current system to determine skill. Both the multi-model methods considered showed skill over the reference forecasts. The version that combined the historical modelling chain, dynamical modelling chain, and statistical modelling chain performed better than the other and was chosen for the prototype. The prototype was able to outperform the current operational system 57 % of the time on average and reduce the error in the SFV by ˜ 6 % across all sub-basins and forecast dates.

Improving the Representation of Snow Crystal Properties Within a Single-Moment Microphysics Scheme

NASA Technical Reports Server (NTRS)

Molthan, Andrew L.; Petersen, Walter A.; Case, Jonathan L.; Dembek, S. R.

2010-01-01

As computational resources continue their expansion, weather forecast models are transitioning to the use of parameterizations that predict the evolution of hydrometeors and their microphysical processes, rather than estimating the bulk effects of clouds and precipitation that occur on a sub-grid scale. These parameterizations are referred to as single-moment, bulk water microphysics schemes, as they predict the total water mass among hydrometeors in a limited number of classes. Although the development of single moment microphysics schemes have often been driven by the need to predict the structure of convective storms, they may also provide value in predicting accumulations of snowfall. Predicting the accumulation of snowfall presents unique challenges to forecasters and microphysics schemes. In cases where surface temperatures are near freezing, accumulated depth often depends upon the snowfall rate and the ability to overcome an initial warm layer. Precipitation efficiency relates to the dominant ice crystal habit, as dendrites and plates have relatively large surface areas for the accretion of cloud water and ice, but are only favored within a narrow range of ice supersaturation and temperature. Forecast models and their parameterizations must accurately represent the characteristics of snow crystal populations, such as their size distribution, bulk density and fall speed. These properties relate to the vertical distribution of ice within simulated clouds, the temperature profile through latent heat release, and the eventual precipitation rate measured at the surface. The NASA Goddard, single-moment microphysics scheme is available to the operational forecast community as an option within the Weather Research and Forecasting (WRF) model. The NASA Goddard scheme predicts the occurrence of up to six classes of water mass: vapor, cloud ice, cloud water, rain, snow and either graupel or hail.

Debris flow early warning systems in Norway: organization and tools

NASA Astrophysics Data System (ADS)

Kleivane, I.; Colleuille, H.; Haugen, L. E.; Alve Glad, P.; Devoli, G.

2012-04-01

In Norway, shallow slides and debris flows occur as a combination of high-intensity precipitation, snowmelt, high groundwater level and saturated soil. Many events have occurred in the last decades and are often associated with (or related to) floods events, especially in the Southern of Norway, causing significant damages to roads, railway lines, buildings, and other infrastructures (i.e November 2000; August 2003; September 2005; November 2005; Mai 2008; June and Desember 2011). Since 1989 the Norwegian Water Resources and Energy Directorate (NVE) has had an operational 24 hour flood forecasting system for the entire country. From 2009 NVE is also responsible to assist regions and municipalities in the prevention of disasters posed by landslides and snow avalanches. Besides assisting the municipalities through implementation of digital landslides inventories, susceptibility and hazard mapping, areal planning, preparation of guidelines, realization of mitigation measures and helping during emergencies, NVE is developing a regional scale debris flow warning system that use hydrological models that are already available in the flood warning systems. It is well known that the application of rainfall thresholds is not sufficient to evaluate the hazard for debris flows and shallow slides, and soil moisture conditions play a crucial role in the triggering conditions. The information on simulated soil and groundwater conditions and water supply (rain and snowmelt) based on weather forecast, have proved to be useful variables that indicate the potential occurrence of debris flows and shallow slides. Forecasts of runoff and freezing-thawing are also valuable information. The early warning system is using real-time measurements (Discharge; Groundwater level; Soil water content and soil temperature; Snow water equivalent; Meteorological data) and model simulations (a spatially distributed version of the HBV-model and an adapted version of 1-D soil water and energy balance model COUP). The data are presented in a web- and GIS-based system with daily nationwide maps showing the meteorological and hydrological conditions for the present and the near future from quantitative weather prognosis. In addition a division of the country in homogenous debris flow-prone regions is also under progress based on geomorfological, topographic parameters and loose quaternary deposits distribution. Threshold-levels are being investigated by using statistical analyses of historical debris flows events and measured hydro-meteorological parameters. The debris flow early warning system is currently being tested and is expected to be operational in 2013. Final products will be warning messages and a map showing the different hazard levels, from low to high, indicating the landslide probability and the type of expected damages in a certain area. Many activities are realized in strong collaboration with the road and railway authorities, the geological survey and private consultant companies.

Flood forecasting with DDD-application of a parsimonious hydrological model in operational flood forecasting in Norway

NASA Astrophysics Data System (ADS)

Skaugen, Thomas; Haddeland, Ingjerd

2014-05-01

A new parameter-parsimonious rainfall-runoff model, DDD (Distance Distribution Dynamics) has been run operationally at the Norwegian Flood Forecasting Service for approximately a year. DDD has been calibrated for, altogether, 104 catchments throughout Norway, and provide runoff forecasts 8 days ahead on a daily temporal resolution driven by precipitation and temperature from the meteorological forecast models AROME (48 hrs) and EC (192 hrs). The current version of DDD differs from the standard model used for flood forecasting in Norway, the HBV model, in its description of the subsurface and runoff dynamics. In DDD, the capacity of the subsurface water reservoir M, is the only parameter to be calibrated whereas the runoff dynamics is completely parameterised from observed characteristics derived from GIS and runoff recession analysis. Water is conveyed through the soils to the river network by waves with celerities determined by the level of saturation in the catchment. The distributions of distances between points in the catchment to the nearest river reach and of the river network give, together with the celerities, distributions of travel times, and, consequently unit hydrographs. DDD has 6 parameters less to calibrate in the runoff module than the HBV model. Experiences using DDD show that especially the timing of flood peaks has improved considerably and in a comparison between DDD and HBV, when assessing timeseries of 64 years for 75 catchments, DDD had a higher hit rate and a lower false alarm rate than HBV. For flood peaks higher than the mean annual flood the median hit rate is 0.45 and 0.41 for the DDD and HBV models respectively. Corresponding number for the false alarm rate is 0.62 and 0.75 For floods over the five year return interval, the median hit rate is 0.29 and 0.28 for the DDD and HBV models, respectively with false alarm rates equal to 0.67 and 0.80. During 2014 the Norwegian flood forecasting service will run DDD operationally at a 3h temporal resolution. Running DDD at a 3h resolution will give a better prediction of flood peaks in small catchments, where the averaging over 24 hrs will lead to a underestimation of high events, and we can better describe the progress floods in larger catchments. Also, at a 3h temporal resolution we make better use of the meteorological forecasts that for long have been provided at a very detailed temporal resolution.

Wave ensemble forecast in the Western Mediterranean Sea, application to an early warning system.

NASA Astrophysics Data System (ADS)

Pallares, Elena; Hernandez, Hector; Moré, Jordi; Espino, Manuel; Sairouni, Abdel

2015-04-01

The Western Mediterranean Sea is a highly heterogeneous and variable area, as is reflected on the wind field, the current field, and the waves, mainly in the first kilometers offshore. As a result of this variability, the wave forecast in these regions is quite complicated to perform, usually with some accuracy problems during energetic storm events. Moreover, is in these areas where most of the economic activities take part, including fisheries, sailing, tourism, coastal management and offshore renewal energy platforms. In order to introduce an indicator of the probability of occurrence of the different sea states and give more detailed information of the forecast to the end users, an ensemble wave forecast system is considered. The ensemble prediction systems have already been used in the last decades for the meteorological forecast; to deal with the uncertainties of the initial conditions and the different parametrizations used in the models, which may introduce some errors in the forecast, a bunch of different perturbed meteorological simulations are considered as possible future scenarios and compared with the deterministic forecast. In the present work, the SWAN wave model (v41.01) has been implemented for the Western Mediterranean sea, forced with wind fields produced by the deterministic Global Forecast System (GFS) and Global Ensemble Forecast System (GEFS). The wind fields includes a deterministic forecast (also named control), between 11 and 21 ensemble members, and some intelligent member obtained from the ensemble, as the mean of all the members. Four buoys located in the study area, moored in coastal waters, have been used to validate the results. The outputs include all the time series, with a forecast horizon of 8 days and represented in spaghetti diagrams, the spread of the system and the probability at different thresholds. The main goal of this exercise is to be able to determine the degree of the uncertainty of the wave forecast, meaningful between the 5th and the 8th day of the prediction. The information obtained is then included in an early warning system, designed in the framework of the European project iCoast (ECHO/SUB/2013/661009) with the aim of set alarms in coastal areas depending on the wave conditions, the sea level, the flooding and the run up in the coast.

Flood-inundation maps for the East Fork White River at Shoals, Indiana

USGS Publications Warehouse

Boldt, Justin A.

2016-05-06

Digital flood-inundation maps for a 5.9-mile reach of the East Fork White River at Shoals, Indiana (Ind.), were created by the U.S. Geological Survey (USGS) in cooperation with the Indiana Office of Community and Rural Affairs. The flood-inundation maps, which can be accessed through the USGS Flood Inundation Mapping Science Web site at http://water.usgs.gov/osw/flood_inundation/ depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at the USGS streamgage on the East Fork White River at Shoals, Ind. (USGS station number 03373500). Near-real-time stages at this streamgage may be obtained on the Internet from the USGS National Water Information System at http://waterdata.usgs.gov/ or the National Weather Service (NWS) Advanced Hydrologic Prediction Service (AHPS) at http://water.weather.gov/ahps/, which also forecasts flood hydrographs at this site (NWS AHPS site SHLI3). NWS AHPS forecast peak stage information may be used in conjunction with the maps developed in this study to show predicted areas of flood inundation.Flood profiles were computed for the East Fork White River reach by means of a one-dimensional, step-backwater model developed by the U.S. Army Corps of Engineers. The hydraulic model was calibrated by using the current stage-discharge relation (USGS rating no. 43.0) at USGS streamgage 03373500, East Fork White River at Shoals, Ind. The calibrated hydraulic model was then used to compute 26 water-surface profiles for flood stages at 1-foot (ft) intervals referenced to the streamgage datum and ranging from approximately bankfull (10 ft) to the highest stage of the current stage-discharge rating curve (35 ft). The simulated water-surface profiles were then combined with a geographic information system (GIS) digital elevation model (DEM), derived from light detection and ranging (lidar) data, to delineate the area flooded at each water level. The areal extent of the 24-ft flood-inundation map was verified with photographs from a flood event on July 20, 2015.The availability of these maps, along with information on the Internet regarding current stage from the USGS streamgage at East Fork White River at Shoals, Ind., and forecasted stream stages from the NWS AHPS, provides emergency management personnel and residents with information that is critical for flood response activities such as evacuations and road closures, as well as for post-flood recovery efforts.

Changes in the relation between snow station observations and basin scale snow water resources

NASA Astrophysics Data System (ADS)

Sexstone, G. A.; Penn, C. A.; Clow, D. W.; Moeser, D.; Liston, G. E.

2017-12-01

Snow monitoring stations that measure snow water equivalent or snow depth provide fundamental observations used for predicting water availability and flood risk in mountainous regions. In the western United States, snow station observations provided by the Natural Resources Conservation Service Snow Telemetry (SNOTEL) network are relied upon for forecasting spring and summer streamflow volume. Streamflow forecast accuracy has declined for many regions over the last several decades. Changes in snow accumulation and melt related to climate, land use, and forest cover are not accounted for in current forecasts, and are likely sources of error. Therefore, understanding and updating relations between snow station observations and basin scale snow water resources is crucial to improve accuracy of streamflow prediction. In this study, we investigated the representativeness of snow station observations when compared to simulated basin-wide snow water resources within the Rio Grande headwaters of Colorado. We used the combination of a process-based snow model (SnowModel), field-based measurements, and remote sensing observations to compare the spatiotemporal variability of simulated basin-wide snow accumulation and melt with that of SNOTEL station observations. Results indicated that observations are comparable to simulated basin-average winter precipitation but overestimate both the simulated basin-average snow water equivalent and snowmelt rate. Changes in the representation of snow station observations over time in the Rio Grande headwaters were also investigated and compared to observed streamflow and streamflow forecasting errors. Results from this study provide important insight in the context of non-stationarity for future water availability assessments and streamflow predictions.

An integrated modeling framework for real-time irrigation scheduling: the benefit of spectroscopy and weather forecasts

NASA Astrophysics Data System (ADS)

Brook, Anna; Polinova, Maria; Housh, Mashor

2016-04-01

Agriculture and agricultural landscapes are increasingly under pressure to meet the demands of a constantly increasing human population and globally changing food patterns. At the same time, there is rising concern that climate change and food security will harm agriculture in many regions of the world (Nelson et al., 2009). Facing those treats, majority of Mediterranean countries had chosen irrigated agriculture. For crop plants water is one of the most important inputs, as it is responsible for crop growth, production and it ensures the efficiency of other inputs (e.g. seeds, fertilizers and pesticide) but its use is in competition with other local sectors (e.g. industry, urban human use). Thus, well-timed availability of water is vital to agriculture for ensured yields. The increasing demand for irrigation has necessitated the need for optimal irrigation scheduling techniques that coordinate the timing and amount of irrigation to optimally manage the water use in agriculture systems. The irrigation scheduling problem can be challenging as farmers try to deal with different conflicting objectives of maximizing their yield while minimizing irrigation water use. Another challenge in the irrigation scheduling problem is attributed to the uncertain factors involved in the plant growth process during the growing season. Most notable, the climatic factors such as evapotranspiration and rainfall, these uncertain factors add a third objective to the farmer perspective, namely, minimizing the risk associated with these uncertain factors. Nevertheless, advancements in weather forecasting reduced the uncertainty level associated with future climatic data. Thus, climatic forecasts can be reliably employed to guide optimal irrigation schedule scheme when coupled with stochastic optimization models (Housh et al., 2012). Many studies have concluded that optimal irrigation decisions can provide substantial economic value over conventional irrigation decisions (Wang and Cai 2009). These studies have only incorporated short-term (weekly) forecasts, missing the potential benefit of the mid-term (seasonal) climate forecasts The latest progress in new data acquisition technologies (mainly in the field of Earth observation by remote sensing and imaging spectroscopy systems) as well as the state-of-the-art achievements in the fields of geographical information systems (GIS), computer science and climate and climate impact modelling enable to develop both integrated modelling and realistic spatial simulations. The present method is the use of field spectroscopy technology to keep constant monitoring of the field. The majority of previously developed decision support systems use satellite remote sensing data that provide very limited capabilities (conventional and basic parameters). The alternative is to use a more progressive technology of hyperspectral airborne or ground-based imagery data that provide an exhaustive description of the field. Nevertheless, this alternative is known to be very costly and complex. As such, we will present a low-cost imaging spectroscopy technology supported by detailed and fine-resolution field spectroscopy as a cost effective option for near field real-time monitoring tool. In order to solve the soil water balance and to predict the water irrigation volume a pedological survey is realized in the evaluation study areas.The remote sensing and field spectroscopy were applied to integrate continuous feedbacks from the field (e.g. soil moisture, organic/inorganic carbon, nitrogen, salinity, fertilizers, sulphur acid, texture; crop water-stress, plant stage, LAI , chlorophyll, biomass, yield prediction applying PROSPECT+SILT ; Fraction of Absorbed Photosynthetically Active Radiation FAPAR) estimated based on remote sensing information to minimize the errors associated with crop simulation process. A stochastic optimization model will be formulated that take into account both mid-term seasonal probabilistic climate prediction and short-term weekly forecasts. In order to optimize the water resource use, the irrigation scheduling will be defined by use a simulation model of soil-plant and atmosphere system (e.g. SWAP model, Van Dam et al., 2008). The use of this tool is necessary to: i) take into account the soil spatial variability; ii) to predict the system behaviour under the forecasted climate; iii) define the optimized irrigation water volumes. Given this knowledge in the three domains of optimization under uncertainty, spectroscopy/remote sensing and climate forecasting, we will be presented as an integrated framework for deriving optimal irrigation decisions. References Nelson, Gerald C., et al. Climate change: Impact on agriculture and costs of adaptation. Vol. 21. Intl Food Policy Res Inst, 2009. Housh, Mashor, Avi Ostfeld, and Uri Shamir. "Seasonal multi-year optimal management of quantities and salinities in regional water supply systems." Environmental Modelling & Software 37 (2012): 55-67. Wang, Dingbao, and Ximing Cai. "Irrigation scheduling - Role of weather forecasting and farmers' behavior." Journal of Water Resources Planning and Management 135.5 (2009): 364-372. Van Dam, J. C., et al. SWAP version 3.2: Theory description and user manual. No. 1649. Wageningen, The Netherlands: Alterra, 2008.

Using Temperature Forecasts to Improve Seasonal Streamflow Forecasts in the Colorado and Rio Grande Basins

NASA Astrophysics Data System (ADS)

Lehner, F.; Wood, A.; Llewellyn, D.; Blatchford, D. B.; Goodbody, A. G.; Pappenberger, F.

2017-12-01

Recent studies have documented the influence of increasing temperature on streamflow across the American West, including snow-melt driven rivers such as the Colorado or Rio Grande. At the same time, some basins are reporting decreasing skill in seasonal streamflow forecasts, termed water supply forecasts (WSFs), over the recent decade. While the skill in seasonal precipitation forecasts from dynamical models remains low, their skill in predicting seasonal temperature variations could potentially be harvested for WSFs to account for non-stationarity in regional temperatures. Here, we investigate whether WSF skill can be improved by incorporating seasonal temperature forecasts from dynamical forecasting models (from the North American Multi Model Ensemble and the European Centre for Medium-Range Weather Forecast System 4) into traditional statistical forecast models. We find improved streamflow forecast skill relative to traditional WSF approaches in a majority of headwater locations in the Colorado and Rio Grande basins. Incorporation of temperature into WSFs thus provides a promising avenue to increase the robustness of current forecasting techniques in the face of continued regional warming.

Validation of WRF forecasts for the Chajnantor region

NASA Astrophysics Data System (ADS)

Pozo, Diana; Marín, J. C.; Illanes, L.; Curé, M.; Rabanus, D.

2016-06-01

This study assesses the performance of the Weather Research and Forecasting (WRF) model to represent the near-surface weather conditions and the precipitable water vapour (PWV) in the Chajnantor plateau, in the north of Chile, from 2007 April to December. The WRF model shows a very good performance forecasting the near-surface temperature and zonal wind component, although it overestimates the 2 m water vapour mixing ratio and underestimates the 10 m meridional wind component. The model represents very well the seasonal, intraseasonal and the diurnal variation of PWV. However, the PWV errors increase after the 12 h of simulation. Errors in the simulations are larger than 1.5 mm only during 10 per cent of the study period, they do not exceed 0.5 mm during 65 per cent of the time and they are below 0.25 mm more than 45 per cent of the time, which emphasizes the good performance of the model to forecast the PWV over the region. The misrepresentation of the near-surface humidity in the region by the WRF model may have a negative impact on the PWV forecasts. Thus, having accurate forecasts of humidity near the surface may result in more accurate PWV forecasts. Overall, results from this, as well as recent studies, supports the use of the WRF model to provide accurate weather forecasts for the region, particularly for the PWV, which can be of great benefit for astronomers in the planning of their scientific operations and observing time.

Cloud and DNI nowcasting with MSG/SEVIRI for the optimized operation of concentrating solar power plants

NASA Astrophysics Data System (ADS)

Sirch, Tobias; Bugliaro, Luca; Zinner, Tobias; Möhrlein, Matthias; Vazquez-Navarro, Margarita

2017-02-01

A novel approach for the nowcasting of clouds and direct normal irradiance (DNI) based on the Spinning Enhanced Visible and Infrared Imager (SEVIRI) aboard the geostationary Meteosat Second Generation (MSG) satellite is presented for a forecast horizon up to 120 min. The basis of the algorithm is an optical flow method to derive cloud motion vectors for all cloudy pixels. To facilitate forecasts over a relevant time period, a classification of clouds into objects and a weighted triangular interpolation of clear-sky regions are used. Low and high level clouds are forecasted separately because they show different velocities and motion directions. Additionally a distinction in advective and convective clouds together with an intensity correction for quickly thinning convective clouds is integrated. The DNI is calculated from the forecasted optical thickness of the low and high level clouds. In order to quantitatively assess the performance of the algorithm, a forecast validation against MSG/SEVIRI observations is performed for a period of 2 months. Error rates and Hanssen-Kuiper skill scores are derived for forecasted cloud masks. For a forecast of 5 min for most cloud situations more than 95 % of all pixels are predicted correctly cloudy or clear. This number decreases to 80-95 % for a forecast of 2 h depending on cloud type and vertical cloud level. Hanssen-Kuiper skill scores for cloud mask go down to 0.6-0.7 for a 2 h forecast. Compared to persistence an improvement of forecast horizon by a factor of 2 is reached for all forecasts up to 2 h. A comparison of forecasted optical thickness distributions and DNI against observations yields correlation coefficients larger than 0.9 for 15 min forecasts and around 0.65 for 2 h forecasts.

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.

Flood-inundation maps for the Big Blue River at Shelbyville, Indiana

USGS Publications Warehouse

Fowler, Kathleen K.

2017-02-13

Digital flood-inundation maps for a 4.1-mile reach of the Big Blue River at Shelbyville, Indiana, were created by the U.S. Geological Survey (USGS) in cooperation with the Indiana Office of Community and Rural Affairs. The floodinundation maps, which can be accessed through the USGS Flood Inundation Mapping Science Web site at https://water. usgs.gov/osw/flood_inundation/, depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at the USGS streamgage on the Big Blue River at Shelbyville, Ind. (station number 03361500). Near-real-time stages at this streamgage may be obtained from the USGS National Water Information System at https://waterdata. usgs.gov/ or the National Weather Service (NWS) Advanced Hydrologic Prediction Service at https://water.weather.gov/ ahps/, which also forecasts flood hydrographs at this site (SBVI3). Flood profiles were computed for the stream reach by means of a one-dimensional step-backwater model. The hydraulic model was calibrated by using the most current stage-discharge relation at the Big Blue River at Shelbyville, Ind., streamgage. The calibrated hydraulic model was then used to compute 12 water-surface profiles for flood stages referenced to the streamgage datum and ranging from 9.0 feet, or near bankfull, to 19.4 feet, the highest stage of the current stage-discharge rating curve. The simulated water-surface profiles were then combined with a Geographic Information System digital elevation model (derived from light detection and ranging [lidar] data having a 0.98-foot vertical accuracy and 4.9-foot horizontal resolution) to delineate the area flooded at each water level. The availability of these maps, along with Internet information regarding current stage from the USGS streamgage at the Big Blue River at Shelbyville, Ind., and forecasted stream stages from the NWS, will provide emergency management personnel and residents with information that is critical for flood response activities such as evacuations and road closures as well as for post-flood recovery efforts.