Applications of satellite snow cover in computerized short-term streamflow forecasting. [Conejos River, Colorado

NASA Technical Reports Server (NTRS)

Leaf, C. F.

1975-01-01

A procedure is described whereby the correlation between: (1) satellite derived snow-cover depletion and (2) residual snowpack water equivalent, can be used to update computerized residual flow forecasts for the Conejos River in southern Colorado.

Comparison of Conventional and ANN Models for River Flow Forecasting

NASA Astrophysics Data System (ADS)

Jain, A.; Ganti, R.

2011-12-01

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

Hybrid Forecasting of Daily River Discharges Considering Autoregressive Heteroscedasticity

NASA Astrophysics Data System (ADS)

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

2017-06-01

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

Forecasting models for flow and total dissolved solids in Karoun river-Iran

NASA Astrophysics Data System (ADS)

Salmani, Mohammad Hassan; Salmani Jajaei, Efat

2016-04-01

Water quality is one of the most important factors contributing to a healthy life. From the water quality management point of view, TDS (total dissolved solids) is the most important factor and many water developing plans have been implemented in recognition of this factor. However, these plans have not been perfect and very successful in overcoming the poor water quality problem, so there are a good volume of related studies in the literature. We study TDS and the water flow of the Karoun river in southwest Iran. We collected the necessary time series data from the Harmaleh station located in the river. We present two Univariate Seasonal Autoregressive Integrated Movement Average (ARIMA) models to forecast TDS and water flow in this river. Then, we build up a Transfer Function (TF) model to formulate the TDS as a function of water flow volume. A performance comparison between the Seasonal ARIMA and the TF models are presented.

Application of a medium-range global hydrologic probabilistic forecast scheme to the Ohio River Basin

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

Voisin, Nathalie; Pappenberger, Florian; Lettenmaier, D. P.

2011-08-15

A 10-day globally applicable flood prediction scheme was evaluated using the Ohio River basin as a test site for the period 2003-2007. The Variable Infiltration Capacity (VIC) hydrology model was initialized with the European Centre for Medium Range Weather Forecasts (ECMWF) analysis temperatures and wind, and Tropical Rainfall Monitoring Mission Multi Satellite Precipitation Analysis (TMPA) precipitation up to the day of forecast. In forecast mode, the VIC model was then forced with a calibrated and statistically downscaled ECMWF ensemble prediction system (EPS) 10-day ensemble forecast. A parallel set up was used where ECMWF EPS forecasts were interpolated to the spatialmore » scale of the hydrology model. Each set of forecasts was extended by 5 days using monthly mean climatological variables and zero precipitation in order to account for the effect of initial conditions. The 15-day spatially distributed ensemble runoff forecasts were then routed to four locations in the basin, each with different drainage areas. Surrogates for observed daily runoff and flow were provided by the reference run, specifically VIC simulation forced with ECMWF analysis fields and TMPA precipitation fields. The flood prediction scheme using the calibrated and downscaled ECMWF EPS forecasts was shown to be more accurate and reliable than interpolated forecasts for both daily distributed runoff forecasts and daily flow forecasts. Initial and antecedent conditions dominated the flow forecasts for lead times shorter than the time of concentration depending on the flow forecast amounts and the drainage area sizes. The flood prediction scheme had useful skill for the 10 following days at all sites.« less

Prospects for seasonal forecasting of summer drought and low river flow anomalies in England and Wales

NASA Astrophysics Data System (ADS)

Wedgbrow, C. S.; Wilby, R. L.; Fox, H. R.; O'Hare, G.

2002-02-01

Future climate change scenarios suggest enhanced temporal and spatial gradients in water resources across the UK. Provision of seasonal forecast statistics for surface climate variables could alleviate some negative effects of climate change on water resource infrastructure. This paper presents a preliminary investigation of spatial and temporal relationships between large-scale North Atlantic climatic indices, drought severity and river flow anomalies in England and Wales. Potentially useful predictive relationships are explored between winter indices of the Polar-Eurasian (POL) teleconnection pattern, the North Atlantic oscillation (NAO), North Atlantic sea surface temperature anomalies (SSTAs), and the summer Palmer drought severity index (PDSI) and reconstructed river flows in England and Wales. Correlation analyses, coherence testing and an index of forecast potential, demonstrate that preceding winter values of the POL index, SSTA (and to a lesser extent the NAO), provide indications of summer and early autumn drought severity and river flow anomalies in parts of northwest, southwest and southeast England. Correlation analyses demonstrate that positive winter anomalies of T1, POL index and NAO index are associated with negative PDSI (i.e. drought) across eastern parts of the British Isles in summer (r < 0.51). Coherence tests show that a positive winter SSTA (1871-1995) and POL index (1950-95) have preceded below-average summer river flows in the northwest and southwest of England and Wales in 70 to 100% of summers. The same rivers have also experienced below-average flows during autumn following negative winter phases of the NAO index in 64 to 93% of summers (1865-1995). Possible explanations for the predictor-predictand relationships are considered, including the memory of groundwater, and ocean-atmosphere coupling, and regional manifestations of synoptic rainfall processes. However, further research is necessary to increase the number of years and predictor variables from which it is possible to derive rules that may be useful for forecasting.

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

NASA Astrophysics Data System (ADS)

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

2006-11-01

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

Model-Aided Altimeter-Based Water Level Forecasting System in Mekong River

NASA Astrophysics Data System (ADS)

Chang, C. H.; Lee, H.; Hossain, F.; Okeowo, M. A.; Basnayake, S. B.; Jayasinghe, S.; Saah, D. S.; Anderson, E.; Hwang, E.

2017-12-01

Mekong River, one of the massive river systems in the world, has drainage area of about 795,000 km2 covering six countries. People living in its drainage area highly rely on resources given by the river in terms of agriculture, fishery, and hydropower. Monitoring and forecasting the water level in a timely manner, is urgently needed over the Mekong River. Recently, using TOPEX/Poseidon (T/P) altimetry water level measurements in India, Biancamaria et al. [2011] has demonstrated the capability of an altimeter-based flood forecasting system in Bangladesh, with RMSE from 0.6 - 0.8 m for lead times up to 5 days on 10-day basis due to T/P's repeat period. Hossain et al. [2013] further established a daily water level forecasting system in Bangladesh using observations from Jason-2 in India and HEC-RAS hydraulic model, with RMSE from 0.5 - 1.5 m and an underestimating mean bias of 0.25 - 1.25 m. However, such daily forecasting system relies on a collection of Jason-2 virtual stations (VSs) to ensure frequent sampling and data availability. Since the Mekong River is a meridional river with few number of VSs, the direct application of this system to the Mekong River becomes challenging. To address this problem, we propose a model-aided altimeter-based forecasting system. The discharge output by Variable Infiltration Capacity hydrologic model is used to reconstruct a daily water level product at upstream Jason-2 VSs based on the discharge-to-level rating curve. The reconstructed daily water level is then used to perform regression analysis with downstream in-situ water level to build regression models, which are used to forecast a daily water level. In the middle reach of the Mekong River from Nakhon Phanom to Kratie, a 3-day lead time forecasting can reach RMSE about 0.7 - 1.3 m with correlation coefficient around 0.95. For the lower reach of the Mekong River, the water flow becomes more complicated due to the reversal flow between the Tonle Sap Lake and the Mekong River, while ocean tide can also propagate into this region. By considering the influence of Tonle Sap Lake and the Mekong River through multi-variable regression analysis, the forecasting results from Prek Kdam to Chau Doc/Tan Chau reach RMSE from about 0.3 - 0.65 m and correlation coefficient about 0.93- 0.97 with 5-day lead time.

Evaluating Snow Data Assimilation Framework for Streamflow Forecasting Applications Using Hindcast Verification

NASA Astrophysics Data System (ADS)

Barik, M. G.; Hogue, T. S.; Franz, K. J.; He, M.

2012-12-01

Snow water equivalent (SWE) estimation is a key factor in producing reliable streamflow simulations and forecasts in snow dominated areas. However, measuring or predicting SWE has significant uncertainty. Sequential data assimilation, which updates states using both observed and modeled data based on error estimation, has been shown to reduce streamflow simulation errors but has had limited testing for forecasting applications. In the current study, a snow data assimilation framework integrated with the National Weather System River Forecasting System (NWSRFS) is evaluated for use in ensemble streamflow prediction (ESP). Seasonal water supply ESP hindcasts are generated for the North Fork of the American River Basin (NFARB) in northern California. Parameter sets from the California Nevada River Forecast Center (CNRFC), the Differential Evolution Adaptive Metropolis (DREAM) algorithm and the Multistep Automated Calibration Scheme (MACS) are tested both with and without sequential data assimilation. The traditional ESP method considers uncertainty in future climate conditions using historical temperature and precipitation time series to generate future streamflow scenarios conditioned on the current basin state. We include data uncertainty analysis in the forecasting framework through the DREAM-based parameter set which is part of a recently developed Integrated Uncertainty and Ensemble-based data Assimilation framework (ICEA). Extensive verification of all tested approaches is undertaken using traditional forecast verification measures, including root mean square error (RMSE), Nash-Sutcliffe efficiency coefficient (NSE), volumetric bias, joint distribution, rank probability score (RPS), and discrimination and reliability plots. In comparison to the RFC parameters, the DREAM and MACS sets show significant improvement in volumetric bias in flow. Use of assimilation improves hindcasts of higher flows but does not significantly improve performance in the mid flow and low flow categories.

Evaluation of the satellite derived snow cover area - Runoff forecasting models for the inaccessible basins of western Himalayas

NASA Technical Reports Server (NTRS)

Dey, B.

1985-01-01

In this study, the existing seasonal snow cover area runoff forecasting models of the Indus, Kabul, Sutlej and Chenab basins were evaluated with the concurrent flow correlation model for the period 1975-79. In all the basins under study, correlation of concurrent flow model explained the variability in flow better than by the snow cover area runoff models. Actually, the concurrent flow correlation model explained more than 90 percent of the variability in the flow of these rivers. Compared to this model, the snow cover area runoff models explained less of the variability in flow. In the Himalayan river basins under study and at least for the period under observation, the concurrent flow correlation model provided a set of results with which to compare the estimates from the snow cover area runoff models.

Assessment of a new seasonal to inter-annual operational Great Lakes water supply, water levels, and connecting channel flow forecasting system

NASA Astrophysics Data System (ADS)

Gronewold, A.; Fry, L. M.; Hunter, T.; Pei, L.; Smith, J.; Lucier, H.; Mueller, R.

2017-12-01

The U.S. Army Corps of Engineers (USACE) has recently operationalized a suite of ensemble forecasts of Net Basin Supply (NBS), water levels, and connecting channel flows that was developed through a collaboration among USACE, NOAA's Great Lakes Environmental Research Laboratory, Ontario Power Generation (OPG), New York Power Authority (NYPA), and the Niagara River Control Center (NRCC). These forecasts are meant to provide reliable projections of potential extremes in daily discharge in the Niagara and St. Lawrence Rivers over a long time horizon (5 years). The suite of forecasts includes eight configurations that vary by (a) NBS model configuration, (b) meteorological forcings, and (c) incorporation of seasonal climate projections through the use of weighting. Forecasts are updated on a weekly basis, and represent the first operational forecasts of Great Lakes water levels and flows that span daily to inter-annual horizons and employ realistic regulation logic and lake-to-lake routing. We will present results from a hindcast assessment conducted during the transition from research to operation, as well as early indications of success rates determined through operational verification of forecasts. Assessment will include an exploration of the relative skill of various forecast configurations at different time horizons and the potential for application to hydropower decision making and Great Lakes water management.

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

NASA Astrophysics Data System (ADS)

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

2014-12-01

In the US, the forecasting approach used by the NWS River Forecast Centers and other regional organizations such as the Bonneville Power Administration (BPA) or Tennessee Valley Authority (TVA) has traditionally involved manual model input and state modifications made by forecasters in real-time. This process is time consuming and requires expert knowledge and experience. The benefits of automated data assimilation (DA) as a strategy for avoiding manual modification approaches have been demonstrated in research studies (eg. Seo et al., 2009). This study explores the usage of various ensemble DA algorithms within the operational platform used by TVA. The final goal is to identify a DA algorithm that will guide the manual modification process used by TVA forecasters and realize considerable time gains (without loss of quality or even enhance the quality) within the forecast process. We evaluate the usability of various popular algorithms for DA that have been applied on a limited basis for operational hydrology. To this end, Delft-FEWS was wrapped (via piwebservice) in OpenDA to enable execution of FEWS workflows (and the chained models within these workflows, including SACSMA, UNITHG and LAGK) in a DA framework. Within OpenDA, several filter methods are available. We considered 4 algorithms: particle filter (RRF), Ensemble Kalman Filter and Asynchronous Ensemble Kalman and Particle filter. Retrospective simulation results for one location and algorithm (AEnKF) are illustrated in Figure 1. The initial results are promising. We will present verification results for these methods (and possible more) for a variety of sub basins in the Tennessee River basin. Finally, we will offer recommendations for guided DA based on our results. References Seo, D.-J., L. Cajina, R. Corby and T. Howieson, 2009: Automatic State Updating for Operational Streamflow Forecasting via Variational Data Assimilation, 367, Journal of Hydrology, 255-275. Figure 1. Retrospectively simulated streamflow for the headwater basin above Powell River at Jonesville (red is observed flow, blue is simulated flow without DA, black is simulated flow with DA)

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.

Global Ocean Forecast System (GOFS) Version 2.6. User’s Manual

DTIC Science & Technology

2010-03-31

odimens.D, which takes the rivers.dat flow levels, inputs an SST and sea surface salinity (SSS) climatology from GDEM , and outputs the orivs_1.D...Center for Medium-range Weather Forecast GB GigaByte GDEM Global Digital Elevation Map GOFS Global Ocean Forecast System HPCMP High Performance

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.

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

NASA Astrophysics Data System (ADS)

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

2018-03-01

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

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

NASA Astrophysics Data System (ADS)

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

2003-07-01

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

Season-ahead Drought Forecast Models for the Lower Colorado River Authority in Texas

NASA Astrophysics Data System (ADS)

Block, P. J.; Zimmerman, B.; Grzegorzewski, M.; Watkins, D. W., Jr.; Anderson, R.

2014-12-01

The Lower Colorado River Authority (LCRA) in Austin, Texas, manages the Highland Lakes reservoir system in Central Texas, a series of six lakes on the Lower Colorado River. This system provides water to approximately 1.1 million people in Central Texas, supplies hydropower to a 55-county area, supports rice farming along the Texas Gulf Coast, and sustains in-stream flows in the Lower Colorado River and freshwater inflows to Matagorda Bay. The current, prolonged drought conditions are severely taxing the LCRA's system, making allocation and management decisions exceptionally challenging, and affecting the ability of constituents to conduct proper planning. In this work, we further develop and evaluate season-ahead statistical streamflow and precipitation forecast models for integration into LCRA decision support models. Optimal forecast lead time, predictive skill, form, and communication are all considered.

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.

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.

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.

Science implementation of Forecast Mekong for food and environmental security

USGS Publications Warehouse

Turnipseed, D. Phil

2012-01-01

Forecast Mekong is a significant international thrust under the Delta Research and Global Observation Network (DRAGON) of the U.S. Geological Survey (USGS) and was launched in 2009 by the U.S. Department of State and the Foreign Ministers of Cambodia, Laos, Thailand, and Vietnam under U.S. Department of State Secretary Hillary R. Clinton's Lower Mekong Initiative to enhance U.S. engagement with countries of the Lower Mekong River Basin in the areas of environment, health, education, and infrastructure. Since 2009, the USGS has worked closely with the U.S. Department of State; personnel from Cambodia, Laos, Thailand, and Vietnam; nongovernmental organizations; and academia to collect and use research and data from the Lower Mekong River Basin to provide hands-on results that will help decisionmakers in future planning and design for restoration, conservation, and management efforts in the Lower Mekong River Basin. In 2012 Forecast Mekong is highlighting the increasing cooperation between the United States and Lower Mekong River Basin countries in the areas of food and environmental security. Under the DRAGON, Forecast Mekong continues work in interactive data integration, modeling, and visualization system by initiating three-dimensional bathymetry and river flow data along with a pilot study of fish distribution, population, and migratory patterns in the Lower Mekong River Basin. When fully developed by the USGS, in partnership with local governments and universities throughout the Mekong River region, Forecast Mekong will provide valuable planning tools to visualize the consequences of climate change and river management.

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

NASA Astrophysics Data System (ADS)

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

2013-08-01

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

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.

Untangling Trends and Drivers of Changing River Discharge Along Florida's Gulf Coast

NASA Astrophysics Data System (ADS)

Glodzik, K.; Kaplan, D. A.; Klarenberg, G.

2017-12-01

Along the relatively undeveloped Big Bend coastline of Florida, discharge in many rivers and springs is decreasing. The causes are unclear, though they likely include a combination of groundwater extraction for water supply, climate variability, and altered land use. Saltwater intrusion from altered freshwater influence and sea level rise is causing transformative ecosystem impacts along this flat coastline, including coastal forest die-off and oyster reef collapse. A key uncertainty for understanding river discharge change is predicting discharge from rainfall, since Florida's karstic bedrock stores large amounts of groundwater, which has a long residence time. This study uses Dynamic Factor Analysis (DFA), a multivariate data reduction technique for time series, to find common trends in flow and reveal hydrologic variables affecting flow in eight Big Bend rivers since 1965. The DFA uses annual river flows as response time series, and climate data (annual rainfall and evapotranspiration by watershed) and climatic indices (El Niño Southern Oscillation [ENSO] Index and North Atlantic Oscillation [NAO] Index) as candidate explanatory variables. Significant explanatory variables (one evapotranspiration and three rainfall time series) explained roughly 50% of discharge variation across rivers. Significant trends (representing unexplained variation) were shared among rivers, with geographical grouping of five northern rivers and three southern rivers, along with a strong downward trend affecting six out of eight systems. ENSO and NAO had no significant impact. Advancing knowledge of these dynamics is necessary for forecasting how altered rainfall and temperatures from climate change may impact flows. Improved forecasting is especially important given Florida's reliance on groundwater extraction to support its growing population.

Explaining and forecasting interannual variability in the flow of the Nile River

NASA Astrophysics Data System (ADS)

Siam, M. S.; Eltahir, E. A. B.

2014-05-01

The natural interannual variability in the flow of Nile River had a significant impact on the ancient civilizations and cultures that flourished on the banks of the river. This is evident from stories in the Bible and Koran, and from the numerous Nilometers discovered near ancient temples. Here, we analyze extensive data sets collected during the 20th century and define four modes of natural variability in the flow of Nile River, identifying a new significant potential for improving predictability of floods and droughts. Previous studies have identified a significant teleconnection between the Nile flow and the Eastern Pacific Ocean. El Niño-Southern Oscillation (ENSO) explains about 25% of the interannual variability in the Nile flow. Here, we identify, for the first time, a region in the southern Indian Ocean with similarly strong teleconnection to the Nile flow. Sea Surface Temperature (SST) in the region (50-80° E and 25-35° S) explains 28% of the interannual variability in the Nile flow. During those years with anomalous SST conditions in both Oceans, we estimate that indices of the SSTs in the Pacific and Indian Oceans can collectively explain up to 84% of the interannual variability in the flow of Nile. Building on these findings, we use classical Bayesian theorem to develop a new hybrid forecasting algorithm that predicts the Nile flow based on global models predictions of indices of the SST in the Eastern Pacific and Southern Indian Oceans.

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

NASA Astrophysics Data System (ADS)

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

2017-04-01

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

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.

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.

Statistical analysis and ANN modeling for predicting hydrological extremes under climate change scenarios: the example of a small Mediterranean agro-watershed.

PubMed

Kourgialas, Nektarios N; Dokou, Zoi; Karatzas, George P

2015-05-01

The purpose of this study was to create a modeling management tool for the simulation of extreme flow events under current and future climatic conditions. This tool is a combination of different components and can be applied in complex hydrogeological river basins, where frequent flood and drought phenomena occur. The first component is the statistical analysis of the available hydro-meteorological data. Specifically, principal components analysis was performed in order to quantify the importance of the hydro-meteorological parameters that affect the generation of extreme events. The second component is a prediction-forecasting artificial neural network (ANN) model that simulates, accurately and efficiently, river flow on an hourly basis. This model is based on a methodology that attempts to resolve a very difficult problem related to the accurate estimation of extreme flows. For this purpose, the available measurements (5 years of hourly data) were divided in two subsets: one for the dry and one for the wet periods of the hydrological year. This way, two ANNs were created, trained, tested and validated for a complex Mediterranean river basin in Crete, Greece. As part of the second management component a statistical downscaling tool was used for the creation of meteorological data according to the higher and lower emission climate change scenarios A2 and B1. These data are used as input in the ANN for the forecasting of river flow for the next two decades. The final component is the application of a meteorological index on the measured and forecasted precipitation and flow data, in order to assess the severity and duration of extreme events. Copyright © 2015 Elsevier Ltd. All rights reserved.

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

NASA Astrophysics Data System (ADS)

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

2016-12-01

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

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.

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

NASA Astrophysics Data System (ADS)

Weil, P.

2013-12-01

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

Ice flood velocity calculating approach based on single view metrology

NASA Astrophysics Data System (ADS)

Wu, X.; Xu, L.

2017-02-01

Yellow River is the river in which the ice flood occurs most frequently in China, hence, the Ice flood forecasting has great significance for the river flood prevention work. In various ice flood forecast models, the flow velocity is one of the most important parameters. In spite of the great significance of the flow velocity, its acquisition heavily relies on manual observation or deriving from empirical formula. In recent years, with the high development of video surveillance technology and wireless transmission network, the Yellow River Conservancy Commission set up the ice situation monitoring system, in which live videos can be transmitted to the monitoring center through 3G mobile networks. In this paper, an approach to get the ice velocity based on single view metrology and motion tracking technique using monitoring videos as input data is proposed. First of all, River way can be approximated as a plane. On this condition, we analyze the geometry relevance between the object side and the image side. Besides, we present the principle to measure length in object side from image. Secondly, we use LK optical flow which support pyramid data to track the ice in motion. Combining the result of camera calibration and single view metrology, we propose a flow to calculate the real velocity of ice flood. At last we realize a prototype system by programming and use it to test the reliability and rationality of the whole solution.

Bayesian forecasting and uncertainty quantifying of stream flows using Metropolis-Hastings Markov Chain Monte Carlo algorithm

NASA Astrophysics Data System (ADS)

Wang, Hongrui; Wang, Cheng; Wang, Ying; Gao, Xiong; Yu, Chen

2017-06-01

This paper presents a Bayesian approach using Metropolis-Hastings Markov Chain Monte Carlo algorithm and applies this method for daily river flow rate forecast and uncertainty quantification for Zhujiachuan River using data collected from Qiaotoubao Gage Station and other 13 gage stations in Zhujiachuan watershed in China. The proposed method is also compared with the conventional maximum likelihood estimation (MLE) for parameter estimation and quantification of associated uncertainties. While the Bayesian method performs similarly in estimating the mean value of daily flow rate, it performs over the conventional MLE method on uncertainty quantification, providing relatively narrower reliable interval than the MLE confidence interval and thus more precise estimation by using the related information from regional gage stations. The Bayesian MCMC method might be more favorable in the uncertainty analysis and risk management.

The Western U.S. Drought: How Bad Is It?

NASA Astrophysics Data System (ADS)

Piechota, Thomas; Timilsena, Janek; Tootle, Glenn; Hidalgo, Hugo

2004-08-01

Historical stream flow records and the forecast for 2004 make the current (lpar1999-2004) drought in the southwestern United States the worst one in the past 80 years for portions of the Upper Colorado River Basin (UCRB). For the Colorado River (near Cisco, Utah), the cumulative stream flow deficit (departure from long term mean) for the current drought is almost 11 km3, or approximately 2 years of average stream flow. Although the current drought is the most significant, based on stream flow records, is it the worst ever?

Seasonal forecasting of discharge for the Raccoon River, Iowa

NASA Astrophysics Data System (ADS)

Slater, Louise; Villarini, Gabriele; Bradley, Allen; Vecchi, Gabriel

2016-04-01

The state of Iowa (central United States) is regularly afflicted by severe natural hazards such as the 2008/2013 floods and the 2012 drought. To improve preparedness for these catastrophic events and allow Iowans to make more informed decisions about the most suitable water management strategies, we have developed a framework for medium to long range probabilistic seasonal streamflow forecasting for the Raccoon River at Van Meter, a 8900-km2 catchment located in central-western Iowa. Our flow forecasts use statistical models to predict seasonal discharge for low to high flows, with lead forecasting times ranging from one to ten months. Historical measurements of daily discharge are obtained from the U.S. Geological Survey (USGS) at the Van Meter stream gage, and used to compute quantile time series from minimum to maximum seasonal flow. The model is forced with basin-averaged total seasonal precipitation records from the PRISM Climate Group and annual row crop production acreage from the U.S. Department of Agriculture's National Agricultural Statistics Services database. For the forecasts, we use corn and soybean production from the previous year (persistence forecast) as a proxy for the impacts of agricultural practices on streamflow. The monthly precipitation forecasts are provided by eight Global Climate Models (GCMs) from the North American Multi-Model Ensemble (NMME), with lead times ranging from 0.5 to 11.5 months, and a resolution of 1 decimal degree. Additionally, precipitation from the month preceding each season is used to characterize antecedent soil moisture conditions. The accuracy of our modelled (1927-2015) and forecasted (2001-2015) discharge values is assessed by comparison with the observed USGS data. We explore the sensitivity of forecast skill over the full range of lead times, flow quantiles, forecast seasons, and with each GCM. Forecast skill is also examined using different formulations of the statistical models, as well as NMME forecast weighting procedures based on the computed potential skill (historical forecast accuracy) of the different GCMs. We find that the models describe the year-to-year variability in streamflow accurately, as well as the overall tendency towards increasing (and more variable) discharge over time. Surprisingly, forecast skill does not decrease markedly with lead time, and high flows tend to be well predicted, suggesting that these forecasts may have considerable practical applications. Further, the seasonal flow forecast accuracy is substantially improved by weighting the contribution of individual GCMs to the forecasts, and also by the inclusion of antecedent precipitation. Our results can provide critical information for adaptation strategies aiming to mitigate the costs and disruptions arising from flood and drought conditions, and allow us to determine how far in advance skillful forecasts can be issued. The availability of these discharge forecasts would have major societal and economic benefits for hydrology and water resources management, agriculture, disaster forecasts and prevention, energy, finance and insurance, food security, policy-making and public authorities, and transportation.

On the forecast of runoff based on the harmonic analysis of time series of precipitation in the catchment area

NASA Astrophysics Data System (ADS)

Cherednichenko, A. V.; Cherednichenko, A. V.; Cherednichenko, V. S.

2018-01-01

It is shown that a significant connection exists between the most important harmonics, extracted in the process of harmonic analysis of time series of precipitation in the catchment area of rivers and the amount of runoff. This allowed us to predict the size of the flow for a period of up to 20 years, assuming that the main parameters of the harmonics are preserved at the predicted time interval. The results of such a forecast for three river basins of Kazakhstan are presented.

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

NASA Astrophysics Data System (ADS)

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

2017-09-01

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

Bayesian forecasting and uncertainty quantifying of stream flows using Metropolis–Hastings Markov Chain Monte Carlo algorithm

DOE PAGES

Wang, Hongrui; Wang, Cheng; Wang, Ying; ...

2017-04-05

This paper presents a Bayesian approach using Metropolis-Hastings Markov Chain Monte Carlo algorithm and applies this method for daily river flow rate forecast and uncertainty quantification for Zhujiachuan River using data collected from Qiaotoubao Gage Station and other 13 gage stations in Zhujiachuan watershed in China. The proposed method is also compared with the conventional maximum likelihood estimation (MLE) for parameter estimation and quantification of associated uncertainties. While the Bayesian method performs similarly in estimating the mean value of daily flow rate, it performs over the conventional MLE method on uncertainty quantification, providing relatively narrower reliable interval than the MLEmore » confidence interval and thus more precise estimation by using the related information from regional gage stations. As a result, the Bayesian MCMC method might be more favorable in the uncertainty analysis and risk management.« less

Support vector machine-an alternative to artificial neuron network for water quality forecasting in an agricultural nonpoint source polluted river?

PubMed

Liu, Mei; Lu, Jun

2014-09-01

Water quality forecasting in agricultural drainage river basins is difficult because of the complicated nonpoint source (NPS) pollution transport processes and river self-purification processes involved in highly nonlinear problems. Artificial neural network (ANN) and support vector model (SVM) were developed to predict total nitrogen (TN) and total phosphorus (TP) concentrations for any location of the river polluted by agricultural NPS pollution in eastern China. River flow, water temperature, flow travel time, rainfall, dissolved oxygen, and upstream TN or TP concentrations were selected as initial inputs of the two models. Monthly, bimonthly, and trimonthly datasets were selected to train the two models, respectively, and the same monthly dataset which had not been used for training was chosen to test the models in order to compare their generalization performance. Trial and error analysis and genetic algorisms (GA) were employed to optimize the parameters of ANN and SVM models, respectively. The results indicated that the proposed SVM models performed better generalization ability due to avoiding the occurrence of overtraining and optimizing fewer parameters based on structural risk minimization (SRM) principle. Furthermore, both TN and TP SVM models trained by trimonthly datasets achieved greater forecasting accuracy than corresponding ANN models. Thus, SVM models will be a powerful alternative method because it is an efficient and economic tool to accurately predict water quality with low risk. The sensitivity analyses of two models indicated that decreasing upstream input concentrations during the dry season and NPS emission along the reach during average or flood season should be an effective way to improve Changle River water quality. If the necessary water quality and hydrology data and even trimonthly data are available, the SVM methodology developed here can easily be applied to other NPS-polluted rivers.

Model based hydropower gate operation for mitigation of CSO impacts by means of river base flow increase.

PubMed

Achleitner, S; De Toffol, S; Engelhard, C; Rauch, W

2005-01-01

In river stretches being subjected to flow regulation, usually for the purpose of energy production (e.g. Hydropower) or flood protection (river barrage), a special measure can be taken against the effect of combined sewer overflows (CSOs). The basic idea is the temporal increase of the river base flow (during storm weather) as an in-stream measure for mitigation of CSO spilling. The focus is the mitigation of the negative effect of acute pollution of substances. The measure developed can be seen as an application of the classic real time control (RTC) concept onto the river system. Upstream gate operation is to be based on real time monitoring and forecasting of precipitation. The main objective is the development of a model based predictive control system for the gate operation, by modelling of the overall wastewater system (incl. the receiving water). The main emphasis is put on the operational strategy and the appropriate short-term forecast of spilling events. The potential of the measure is tested for the application of the operational strategy and its ecological and economic feasibility. The implementation of such an in-stream measure into the hydropower's operational scheme is unique. Advantages are (a) the additional in-stream dilution of acute pollutants entering the receiving water and (b) the resulting minimization of the required CSO storage volume.

Two-Dimensional Flow Modeling: Proceedings of the National U.S. Army Corps of Engineers-Sponsored Seminar on the Two-Dimensional Flow Modeling (1st), 7-9 July 1981,

DTIC Science & Technology

1982-03-01

Niederauer (Enerly, Inc.), Lee Butler (WES), Danny Fread (NOAA). Theodor Strelkoff (Hydraulic Enuineering), Robert MacArthur (HEC), Warren Mellema (4RD), Jaime ...Mississippi, Sabine , and Neches Rivers as shown in Figures 1 and 2. Work is on-going to gradually extend this service to other Gulf Coast rivers in Texas...MEXICO FIG. 2-HURRICANE STORM SURGE FORECASTING OF SABINE AND NECHES RIVERS 16 DAM -Z - PLAN VIEW- SEC. VIEW f-f FIG. 3- DAM-BREAK FLOOD ONTO A VERY WIDE

Winter-spring 2001 United States streamflow probabilities based on anticipated neutral ENSO conditions and recent NPO status

USGS Publications Warehouse

Dettinger, M.D.; Cayan, D.R.; McCabe, G.J.; Redmond, K.T.

2000-01-01

An analysis of historical floods and seasonal streamflows during years with neutral El NiñoSouthern Oscillation (ENSO) conditions in the tropical Pacific and “negative” states of the North Pacific Oscillation (NPO) in the North Pacific—like those expected next year—indicates that (1) chances of having maximum-daily flows next year that are near the longterm averages in many rivers are enhanced, especially in the western states, (2) chances of having near-average seasonal-average flows also may be enhanced across the country, and (3) locally, chances of large floods and winter-season flows may be enhanced in the extreme Northwest, chances of large winter flows may be diminished in rivers in and around Wisconsin, and chances of large spring flows may be diminished in the interior southwest and southeastern coastal plain. The background, methods, and forecast results that lead to these statements are detailed below, followed by a summary of the successes and failures of last year’s streamflow forecast by Dettinger et al. (1999).

Application of HEC-RAS for flood forecasting in perched river-A case study of hilly region, China

NASA Astrophysics Data System (ADS)

Sun, Pingping; Wang, Shuqian; Gan, Hong; Liu, Bin; Jia, Ling

2017-04-01

Flooding in small and medium rivers are seriously threatening the safety of human beings’ life and property. The simulation forecasting of the river flood and bank risk in hilly region has gradually become a hotspot. At present, there are few studies on the simulation of hilly perched river, especially in the case of lacking section flow data. And the method of how to determine the position of the levee breach along the river bank is not much enough. Based on the characteristics of the sections in hilly perched river, an attempt is applied in this paper which establishes the correlation between the flow profile computed by HEC-RAS model and the river bank. A hilly perched river in Lingshi County, Shanxi Province of China, is taken as the study object, the levee breach positions along the bank are simulated under four different design storm. The results show that the flood control standard of upper reach is high, which can withstand the design storm of 100 years. The current standard of lower reach is low, which is the flooding channel with high frequency. As the standard of current channel between the 2rd and the 11th section is low, levee along that channel of the river bank is considered to be heighten and reinforced. The study results can provide some technical support for flood proofing in hilly region and some reference for the reinforcement of river bank.

Operational Hydrologic Forecasts in the Columbia River Basin

NASA Astrophysics Data System (ADS)

Shrestha, K. Y.; Curry, J. A.; Webster, P. J.; Toma, V. E.; Jelinek, M.

2013-12-01

The Columbia River Basin (CRB) covers an area of ~670,000 km2 and stretches across parts of seven U.S. states and one Canadian province. The basin is subject to a variable climate, and moisture stored in snowpack during the winter is typically released in spring and early summer. These releases contribute to rapid increases in flow. A number of impoundments have been constructed on the Columbia River main stem and its tributaries for the purposes of flood control, navigation, irrigation, recreation, and hydropower. Storage reservoirs allow water managers to adjust natural flow patterns to benefit water and energy demands. In the past decade, the complexity of water resource management issues in the basin has amplified the importance of streamflow forecasting. Medium-range (1-10 day) numerical weather forecasts of precipitation and temperature can be used to drive hydrological models. In this work, probabilistic meteorological variables from the European Center for Medium Range Weather Forecasting (ECMWF) are used to force the Variable Infiltration Capacity (VIC) model. Soil textures were obtained from FAO data; vegetation types / land cover information from UMD land cover data; stream networks from USGS HYDRO1k; and elevations from CGIAR version 4 SRTM data. The surface energy balance in 0.25° (~25 km) cells is closed through an iterative process operating at a 6 hour timestep. Output fluxes from a number of cells in the basin are combined through one-dimensional flow routing predicated on assumptions of linearity and time invariance. These combinations lead to daily mean streamflow estimates at key locations throughout the basin. This framework is suitable for ingesting daily numerical weather prediction data, and was calibrated using USGS mean daily streamflow data at the Dalles Dam (TDA). Operational streamflow forecasts in the CRB have been active since October 2012. These are 'naturalized' or unregulated forecasts. In 2013, increases of ~2600 m3/s (~48% of average discharge for water years 1879-2012) or greater were observed at TDA during the following periods: 29 March to 12 April, 5 May to 11 May, and 19 June to 29 June. Precipitation and temperature forecasts during these periods are shown along with changes in the model simulated snowpack. We evaluate the performance of the ensemble mean 10 days in advance of each of these three events, and comment on how the distribution of ensemble members affected forecast confidence in each situation.

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.

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.

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.

New River Inlet DRI: Observations and Modeling of Flow and Material Exchange & Field and Numerical Study of the Columbia River Mouth

DTIC Science & Technology

2013-09-30

analyze the MCR drifter, in situ mini-catamaran, pressure, and USGS tripod observations; • describe the tidal chocking behavior at New River Inlet (NRI...i.e. waves , wind and potentially stratification) APPROACH Our approach is to collect field observations to evaluate the sensitivity of Delft3D at...forecast model using the predicted tides, wind, wave and river discharge conditions to optimize spatial coverage and drifter retrieval operations. On

Challenges in Understanding and Forecasting Winds in Complex Terrain.

NASA Astrophysics Data System (ADS)

Mann, J.; Fernando, J.; Wilczak, J. M.

2017-12-01

An overview will be given of some of the challenges in understanding and forecasting winds in complex terrain. These challenges can occur for several different reasons including 1) gaps in our understanding of fundamental physical boundary layer processes occurring in complex terrain; 2) a lack of adequate parameterizations and/or numerical schemes in NWP models; and 3) inadequate observations for initialization of NWP model forecasts. Specific phenomena that will be covered include topographic wakes/vortices, cold pools, gap flows, and mountain-valley winds, with examples taken from several air quality and wind energy related field programs in California as well as from the recent Second Wind Forecast Improvement Program (WFIP2) field campaign in the Columbia River Gorge/Basin area of Washington and Oregon States. Recent parameterization improvements discussed will include those for boundary layer turbulence, including 3D turbulence schemes, and gravity wave drag. Observational requirements for improving wind forecasting in complex terrain will be discussed, especially in the context of forecasting pressure gradient driven gap flow events.

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.

The Impact of Corps Flood Control Reservoirs in the June 2008 Upper Mississippi Flood

NASA Astrophysics Data System (ADS)

Charley, W. J.; Stiman, J. A.

2008-12-01

The US Army Corps of Engineers is responsible for a multitude of flood control project on the Mississippi River and its tributaries, including levees that protect land from flooding, and dams to help regulate river flows. The first six months of 2008 were the wettest on record in the upper Mississippi Basin. During the first 2 weeks of June, rainfall over the Midwest ranged from 6 to as much as 16 inches, overwhelming the flood protection system, causing massive flooding and damage. Most severely impacted were the States of Iowa, Illinois, Indiana, Missouri, and Wisconsin. In Iowa, flooding occurred on almost every river in the state. On the Iowa River, record flooding occurred from Marshalltown, Iowa, downstream to its confluence with the Mississippi River. At several locations, flooding exceeded the 500-year event. The flooding affected agriculture, transportation, and infrastructure, including homes, businesses, levees, and other water-control structures. It has been estimated that there was at least 7 billion dollars in damages. While the flooding in Iowa was extraordinary, Corps of Engineers flood control reservoirs helped limit damage and prevent loss of life, even though some reservoirs were filled beyond their design capacity. Coralville Reservoir on the Iowa River, for example, filled to 135% of its design flood storage capacity, with stage a record five feet over the crest of the spillway. In spite of this, the maximum reservoir release was limited to 39,500 cfs, while a peak inflow of 57,000 cfs was observed. CWMS, the Corps Water Management System, is used to help regulate Corps reservoirs, as well as track and evaluate flooding and flooding potential. 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. The system uses precipitation and flow data, collected in real-time, along with forecasted flow from the National Weather Service to model and optimize reservoir operations and forecast downstream flows and stages, providing communities accurate and timely information to aid their flood-fighting. This involves integrating several simulation modeling programs, including HEC-HMS to forecast flows, HEC-ResSim to model reservoir operations and HEC-RAS to compute forecasted stage hydrographs. 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. The effectiveness of this tool and Corps reservoirs are examined.

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.

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

NASA Astrophysics Data System (ADS)

Bao, Hongjun; Zhao, Linna

2012-02-01

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

Streamflow Forecasting Using Nuero-Fuzzy Inference System

NASA Astrophysics Data System (ADS)

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

2005-12-01

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

Dynamic Change in Glacial Dammed Lake Behavior of Suicide Basin, Mendenhall Glacier, Juneau Alaska

NASA Astrophysics Data System (ADS)

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

2016-12-01

Suicide Basin Jökulhlaups, since 2011, have resulted in moderate flooding on the Mendenhall Lake and River in Juneau, AK. At this time, the USGS recorded peak streamflow of 20,000 cfs in 2014, the highest flows officially reported by the USGS which was attributed to a Suicide Basin glacial-dammed lake release. However, the USGS estimated a peak flow of 27,000 cfs in 1961 and we suspect this event is partially the result of a glacial dammed lake release. From 2011 to 2015, data indicates that yearly outburst from Suicide Basin were the norm; however, in 2015 and 2016, multiple outbursts during the summer were observed suggesting a dynamic change in glacial behavior. For public safety and awareness, the University of Alaska Southeast and U.S. Geologic Survey began monitoring real-time Suicide Basin lake levels. A real-time model was developed by the National Weather Service Alaska-Pacific River Forecast Center capable of forecasting potential timing and magnitude of the flood-wave crest from this Suicide Basin release. However, the model now is being modified because data not previously available has become available and adapted to the change in state of glacial behavior. The importance of forecasting time and level of crest on the Mendenhall River system owing to these outbursts floods is an essential aid to emergency managers and the general public to provide impact decision support services (IDSS). The National Weather Service has been able to provide 36 to 24 hour forecasts for these large events, but with the change in glacial state on the Mendenhall Glacier, the success of forecasting these events is getting more challenging. We will show the success of the hydrologic model but at the same time show the challenges we have seen with the changing glacier dynamics.

River flow simulation using a multilayer perceptron-firefly algorithm model

NASA Astrophysics Data System (ADS)

Darbandi, Sabereh; Pourhosseini, Fatemeh Akhoni

2018-06-01

River flow estimation using records of past time series is importance in water resources engineering and management and is required in hydrologic studies. In the past two decades, the approaches based on the artificial neural networks (ANN) were developed. River flow modeling is a non-linear process and highly affected by the inputs to the modeling. In this study, the best input combination of the models was identified using the Gamma test then MLP-ANN and hybrid multilayer perceptron (MLP-FFA) is used to forecast monthly river flow for a set of time intervals using observed data. The measurements from three gauge at Ajichay watershed, East Azerbaijani, were used to train and test the models approach for the period from January 2004 to July 2016. Calibration and validation were performed within the same period for MLP-ANN and MLP-FFA models after the preparation of the required data. Statistics, the root mean square error and determination coefficient, are used to verify outputs from MLP-ANN to MLP-FFA models. The results show that MLP-FFA model is satisfactory for monthly river flow simulation in study area.

A channel dynamics model for real-time flood forecasting

USGS Publications Warehouse

Hoos, Anne B.; Koussis, Antonis D.; Beale, Guy O.

1989-01-01

A new channel dynamics scheme (alternative system predictor in real time (ASPIRE)), designed specifically for real-time river flow forecasting, is introduced to reduce uncertainty in the forecast. ASPIRE is a storage routing model that limits the influence of catchment model forecast errors to the downstream station closest to the catchment. Comparisons with the Muskingum routing scheme in field tests suggest that the ASPIRE scheme can provide more accurate forecasts, probably because discharge observations are used to a maximum advantage and routing reaches (and model errors in each reach) are uncoupled. Using ASPIRE in conjunction with the Kalman filter did not improve forecast accuracy relative to a deterministic updating procedure. Theoretical analysis suggests that this is due to a large process noise to measurement noise ratio.

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

NASA Astrophysics Data System (ADS)

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

2016-04-01

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

Upper Rio Grande water operations model: A tool for enhanced system management

Treesearch

Gail Stockton; D. Michael Roark

1999-01-01

The Upper Rio Grande Water Operations Model (URGWOM) under development through a multi-agency effort has demonstrated capability to represent the physical river/reservoir system, to track and account for Rio Grande flows and imported San Juan flows, and to forecast flows at various points in the system. Testing of the Rio Chama portion of the water operations model was...

First Assessment of Itaipu Dam Ensemble Inflow Forecasting System

NASA Astrophysics Data System (ADS)

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

2017-04-01

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

Going with the flow: using species-discharge relationships to forecast losses in fish biodiversity.

PubMed

Xenopoulos, Marguerite A; Lodge, David M

2006-08-01

In response to the scarcity of tools to make quantitative forecasts of the loss of aquatic species from anthropogenic effects, we present a statistical model that relates fish species richness to river discharge. Fish richness increases logarithmically with discharge, an index of habitat space, similar to a species-area curve in terrestrial systems. We apply the species-discharge model as a forecasting tool to build scenarios of changes in riverine fish richness from climate change, water consumption, and other anthropogenic drivers that reduce river discharge. Using hypothetical reductions in discharges (of magnitudes that have been observed in other rivers), we predict that reductions of 20-90% in discharge would result in losses of 2-38% of the fish species in two biogeographical regions in the United States (Lower Ohio-Upper Mississippi and Southeastern). Additional data on the occurrence of specific species relative to specific discharge regimes suggests that fishes found exclusively in high discharge environments (e.g., Shovelnose sturgeon) would be most vulnerable to reductions in discharge. Lag times in species extinctions after discharge reduction provide a window of opportunity for conservation efforts. Applications of the species-discharge model can help prioritize such management efforts among species and rivers.

An Application of Data Mining Techniques for Flood Forecasting: Application in Rivers Daya and Bhargavi, India

NASA Astrophysics Data System (ADS)

Panigrahi, Binay Kumar; Das, Soumya; Nath, Tushar Kumar; Senapati, Manas Ranjan

2018-05-01

In the present study, with a view to speculate the water flow of two rivers in eastern India namely river Daya and river Bhargavi, the focus was on developing Cascaded Functional Link Artificial Neural Network (C-FLANN) model. Parameters of C-FLANN architecture were updated using Harmony Search (HS) and Differential Evolution (DE). As the numbers of samples are very low, there is a risk of over fitting. To avoid this Map reduce based ANOVA technique is used to select important features. These features were used and provided to the architecture which is used to predict the water flow in both the rivers, one day, one week and two weeks ahead. The results of both the techniques were compared with Radial Basis Functional Neural Network (RBFNN) and Multilayer Perceptron (MLP), two widely used artificial neural network for prediction. From the result it was confirmed that C-FLANN trained through HS gives better prediction result than being trained through DE or RBFNN or MLP and can be used for predicting water flow in different rivers.

Economic Analysis of the Impacts of Climate-Induced Changes in River Flow on Hydropower and Fisheries in Himalayan region.

NASA Astrophysics Data System (ADS)

Khadka Mishra, S.; Hayse, J.; Veselka, T.; Yan, E.; Kayastha, R. B.; McDonald, K.; Steiner, N.; Lagory, K.

2017-12-01

Climate-mediated changes in melting of snow and glaciers and in precipitation patterns are expected to significantly alter the water flow of rivers at various spatial and temporal scales. Hydropower generation and fisheries are likely to be impacted annually and over the century by the seasonal as well as long-term changes in hydrological conditions. In order to quantify the interactions between the drivers of climate change, the hydropower sector and the ecosystem we developed an integrated assessment framework that links climate models with process-based bio-physical and economic models. This framework was applied to estimate the impacts of changes in snow and glacier melt on the stream flow of the Trishuli River of the High Mountain Asia Region. Remotely-sensed data and derived products, as well as in-situ data, were used to quantify the changes in snow and glacier melt. The hydrological model was calibrated and validated for stream flows at various points in the Trishuli river in order to forecast conditions at the location of a stream gauge station upstream of the Trishuli hydropower plant. The flow of Trishuli River was projected to increase in spring and decrease in summer over the period of 2020-2100 under RCP 8.5 and RCP 4.5 scenarios as compared to respective mean seasonal discharge observed over 1981-2014. The simulated future annual mean stream flow would increase by 0.6 m3/s under RCP 8.5 scenario but slightly decrease under RCP 4.5. The Argonne Hydropower Energy and Economic toolkit was used to estimate and forecast electricity generation at the Trishuli power plant under various flow conditions and upgraded infrastructure. The increased spring flow is expected to increase dry-season electricity generation by 18% under RCP 8.5 in comparison to RCP 4.5. A fishery suitability model developed for the basin indicated that fishery suitability in the Trishuli River would be greater than 70% of optimal, even during dry months under both RCP 4.5 and RCP 8.5. The estimated economic value (preliminary result) of electricity generated from the Trishuli hydropower plant under RCP 4.5 and RCP 8.5 were projected to be 3.7% to 7.5% higher for the month of March while for the months of April and May the values were1.5% to 9.4% lower.

Forecasting risk along a river basin using a probabilistic and deterministic model for environmental risk assessment of effluents through ecotoxicological evaluation and GIS.

PubMed

Gutiérrez, Simón; Fernandez, Carlos; Barata, Carlos; Tarazona, José Vicente

2009-12-20

This work presents a computer model for Risk Assessment of Basins by Ecotoxicological Evaluation (RABETOX). The model is based on whole effluent toxicity testing and water flows along a specific river basin. It is capable of estimating the risk along a river segment using deterministic and probabilistic approaches. The Henares River Basin was selected as a case study to demonstrate the importance of seasonal hydrological variations in Mediterranean regions. As model inputs, two different ecotoxicity tests (the miniaturized Daphnia magna acute test and the D.magna feeding test) were performed on grab samples from 5 waste water treatment plant effluents. Also used as model inputs were flow data from the past 25 years, water velocity measurements and precise distance measurements using Geographical Information Systems (GIS). The model was implemented into a spreadsheet and the results were interpreted and represented using GIS in order to facilitate risk communication. To better understand the bioassays results, the effluents were screened through SPME-GC/MS analysis. The deterministic model, performed each month during one calendar year, showed a significant seasonal variation of risk while revealing that September represents the worst-case scenario with values up to 950 Risk Units. This classifies the entire area of study for the month of September as "sublethal significant risk for standard species". The probabilistic approach using Monte Carlo analysis was performed on 7 different forecast points distributed along the Henares River. A 0% probability of finding "low risk" was found at all forecast points with a more than 50% probability of finding "potential risk for sensitive species". The values obtained through both the deterministic and probabilistic approximations reveal the presence of certain substances, which might be causing sublethal effects in the aquatic species present in the Henares River.

Toward global mapping of river discharge using satellite images and at-many-stations hydraulic geometry.

PubMed

Gleason, Colin J; Smith, Laurence C

2014-04-01

Rivers provide critical water supply for many human societies and ecosystems, yet global knowledge of their flow rates is poor. We show that useful estimates of absolute river discharge (in cubic meters per second) may be derived solely from satellite images, with no ground-based or a priori information whatsoever. The approach works owing to discovery of a characteristic scaling law uniquely fundamental to natural rivers, here termed a river's at-many-stations hydraulic geometry. A first demonstration using Landsat Thematic Mapper images over three rivers in the United States, Canada, and China yields absolute discharges agreeing to within 20-30% of traditional in situ gauging station measurements and good tracking of flow changes over time. Within such accuracies, the door appears open for quantifying river resources globally with repeat imaging, both retroactively and henceforth into the future, with strong implications for water resource management, food security, ecosystem studies, flood forecasting, and geopolitics.

A multimodel approach to interannual and seasonal prediction of Danube discharge anomalies

NASA Astrophysics Data System (ADS)

Rimbu, Norel; Ionita, Monica; Patrut, Simona; Dima, Mihai

2010-05-01

Interannual and seasonal predictability of Danube river discharge is investigated using three model types: 1) time series models 2) linear regression models of discharge with large-scale climate mode indices and 3) models based on stable teleconnections. All models are calibrated using discharge and climatic data for the period 1901-1977 and validated for the period 1978-2008 . Various time series models, like autoregressive (AR), moving average (MA), autoregressive and moving average (ARMA) or singular spectrum analysis and autoregressive moving average (SSA+ARMA) models have been calibrated and their skills evaluated. The best results were obtained using SSA+ARMA models. SSA+ARMA models proved to have the highest forecast skill also for other European rivers (Gamiz-Fortis et al. 2008). Multiple linear regression models using large-scale climatic mode indices as predictors have a higher forecast skill than the time series models. The best predictors for Danube discharge are the North Atlantic Oscillation (NAO) and the East Atlantic/Western Russia patterns during winter and spring. Other patterns, like Polar/Eurasian or Tropical Northern Hemisphere (TNH) are good predictors for summer and autumn discharge. Based on stable teleconnection approach (Ionita et al. 2008) we construct prediction models through a combination of sea surface temperature (SST), temperature (T) and precipitation (PP) from the regions where discharge and SST, T and PP variations are stable correlated. Forecast skills of these models are higher than forecast skills of the time series and multiple regression models. The models calibrated and validated in our study can be used for operational prediction of interannual and seasonal Danube discharge anomalies. References Gamiz-Fortis, S., D. Pozo-Vazquez, R.M. Trigo, and Y. Castro-Diez, Quantifying the predictability of winter river flow in Iberia. Part I: intearannual predictability. J. Climate, 2484-2501, 2008. Gamiz-Fortis, S., D. Pozo-Vazquez, R.M. Trigo, and Y. Castro-Diez, Quantifying the predictability of winter river flow in Iberia. Part II: seasonal predictability. J. Climate, 2503-2518, 2008. Ionita, M., G. Lohmann, and N. Rimbu, Prediction of spring Elbe river discharge based on stable teleconnections with global temperature and precipitation. J. Climate. 6215-6226, 2008.

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.

Derivation and Application of Idealized Flow Conditions in River Network Simulation

NASA Astrophysics Data System (ADS)

Afshari Tork, S.; Fekete, B. M.

2015-12-01

Stream flow information is essential for many applications across broad range of scales, e.g. global water balances, engineering design, flood forecasting, environmental management, etc. Quantitative assessment of flow dynamics of natural streams, requires detailed knowledge of all the geometrical and geophysical variables (e.g. bed-slope, bed roughness, etc.) along river reaches. Simplifying the river bed geometries could reduce both the computational burden implementing flow simulations and challenges in assembling the required data, especially for large domains. Average flow conditions expressed as empirical "at-a-station" hydraulic geometry relationships between key channel components, (i.e. water depth, top-width, flow velocity, flow area against discharge) have been studied since 60's. Recent works demonstrated that power-function as idealized riverbed geometry whose parameters are correlated to those of exponential relationship between mean water depth and top-width, are consistent with empirical "at-a-station" relations.US Geological Surveys' National Water Information System web-interface provides huge amount of river discharge and corresponding stage height data from several thousands of streamflow monitoring stations over United States accompanied by river survey summaries providing additional flow informations (width, mean velocity, cross-sectional area). We conducted a series of analyses to indentify consistent data daily monitoring and corresponding survey records that are suitable to refine our current understanding of how the "at-a-station" properties of river channels relate to channel forming characteristics (e.g. riverbed slope, flow regime, geology, etc.). The resulting ~1,200 actively operating USGS stations with over ~225,000 corresponding survery records (almost 200 survey per gauge on average) is the largest river survey database ever studied in the past.Our presentation will show our process assembling our river monitoring and survey data base and we will present our first results translating "at-a-station" relations into he hydraulic geometry of river channels based on idealized power-law riverbed geometries. We also will also present a series of application (e.g. improved flow rounting, simplyfied river surveying).

Fusing enhanced radar precipitation, in-situ hydrometeorological measurements and airborne LIDAR snowpack estimates in a hyper-resolution hydrologic model to improve seasonal water supply forecasts

NASA Astrophysics Data System (ADS)

Gochis, D. J.; Busto, J.; Howard, K.; Mickey, J.; Deems, J. S.; Painter, T. H.; Richardson, M.; Dugger, A. L.; Karsten, L. R.; Tang, L.

2015-12-01

Scarcity of spatially- and temporally-continuous observations of precipitation and snowpack conditions in remote mountain watersheds results in fundamental limitations in water supply forecasting. These limitationsin observational capabilities can result in strong biases in total snowmelt-driven runoff amount, the elevational distribution of runoff, river basin tributary contributions to total basin runoff and, equally important for water management, the timing of runoff. The Upper Rio Grande River basin in Colorado and New Mexico is one basin where observational deficiencies are hypothesized to have significant adverse impacts on estimates of snowpack melt-out rates and on water supply forecasts. We present findings from a coordinated observational-modeling study within Upper Rio Grande River basin whose aim was to quanitfy the impact enhanced precipitation, meteorological and snowpack measurements on the simulation and prediction of snowmelt driven streamflow. The Rio Grande SNOwpack and streamFLOW (RIO-SNO-FLOW) Prediction Project conducted enhanced observing activities during the 2014-2015 water year. Measurements from a gap-filling, polarimetric radar (NOXP) and in-situ meteorological and snowpack measurement stations were assimilated into the WRF-Hydro modeling framework to provide continuous analyses of snowpack and streamflow conditions. Airborne lidar estimates of snowpack conditions from the NASA Airborne Snow Observatory during mid-April and mid-May were used as additional independent validations against the various model simulations and forecasts of snowpack conditions during the melt-out season. Uncalibrated WRF-Hydro model performance from simulations and forecasts driven by enhanced observational analyses were compared against results driven by currently operational data inputs. Precipitation estimates from the NOXP research radar validate significantly better against independent in situ observations of precipitation and snow-pack increases. Correcting the operational NLDAS2 forcing data with the experimental observations led to significant improvements in the seasonal accumulation and ablation of mountain snowpack and ultimately led to marked improvement in model simulated streamflow as compared with streamflow observations.

Real-time flood forecasting

USGS Publications Warehouse

Lai, C.; Tsay, T.-K.; Chien, C.-H.; Wu, I.-L.

2009-01-01

Researchers at the Hydroinformatic Research and Development Team (HIRDT) of the National Taiwan University undertook a project to create a real time flood forecasting model, with an aim to predict the current in the Tamsui River Basin. The model was designed based on deterministic approach with mathematic modeling of complex phenomenon, and specific parameter values operated to produce a discrete result. The project also devised a rainfall-stage model that relates the rate of rainfall upland directly to the change of the state of river, and is further related to another typhoon-rainfall model. The geographic information system (GIS) data, based on precise contour model of the terrain, estimate the regions that were perilous to flooding. The HIRDT, in response to the project's progress, also devoted their application of a deterministic model to unsteady flow of thermodynamics to help predict river authorities issue timely warnings and take other emergency measures.

A Data-driven Approach for Forecasting Next-day River Discharge

NASA Astrophysics Data System (ADS)

Sharif, H. O.; Billah, K. S.

2017-12-01

This study focuses on evaluating the performance of the Soil and Water Assessment Tool (SWAT) eco-hydrological model, a simple Auto-Regressive with eXogenous input (ARX) model, and a Gene expression programming (GEP)-based model in one-day-ahead forecasting of discharge of a subtropical basin (the upper Kentucky River Basin). The three models were calibrated with daily flow at the US Geological Survey (USGS) stream gauging station not affected by flow regulation for the period of 2002-2005. The calibrated models were then validated at the same gauging station as well as another USGS gauge 88 km downstream for the period of 2008-2010. The results suggest that simple models outperform a sophisticated hydrological model with GEP having the advantage of being able to generate functional relationships that allow scientific investigation of the complex nonlinear interrelationships among input variables. Unlike SWAT, GEP, and to some extent, ARX are less sensitive to the length of the calibration time series and do not require a spin-up period.

Collaborative Initiative toward Developing River Forecasting in South America

NASA Astrophysics Data System (ADS)

Cabrera, R.

2015-12-01

In the United States, river floods have been discussed as early as 1884. Following a disastrous flooding in 1903, Congress passed legislation and river and flood services became a separate division within the U.S. Weather Bureau. The first River Forecast Center started in 1946 and today the whole country is served by thirteen River Forecast Centers. News from Latin American and Caribbean Countries often report of devastating flooding. However, river forecast services are not fully developed yet. This presentation suggests the utilization of a multinational collaborative approach toward the development of river forecasts in order to mitigate flooding in South America. The benefit of an international strategy resides in the strength created by a team of professionals with different capabilities and expertise.

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.

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.

Toward global mapping of river discharge using satellite images and at-many-stations hydraulic geometry

PubMed Central

Gleason, Colin J.; Smith, Laurence C.

2014-01-01

Rivers provide critical water supply for many human societies and ecosystems, yet global knowledge of their flow rates is poor. We show that useful estimates of absolute river discharge (in cubic meters per second) may be derived solely from satellite images, with no ground-based or a priori information whatsoever. The approach works owing to discovery of a characteristic scaling law uniquely fundamental to natural rivers, here termed a river’s at-many-stations hydraulic geometry. A first demonstration using Landsat Thematic Mapper images over three rivers in the United States, Canada, and China yields absolute discharges agreeing to within 20–30% of traditional in situ gauging station measurements and good tracking of flow changes over time. Within such accuracies, the door appears open for quantifying river resources globally with repeat imaging, both retroactively and henceforth into the future, with strong implications for water resource management, food security, ecosystem studies, flood forecasting, and geopolitics. PMID:24639551

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.

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.

A fully-online Neuro-Fuzzy model for flow forecasting in basins with limited data

NASA Astrophysics Data System (ADS)

Ashrafi, Mohammad; Chua, Lloyd Hock Chye; Quek, Chai; Qin, Xiaosheng

2017-02-01

Current state-of-the-art online neuro fuzzy models (NFMs) such as DENFIS (Dynamic Evolving Neural-Fuzzy Inference System) have been used for runoff forecasting. Online NFMs adopt a local learning approach and are able to adapt to changes continuously. The DENFIS model however requires upper/lower bound for normalization and also the number of rules increases monotonically. This requirement makes the model unsuitable for use in basins with limited data, since a priori data is required. In order to address this and other drawbacks of current online models, the Generic Self-Evolving Takagi-Sugeno-Kang (GSETSK) is adopted in this study for forecast applications in basins with limited data. GSETSK is a fully-online NFM which updates its structure and parameters based on the most recent data. The model does not require the need for historical data and adopts clustering and rule pruning techniques to generate a compact and up-to-date rule-base. GSETSK was used in two forecast applications, rainfall-runoff (a catchment in Sweden) and river routing (Lower Mekong River) forecasts. Each of these two applications was studied under two scenarios: (i) there is no prior data, and (ii) only limited data is available (1 year for the Swedish catchment and 1 season for the Mekong River). For the Swedish Basin, GSETSK model results were compared to available results from a calibrated HBV (Hydrologiska Byråns Vattenbalansavdelning) model. For the Mekong River, GSETSK results were compared against the URBS (Unified River Basin Simulator) model. Both comparisons showed that results from GSETSK are comparable with the physically based models, which were calibrated with historical data. Thus, even though GSETSK was trained with a very limited dataset in comparison with HBV or URBS, similar results were achieved. Similarly, further comparisons between GSETSK with DENFIS and the RBF (Radial Basis Function) models highlighted further advantages of GSETSK as having a rule-base (compared to opaque RBF) which is more compact, up-to-date and more easily interpretable.

Performance and robustness of probabilistic river forecasts computed with quantile regression based on multiple independent variables in the North Central USA

NASA Astrophysics Data System (ADS)

Hoss, F.; Fischbeck, P. S.

2014-10-01

This study further develops the method of quantile regression (QR) to predict exceedance probabilities of flood stages by post-processing forecasts. Using data from the 82 river gages, for which the National Weather Service's North Central River Forecast Center issues forecasts daily, this is the first QR application to US American river gages. Archived forecasts for lead times up to six days from 2001-2013 were analyzed. Earlier implementations of QR used the forecast itself as the only independent variable (Weerts et al., 2011; López López et al., 2014). This study adds the rise rate of the river stage in the last 24 and 48 h and the forecast error 24 and 48 h ago to the QR model. Including those four variables significantly improved the forecasts, as measured by the Brier Skill Score (BSS). Mainly, the resolution increases, as the original QR implementation already delivered high reliability. Combining the forecast with the other four variables results in much less favorable BSSs. Lastly, the forecast performance does not depend on the size of the training dataset, but on the year, the river gage, lead time and event threshold that are being forecast. We find that each event threshold requires a separate model configuration or at least calibration.

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.

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).

Analysis and prediction of flow from local source in a river basin using a Neuro-fuzzy modeling tool.

PubMed

Aqil, Muhammad; Kita, Ichiro; Yano, Akira; Nishiyama, Soichi

2007-10-01

Traditionally, the multiple linear regression technique has been one of the most widely used models in simulating hydrological time series. However, when the nonlinear phenomenon is significant, the multiple linear will fail to develop an appropriate predictive model. Recently, neuro-fuzzy systems have gained much popularity for calibrating the nonlinear relationships. This study evaluated the potential of a neuro-fuzzy system as an alternative to the traditional statistical regression technique for the purpose of predicting flow from a local source in a river basin. The effectiveness of the proposed identification technique was demonstrated through a simulation study of the river flow time series of the Citarum River in Indonesia. Furthermore, in order to provide the uncertainty associated with the estimation of river flow, a Monte Carlo simulation was performed. As a comparison, a multiple linear regression analysis that was being used by the Citarum River Authority was also examined using various statistical indices. The simulation results using 95% confidence intervals indicated that the neuro-fuzzy model consistently underestimated the magnitude of high flow while the low and medium flow magnitudes were estimated closer to the observed data. The comparison of the prediction accuracy of the neuro-fuzzy and linear regression methods indicated that the neuro-fuzzy approach was more accurate in predicting river flow dynamics. The neuro-fuzzy model was able to improve the root mean square error (RMSE) and mean absolute percentage error (MAPE) values of the multiple linear regression forecasts by about 13.52% and 10.73%, respectively. Considering its simplicity and efficiency, the neuro-fuzzy model is recommended as an alternative tool for modeling of flow dynamics in the study area.

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.

Measuring real-time streamflow using emerging technologies: Radar, hydroacoustics, and the probability concept

NASA Astrophysics Data System (ADS)

Fulton, John; Ostrowski, Joseph

2008-07-01

SummaryForecasting streamflow during extreme hydrologic events such as floods can be problematic. This is particularly true when flow is unsteady, and river forecasts rely on models that require uniform-flow rating curves to route water from one forecast point to another. As a result, alternative methods for measuring streamflow are needed to properly route flood waves and account for inertial and pressure forces in natural channels dominated by nonuniform-flow conditions such as mild water surface slopes, backwater, tributary inflows, and reservoir operations. The objective of the demonstration was to use emerging technologies to measure instantaneous streamflow in open channels at two existing US Geological Survey streamflow-gaging stations in Pennsylvania. Surface-water and instream-point velocities were measured using hand-held radar and hydroacoustics. Streamflow was computed using the probability concept, which requires velocity data from a single vertical containing the maximum instream velocity. The percent difference in streamflow at the Susquehanna River at Bloomsburg, PA ranged from 0% to 8% with an average difference of 4% and standard deviation of 8.81 m 3/s. The percent difference in streamflow at Chartiers Creek at Carnegie, PA ranged from 0% to 11% with an average difference of 5% and standard deviation of 0.28 m 3/s. New generation equipment is being tested and developed to advance the use of radar-derived surface-water velocity and instantaneous streamflow to facilitate the collection and transmission of real-time streamflow that can be used to parameterize hydraulic routing models.

Measuring real-time streamflow using emerging technologies: Radar, hydroacoustics, and the probability concept

USGS Publications Warehouse

Fulton, J.; Ostrowski, J.

2008-01-01

Forecasting streamflow during extreme hydrologic events such as floods can be problematic. This is particularly true when flow is unsteady, and river forecasts rely on models that require uniform-flow rating curves to route water from one forecast point to another. As a result, alternative methods for measuring streamflow are needed to properly route flood waves and account for inertial and pressure forces in natural channels dominated by nonuniform-flow conditions such as mild water surface slopes, backwater, tributary inflows, and reservoir operations. The objective of the demonstration was to use emerging technologies to measure instantaneous streamflow in open channels at two existing US Geological Survey streamflow-gaging stations in Pennsylvania. Surface-water and instream-point velocities were measured using hand-held radar and hydroacoustics. Streamflow was computed using the probability concept, which requires velocity data from a single vertical containing the maximum instream velocity. The percent difference in streamflow at the Susquehanna River at Bloomsburg, PA ranged from 0% to 8% with an average difference of 4% and standard deviation of 8.81 m3/s. The percent difference in streamflow at Chartiers Creek at Carnegie, PA ranged from 0% to 11% with an average difference of 5% and standard deviation of 0.28 m3/s. New generation equipment is being tested and developed to advance the use of radar-derived surface-water velocity and instantaneous streamflow to facilitate the collection and transmission of real-time streamflow that can be used to parameterize hydraulic routing models.

New Techniques for Real-Time Stage Forecasting for Tributaries in the Nashville Area

NASA Astrophysics Data System (ADS)

Charley, W.; Moran, B.; LaRosa, J.

2011-12-01

On Saturday, May 1, 2010, heavy rain began falling in the Cumberland River Valley, Tennessee, and continued through the following day. 13.5 inches was measured at Nashville, an unprecedented amount that doubled the previous 2-day record, and exceeded the May monthly total record of 11 inches. Elsewhere in the valley, amounts of over 19 inches were measured. This intensity of rainfall quickly overwhelmed tributaries to the Cumberland in the Nashville area, causing wide-spread and serious flooding. Tractor-trailers and houses were seen floating down Mill Creek, a primary tributary in the south eastern area of Nashville. Twenty-six people died and over 2 billion dollars in damage occurred as a result of the flood. Since that time, several other significant rainfall events have occurred in the area. As a result of the flood, agencies in the Nashville area want better capabilities to forecast stages for the local tributaries. Better stage forecasting will help local agencies close roads, evacuate homes and businesses and similar actions. An interagency group, consisting of Metro Nashville Water Services and Office of Emergency Management, the National Weather Service, the US Geological Survey and the US Army Corps of Engineers, has been established to seek ways to better forecast short-term events in the region. It should be noted that the National Weather Service has the official responsibility of forecasting stages. This paper examines techniques and algorithms that are being developed to meet this need and the practical aspects of integrating them into a usable product that can quickly and accurately forecast stages in the short-time frame of the tributaries. This includes not only the forecasting procedure, but also the procedure to acquire the latest precipitation and stage data to make the forecasts. These procedures are integrated into the program HEC-RTS, the US Army Corps of Engineers Real-Time Simulation program. HEC-RTS is a Java-based integration tool that has been derived from the Corps Water Management System (CWMS). The modeling component takes observed and forecasted rainfall to compute river flow with the program HEC-HMS. The river hydraulics program, HEC-RAS, computes river stages and water surface profiles. An inundation boundary and depth map of water in the flood plain is computed from HEC-RAS Mapper. The user-configurable sequence of modeling software allows engineers to evaluate and compare hydraulic impacts for various "what if?" scenarios. The implementation of these techniques and HEC-RTS is examined for the Mill Creek basin, the 108 square mile tributary basin south east of Nashville. Mill Creek has an average annual flow of 150 CFS and a short response time. It has suffered major damage from the 2010 and other events. The accuracy and effectiveness of the techniques in the integrated tool HEC-RTS is evaluated.

Coupled Teleconnections and River Dynamics for Enhanced Hydrologic Forecasting in the Upper Colorado River Basin USA

NASA Astrophysics Data System (ADS)

Matter, M. A.; Garcia, L. A.; Fontane, D. G.

2005-12-01

Accuracy of water supply forecasts has improved for some river basins in the western U.S.A. by integrating knowledge of climate teleconnections, such as El Niño/Southern Oscillation (ENSO), into forecasting routines, but in other basins, such as the Colorado River Basin (CRB), forecast accuracy has declined (Pagano et al. 2004). Longer lead time and more accurate seasonal forecasts, particularly during floods or drought, could help reduce uncertainty and risk in decision-making and lengthen the period for planning more efficient and effective strategies for water use and ecosystem management. The goal of this research is to extend the lead time for snowmelt hydrograph estimation by 4-6 months (from spring to the preceding fall), and at the same time increase the accuracy of snowmelt runoff estimates in the Upper CRB (UCRB). We hypothesize that: (1) UCRB snowpack accumulation and melt are driven by large scale climate modes, including ENSO, PDO and AMO, that establish by fall and persist into early spring; (2) forecast analysis may begin in the fall prior to the start of the primary snow accumulation period and when energy to change the climate system is decreasing; and (3) between fall and early spring, streamflow hydrographs will amplify precipitation and temperature signals, and thus will evolve characteristically in response to wet, dry or average hydroclimatic conditions. Historical in situ records from largely unregulated river reaches and undeveloped time periods of the UCRB are used to test this hypothesis. Preliminary results show that, beginning in the fall (e.g., October or November) streamflow characteristics, including magnitude, rate of change and variability, as well as timing and magnitude of fall/early winter and late winter/early spring season flow volumes, are directly correlated with the magnitude of the upcoming snowmelt runoff (or annual basin yield). The use of climate teleconnections to determine characteristic streamflow responses in the UCRB advances understanding of atmosphere/land surface processes and interactions in complex terrain and subsequent effects on snowpack development and runoff (i.e., water supply), and may be used to improve seasonal forecast accuracy and extend lead time to develop more efficient and effective management strategies for water resources and ecosystems.

Use of satellite data in runoff forecasting in the heavily forested, cloud-covered Pacific Northwest. [Upper Snake, Boise, Dworshak, Libby and Hungry Horse River Basins

NASA Technical Reports Server (NTRS)

Dillard, J. P.; Orwig, C. F. (Principal Investigator)

1980-01-01

The author has identified the following significant results. Satellite-derived snow cover data improves forecasts of stream flow but not at a statistically significant amount and should not be used exclusively because of persistent cloud cover. Based upon reconstruction runs, satellite data can be used to augment snow-flight data in the Upper Snake, Boise, Dworshak, and Hungry Horse basins. Satellite data does not compare well with aerial snow-flight data in the Libby basin.

Integration of Water Resource Models with Fayetteville Shale Decision Support and Information System

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

Cothren, Jackson; Thoma, Greg; DiLuzio, Mauro

2013-06-30

Significant issues can arise with the timing, location, and volume of surface water withdrawals associated with hydraulic fracturing of gas shale reservoirs as impacted watersheds may be sensitive, especially in drought years, during low flow periods, or during periods of the year when activities such as irrigation place additional demands on the surface supply of water. Significant energy production and associated water withdrawals may have a cumulative impact to watersheds over the short-term. Hence, hydraulic fracturing based on water withdrawal could potentially create shifts in the timing and magnitude of low or high flow events or change the magnitude ofmore » river flow at daily, monthly, seasonal, or yearly time scales. These changes in flow regimes can result in dramatically altered river systems. Currently little is known about the impact of fracturing on stream flow behavior. Within this context the objective of this study is to assess the impact of the hydraulic fracturing on the water balance of the Fayetteville Shale play area and examine the potential impacts of hydraulic fracturing on river flow regime at subbasin scale. This project addressed that need with four unique but integrated research and development efforts: 1) Evaluate the predictive reliability of the Soil and Water Assessment Tool (SWAT) model based at a variety of scales (Task/Section 3.5). The Soil and Water Assessment Tool (SWAT) model was used to simulate the across-scale water balance and the respective impact of hydraulic fracturing. A second hypothetical scenario was designed to assess the current and future impacts of water withdrawals for hydraulic fracturing on the flow regime and on the environmental flow components (EFCs) of the river. The shifting of these components, which present critical elements to water supply and water quality, could influence the ecological dynamics of river systems. For this purpose, we combined the use of SWAT model and Richter et al.’s (1996) methodology to assess the shifting and alteration of the flow regime within the river and streams of the study area. 2) Evaluate the effect of measurable land use changes related to gas development (well-pad placement, access road completion, etc.) on surface water flow in the region (Task/Section 3.7). Results showed that since the upsurge in shale-gas related activities in the Fayetteville Shale Play (between 2006 and 2010), shale-gas related infrastructure in the region have increase by 78%. This change in land-cover in comparison with other land-cover classes such as forest, urban, pasture, agricultural and water indicates the highest rate of change in any land-cover category for the study period. A Soil and Water Assessment Tool (SWAT) flow model of the Little Red River watershed simulated from 2000 to 2009 showed a 10% increase in storm water runoff. A forecast scenario based on the assumption that 2010 land-cover does not see any significant change over the forecast period (2010 to 2020) also showed a 10% increase in storm water runoff. Further analyses showed that this change in the stream-flow regime for the forecast period is attributable to the increase in land-cover as introduced by the shale-gas infrastructure. 3) Upgrade the Fayetteville Shale Information System to include information on watershed status. (Tasks/Sections 2.1 and 2.2). This development occurred early in the project period, and technological improvements in web-map API’s have made it possible to further improve the map. The current sites (http://lingo.cast.uark.edu) is available but is currently being upgraded to a more modern interface and robust mapping engine using funds outside this project. 4) Incorporate the methodologies developed in Tasks/Sections 3.5 and 3.7 into a Spatial Decision Support System for use by regulatory agencies and producers in the play. The resulting system is available at http://fayshale.cast.uark.edu and is under review the Arkansas Natural Resources Commission.« less

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.

Operational flood forecasting: further lessons learned form a recent inundation in Tuscany, Italy

NASA Astrophysics Data System (ADS)

Caparrini, F.; Castelli, F.; di Carlo, E.

2010-09-01

After a few years of experimental setup, model refinement and parameters calibration, a distributed flood forecasting system for the Tuscany region was promoted to operational use in early 2008. The hydrologic core of the system, MOBIDIC, is a fully distributed soil moisture accounting model, with sequential assimilation of hydrometric data. The model is forced by the real-time dense hydrometeorological network of the Regional Hydrologic Service as well from the QPF products of a number of different limited area meteorological models (LAMI, WRF+ECMWF, WRF+GFS). Given the relatively short response time of the Tuscany basins, the river flow forecasts based on ground measured precipitation are operationally used mainly as a monitoring tool, while the true usable predictions are necessarily based on the QPF input. The first severe flooding event the system had to face occurred in late December 2009, when a failure of the right levee of the Serchio river caused an extensive inundation (on December 25th). In the days following the levee breaking, intensive monitoring and forecast was needed (another flood peak occurred on the night between December 29th and January 1st 2010) as a support for decisions regarding the management of the increased vulnerability of the area and the planning of emergency reparation works at the river banks. The operational use of the system during such a complex event, when both the meteorological and the hydrological components may be said to have performed well form a strict modeling point of view, brought to attention a number of additional issues about the system as a whole. The main of these issues may be phrased in terms of additional system requirements, namely: the ranking of different QPF products in terms of some likelihood measure; the rapid redefinition of alarm thresholds due to sudden changes in the river flow capacity; the supervised prediction for evaluating the consequences of different management scenarios for reservoirs, regulated floodplains, levees, etc. In order to quantitatively address these issues, a multivariate sensitivity hindcast of the above event is presented here, where variation of model predictions and subsequent likely decision making are measured against QPF accuracy, other possible levees failures, different reservoir releases.

Total probabilities of ensemble runoff forecasts

NASA Astrophysics Data System (ADS)

Olav Skøien, Jon; Bogner, Konrad; Salamon, Peter; Smith, Paul; Pappenberger, Florian

2017-04-01

Ensemble forecasting has a long history from meteorological modelling, as an indication of the uncertainty of the forecasts. However, it is necessary to calibrate and post-process the ensembles as the they often exhibit both bias and dispersion errors. 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 varying in space and time, while giving a spatially and temporally consistent output. However, their method is computationally complex for our larger number of stations, which makes it unsuitable for our purpose. Our post-processing method of the ensembles is developed in the framework of the European Flood Awareness System (EFAS - http://www.efas.eu), where we are making forecasts for whole Europe, and based on observations from around 700 catchments. As the target is flood forecasting, we are also more interested in improving the forecast skill for high-flows rather than in a good prediction of the entire flow regime. EFAS uses a combination of ensemble forecasts and deterministic forecasts from different meteorological 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 estimate the total probability, the post-processed mean and uncertainty of all ensembles. The post-processing parameters are first calibrated for each calibration location, but we are adding a spatial penalty in the calibration process to force a spatial correlation of the parameters. The penalty takes distance, stream-connectivity and size of the catchment areas into account. This can in some cases have a slight negative impact on the calibration error, but avoids large differences between parameters of nearby locations, whether stream connected or not. The spatial calibration also 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.

Testing the Joint UK Land Environment Simulator (JULES) for flood forecasting

NASA Astrophysics Data System (ADS)

Batelis, Stamatios-Christos; Rosolem, Rafael; Han, Dawei; Rahman, Mostaquimur

2017-04-01

Land Surface Models (LSM) are based on physics principles and simulate the exchanges of energy, water and biogeochemical cycles between the land surface and lower atmosphere. Such models are typically applied for climate studies or effects of land use changes but as the resolution of LSMs and supporting observations are continuously increasing, its representation of hydrological processes need to be addressed adequately. For example, changes in climate and land use can alter the hydrology of a region, for instance, by altering its flooding regime. LSMs can be a powerful tool because of their ability to spatially represent a region with much finer resolution. However, despite such advantages, its performance has not been extensively assessed for flood forecasting simply because its representation of typical hydrological processes, such as overland flow and river routing, are still either ignored or roughly represented. In this study, we initially test the Joint UK Land Environment Simulator (JULES) as a flood forecast tool focusing on its river routing scheme. In particular, JULES river routing parameterization is based on the Rapid Flow Model (RFM) which relies on six prescribed parameters (two surface and two subsurface wave celerities, and two return flow fractions). Although this routing scheme is simple, the prescription of its six default parameters is still too generalized. Our aim is to understand the importance of each RFM parameter in a series of JULES simulations at a number of catchments in the UK for the 2006-2015 period. This is carried out, for instance, by making a number of assumptions of parameter behaviour (e.g., spatially uniform versus varying and/or temporally constant or time-varying parameters within each catchment). Hourly rainfall radar in combination with the CHESS (Climate, Hydrological and Ecological research Support System) meteorological daily data both at 1 km2 resolution are used. The evaluation of the model is based on hourly runoff data provided by the National River Flood Archive using a number of model performance metrics. We use a calibrated conceptually-based lumped model, more typically applied in flood studies, as a benchmark for our analysis.

Snow cover, snowmelt and runoff in the Himalayan River basins

NASA Technical Reports Server (NTRS)

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

1988-01-01

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

Development and Evaluation of an Integrated Hydrological Modeling Framework for Monitoring and Understanding Floods and Droughts

NASA Astrophysics Data System (ADS)

Yang, Z. L.; Wu, W. Y.; Lin, P.; Maidment, D. R.

2017-12-01

Extreme water events such as catastrophic floods and severe droughts have increased in recent decades. Mitigating the risk to lives, food security, infrastructure, energy supplies, as well as numerous other industries posed by these extreme events requires informed decision-making and planning based on sound science. We are developing a global water modeling capability by building models that will provide total operational water predictions (evapotranspiration, soil moisture, groundwater, channel flow, inundation, snow) at unprecedented spatial resolutions and updated frequencies. Toward this goal, this talk presents an integrated global hydrological modeling framework that takes advantage of gridded meteorological forcing, land surface modeling, channeled flow modeling, ground observations, and satellite remote sensing. Launched in August 2016, the National Water Model successfully incorporates weather forecasts to predict river flows for more than 2.7 million rivers across the continental United States, which transfers a "synoptic weather map" to a "synoptic river flow map" operationally. In this study, we apply a similar framework to a high-resolution global river network database, which is developed from a hierarchical Dominant River Tracing (DRT) algorithm, and runoff output from the Global Land Data Assimilation System (GLDAS) to a vector-based river routing model (The Routing Application for Parallel Computation of Discharge, RAPID) to produce river flows from 2001 to 2016 using Message Passing Interface (MPI) on Texas Advanced Computer Center's Stampede system. In this simulation, global river discharges for more than 177,000 rivers are computed every 30 minutes. The modeling framework's performance is evaluated with various observations including river flows at more than 400 gauge stations globally. Overall, the model exhibits a reasonably good performance in simulating the averaged patterns of terrestrial water storage, evapotranspiration and runoff. The system is appropriate for monitoring and studying floods and droughts. Directions for future research will be outlined and discussed.

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

NASA Astrophysics Data System (ADS)

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

2014-12-01

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

A method for characterizing late-season low-flow regime in the upper Grand Ronde River Basin, Oregon

USGS Publications Warehouse

Kelly, Valerie J.; White, Seth

2016-04-19

This report describes a method for estimating ecologically relevant low-flow metrics that quantify late‑season streamflow regime for ungaged sites in the upper Grande Ronde River Basin, Oregon. The analysis presented here focuses on sites sampled by the Columbia River Inter‑Tribal Fish Commission as part of their efforts to monitor habitat restoration to benefit spring Chinook salmon recovery in the basin. Streamflow data were provided by the U.S. Geological Survey and the Oregon Water Resources Department. Specific guidance was provided for selection of streamgages, development of probabilistic frequency distributions for annual 7-day low-flow events, and regionalization of the frequency curves based on multivariate analysis of watershed characteristics. Evaluation of the uncertainty associated with the various components of this protocol indicates that the results are reliable for the intended purpose of hydrologic classification to support ecological analysis of factors contributing to juvenile salmon success. They should not be considered suitable for more standard water-resource evaluations that require greater precision, especially those focused on management and forecasting of extreme low-flow conditions.

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.

Flood-inundation maps for the Hoosic River, North Adams and Williamstown, Massachusetts, from the confluence with the North Branch Hoosic River to the Vermont State line

USGS Publications Warehouse

Lombard, Pamela J.; Bent, Gardner C.

2015-01-01

The availability of the flood-inundation maps, combined with information regarding current (near real-time) stage from USGS streamgage Hoosic River near Williamstown, and forecasted flood stages from the National Weather Service Advanced Hydrologic Prediction Service will provide emergency management personnel and residents with information that is critical for flood response activities such as evacuations and road closures, and post-flood recovery efforts. The flood-inundation maps are nonregulatory, but provide Federal, State, and local agencies and the public with estimates of the potential extent of flooding during selected peak-flow events.

A national framework for flood forecasting model assessment for use in operations and investment planning over England and Wales

NASA Astrophysics Data System (ADS)

Moore, Robert J.; Wells, Steven C.; Cole, Steven J.

2016-04-01

It has been common for flood forecasting systems to be commissioned at a catchment or regional level in response to local priorities and hydrological conditions, leading to variety in system design and model choice. As systems mature and efficiencies of national management are sought, there can be a drive towards system rationalisation, gaining an overview of model performance and consideration of simplification through model-type convergence. Flood forecasting model assessments, whilst overseen at a national level, may be commissioned and managed at a catchment and regional level, take a variety of forms and be large in number. This presents a challenge when an integrated national assessment is required to guide operational use of flood forecasts and plan future investment in flood forecasting models and supporting hydrometric monitoring. This contribution reports on how a nationally consistent framework for flood forecasting model performance has been developed to embrace many past, ongoing and future assessments for local river systems by engineering consultants across England & Wales. The outcome is a Performance Summary for every site model assessed which, on a single page, contains relevant catchment information for context, a selection of overlain forecast and observed hydrographs and a set of performance statistics with associated displays of novel condensed form. One display provides performance comparison with other models that may exist for the site. The performance statistics include skill scores for forecasting events (flow/level threshold crossings) of differing severity/rarity, indicating their probability and likely timing, which have real value in an operational setting. The local models assessed can be of any type and span rainfall-runoff (conceptual and transfer function) and flow routing (hydrological and hydrodynamic) forms. Also accommodated by the framework is the national G2G (Grid-to-Grid) distributed hydrological model, providing area-wide coverage across the fluvial rivers of England and Wales, which can be assessed at gauged sites. Thus the performance of the national G2G model forecasts can be directly compared with that from the local models. The Performance Summary for each site model is complemented by a national spatial analysis of model performance stratified by model-type, geographical region and forecast lead-time. The map displays provide an extensive evidence-base that can be interrogated, through a Flood Forecasting Model Performance web portal, to reveal fresh insights into comparative performance across locations, lead-times and models. This work was commissioned by the Environment Agency in partnership with Natural Resources Wales and the Flood Forecasting Centre for England and Wales.

A simple snowmelt lysimeter

Treesearch

Harold F. Haupt

1969-01-01

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

Flood-inundation maps for the White River at Indianapolis, Indiana, 2014

USGS Publications Warehouse

Nystrom, Elizabeth A.

2015-01-01

The availability of these maps, along with Internet information regarding current stage from the USGS streamgage and forecasted high-flow 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 postflood recovery efforts.

A two-model hydrologic ensemble prediction of hydrograph: case study from the upper Nysa Klodzka river basin (SW Poland)

NASA Astrophysics Data System (ADS)

Niedzielski, Tomasz; Mizinski, Bartlomiej

2016-04-01

The HydroProg system has been elaborated in frame of the research project no. 2011/01/D/ST10/04171 of the National Science Centre of Poland and is steadily producing multimodel ensemble predictions of hydrograph in real time. Although there are six ensemble members available at present, the longest record of predictions and their statistics is available for two data-based models (uni- and multivariate autoregressive models). Thus, we consider 3-hour predictions of water levels, with lead times ranging from 15 to 180 minutes, computed every 15 minutes since August 2013 for the Nysa Klodzka basin (SW Poland) using the two approaches and their two-model ensemble. Since the launch of the HydroProg system there have been 12 high flow episodes, and the objective of this work is to present the performance of the two-model ensemble in the process of forecasting these events. For a sake of brevity, we limit our investigation to a single gauge located at the Nysa Klodzka river in the town of Klodzko, which is centrally located in the studied basin. We identified certain regular scenarios of how the models perform in predicting the high flows in Klodzko. At the initial phase of the high flow, well before the rising limb of hydrograph, the two-model ensemble is found to provide the most skilful prognoses of water levels. However, while forecasting the rising limb of hydrograph, either the two-model solution or the vector autoregressive model offers the best predictive performance. In addition, it is hypothesized that along with the development of the rising limb phase, the vector autoregression becomes the most skilful approach amongst the scrutinized ones. Our simple two-model exercise confirms that multimodel hydrologic ensemble predictions cannot be treated as universal solutions suitable for forecasting the entire high flow event, but their superior performance may hold only for certain phases of a high flow.

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

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

NASA Astrophysics Data System (ADS)

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

2016-12-01

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

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.

Comparative Performance Evaluation of Rainfall-runoff Models, Six of Black-box Type and One of Conceptual Type, From The Galway Flow Forecasting System (gffs) Package, Applied On Two Irish Catchments

NASA Astrophysics Data System (ADS)

Goswami, M.; O'Connor, K. M.; Shamseldin, A. Y.

The "Galway Real-Time River Flow Forecasting System" (GFFS) is a software pack- age developed at the Department of Engineering Hydrology, of the National University of Ireland, Galway, Ireland. It is based on a selection of lumped black-box and con- ceptual rainfall-runoff models, all developed in Galway, consisting primarily of both the non-parametric (NP) and parametric (P) forms of two black-box-type rainfall- runoff models, namely, the Simple Linear Model (SLM-NP and SLM-P) and the seasonally-based Linear Perturbation Model (LPM-NP and LPM-P), together with the non-parametric wetness-index-based Linearly Varying Gain Factor Model (LVGFM), the black-box Artificial Neural Network (ANN) Model, and the conceptual Soil Mois- ture Accounting and Routing (SMAR) Model. Comprised of the above suite of mod- els, the system enables the user to calibrate each model individually, initially without updating, and it is capable also of producing combined (i.e. consensus) forecasts us- ing the Simple Average Method (SAM), the Weighted Average Method (WAM), or the Artificial Neural Network Method (NNM). The updating of each model output is achieved using one of four different techniques, namely, simple Auto-Regressive (AR) updating, Linear Transfer Function (LTF) updating, Artificial Neural Network updating (NNU), and updating by the Non-linear Auto-Regressive Exogenous-input method (NARXM). The models exhibit a considerable range of variation in degree of complexity of structure, with corresponding degrees of complication in objective func- tion evaluation. Operating in continuous river-flow simulation and updating modes, these models and techniques have been applied to two Irish catchments, namely, the Fergus and the Brosna. A number of performance evaluation criteria have been used to comparatively assess the model discharge forecast efficiency.

Why Does a Shift in Precipitation from Snow Towards Rain Lead to a Decrease in Long Term Mean River Flow?

NASA Astrophysics Data System (ADS)

Woods, R. A.

2017-12-01

Empirical evidence suggests that a shift in precipitation from snow towards rain leads to a strong decrease in long term mean river flow, for a diverse set of snow-dominated catchments across the USA (Berghuijs et al, 2014, Nature Climate Change). Mutually inconsistent hypotheses have been proposed, but no comprehensive explanations are available to explain the observations. Why does less snow apparently lead to less river flow and more evaporation? Is it caused by changes in snow cover, soil freezing, infiltration processes, timing of plant water uptake or something else? Which processes are important where? Solving this scientific puzzle will have significant follow-on impacts for hydrological models, flood risk assessment, seasonal water forecasts, and climate change impacts on water availability, ecosystem functions and other systems impacted by long-term reductions in river flow and evaporation, and their feedbacks to the water cycle.A large international research collaboration (CHIPPER, 35 groups from 15 countries) has formed to make a joint contribution to improved understanding of links between the phase of precipitation and the hydrological cycle, with a particular focus on water balance. This presentation will review progress since 2014 on the topic, outline intended future lines of investigation, and invite feedback and additional collaborations.

Why Does a Shift in Precipitation from Snow Towards Rain Lead to a Decrease in Long Term Mean River Flow?

NASA Astrophysics Data System (ADS)

Woods, Ross

2017-04-01

Empirical evidence suggests that a shift in precipitation from snow towards rain leads to a strong decrease in long term mean river flow, for a diverse set of snow-dominated catchments across the USA (Berghuijs et al, 2014, Nature Climate Change). Mutually inconsistent hypotheses have been proposed, but no comprehensive explanations are available to explain the observations. Why does less snow apparently lead to less river flow and more evaporation? Is it caused by changes in snow cover, soil freezing, infiltration processes, timing of plant water uptake or something else? Which processes are important where? Solving this scientific puzzle will have significant follow-on impacts for hydrological models, flood risk assessment, seasonal water forecasts, and climate change impacts on water availability, ecosystem functions and other systems impacted by long-term reductions in river flow and evaporation, and their feedbacks to the water cycle. A large international research collaboration (CHIPPER, 34 groups from 14 countries) has formed to make a joint contribution to improved understanding of links between the phase of precipitation and the hydrological cycle, with a particular focus on water balance. This presentation will review progress since 2014 on the topic, outline intended future lines of investigation, and invite feedback and additional collaborations.

Towards real-time flood forecasting in hydraulics: merits of in situ discharge and water level data assimilation for the modeling of the Marne catchment in France

NASA Astrophysics Data System (ADS)

Ricci, S. M.; Habert, J.; Le Pape, E.; Piacentini, A.; Jonville, G.; Thual, O.; Zaoui, F.

2011-12-01

The present study describes the assimilation of river flow and water level observations and the resulting improvement in flood forecasting. The Kalman Filter algorithm was built on top of the one-dimensional hydraulic model, MASCARET, [1] which describes the Saint-Venant equations. The assimilation algorithm folds in two steps: the first one was based on the assumption that the upstream flow can be adjusted using a three-parameter correction; the second one consisted of directly correcting the hydraulic state. This procedure was previously applied on the Adour Maritime Catchment using water level observations [2]. On average, it was shown that the data assimilation procedure enables an improvement of 80% in the simulated water level over the reanalysis period, 60 % in the forecast water level at a one-hour lead time, and 25% at a twelve-hour lead time. The procedure was then applied on the Marne Catchment, which includes karstic tributaries, located East of the Paris basin, characterized by long flooding periods and strong sensitivity to local precipitations. The objective was to geographically extend and improve the existing model used by the flood forecasting service located in Chalons-en-Champagne. A hydrological study first enabled the specification of boundary conditions (upstream flow or lateral inflow), then the hydraulic model was calibrated using in situ discharge data (adjustment of Strickler coefficients or cross sectional geometry). The assimilation of water level data enabled the reduction of the uncertainty in the hydrological boundary conditions and led to significant improvement of the simulated water level in re-analysis and forecast modes. Still, because of errors in the Strickler coefficients or cross section geometry, the improvement of the simulated water level sometimes resulted in a degradation of discharge values. This problem was overcome by controlling the correction of the hydrological boundary conditions by directly assimilating discharge observations rather than water level observations. As this approach leads to a satisfying simulation of flood events in the Marne catchment in re-analysis and forecast mode, ongoing work aims at controlling Strickler coefficients through data assimilation procedures in order to simultaneously improve the water level and discharge state. [1] N. Goutal, F. Maurel: A finite volume solver for 1D shallow water equations applied to an actual river, Int. J. Numer. Meth. Fluids, 38(2), 1--19, 2002. [2] S. Ricci, A. Piacentini, O. Thual, E. Le Pape, G. Jonville, 2011: Correction of upstream flow and hydraulic state with data assimilation on the context of flood forecasting. Submitted to Hydrol. Earth Syst. Sci, In review.

Application of current and future satellite missions to hydrologic prediction in transboundary rivers

NASA Astrophysics Data System (ADS)

Biancamaria, S.; Clark, E.; Lettenmaier, D. P.

2010-12-01

More than 256 major global river basins, which cover about 45% of the continental land surface, are shared among two or more countries. The flow of such a large part of the global runoff across international boundaries has led to tension in many cases between upstream and downstream riparian countries. Among many examples, this is the case of the Ganges and the Brahmaputra Rivers, which cross the boundary between India and Bangladesh. Hydrological data (river discharge, reservoir storage) are viewed as sensitive by India (the upstream country) and are therefore not shared with Bangladesh, which can only monitor river discharge and water depth at the international border crossing. These measurements only allow forecasting of floods in the interior and southern portions of the country two to three days in advance. These forecasts are not long enough either for agricultural water management purposes (for which knowledge of upstream reservoir storage is essential) or for disaster preparedness purposes. Satellite observations of river spatial extent, surface slope, reservoir area and surface elevation have the potential to make tremendous changes in management of water within the basins. In this study, we examine the use of currently available satellite measurements (in India) and in-situ measurements in Bangladesh to increase forecast lead time in the Ganges and Brahmaputra Rivers. Using nadir altimeters, we find that it is possible to forecast the discharge of the Ganges River at the Bangladesh border with lead time 3 days and mean absolute error of around 25%. On the Ganges River, 2-day forecasts are possible with a mean absolute error of around 20%. When combined with optical/infra-red MODIS images, it is possible to map water elevations along the river and its floodplain upstream of the boundary, and to compute water storage. However, the high frequency of clouds in this region results in relatively large errors in the water mask. Due to the nadir altimeter temporal repeat (10 days for current satellites) and to gaps in the water mask, water volume estimates are meaningful only at the monthly scale. Furthermore, this information is limited to channels with wider than 250-500 m. The future Surface Water and Ocean Topography (SWOT) mission, which is intended to be launched in 2020, will provide global maps of water elevations, with a spatial resolution of 100 m and errors on the water elevation equal to or below 10 cm. The SWOT Ka band interferometric Synthetic Aperture Radar (SAR), will not be affected by cloud cover (aside from infrequent heavy rain); therefore, estimation of the water volume change on the Ganges and on the Brahmaputra upstream to the Bangladesh provided by SWOT should be much more accurate in space and time than can currently be achieved. We discuss the implications of future SWOT observations in the context of our preliminary work on the Ganges-Brahmaputra Rivers using current generation satellite data.

The value of the North American Multi Model Ensemble phase 2 for sub-seasonal hydrological forecasting

NASA Astrophysics Data System (ADS)

Wanders, Niko; Wood, Eric

2016-04-01

Sub-seasonal to seasonal weather and hydrological forecasts have the potential to provide vital information for a variety of water-related decision makers. For example, seasonal forecasts of drought risk can enable farmers to make adaptive choices on crop varieties, labour usage, and technology investments. Seasonal and sub-seasonal predictions can increase preparedness to hydrological extremes that regularly occur in all regions of the world with large impacts on society. We investigated the skill of six seasonal forecast models from the NMME-2 ensemble coupled to two global hydrological models (VIC and PCRGLOBWB) for the period 1982-2012. The 31 years of NNME-2 hindcast data is used in combination with an ensemble mean and ESP forecast, to forecast important hydrological variables (e.g. soil moisture, groundwater storage, snow, reservoir levels and river discharge). By using two global hydrological models we are able to quantify both the uncertainty in the meteorological input and the uncertainty created by the different hydrological models. We show that the NMME-2 forecast outperforms the ESP forecasts in terms of anomaly correlation and brier skill score for all forecasted hydrological variables, with a low uncertainty in the performance amongst the hydrological models. However, the continuous ranked probability score (CRPS) of the NMME-2 ensemble is inferior to the ESP due to a large spread between the individual ensemble members. We use a cost analysis to show that the damage caused by floods and droughts in large scale rivers can globally be reduced by 48% (for leads from 1-2 months) to 20% (for leads between 6-9 months) when precautions are taken based on the NMME-2 ensemble instead of an ESP forecast. In collaboration with our local partner in West Africa (AGHRYMET), we looked at the performance of the sub-seasonal forecasts for crop planting dates and high flow season in West Africa. We show that the uncertainty in the optimal planting date is reduced from 30 days to 12 days (2.5 month lead) and an increased predictability of the high flow season from 45 days to 20 days (3-4 months lead). Additionally, we show that snow accumulation and melt onset in the Northern hemisphere can be forecasted with an uncertainty of 10 days (2.5 months lead). Both the overall skill, and the skill found in these last two examples, indicates that the new NMME-2 forecast dataset is valuable for sub-seasonal forecast applications. The high temporal resolution (daily), long leads (one year leads) and large hindcast archive enable new sub-seasonal forecasting applications to be explored. We show that the NMME-2 has a large potential for sub-seasonal hydrological forecasting and other potential hydrological applications (e.g. reservoir management), which could benefit from these new forecasts.

Monthly to seasonal low flow prediction: statistical versus dynamical models

NASA Astrophysics Data System (ADS)

Ionita-Scholz, Monica; Klein, Bastian; Meissner, Dennis; Rademacher, Silke

2016-04-01

While the societal and economical impacts of floods are well documented and assessable, the impacts of lows flows are less studied and sometimes overlooked. For example, over the western part of Europe, due to intense inland waterway transportation, the economical loses due to low flows are often similar compared to the ones due to floods. In general, the low flow aspect has the tendency to be underestimated by the scientific community. One of the best examples in this respect is the facts that at European level most of the countries have an (early) flood alert system, but in many cases no real information regarding the development, evolution and impacts of droughts. Low flows, occurring during dry periods, may result in several types of problems to society and economy: e.g. lack of water for drinking, irrigation, industrial use and power production, deterioration of water quality, inland waterway transport, agriculture, tourism, issuing and renewing waste disposal permits, and for assessing the impact of prolonged drought on aquatic ecosystems. As such, the ever-increasing demand on water resources calls for better a management, understanding and prediction of the water deficit situation and for more reliable and extended studies regarding the evolution of the low flow situations. In order to find an optimized monthly to seasonal forecast procedure for the German waterways, the Federal Institute of Hydrology (BfG) is exploring multiple approaches at the moment. On the one hand, based on the operational short- to medium-range forecasting chain, existing hydrological models are forced with two different hydro-meteorological inputs: (i) resampled historical meteorology generated by the Ensemble Streamflow Prediction approach and (ii) ensemble (re-) forecasts of ECMWF's global coupled ocean-atmosphere general circulation model, which have to be downscaled and bias corrected before feeding the hydrological models. As a second approach BfG evaluates in cooperation with the Alfred Wegener Institute a purely statistical scheme to generate streamflow forecasts for several months ahead. Instead of directly using teleconnection indices (e.g. NAO, AO) the idea is to identify regions with stable teleconnections between different global climate information (e.g. sea surface temperature, geopotential height etc.) and streamflow at different gauges relevant for inland waterway transport. So-called stability (correlation) maps are generated showing regions where streamflow and climate variable from previous months are significantly correlated in a 21 (31) years moving window. Finally, the optimal forecast model is established based on a multiple regression analysis of the stable predictors. We will present current results of the aforementioned approaches with focus on the River Rhine (being one of the world's most frequented waterways and the backbone of the European inland waterway network) and the Elbe River. Overall, our analysis reveals the existence of a valuable predictability of the low flows at monthly and seasonal time scales, a result that may be useful to water resources management. Given that all predictors used in the models are available at the end of each month, the forecast scheme can be used operationally to predict extreme events and to provide early warnings for upcoming low flows.

Impact of river discharge on the California coastal ocean circulation and variability

NASA Astrophysics Data System (ADS)

Leiva, J.; Chao, Y.; Farrara, J. D.; Zhang, H.

2016-12-01

A real-time California coastal ocean nowcast and forecast system is used to quantify the impact of river discharge on the California coastal ocean circulation and variability. River discharge and freshwater runoff is monitored by an extensive network of stream gages maintained through the U.S. Geological Survey, that offers archived stream flow records as well as real-time datasets. Of all the rivers monitored by the USGS, 25 empty into the Pacific Ocean and contribute a potential source of runoff data. Monthly averages for the current water year yield discharge estimates as high as 6,000 cubic meters per second of additional freshwater input into our present model. Using Regional Ocean Modeling System (ROMS), we performed simulations from October 2015 to May 2016 with and without the river discharge. Results of these model simulations are compared with available observations including both in situ and satellite. Particular attention is paid to the salinity simulation. Validation is done with comparisons to sea glider data available through Oregon State University and UC San Diego, which provides depth profiles along the California coast during this time period. Additional validation is performed through comparisons with sea surface salinity measurements from the Soil Moisture and Ocean Salinity (SMOS) mission. Continued testing for previous years, e.g. between 2011 and 2015, is being made using the Aquarius sea surface salinity data. Discharge data collected by the USGS stream gages provides a necessary source of freshwater input that must be accounted for. Incorporating a new runoff source produces a more robust model that generates improved forecasts. Following validation with available sea glider and satellite data, the enhanced model can be adapted to real-time forecasting.

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.

Hydrometeorology as an Inversion Problem: Can River Discharge Observations Improve the Atmosphere by Ensemble Data Assimilation?

NASA Astrophysics Data System (ADS)

Sawada, Yohei; Nakaegawa, Tosiyuki; Miyoshi, Takemasa

2018-01-01

We examine the potential of assimilating river discharge observations into the atmosphere by strongly coupled river-atmosphere ensemble data assimilation. The Japan Meteorological Agency's Non-Hydrostatic atmospheric Model (JMA-NHM) is first coupled with a simple rainfall-runoff model. Next, the local ensemble transform Kalman filter is used for this coupled model to assimilate the observations of the rainfall-runoff model variables into the JMA-NHM model variables. This system makes it possible to do hydrometeorology backward, i.e., to inversely estimate atmospheric conditions from the information of river flows or a flood on land surfaces. We perform a proof-of-concept Observing System Simulation Experiment, which reveals that the assimilation of river discharge observations into the atmospheric model variables can improve the skill of the short-term severe rainfall forecast.

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.

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.

Decision Support System for Evaluation of Gunnison River Flow Regimes With Respect To Resources of the Black Canyon of the Gunnison National Park

USGS Publications Warehouse

Auble, Gregor T.; Wondzell, Mark; Talbert, Colin

2009-01-01

This report describes and documents a decision support system for the Gunnison River in Black Canyon of the Gunnison National Park. It is a macro-embedded EXCEL program that calculates and displays indicators representing valued characteristics or processes in the Black Canyon based on daily flows of the Gunnison River. The program is designed to easily accept input from downloaded stream gage records or output from the RIVERWARE reservoir operations model being used for the upstream Aspinall Unit. The decision support system is structured to compare as many as eight alternative flow regimes, where each alternative is represented by a daily sequence of at least 20 calendar years of streamflow. Indicators include selected flow statistics, riparian plant community distribution, clearing of box elder by inundation and scour, several measures of sediment mobilization, trout fry habitat, and federal reserved water rights. Calculation of variables representing National Park Service federal reserved water rights requires additional secondary input files pertaining to forecast and actual basin inflows and storage levels in Blue Mesa reservoir. Example input files representing a range of situations including historical, reconstructed natural, and simulated alternative reservoir operations are provided with the software.

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.

The Sensitivity of Orographic Precipitation to Flow Direction

NASA Astrophysics Data System (ADS)

Mass, C.; Picard, L.

2015-12-01

An area of substantial interest is the sensitivity of orographic precipitation to the characteristics of the incoming flow and to the surrounding environment. Some studies have suggested substantial sensitivity of precipitation within individual river drainages for relatively small directional or stability variations of incoming flow. A characterization of such flow sensitivity would be of great value for hydrometeorological prediction, the determination of Probable Maximum Precipitation statistics, and for quantifying the uncertainty in precipitation and hydrological forecasts. To gain insight into this problem, an idealized version of the Weather Research and Forecasting (WRF) modeling system was created in which simulations are driven by a single vertical sounding, with the assumption of thermal wind balance. The actual terrain is used and the full physics complement of the modeling system. The presentation will show how precipitation over the Olympic Mountains of Washington State varies as flow direction changes. This analysis will include both the aggregate precipitation over the barrier and the precipitation within individual drainages or areas. The role of surrounding terrain and the nearby coastline are also examined by removing these features from simulations. Finally, the impact of varying flow stability and speed on the precipitation over this orographic feature will be described.

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.

Forecast Tools for Alaska River Ice Breakup Timing and Severity

NASA Astrophysics Data System (ADS)

Moran, E. H.; Lindsey, S.; van Breukelen, C. M.; Thoman, R.

2016-12-01

Spring Breakup on the large interior rivers in Alaska means a time of nervous anticipation for many of the residents in the villages alongside those rivers. On the Yukon and Kuskokwim Rivers the record flood for most villages occurred as a result of ice jams that backed up water and dump truck sized ice floes into the village. Those floods can occur suddenly and can literally wipe out a village. The challenge is that with a limited observation network (3 automated USGS gages along the 1200 miles of the Yukon River flowing through Alaska) and the inherently transient nature of ice jam formation, prediction of the timing and severity of these events has been a tremendous challenge. Staff at the Alaska Pacific River Forecast Center as well as the Alaska Region Climate Program Manager have been developing more quantitative tools to attempt to provide a longer lead time for villages to prepare for potentially devastating flooding. In the past, a very qualitative assessment of the primary drivers of Spring Breakup (snow pack, river ice thickness and forecast spring weather) have led to the successful identification of years when flood severity was likely to be elevated or significantly decreased. These qualitative assessments have also allowed the forecasting of the probability of either a thermal or a dynamic breakup. But there has continued to be a need for an objective tool that can handle weather patterns that border on the tails of the climatic distributions as well as the timing and flood potential from weather patterns that are closer to the median of the distribution. Over the past 8 years there have been a significant number of years with anomalous spring weather patterns including cold springs followed by rapid warmups leading to record flooding from ice jams during spring breakup (2009, 2013), record late breakup (2013), record early breakup (2016), record high snowfall (2012), record snowmelt and aufeis flooding (2015) and record low snowfall (2015). The need for improved tools that can handle these events over the full breadth of the distribution has never been greater. This talk will describe efforts to incorporate climate signals into the spring breakup outlook and show results of some temperature based indices as an indicator of breakup timing.

Assessing the performance of eight real-time updating models and procedures for the Brosna River

NASA Astrophysics Data System (ADS)

Goswami, M.; O'Connor, K. M.; Bhattarai, K. P.; Shamseldin, A. Y.

2005-10-01

The flow forecasting performance of eight updating models, incorporated in the Galway River Flow Modelling and Forecasting System (GFMFS), was assessed using daily data (rainfall, evaporation and discharge) of the Irish Brosna catchment (1207 km2), considering their one to six days lead-time discharge forecasts. The Perfect Forecast of Input over the Forecast Lead-time scenario was adopted, where required, in place of actual rainfall forecasts. The eight updating models were: (i) the standard linear Auto-Regressive (AR) model, applied to the forecast errors (residuals) of a simulation (non-updating) rainfall-runoff model; (ii) the Neural Network Updating (NNU) model, also using such residuals as input; (iii) the Linear Transfer Function (LTF) model, applied to the simulated and the recently observed discharges; (iv) the Non-linear Auto-Regressive eXogenous-Input Model (NARXM), also a neural network-type structure, but having wide options of using recently observed values of one or more of the three data series, together with non-updated simulated outflows, as inputs; (v) the Parametric Simple Linear Model (PSLM), of LTF-type, using recent rainfall and observed discharge data; (vi) the Parametric Linear perturbation Model (PLPM), also of LTF-type, using recent rainfall and observed discharge data, (vii) n-AR, an AR model applied to the observed discharge series only, as a naïve updating model; and (viii) n-NARXM, a naive form of the NARXM, using only the observed discharge data, excluding exogenous inputs. The five GFMFS simulation (non-updating) models used were the non-parametric and parametric forms of the Simple Linear Model and of the Linear Perturbation Model, the Linearly-Varying Gain Factor Model, the Artificial Neural Network Model, and the conceptual Soil Moisture Accounting and Routing (SMAR) model. As the SMAR model performance was found to be the best among these models, in terms of the Nash-Sutcliffe R2 value, both in calibration and in verification, the simulated outflows of this model only were selected for the subsequent exercise of producing updated discharge forecasts. All the eight forms of updating models for producing lead-time discharge forecasts were found to be capable of producing relatively good lead-1 (1-day ahead) forecasts, with R2 values almost 90% or above. However, for higher lead time forecasts, only three updating models, viz., NARXM, LTF, and NNU, were found to be suitable, with lead-6 values of R2 about 90% or higher. Graphical comparisons were made of the lead-time forecasts for the two largest floods, one in the calibration period and the other in the verification period.

Relating the dynamics of climatological and hydrological droughts in semiarid Botswana

NASA Astrophysics Data System (ADS)

Byakatonda, Jimmy; Parida, B. P.; Kenabatho, Piet K.

2018-06-01

Dynamics of droughts have been an associated feature of climate variability particularly in semiarid regions which impact on the response of hydrological systems. This study attempts to determine drought timescale that is suitable for monitoring the effects of drought on hydrological systems which can then be used to assess the long term persistence or reversion and forecasts of the dynamics. Based on this, climatological and hydrological drought indices characterized by Standardized precipitation evapotranspiration index (SPEI) and Standardized flow index (SFI) respectively have been determined using monthly rainfall, temperature and flow data from two major river systems. The association between climatological and hydrological droughts in Botswana has been investigated using these river systems namely: Okavango that is predominantly a storage type and Limpopo which is non-storage for a period of 1975-2014. Dynamics of climatological and hydrological droughts are showing trends towards drying conditions at both river systems. It was also observed that hydrological droughts lag climatological droughts by 7 months in Limpopo and 6 months in Okavango river systems respectively. Analyses of the association between climatic and flow indices indicate that the degree of association becomes stronger with increasing timescale at the Okavango river system. However in the Limpopo river system, it was observed that high timescales of 18- and 24-months were not useful in drought monitoring. 15-months timescale was identified to best monitor drought dynamics at both locations. Therefore SPEIs and SFIs computed at 15-months timescale have been used to assess the variability and long term persistence in drought dynamics through rescaled range analysis (R/S). H-coefficients of 0.06 and 0.08 resulted for Limpopo and Okavango respectively. These H-coefficients being significantly less than 0.5 is an indication of high variability and suggests a change in dynamics from the existing conditions in these river systems. To forecast possible changes, the nonlinear autoregressive with exogenous input (NARX) artificial neural network model has been used. Results from this model agree with those of the R/S and projects generally dry conditions for the next 40 months. Results from this study are helpful not only in choosing a proper timescale but also in evaluating the futuristic drought dynamics necessary for water resources planning and management.

Hydrological time series modeling: A comparison between adaptive neuro-fuzzy, neural network and autoregressive techniques

NASA Astrophysics Data System (ADS)

Lohani, A. K.; Kumar, Rakesh; Singh, R. D.

2012-06-01

SummaryTime series modeling is necessary for the planning and management of reservoirs. More recently, the soft computing techniques have been used in hydrological modeling and forecasting. In this study, the potential of artificial neural networks and neuro-fuzzy system in monthly reservoir inflow forecasting are examined by developing and comparing monthly reservoir inflow prediction models, based on autoregressive (AR), artificial neural networks (ANNs) and adaptive neural-based fuzzy inference system (ANFIS). To take care the effect of monthly periodicity in the flow data, cyclic terms are also included in the ANN and ANFIS models. Working with time series flow data of the Sutlej River at Bhakra Dam, India, several ANN and adaptive neuro-fuzzy models are trained with different input vectors. To evaluate the performance of the selected ANN and adaptive neural fuzzy inference system (ANFIS) models, comparison is made with the autoregressive (AR) models. The ANFIS model trained with the input data vector including previous inflows and cyclic terms of monthly periodicity has shown a significant improvement in the forecast accuracy in comparison with the ANFIS models trained with the input vectors considering only previous inflows. In all cases ANFIS gives more accurate forecast than the AR and ANN models. The proposed ANFIS model coupled with the cyclic terms is shown to provide better representation of the monthly inflow forecasting for planning and operation of reservoir.

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

NASA Astrophysics Data System (ADS)

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

2009-08-01

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

Probabilistic postprocessing models for flow forecasts for a system of catchments and several lead times

NASA Astrophysics Data System (ADS)

Engeland, Kolbjorn; Steinsland, Ingelin

2014-05-01

This study introduces a methodology for the construction of probabilistic inflow forecasts for multiple catchments and lead times, and investigates criterions for evaluation of multi-variate forecasts. A post-processing approach is used, and a Gaussian model is applied for transformed variables. The post processing model has two main components, the mean model and the dependency model. The mean model is used to estimate the marginal distributions for forecasted inflow for each catchment and lead time, whereas the dependency models was used to estimate the full multivariate distribution of forecasts, i.e. co-variances between catchments and lead times. In operational situations, it is a straightforward task to use the models to sample inflow ensembles which inherit the dependencies between catchments and lead times. The methodology was tested and demonstrated in the river systems linked to the Ulla-Førre hydropower complex in southern Norway, where simultaneous probabilistic forecasts for five catchments and ten lead times were constructed. The methodology exhibits sufficient flexibility to utilize deterministic flow forecasts from a numerical hydrological model as well as statistical forecasts such as persistent forecasts and sliding window climatology forecasts. It also deals with variation in the relative weights of these forecasts with both catchment and lead time. When evaluating predictive performance in original space using cross validation, the case study found that it is important to include the persistent forecast for the initial lead times and the hydrological forecast for medium-term lead times. Sliding window climatology forecasts become more important for the latest lead times. Furthermore, operationally important features in this case study such as heteroscedasticity, lead time varying between lead time dependency and lead time varying between catchment dependency are captured. Two criterions were used for evaluating the added value of the dependency model. The first one was the Energy score (ES) that is a multi-dimensional generalization of continuous rank probability score (CRPS). ES was calculated for all lead-times and catchments together, for each catchment across all lead times and for each lead time across all catchments. The second criterion was to use CRPS for forecasted inflows accumulated over several lead times and catchments. The results showed that ES was not very sensitive to correct covariance structure, whereas CRPS for accumulated flows where more suitable for evaluating the dependency model. This indicates that it is more appropriate to evaluate relevant univariate variables that depends on the dependency structure then to evaluate the multivariate forecast directly.

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.

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.

Adequacy of satellite derived rainfall data for stream flow modeling

USGS Publications Warehouse

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

2007-01-01

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

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

USGS Publications Warehouse

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

2009-01-01

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

Radar-driven high-resolution hydro-meteorological forecasts of the 26 September 2007 Venice flash flood

NASA Astrophysics Data System (ADS)

Rossa, Andrea M.; Laudanna Del Guerra, Franco; Borga, Marco; Zanon, Francesco; Settin, Tommaso; Leuenberger, Daniel

2010-11-01

SummaryThis study aims to assess the feasibility of assimilating carefully checked radar rainfall estimates into a numerical weather prediction (NWP) to extend the forecasting lead time for an extreme flash flood. The hydro-meteorological modeling chain includes the convection-permitting NWP model COSMO-2 and a coupled hydrological-hydraulic model. Radar rainfall estimates are assimilated into the NWP model via the latent heat nudging method. The study is focused on 26 September 2007 extreme flash flood which impacted the coastal area of North-eastern Italy around Venice. The hydro-meteorological modeling system is implemented over the 90 km2 Dese river basin draining to the Venice Lagoon. The radar rainfall observations are carefully checked for artifacts, including rain-induced signal attenuation, by means of physics-based correction procedures and comparison with a dense network of raingauges. The impact of the radar rainfall estimates in the assimilation cycle of the NWP model is very significant. The main individual organized convective systems are successfully introduced into the model state, both in terms of timing and localization. Also, high-intensity incorrectly localized precipitation is correctly reduced to about the observed levels. On the other hand, the highest rainfall intensities computed after assimilation underestimate the observed values by 20% and 50% at a scale of 20 km and 5 km, respectively. The positive impact of assimilating radar rainfall estimates is carried over into the free forecast for about 2-5 h, depending on when the forecast was started. The positive impact is larger when the main mesoscale convective system is present in the initial conditions. The improvements in the precipitation forecasts are propagated to the river flow simulations, with an extension of the forecasting lead time up to 3 h.

Assessment of SWE data assimilation for ensemble streamflow predictions

NASA Astrophysics Data System (ADS)

Franz, Kristie J.; Hogue, Terri S.; Barik, Muhammad; He, Minxue

2014-11-01

An assessment of data assimilation (DA) for Ensemble Streamflow Prediction (ESP) using seasonal water supply hindcasting in the North Fork of the American River Basin (NFARB) and the National Weather Service (NWS) hydrologic forecast models is undertaken. Two parameter sets, one from the California Nevada River Forecast Center (RFC) and one from the Differential Evolution Adaptive Metropolis (DREAM) algorithm, are tested. For each parameter set, hindcasts are generated using initial conditions derived with and without the inclusion of a DA scheme that integrates snow water equivalent (SWE) observations. The DREAM-DA scenario uses an Integrated Uncertainty and Ensemble-based data Assimilation (ICEA) framework that also considers model and parameter uncertainty. Hindcasts are evaluated using deterministic and probabilistic forecast verification metrics. In general, the impact of DA on the skill of the seasonal water supply predictions is mixed. For deterministic (ensemble mean) predictions, the Percent Bias (PBias) is improved with integration of the DA. DREAM-DA and the RFC-DA have the lowest biases and the RFC-DA has the lowest Root Mean Squared Error (RMSE). However, the RFC and DREAM-DA have similar RMSE scores. For the probabilistic predictions, the RFC and DREAM have the highest Continuous Ranked Probability Skill Scores (CRPSS) and the RFC has the best discrimination for low flows. Reliability results are similar between the non-DA and DA tests and the DREAM and DREAM-DA have better reliability than the RFC and RFC-DA for forecast dates February 1 and later. Despite producing improved streamflow simulations in previous studies, the hindcast analysis suggests that the DA method tested may not result in obvious improvements in streamflow forecasts. We advocate that integration of hindcasting and probabilistic metrics provides more rigorous insight on model performance for forecasting applications, such as in this study.

Artificial Neural Network Models for Long Lead Streamflow Forecasts using Climate Information

NASA Astrophysics Data System (ADS)

Kumar, J.; Devineni, N.

2007-12-01

Information on season ahead stream flow forecasts is very beneficial for the operation and management of water supply systems. Daily streamflow conditions at any particular reservoir primarily depend on atmospheric and land surface conditions including the soil moisture and snow pack. On the other hand recent studies suggest that developing long lead streamflow forecasts (3 months ahead) typically depends on exogenous climatic conditions particularly Sea Surface Temperature conditions (SST) in the tropical oceans. Examples of some oceanic variables are El Nino Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO). Identification of such conditions that influence the moisture transport into a given basin poses many challenges given the nonlinear dependency between the predictors (SST) and predictand (stream flows). In this study, we apply both linear and nonlinear dependency measures to identify the predictors that influence the winter flows into the Neuse basin. The predictor identification approach here adopted uses simple correlation coefficients to spearman rank correlation measures for detecting nonlinear dependency. All these dependency measures are employed with a lag 3 time series of the high flow season (January - February - March) using 75 years (1928-2002) of stream flows recorded in to the Falls Lake, Neuse River Basin. Developing streamflow forecasts contingent on these exogenous predictors will play an important role towards improved water supply planning and management. Recently, the soft computing techniques, such as artificial neural networks (ANNs) have provided an alternative method to solve complex problems efficiently. ANNs are data driven models which trains on the examples given to it. The ANNs functions as universal approximators and are non linear in nature. This paper presents a study aiming towards using climatic predictors for 3 month lead time streamflow forecast. ANN models representing the physical process of the system are developed between the identified predictors and the predictand. Predictors used are the scores of Principal Components Analysis (PCA). The models were tested and validated. The feed- forward multi-layer perceptron (MLP) type neural networks trained using the back-propagation algorithms are employed in the current study. The performance of the ANN-model forecasts are evaluated using various performance evaluation measures such as correlation coefficient, root mean square error (RMSE). The preliminary results shows that ANNs are efficient to forecast long lead time streamflows using climatic predictors.

The Cumberland River Flood of 2010 and Corps Reservoir Operations

NASA Astrophysics Data System (ADS)

Charley, W.; Hanbali, F.; Rohrbach, B.

2010-12-01

On Saturday, May 1, 2010, heavy rain began falling in the Cumberland River Valley and continued through the following day. 13.5 inches was measured at Nashville, an unprecedented amount that doubled the previous 2-day record, and exceeded the May monthly total record of 11 inches. Elsewhere in the valley, amounts of over 19 inches were measured. The frequency of this storm was estimated to exceed the one-thousand year event. This historic rainfall brought large scale flooding to the Cumberland-Ohio-Tennessee River Valleys, and caused over 2 billion dollars in damages, despite the numerous flood control projects in the area, including eight U.S. Army Corps of Engineers projects. The vast majority of rainfall occurred in drainage areas that are uncontrolled by Corps flood control projects, which lead to the wide area flooding. However, preliminary analysis indicates that operations of the Corps projects reduced the Cumberland River flood crest in Nashville by approximately five feet. With funding from the American Recovery and Reinvestment Act (ARRA) of 2009, hydrologic, hydraulic and reservoir simulation models have just been completed for the Cumberland-Ohio-Tennessee River Valleys. These models are being implemented in the Corps Water Management System (CWMS), a comprehensive data acquisition and hydrologic modeling system for short-term decision support of water control operations in real time. The CWMS modeling component uses observed rainfall and forecasted rainfall to compute forecasts of river flows into and downstream of reservoirs, using HEC-HMS. Simulation of reservoir operations, utilizing either the HEC-ResSim or CADSWES RiverWare program, uses these flow scenarios to provide operational decision information for the engineer. The river hydraulics program, HEC-RAS, computes river stages and water surface profiles for these scenarios. An inundation boundary and depth map of water in the flood plain can be calculated from the HEC-RAS results using ArcInfo. The economic impacts of the different inundation depths are computed by HEC-FIA. The user-configurable sequence of modeling software allows engineers to evaluate operational decisions for reservoirs and other control structures, and view and compare hydraulic and economic impacts for various “what if?” scenarios. This paper reviews the Cumberland River May 2010 event, the impact of Corps reservoirs and reservoir operations and the expected future benefits and effects of the ARRA funded models and CWMS on future events for this area.

Real-time flood forecasts & risk assessment using a possibility-theory based fuzzy neural network

NASA Astrophysics Data System (ADS)

Khan, U. T.

2016-12-01

Globally floods are one of the most devastating natural disasters and improved flood forecasting methods are essential for better flood protection in urban areas. Given the availability of high resolution real-time datasets for flood variables (e.g. streamflow and precipitation) in many urban areas, data-driven models have been effectively used to predict peak flow rates in river; however, the selection of input parameters for these types of models is often subjective. Additionally, the inherit uncertainty associated with data models along with errors in extreme event observations means that uncertainty quantification is essential. Addressing these concerns will enable improved flood forecasting methods and provide more accurate flood risk assessments. In this research, a new type of data-driven model, a quasi-real-time updating fuzzy neural network is developed to predict peak flow rates in urban riverine watersheds. A possibility-to-probability transformation is first used to convert observed data into fuzzy numbers. A possibility theory based training regime is them used to construct the fuzzy parameters and the outputs. A new entropy-based optimisation criterion is used to train the network. Two existing methods to select the optimum input parameters are modified to account for fuzzy number inputs, and compared. These methods are: Entropy-Wavelet-based Artificial Neural Network (EWANN) and Combined Neural Pathway Strength Analysis (CNPSA). Finally, an automated algorithm design to select the optimum structure of the neural network is implemented. The overall impact of each component of training this network is to replace the traditional ad hoc network configuration methods, with one based on objective criteria. Ten years of data from the Bow River in Calgary, Canada (including two major floods in 2005 and 2013) are used to calibrate and test the network. The EWANN method selected lagged peak flow as a candidate input, whereas the CNPSA method selected lagged precipitation and lagged mean daily flow as candidate inputs. Model performance metric show that the CNPSA method had higher performance (with an efficiency of 0.76). Model output was used to assess the risk of extreme peak flows for a given day using an inverse possibility-to-probability transformation.

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.

United States streamflow probabilities based on forecasted La Nina, winter-spring 2000

USGS Publications Warehouse

Dettinger, M.D.; Cayan, D.R.; Redmond, K.T.

1999-01-01

Although for the last 5 months the TahitiDarwin Southern Oscillation Index (SOI) has hovered close to normal, the “equatorial” SOI has remained in the La Niña category and predictions are calling for La Niña conditions this winter. In view of these predictions of continuing La Niña and as a direct extension of previous studies of the relations between El NiñoSouthern Oscil-lation (ENSO) conditions and streamflow in the United States (e.g., Redmond and Koch, 1991; Cayan and Webb, 1992; Redmond and Cayan, 1994; Dettinger et al., 1998; Garen, 1998; Cayan et al., 1999; Dettinger et al., in press), the probabilities that United States streamflows from December 1999 through July 2000 will be in upper and lower thirds (terciles) of the historical records are estimated here. The processes that link ENSO to North American streamflow are discussed in detail in these diagnostics studies. Our justification for generating this forecast is threefold: (1) Cayan et al. (1999) recently have shown that ENSO influences on streamflow variations and extremes are proportionately larger than the corresponding precipitation teleconnections. (2) Redmond and Cayan (1994) and Dettinger et al. (in press) also have shown that the low-frequency evolution of ENSO conditions support long-lead correlations between ENSO and streamflow in many rivers of the conterminous United States. (3) In many rivers, significant (weeks-to-months) delays between precipitation and the release to streams of snowmelt or ground-water discharge can support even longer term forecasts of streamflow than is possible for precipitation. The relatively slow, orderly evolution of El Niño-Southern Oscillation episodes, the accentuated dependence of streamflow upon ENSO, and the long lags between precipitation and flow encourage us to provide the following analysis as a simple prediction of this year’s river flows.

Assessing potential removal of low-head dams in urban settings: an example from the Ottawa River, NW Ohio.

PubMed

Roberts, Sheila J; Gottgens, Johan F; Spongberg, Alison L; Evans, James E; Levine, Norman S

2007-01-01

This is a study of the scientific component of an effort to restore an urban river by removing a low-head dam. The Secor Dam is owned by a local government entity near Toledo, Ohio. The proposed removal of the last structure impeding flow on the Ottawa River has broad appeal, but the owner is concerned about liability issues, particularly potential changes to the flood regime, the presence of contaminated sediments behind the dam, and possible downstream transport of reservoir sediments. Assessing sediment contamination involved sediment sampling and analysis of trace metals and organic contaminants. Forecasting sediment transport involved field methods to determine the volume and textural properties of reservoir and upstream sediment and calculations to determine the fate of reservoir sediments. Forecasting changes in the flood regime involved HEC-RAS hydrological models to determine before and after dam removal flood scenarios using LiDAR data imported into an ArcGIS database. The resulting assessment found potential sediment contamination to be minor, and modeling showed that the removal of the dam would have minimal impacts on sediment transport and flood hazards. Based on the assessment, the removal of the dam has been approved by its owners.

Developing Snow Model Forcing Data From WRF Model Output to Aid in Water Resource Forecasting

NASA Astrophysics Data System (ADS)

Havens, S.; Marks, D. G.; Watson, K. A.; Masarik, M.; Flores, A. N.; Kormos, P.; Hedrick, A. R.

2015-12-01

Traditional operational modeling tools used by water managers in the west are challenged by more frequently occurring uncharacteristic stream flow patterns caused by climate change. Water managers are now turning to new models based on the physical processes within a watershed to combat the increasing number of events that do not follow the historical patterns. The USDA-ARS has provided near real time snow water equivalent (SWE) maps using iSnobal since WY2012 for the Boise River Basin in southwest Idaho and since WY2013 for the Tuolumne Basin in California that feeds the Hetch Hetchy reservoir. The goal of these projects is to not only provide current snowpack estimates but to use the Weather Research and Forecasting (WRF) model to drive iSnobal in order to produce a forecasted stream flow when coupled to a hydrology model. The first step is to develop methods on how to create snow model forcing data from WRF outputs. Using a reanalysis 1km WRF dataset from WY2009 over the Boise River Basin, WRF model results like surface air temperature, relative humidity, wind, precipitation, cloud cover, and incoming long wave radiation must be downscaled for use in iSnobal. iSnobal results forced with WRF output are validated at point locations throughout the basin, as well as compared with iSnobal results forced with traditional weather station data. The presentation will explore the differences in forcing data derived from WRF outputs and weather stations and how this affects the snowpack distribution.

Estimated flood-inundation mapping for the Lower Blue River in Kansas City, Missouri, 2003-2005

USGS Publications Warehouse

Kelly, Brian P.; Rydlund, Jr., Paul H.

2006-01-01

The U.S. Geological Survey, in cooperation with the city of Kansas City, Missouri, began a study in 2003 of the lower Blue River in Kansas City, Missouri, from Gregory Boulevard to the mouth at the Missouri River to determine the estimated extent of flood inundation in the Blue River valley from flooding on the lower Blue River and from Missouri River backwater. Much of the lower Blue River flood plain is covered by industrial development. Rapid development in the upper end of the watershed has increased the volume of runoff, and thus the discharge of flood events for the Blue River. Modifications to the channel of the Blue River began in late 1983 in response to the need for flood control. By 2004, the channel had been widened and straightened from the mouth to immediately downstream from Blue Parkway to convey a 30-year flood. A two-dimensional depth-averaged flow model was used to simulate flooding within a 2-mile study reach of the Blue River between 63rd Street and Blue Parkway. Hydraulic simulation of the study reach provided information for the design and performance of proposed hydraulic structures and channel improvements and for the production of estimated flood-inundation maps and maps representing an areal distribution of water velocity, both magnitude and direction. Flood profiles of the Blue River were developed between Gregory Boulevard and 63rd Street from stage elevations calculated from high water marks from the flood of May 19, 2004; between 63rd Street and Blue Parkway from two-dimensional hydraulic modeling conducted for this study; and between Blue Parkway and the mouth from an existing one-dimensional hydraulic model by the U.S. Army Corps of Engineers. Twelve inundation maps were produced at 2-foot intervals for Blue Parkway stage elevations from 750 to 772 feet. Each map is associated with National Weather Service flood-peak forecast locations at 63rd Street, Blue Parkway, Stadium Drive, U.S. Highway 40, 12th Street, and the Missouri River at the Hannibal railroad bridge in Kansas City. The National Weather Service issues peak-stage forecasts for these locations during times of flooding. Missouri River backwater inundation profiles were developed using interpolated Missouri River stage elevations at the mouth of the Blue River. Twelve backwater-inundation maps were produced at 2-foot intervals for the mouth of the Blue River from 730.9 to 752.9. To provide public access to the information presented in this report, a World Wide Web site (http://mo.water.usgs.gov/indep/kelly/blueriver/index.htm) was created that displays the results of two-dimensional modeling between 63rd Street and Blue Parkway, estimated flood-inundation maps, estimated backwater-inundation maps, and the latest gage heights and National Weather Service stage forecast for each forecast location within the study area. In addition, the full text of this report, all tables, and all plates are available for download at http://pubs.water.usgs.gov/sir2006-5089.

Using HPC within an operational forecasting configuration

NASA Astrophysics Data System (ADS)

Jagers, H. R. A.; Genseberger, M.; van den Broek, M. A. F. H.

2012-04-01

Various natural disasters are caused by high-intensity events, for example: extreme rainfall can in a short time cause major damage in river catchments, storms can cause havoc in coastal areas. To assist emergency response teams in operational decisions, it's important to have reliable information and predictions as soon as possible. This starts before the event by providing early warnings about imminent risks and estimated probabilities of possible scenarios. In the context of various applications worldwide, Deltares has developed an open and highly configurable forecasting and early warning system: Delft-FEWS. Finding the right balance between simulation time (and hence prediction lead time) and simulation accuracy and detail is challenging. Model resolution may be crucial to capture certain critical physical processes. Uncertainty in forcing conditions may require running large ensembles of models; data assimilation techniques may require additional ensembles and repeated simulations. The computational demand is steadily increasing and data streams become bigger. Using HPC resources is a logical step; in different settings Delft-FEWS has been configured to take advantage of distributed computational resources available to improve and accelerate the forecasting process (e.g. Montanari et al, 2006). We will illustrate the system by means of a couple of practical applications including the real-time dynamic forecasting of wind driven waves, flow of water, and wave overtopping at dikes of Lake IJssel and neighboring lakes in the center of The Netherlands. Montanari et al., 2006. Development of an ensemble flood forecasting system for the Po river basin, First MAP D-PHASE Scientific Meeting, 6-8 November 2006, Vienna, Austria.

A water balance approach to enhance national (GB) Daily Landslide Hazard Assessments

NASA Astrophysics Data System (ADS)

Dijkstra, Tom; Reeves, Helen; Freeborough, Katy; Dashwood, Claire; Pennington, Catherine; Jordan, Hannah; Hobbs, Peter; Richardson, Jennifer; Banks, Vanessa; Cole, Steven; Wells, Steven; Moore, Robert

2017-04-01

The British Geological Survey (BGS) is a member of the Natural Hazards Partnership (NHP) and delivers a national (GB) daily landslide hazard assessment (DLHA). The DLHA is based largely on 'expert' driven evaluations of the likelihood of landslides in response to antecedent ground conditions, adverse weather and reported landslide events. It concentrates on shallow translational slides and debris flows - events that most frequently have societal consequences by disrupting transport infrastructure and affecting buildings. Considerable experience with the issuing of DLHAs has been gained since 2012. However, it remains very difficult to appropriately assess changing ground conditions throughout GB even when good quality precipitation forecasts are available. Soil moisture sensors are available, but the network is sparse and not yet capable of covering GB to the detail required to underpin the forecasts. Therefore, we developed an approach where temporal and spatial variations in soil moisture can be obtained from a water balance model, representing processes in the near-surface and configured on a relatively coarse grid of 1 km2. Model outputs are not intended to be relevant to the slope scale. The assumption is that the likelihood of landslides being triggered by rainfall is dependent upon the soil moisture conditions of the near-surface, in combination with how much rain is forecast to occur for the following day. These variables form the basis for establishing thresholds to guide the issuing of DLHA and early warnings. The main aim is to obtain an insight into regional patterns of change and threshold exceedance. The BGS water balance model is still in its infancy and it requires substantial work to fine-tune and validate it. To test the performance of the BGS model we focused on an analysis of Scottish landslides (2004-2015) comprising translational slides and debris flows where the BGS model is conditionally evaluated against the Grid-to-Grid (G2G) Model. G2G is a physical-conceptual distributed hydrological model developed by the Centre for Ecology & Hydrology, also an NHP member. G2G is especially suited to simulate river flows over ungauged areas and has the capability to forecast fluvial river flows at any location across a gridded model domain. This is achieved by using spatial datasets on landscape properties - terrain, land-cover, soil and geology - in combination with gridded time-series of rainfall to shape a rainfall pattern into a river flow response over the model domain. G2G is operational on a 1 km2 grid over the GB and outputs soil moisture estimates that take some account of terrain slope in its water balance calculation. This research is part of an evolutionary process where capabilities of establishing the likelihood of landslides will develop as datasets are becoming increasingly detailed (and accessible) and the representation of hydrogeological and geotechnical processes continues to develop.

Why understanding the impacts of the changing environment on river basin hydrology matters in Texas?

NASA Astrophysics Data System (ADS)

Gao, H.; Zhao, G.; Lee, K.; Zhang, S.; Shen, X.; Shao, M.; Nickelson, C.

2017-12-01

The State of Texas is prone to floods and droughts—both of which are expected to become more frequent, and more intensified, under a changing climate. This has a direct negative effect on agricultural productivity, which is a major revenue source for the state. Meanwhile, with the rapid population growth and economic development, the burden to Texas water resources is exacerbated by the ever increasing demands from users. From a hydrological processes perspective, the direct consequence of the increased impervious area due to urbanization is greater surface runoff and higher flood peaks. Although many reservoirs have been built during the past several decades to regulate river flows and increase water supply, the role of these reservoirs in the context of different future climate change and urbanization scenarios needs to be explored. Furthermore, phytoplankton productivity—an important indicator of coastal ecosystem health— is significantly affected by river discharge. The objective of this presentation is to reveal the importance of understanding the impacts of climate change, urbanization, and flow regulation on Texas river flows, water resources, and coastal water quality. Using state-of-the-art modeling and remote sensing techniques, we will showcase our results over representative Texas river basins and bay areas. A few examples include modeling peak flows in the San Antonio River Basin, evaluating water supply resilience under future drought and urbanization over the Dallas metropolitan area, projecting future crop yields from Texas agricultural lands, and monitoring and forecasting Chlorophyll-a concentrations over Galveston Bay. Results from these studies are expected to provide information relevant to decision making, both with regard to water resources management and to ecosystem protection.

Experiments with Interaction between the National Water Model and the Reservoir System Simulation Model: A Case Study of Russian River Basin

NASA Astrophysics Data System (ADS)

Kim, J.; Johnson, L.; Cifelli, R.; Chandra, C. V.; Gochis, D.; McCreight, J. L.; Yates, D. N.; Read, L.; Flowers, T.; Cosgrove, B.

2017-12-01

NOAA National Water Center (NWC) in partnership with the National Centers for Environmental Prediction (NCEP), the National Center for Atmospheric Research (NCAR) and other academic partners have produced operational hydrologic predictions for the nation using a new National Water Model (NWM) that is based on the community WRF-Hydro modeling system since the summer of 2016 (Gochis et al., 2015). The NWM produces a variety of hydrologic analysis and prediction products, including gridded fields of soil moisture, snowpack, shallow groundwater levels, inundated area depths, evapotranspiration as well as estimates of river flow and velocity for approximately 2.7 million river reaches. Also included in the NWM are representations for more than 1,200 reservoirs which are linked into the national channel network defined by the USGS NHDPlusv2.0 hydrography dataset. Despite the unprecedented spatial and temporal coverage of the NWM, many known deficiencies exist, including the representation of lakes and reservoirs. This study addresses the implementation of a reservoir assimilation scheme through coupling of a reservoir simulation model to represent the influence of managed flows. We examine the use of the reservoir operations to dynamically update lake/reservoir storage volume states, characterize flow characteristics of river reaches flowing into and out of lakes and reservoirs, and incorporate enhanced reservoir operating rules for the reservoir model options within the NWM. Model experiments focus on a pilot reservoir domain-Lake Mendocino, CA, and its contributing watershed, the East Fork Russian River. This reservoir is modeled using United States Army Corps of Engineers (USACE) HEC-ResSim developed for application to examine forecast informed reservoir operations (FIRO) in the Russian River basin.

Managing Environmental Flows for Impounded Rivers in Semi-Arid Regions- A Habitat Suitability Index (HSI) Approach for the Assessment of River Habitat for Salmonid Populations

NASA Astrophysics Data System (ADS)

Pai, H.; Sivakumaran, K.; Villamizar, S. R.; Flanagan, J.; Guo, Q.; Harmon, T. C.

2013-12-01

Balancing ecosystem health in water-scarce, agriculturally dominated river basins remains a challenge. In dry water years, maintaining conditions for restored and sustained indigenous fish populations (a frequently used indicator for ecosystem health) is particularly challenging. Competing human demands include urban and agricultural water supplies, hydropower, and flood control. In many semi-arid regions, increasing drought intensity and frequency under future climate scenarios will combine with population increases to water scarcity. The goal of this work is to better understand how reservoir releases affect fish habitat and overall river aquatic ecosystem quality. Models integrating a diverse array of physical and biological processes and system state are used to forecast the river ecosystem response to changing drivers. We propose a distributed parameter-based Habitat Suitability Index (HSI) approach for assessing fish habitat quality. Our river ecosystem HSI maps are based on a combination of the following: (1) In situ data describing stream flow and water quality conditions; (2) Spatial observations, including surveyed cross-sections, aerial imagery and digital elevation maps (DEM) of the river and its riparian corridor; and (3) Simulated spatially distributed water depths, flow velocities, and temperatures estimated from 1D and 2D river flow and temperature models (HEC-RAS and CE-QUAL-W2, respectively). With respect to (2), image processing schemes are used to classify and map key habitat features, namely riparian edge and shallow underwater vegetation. HSI maps can be modified temporally to address specific life cycle requirements of indicator fish species. Results are presented for several reaches associated with the San Joaquin River Restoration Project, focusing on several components of the Chinook salmon life cycle. HSI maps and interpretations are presented in the context of a range of prescribed reservoir release hydrographs linked to California water year descriptors (wet, dry, critical low, etc.). Implications are discussed with respect to effective reservoir operation (requisite flow releases and temperature) and restorative actions (e.g., riparian vegetation) in the context of habitat suitability.

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.

The impacts of wind power integration on sub-daily variation in river flows downstream of hydroelectric dams.

PubMed

Kern, Jordan D; Patino-Echeverri, Dalia; Characklis, Gregory W

2014-08-19

Due to their operational flexibility, hydroelectric dams are ideal candidates to compensate for the intermittency and unpredictability of wind energy production. However, more coordinated use of wind and hydropower resources may exacerbate the impacts dams have on downstream environmental flows, that is, the timing and magnitude of water flows needed to sustain river ecosystems. In this paper, we examine the effects of increased (i.e., 5%, 15%, and 25%) wind market penetration on prices for electricity and reserves, and assess the potential for altered price dynamics to disrupt reservoir release schedules at a hydroelectric dam and cause more variable and unpredictable hourly flow patterns (measured in terms of the Richards-Baker Flashiness (RBF) index). Results show that the greatest potential for wind energy to impact downstream flows occurs at high (∼25%) wind market penetration, when the dam sells more reserves in order to exploit spikes in real-time electricity prices caused by negative wind forecast errors. Nonetheless, compared to the initial impacts of dam construction (and the dam's subsequent operation as a peaking resource under baseline conditions) the marginal effects of any increased wind market penetration on downstream flows are found to be relatively minor.

Power law time dependence of river flood decay and its relationship to long term discharge frequency distribution. [California

NASA Technical Reports Server (NTRS)

Schubert, G.; Lingenfelter, R. E.

1973-01-01

Investigations have continued into the possibility that significant information on stream flow rates can be obtained from aerial and satellite imagery of river meander patterns by seeking a correlation between the meander and discharge spectra of rivers. Such a correlation could provide the basis for a simple and inexpensive technique for remote sensing of the water resources of large geographical areas, eliminating the need for much hydrologic recording. The investigation of the nature of the meander and discharge spectra and their interrelationship can also contribute to a more fundamental understanding of the processes of both river meander formation and drainage of large basins. It has been found that floods decay with an inverse power law dependence on time. The exponent of this dependence varies from river to river and even from station to station along the same river. This power law time dependence makes possible the forecasting of river discharge with an uncertainty of about 5% for as long as a month following the flood peak.

Parameter Identification and Uncertainty Analysis for Visual MODFLOW based Groundwater Flow Model in a Small River Basin, Eastern India

NASA Astrophysics Data System (ADS)

Jena, S.

2015-12-01

The overexploitation of groundwater resulted in abandoning many shallow tube wells in the river Basin in Eastern India. For the sustainability of groundwater resources, basin-scale modelling of groundwater flow is essential for the efficient planning and management of the water resources. The main intent of this study is to develope a 3-D groundwater flow model of the study basin using the Visual MODFLOW package and successfully calibrate and validate it using 17 years of observed data. The sensitivity analysis was carried out to quantify the susceptibility of aquifer system to the river bank seepage, recharge from rainfall and agriculture practices, horizontal and vertical hydraulic conductivities, and specific yield. To quantify the impact of parameter uncertainties, Sequential Uncertainty Fitting Algorithm (SUFI-2) and Markov chain Monte Carlo (MCMC) techniques were implemented. Results from the two techniques were compared and the advantages and disadvantages were analysed. Nash-Sutcliffe coefficient (NSE) and coefficient of determination (R2) were adopted as two criteria during calibration and validation of the developed model. NSE and R2 values of groundwater flow model for calibration and validation periods were in acceptable range. Also, the MCMC technique was able to provide more reasonable results than SUFI-2. The calibrated and validated model will be useful to identify the aquifer properties, analyse the groundwater flow dynamics and the change in groundwater levels in future forecasts.

Use of medium-range numerical weather prediction model output to produce forecasts of streamflow

USGS Publications Warehouse

Clark, M.P.; Hay, L.E.

2004-01-01

This paper examines an archive containing over 40 years of 8-day atmospheric forecasts over the contiguous United States from the NCEP reanalysis project to assess the possibilities for using medium-range numerical weather prediction model output for predictions of streamflow. This analysis shows the biases in the NCEP forecasts to be quite extreme. In many regions, systematic precipitation biases exceed 100% of the mean, with temperature biases exceeding 3??C. In some locations, biases are even higher. The accuracy of NCEP precipitation and 2-m maximum temperature forecasts is computed by interpolating the NCEP model output for each forecast day to the location of each station in the NWS cooperative network and computing the correlation with station observations. Results show that the accuracy of the NCEP forecasts is rather low in many areas of the country. Most apparent is the generally low skill in precipitation forecasts (particularly in July) and low skill in temperature forecasts in the western United States, the eastern seaboard, and the southern tier of states. These results outline a clear need for additional processing of the NCEP Medium-Range Forecast Model (MRF) output before it is used for hydrologic predictions. Techniques of model output statistics (MOS) are used in this paper to downscale the NCEP forecasts to station locations. Forecasted atmospheric variables (e.g., total column precipitable water, 2-m air temperature) are used as predictors in a forward screening multiple linear regression model to improve forecasts of precipitation and temperature for stations in the National Weather Service cooperative network. This procedure effectively removes all systematic biases in the raw NCEP precipitation and temperature forecasts. MOS guidance also results in substantial improvements in the accuracy of maximum and minimum temperature forecasts throughout the country. For precipitation, forecast improvements were less impressive. MOS guidance increases he accuracy of precipitation forecasts over the northeastern United States, but overall, the accuracy of MOS-based precipitation forecasts is slightly lower than the raw NCEP forecasts. Four basins in the United States were chosen as case studies to evaluate the value of MRF output for predictions of streamflow. Streamflow forecasts using MRF output were generated for one rainfall-dominated basin (Alapaha River at Statenville, Georgia) and three snowmelt-dominated basins (Animas River at Durango, Colorado: East Fork of the Carson River near Gardnerville, Nevada: and Cle Elum River near Roslyn, Washington). Hydrologic model output forced with measured-station data were used as "truth" to focus attention on the hydrologic effects of errors in the MRF forecasts. Eight-day streamflow forecasts produced using the MOS-corrected MRF output as input (MOS) were compared with those produced using the climatic Ensemble Streamflow Prediction (ESP) technique. MOS-based streamflow forecasts showed increased skill in the snowmelt-dominated river basins, where daily variations in streamflow are strongly forced by temperature. In contrast, the skill of MOS forecasts in the rainfall-dominated basin (the Alapaha River) were equivalent to the skill of the ESP forecasts. Further improvements in streamflow forecasts require more accurate local-scale forecasts of precipitation and temperature, more accurate specification of basin initial conditions, and more accurate model simulations of streamflow. ?? 2004 American Meteorological Society.

Assessment of Folsom Lake Watershed response to historical and potential future climate scenarios

USGS Publications Warehouse

Carpenter, Theresa M.; Georgakakos, Konstantine P.

2000-01-01

An integrated forecast-control system was designed to allow the profitable use of ensemble forecasts for the operational management of multi-purpose reservoirs. The system ingests large-scale climate model monthly precipitation through the adjustment of the marginal distribution of reservoir-catchment precipitation to reflect occurrence of monthly climate precipitation amounts in the extreme terciles of their distribution. Generation of ensemble reservoir inflow forecasts is then accomplished with due account for atmospheric- forcing and hydrologic- model uncertainties. These ensemble forecasts are ingested by the decision component of the integrated system, which generates non- inferior trade-off surfaces and, given management preferences, estimates of reservoir- management benefits over given periods. In collaboration with the Bureau of Reclamation and the California Nevada River Forecast Center, the integrated system is applied to Folsom Lake in California to evaluate the benefits for flood control, hydroelectric energy production, and low flow augmentation. In addition to retrospective studies involving the historical period 1964-1993, system simulations were performed for the future period 2001-2030, under a control (constant future greenhouse-gas concentrations assumed at the present levels) and a greenhouse-gas- increase (1-% per annum increase assumed) scenario. The present paper presents and validates ensemble 30-day reservoir- inflow forecasts under a variety of situations. Corresponding reservoir management results are presented in Yao and Georgakakos, A., this issue. Principle conclusions of this paper are that the integrated system provides reliable ensemble inflow volume forecasts at the 5-% confidence level for the majority of the deciles of forecast frequency, and that the use of climate model simulations is beneficial mainly during high flow periods. It is also found that, for future periods with potential sharp climatic increases of precipitation amount and to maintain good reliability levels, operational ensemble inflow forecasting should involve atmospheric forcing from appropriate climatic periods.

Improvements and Lingering Challenges with Modeling Low-Level Winds Over Complex Terrain during the Wind Forecast Improvement Project 2

NASA Astrophysics Data System (ADS)

Olson, J.; Kenyon, J.; Brown, J. M.; Angevine, W. M.; Marquis, M.; Pichugina, Y. L.; Choukulkar, A.; Bonin, T.; Banta, R. M.; Bianco, L.; Djalalova, I.; McCaffrey, K.; Wilczak, J. M.; Lantz, K. O.; Long, C. N.; Redfern, S.; McCaa, J. R.; Stoelinga, M.; Grimit, E.; Cline, J.; Shaw, W. J.; Lundquist, J. K.; Lundquist, K. A.; Kosovic, B.; Berg, L. K.; Kotamarthi, V. R.; Sharp, J.; Jiménez, P.

2017-12-01

The Rapid Refresh (RAP) and High-Resolution Rapid Refresh (HRRR) are NOAA real-time operational hourly updating forecast systems run at 13- and 3-km grid spacing, respectively. Both systems use the Advanced Research version of the Weather Research and Forecasting (WRF-ARW) as the model component of the forecast system. During the second installment of the Wind Forecast Improvement Project (WFIP 2), the RAP/HRRR have been targeted for the improvement of low-level wind forecasts in the complex terrain within the Columbia River Basin (CRB), which requires much finer grid spacing to resolve important terrain peaks in the Cascade Mountains as well as the Columbia River Gorge. Therefore, this project provides a unique opportunity to test and develop the RAP/HRRR physics suite within a very high-resolution nest (Δx = 750 m) over the northwestern US. Special effort is made to incorporate scale-aware aspects into the model physical parameterizations to improve RAP/HRRR wind forecasts for any application at any grid spacing. Many wind profiling and scanning instruments have been deployed in the CRB in support the WFIP 2 field project, which spanned 01 October 2015 to 31 March 2017. During the project, several forecast error modes were identified, such as: (1) too-shallow cold pools during the cool season, which can mix-out more frequently than observed and (2) the low wind speed bias in thermal trough-induced gap flows during the warm season. Development has been focused on the column-based turbulent mixing scheme to improve upon these biases, but investigating the effects of horizontal (and 3D) mixing has also helped improve some of the common forecast failure modes. This presentation will highlight the testing and development of various model components, showing the improvements over original versions for temperature and wind profiles. Examples of case studies and retrospective periods will be presented to illustrate the improvements. We will demonstrate that the improvements made in WFIP 2 will be extendable to other regions, complex or flat terrain. Ongoing and future challenges in RAP/HRRR physics development will be touched upon.

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.

Mapping the Historical Probability of Increased Flood Hazard During ENSO Events Using a New 20th Century River Flow Reanalysis

NASA Astrophysics Data System (ADS)

Emerton, R.; Cloke, H. L.; Stephens, L.; Woolnough, S. J.; Zsoter, E.; Pappenberger, F.

2016-12-01

El Niño Southern Oscillation (ENSO), a mode of variability which sees fluctuations between anomalously high or low sea surface temperatures in the Pacific, is known to influence river flow and flooding at the global scale. The anticipation and forecasting of floods is crucial for flood preparedness, and this link, alongside the predictive skill of ENSO up to seasons ahead, may provide an early indication of upcoming severe flood events. Information is readily available indicating the likely impacts of El Niño and La Niña on precipitation across the globe, which is often used as a proxy for flood hazard. However, the nonlinearity between precipitation and flood magnitude and frequency means that it is important to assess the impact of ENSO events not only on precipitation, but also on river flow and flooding. Historical probabilities provide key information regarding the likely impacts of ENSO events. We estimate, for the first time, the historical probability of increased flood hazard during El Niño and La Niña through a global hydrological analysis, using a new 20thCentury ensemble river flow reanalysis for the global river network. This dataset was produced by running the ECMWF ERA-20CM atmospheric reanalysis through a research set-up of the Global Flood Awareness System (GloFAS) using the CaMa-Flood hydrodynamic model, to produce a 110-year global reanalysis of river flow. We further evaluate the added benefit of the hydrological analysis over the use of precipitation as a proxy for flood hazard. For example, providing information regarding regions that are likely to experience a lagged influence on river flow compared to the influence on precipitation. Our results map, at the global scale, the probability of abnormally high river flow during any given month during an El Niño or La Niña; information such as this is key for organisations that work at the global scale, such as humanitarian aid organisations, providing a seasons-ahead indicator of potential increased flood hazard that can be used as soon as the event onset is declared, or even earlier, when El Niño or La Niña conditions are first predicted.

Dynamic Floodplain representation in hydrologic flood forecasting using WRF-Hydro modeling framework

NASA Astrophysics Data System (ADS)

Gangodagamage, C.; Li, Z.; Maitaria, K.; Islam, M.; Ito, T.; Dhondia, J.

2016-12-01

Floods claim more lives and damage more property than any other category of natural disaster in the Continental United States. A system that can demarcate local flood boundaries dynamically could help flood prone communities prepare for and even prevent from catastrophic flood events. Lateral distance from the centerline of the river to the right and left floodplains for the water levels coming out of the models at each grid location have not been properly integrated with the national hydrography dataset (NHDPlus). The NHDPlus dataset represents the stream network with feature classes such as rivers, tributaries, canals, lakes, ponds, dams, coastlines, and stream gages. The NHDPlus dataset consists of approximately 2.7 million river reaches defining how surface water drains to the ocean. These river reaches have upstream and downstream nodes and basic parameters such as flow direction, drainage area, reach slope etc. We modified an existing algorithm (Gangodagamage et al., 2007) to provide lateral distance from the centerline of the river to the right and left floodplains for the flows simulated by models. Previous work produced floodplain boundaries for static river stages (i.e. 3D metric: distance along the main stem, flow depth, lateral distance from river center line). Our new approach introduces the floodplain boundary for variable water levels at each reach with the fourth dimension, time. We use modeled flows from WRF-Hydro and demarcate the right and left lateral boundaries of inundation dynamically by appropriately mapping discharges into hydraulically corrected stages. Backwater effects from the mainstem to tributaries are considered and proper corrections are applied for the tributary inundations. We obtained river stages by optimizing reach level channel parameters using newly developed stream flow routing algorithm. Non uniform inundations are mapped at each NHDplus reach (upstream and downstream nodes) and spatial interpolation is carried out on a normalized digital elevation model (always streams are at zero elevations) to obtain the smooth flood boundaries between adjacent reaches. The validation of the dynamic inundation boundaries is performed using multi-temporal satellite datasets as well as HEC-RAS hydrodynamic model results for selected streams for previous flood events.

Radar-driven High-resolution Hydrometeorological Forecasts of the 26 September 2007 Venice flash flood

NASA Astrophysics Data System (ADS)

Massimo Rossa, Andrea; Laudanna Del Guerra, Franco; Borga, Marco; Zanon, Francesco; Settin, Tommaso; Leuenberger, Daniel

2010-05-01

Space and time scales of flash floods are such that flash flood forecasting and warning systems depend upon the accurate real-time provision of rainfall information, high-resolution numerical weather prediction (NWP) forecasts and the use of hydrological models. Currently available high-resolution NWP model models can potentially provide warning forecasters information on the future evolution of storms and their internal structure, thereby increasing convective-scale warning lead times. However, it is essential that the model be started with a very accurate representation of on-going convection, which calls for assimilation of high-resolution rainfall data. This study aims to assess the feasibility of using carefully checked radar-derived quantitative precipitation estimates (QPE) for assimilation into NWP and hydrological models. The hydrometeorological modeling chain includes the convection-permitting NWP model COSMO-2 and a hydrologic-hydraulic models built upon the concept of geomorphological transport. Radar rainfall observations are assimilated into the NWP model via the latent heat nudging method. The study is focused on 26 September 2007 extreme flash flood event which impacted the coastal area of north-eastern Italy around Venice. The hydro-meteorological modeling system is implemented over the Dese river, a 90 km2 catchment flowing to the Venice lagoon. The radar rainfall observations are carefully checked for artifacts, including beam attenuation, by means of physics-based correction procedures and comparison with a dense network of raingauges. The impact of the radar QPE in the assimilation cycle of the NWP model is very significant, in that the main individual organized convective systems were successfully introduced into the model state, both in terms of timing and localization. Also, incorrectly localized precipitation in the model reference run without rainfall assimilation was correctly reduced to about the observed levels. On the other hand, the highest rainfall intensities were underestimated by 20% at a scale of 1000 km2, and the local peaks by 50%. The positive impact of the assimilated radar rainfall was carried over into the free forecast for about 2-5 hours, depending on when this forecast was started, and was larger, when the main mesoscale convective system was present in the initial conditions. The improvements of the meteorological model simulations were directly propagated to the river flow simulations, with an extension of the warning lead time up to three hours.

Hydrologic data for water years 1933-97 used in the River and Reservoir Operations Model, Truckee River basin, California and Nevada

USGS Publications Warehouse

Berris, Steven N.; Hess, Glen W.; Bohman, Larry R.

2000-01-01

Title II of Public Law 101-618, the Truckee?Carson?Pyramid Lake Water Rights Settlement Act of 1990, provides direction, authority, and a mechanism for resolving conflicts over water rights in the Truckee and Carson River Basins. The Truckee Carson Program of the U.S. Geological Survey, to support implementation of Public Law 101-618, has developed an operations model to simulate lake/reservoir and river operations for the Truckee River Basin including diversion of Truckee River water to the Truckee Canal for transport to the Carson River Basin. Several types of hydrologic data, formatted in a chronological order with a daily time interval called 'time series,' are described in this report. Time series from water years 1933 to 1997 can be used to run the operations model. Auxiliary hydrologic data not currently used by the model are also described. The time series of hydrologic data consist of flow, lake/reservoir elevation and storage, precipitation, evaporation, evapotranspiration, municipal and industrial (M&I) demand, and streamflow and lake/reservoir level forecast data.

Advancing atmospheric river forecasts into subseasonal-to-seasonal time scales

NASA Astrophysics Data System (ADS)

Baggett, Cory F.; Barnes, Elizabeth A.; Maloney, Eric D.; Mundhenk, Bryan D.

2017-07-01

Atmospheric rivers are elongated plumes of intense moisture transport that are capable of producing extreme and impactful weather. Along the West Coast of North America, they occasionally cause considerable mayhem—delivering flooding rains during periods of heightened activity and desiccating droughts during periods of reduced activity. The intrinsic chaos of the atmosphere makes the prediction of atmospheric rivers at subseasonal-to-seasonal time scales (3 to 5 weeks) an inherently difficult task. We demonstrate here that the potential exists to advance forecast lead times of atmospheric rivers into subseasonal-to-seasonal time scales through knowledge of two of the atmosphere's most prominent oscillations, the Madden-Julian oscillation (MJO) and the quasi-biennial oscillation (QBO). Strong MJO and QBO activity modulates the frequency at which atmospheric rivers strike—offering an opportunity to improve subseasonal-to-seasonal forecast models and thereby skillfully predict atmospheric river activity up to 5 weeks in advance.

Verification of Advances in a Coupled Snow-runoff Modeling Framework for Operational Streamflow Forecasts

NASA Astrophysics Data System (ADS)

Barik, M. G.; Hogue, T. S.; Franz, K. J.; He, M.

2011-12-01

The National Oceanic and Atmospheric Administration's (NOAA's) River Forecast Centers (RFCs) issue hydrologic forecasts related to flood events, reservoir operations for water supply, streamflow regulation, and recreation on the nation's streams and rivers. The RFCs use the National Weather Service River Forecast System (NWSRFS) for streamflow forecasting which relies on a coupled snow model (i.e. SNOW17) and rainfall-runoff model (i.e. SAC-SMA) in snow-dominated regions of the US. Errors arise in various steps of the forecasting system from input data, model structure, model parameters, and initial states. The goal of the current study is to undertake verification of potential improvements in the SNOW17-SAC-SMA modeling framework developed for operational streamflow forecasts. We undertake verification for a range of parameters sets (i.e. RFC, DREAM (Differential Evolution Adaptive Metropolis)) as well as a data assimilation (DA) framework developed for the coupled models. Verification is also undertaken for various initial conditions to observe the influence of variability in initial conditions on the forecast. The study basin is the North Fork America River Basin (NFARB) located on the western side of the Sierra Nevada Mountains in northern California. Hindcasts are verified using both deterministic (i.e. Nash Sutcliffe efficiency, root mean square error, and joint distribution) and probabilistic (i.e. reliability diagram, discrimination diagram, containing ratio, and Quantile plots) statistics. Our presentation includes comparison of the performance of different optimized parameters and the DA framework as well as assessment of the impact associated with the initial conditions used for streamflow forecasts for the NFARB.

Forecasting approaches to the Mekong River

NASA Astrophysics Data System (ADS)

Plate, E. J.

2009-04-01

Hydrologists distinguish between flood forecasts, which are concerned with events of the immediate future, and flood predictions, which are concerned with events that are possible, but whose date of occurrence is not determined. Although in principle both involve the determination of runoff from rainfall, the analytical approaches differ because of different objectives. The differences between the two approaches will be discussed, starting with an analysis of the forecasting process. The Mekong River in south-east Asia is used as an example. Prediction is defined as forecast for a hypothetical event, such as the 100-year flood, which is usually sufficiently specified by its magnitude and its probability of occurrence. It forms the basis for designing flood protection structures and risk management activities. The method for determining these quantities is hydrological modeling combined with extreme value statistics, today usually applied both to rainfall events and to observed river discharges. A rainfall-runoff model converts extreme rainfall events into extreme discharges, which at certain gage points along a river are calibrated against observed discharges. The quality of the model output is assessed against the mean value by means of the Nash-Sutcliffe quality criterion. The result of this procedure is a design hydrograph (or a family of design hydrographs) which are used as inputs into a hydraulic model, which converts the hydrograph into design water levels according to the hydraulic situation of the location. The accuracy of making a prediction in this sense is not particularly high: hydrologists know that the 100-year flood is a statistical quantity which can be estimated only within comparatively wide error bounds, and the hydraulics of a river site, in particular under conditions of heavy sediment loads has many uncertainties. Safety margins, such as additional freeboards are arranged to compensate for the uncertainty of the prediction. Forecasts, on the other hand, have as objective to obtain an accurate hydrograph of the near future. The method by means of which this is done is not as important as the accuracy of the forecast. A mathematical rainfall-runoff model is not necessarily a good forecast model. It has to be very carefully designed, and in many cases statistical models are found to give better results than mathematical models. Forecasters have the advantage of knowing the course of the hydrographs up to the point in time where forecasts have to be made. Therefore, models can be calibrated on line against the hydrograph of the immediate past. To assess the quality of a forecast, the quality criterion should not be based on the mean value, as does the Nash-Sutcliffe criterion, but should be based on the best forecast given the information up to the forecast time. Without any additional information, the best forecast when only the present day value is known is to assume a no-change scenario, i.e. to assume that the present value does not change in the immediate future. For the Mekong there exists a forecasting system which is based on a rainfall-runoff model operated by the Mekong River Commission. This model is found not to be adequate for forecasting for periods longer than one or two days ahead. Improvements are sought through two approaches: a strictly deterministic rainfall-runoff model, and a strictly statistical model based on regression with upstream stations. The two approaches are com-pared, and suggestions are made how to best combine the advantages of both approaches. This requires that due consideration is given to critical hydraulic conditions of the river at and in between the gauging stations. Critical situations occur in two ways: when the river overtops, in which case the rainfall-runoff model is incomplete unless overflow losses are considered, and at the confluence with tributaries. Of particular importance is the role of the large Tonle Sap Lake, which dampens the hydrograph downstream of Phnom Penh. The effect of these components of river hydraulics on forecasting accuracy will be assessed.

Development of a flood-warning system and flood-inundation mapping in Licking County, Ohio

USGS Publications Warehouse

Ostheimer, Chad J.

2012-01-01

Digital flood-inundation maps for selected reaches of South Fork Licking River, Raccoon Creek, North Fork Licking River, and the Licking River in Licking County, Ohio, were created by the U.S. Geological Survey (USGS), in cooperation with the Ohio Department of Transportation; U.S. Department of Transportation, Federal Highway Administration; Muskingum Watershed Conservancy District; U.S. Department of Agriculture, Natural Resources Conservation Service; and the City of Newark and Village of Granville, Ohio. The inundation maps depict estimates of the areal extent of flooding corresponding to water levels (stages) at the following USGS streamgages: South Fork Licking River at Heath, Ohio (03145173); Raccoon Creek below Wilson Street at Newark, Ohio (03145534); North Fork Licking River at East Main Street at Newark, Ohio (03146402); and Licking River near Newark, Ohio (03146500). The maps were provided to the National Weather Service (NWS) for incorporation into a Web-based flood-warning system that can be used in conjunction with NWS flood-forecast data to show areas of predicted flood inundation associated with forecasted flood-peak stages. As part of the flood-warning streamflow network, the USGS re-installed one streamgage on North Fork Licking River, and added three new streamgages, one each on North Fork Licking River, South Fork Licking River, and Raccoon Creek. Additionally, the USGS upgraded a lake-level gage on Buckeye Lake. Data from the streamgages and lake-level gage can be used by emergency-management personnel, in conjunction with the flood-inundation maps, to help determine a course of action when flooding is imminent. Flood profiles for selected reaches were prepared by calibrating steady-state step-backwater models to selected, established streamgage rating curves. The step-backwater models then were used to determine water-surface-elevation profiles for up to 10 flood stages at a streamgage with corresponding streamflows ranging from approximately the 50 to 0.2-percent chance annual-exceedance probabilities for each of the 4 streamgages that correspond to the flood-inundation maps. The computed flood profiles were used in combination with digital elevation data to delineate flood-inundation areas. Maps of Licking County showing flood-inundation areas overlain on digital orthophotographs are presented for the selected floods. The USGS also developed an unsteady-flow model for a reach of South Fork Licking River for use by the NWS to enhance their ability to provide advanced flood warning in the region north of Buckeye Lake, Ohio. The unsteady-flow model was calibrated based on data from four flooding events that occurred from June 2008 to December 2011. Model calibration was approximate due to the fact that there were unmeasured inflows to the river that were not able to be considered during the calibration. Information on unmeasured inflow derived from NWS hydrologic models and additional flood-event data could enable the NWS to further refine the unsteady-flow model.

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?

Water quality in the Schuylkill River, Pennsylvania: the potential for long-lead forecasts

NASA Astrophysics Data System (ADS)

Block, P. J.; Peralez, J.

2012-12-01

Prior analysis of pathogen levels in the Schuylkill River has led to a categorical daily forecast of water quality (denoted as red, yellow, or green flag days.) The forecast, available to the public online through the Philadelphia Water Department, is predominantly based on the local precipitation forecast. In this study, we explore the feasibility of extending the forecast to the seasonal scale by associating large-scale climate drivers with local precipitation and water quality parameter levels. This advance information is relevant for recreational activities, ecosystem health, and water treatment (energy, chemicals), as the Schuylkill provides 40% of Philadelphia's water supply. Preliminary results indicate skillful prediction of average summertime water quality parameters and characteristics, including chloride, coliform, turbidity, alkalinity, and others, using season-ahead oceanic and atmospheric variables, predominantly from the North Atlantic. Water quality parameter trends, including historic land use changes along the river, association with climatic variables, and prediction models will be presented.

Simulating the Snow Water Equivalent and its changing pattern over Nepal

NASA Astrophysics Data System (ADS)

Niroula, S.; Joseph, J.; Ghosh, S.

2016-12-01

Snow fall in the Himalayan region is one of the primary sources of fresh water, which accounts around 10% of total precipitation of Nepal. Snow water is an intricate variable in terms of its global and regional estimates whose complexity is favored by spatial variability linked with rugged topography. The study is primarily focused on simulation of Snow Water Equivalent (SWE) by the use of a macroscale hydrologic model, Variable Infiltration Capacity (VIC). As whole Nepal including its Himalayas lies under the catchment of Ganga River in India, contributing at least 40% of annual discharge of Ganges, this model was run in the entire watershed that covers part of Tibet and Bangladesh as well. Meteorological inputs for 29 years (1979-2007) are drawn from ERA-INTERIM and APHRODITE dataset for horizontal resolution of 0.25 degrees. The analysis was performed to study temporal variability of SWE in the Himalayan region of Nepal. The model was calibrated by observed stream flows of the tributaries of the Gandaki River in Nepal which ultimately feeds river Ganga. Further, the simulated SWE is used to estimate stream flow in this river basin. Since Nepal has a greater snow cover accumulation in monsoon season than in winter at high altitudes, seasonality fluctuations in SWE affecting the stream flows are known. The model provided fair estimates of SWE and stream flow as per statistical analysis. Stream flows are known to be sensitive to the changes in snow water that can bring a negative impact on power generation in a country which has huge hydroelectric potential. In addition, our results on simulated SWE in second largest snow-fed catchment of the country will be helpful for reservoir management, flood forecasting and other water resource management issues. Keywords: Hydrology, Snow Water Equivalent, Variable Infiltration Capacity, Gandaki River Basin, Stream Flow

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.

Estimating river discharge uncertainty by applying the Rating Curve Model

NASA Astrophysics Data System (ADS)

Barbetta, S.; Melone, F.; Franchini, M.; Moramarco, T.

2012-04-01

The knowledge of the flow discharge at a river site is necessary for planning and management of water resources as well as for monitoring and real-time forecasting purposes when significant flood events occur. In the hydrological practice, the operational discharge measurement in medium and large rivers is mostly based on indirect approaches by converting the observed stage into discharge values using steady-flow rating curves. However, the stage-discharge relationship can be unknown for hydrometric sections where flow velocity measurements, particularly during high floods, are not available. To overcome this issue, a simplified approach named Rating Curve Model (RCM) and proposed by Moramarco et al. (Moramarco, T., Barbetta, S., F. Melone, F. & Singh, V.P., Relating local stage and remote discharge with significant lateral inflow, J. Hydrol. Engng ASCE, 10[1], 58?69, 2005) can be conveniently used. RCM turned out able to assess, with a high level of accuracy, the discharge hydrograph at a river site where only the stage is monitored while the flow is recorded at a different section along the river, even when significant lateral flows occur. The simple structure of the model is depending on three parameters of which two can be considered characteristic of the river reach and one of the wave travel time of floods. Considering that RCM well lends itself to predict the stage-discharge relationship at a river site wherein only stages are recorded, an uncertainty analysis on river discharge estimate is of interest for the hydrological practice definitely. To this aim, the uncertainty characterizing the RCM outcomes is addressed in this work by considering two different procedures based on the Monte Carlo approach and the Generalized Likelihood Uncertainty Estimation (GLUE) method, respectively. The statistical distribution of parameters is found and a random re-sampling of parameters is done for assessing the 90% confidence interval (CI) of discharge estimates. In particular, for the latter approach the Nash-Sutcliffe coefficient is used as likelihood measure. Two equipped river reaches of the Upper-Middle Tiber River basin, central Italy, are investigated as case studies. The results provided by the selected methodologies are discussed and compared showing that all the computed CIs are satisfied in term of percentage of included observed discharges with similar percentages characterizing the bands assessed by both Monte Carlo approach and GLUE procedure.

WRF model for precipitation simulation and its application in real-time flood forecasting in the Jinshajiang River Basin, China

NASA Astrophysics Data System (ADS)

Zhou, Jianzhong; Zhang, Hairong; Zhang, Jianyun; Zeng, Xiaofan; Ye, Lei; Liu, Yi; Tayyab, Muhammad; Chen, Yufan

2017-07-01

An accurate flood forecasting with long lead time can be of great value for flood prevention and utilization. This paper develops a one-way coupled hydro-meteorological modeling system consisting of the mesoscale numerical weather model Weather Research and Forecasting (WRF) model and the Chinese Xinanjiang hydrological model to extend flood forecasting lead time in the Jinshajiang River Basin, which is the largest hydropower base in China. Focusing on four typical precipitation events includes: first, the combinations and mode structures of parameterization schemes of WRF suitable for simulating precipitation in the Jinshajiang River Basin were investigated. Then, the Xinanjiang model was established after calibration and validation to make up the hydro-meteorological system. It was found that the selection of the cloud microphysics scheme and boundary layer scheme has a great impact on precipitation simulation, and only a proper combination of the two schemes could yield accurate simulation effects in the Jinshajiang River Basin and the hydro-meteorological system can provide instructive flood forecasts with long lead time. On the whole, the one-way coupled hydro-meteorological model could be used for precipitation simulation and flood prediction in the Jinshajiang River Basin because of its relatively high precision and long lead time.

Modeling the effects of river flow on population dynamics of piping plovers (Charadrius melodus) and least terns (Sternula antillarum) nesting on the Missouri River

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

Buenau, Kate E.; Hiller, Tim L.; Tyre, Andrew J.

Humans make extensive use of rivers and floodplains for economic benefits including agriculture, hydropower, commerce and recreation. Economic development of floodplains subsequently requires control of river levels to avoid flood damage. This process began in the Missouri River basin in the 1890s with the construction of a series of hydropower dams in Montana and escalated to new levels with the approval of the Pick-Sloan plan in the 1944 Flood Control Act. Maximizing these human uses of the river led to changes in and losses of hydrological and ecological processes, ultimately resulting in the federal listing of three fish and wildlifemore » species under the Endangered Species Act: the pallid sturgeon (Scaphirhyncus albus; 1983), the piping plover (Charadrius melodus; 1984), and the interior population of least tern (Sternula antillarum; 1985). The listing of terns and plovers did not affect river management until the United States Army Corps of Engineers (USACE) proposed to modify the governing document of the Missouri River Mainstem System, the Master Manual, a process which was completed in 2003. Although there was little disagreement over the habitat conditions that terns and plovers used for nesting, there was substantial disagreement over the amount of habitat necessary for terns and plovers to meet population recovery goals. Answering this question requires forecasting species-specific population responses to dynamic habitat affected by both human actions (reservoir management and habitat restoration) and natural variability in precipitation. Piping plovers and least terns nest along the Missouri River from Fort Peck, Montana to just north of Sioux City, Iowa (Figure 1). Both species prefer to nest on sand and fine gravel substrates with no or sparse vegetation cover (Prindiville Gaines and Ryan, 1988; Sherfy et al., 2012), such as riverine sandbars (emergent sandbar habitat; ESH). Piping plovers also nest on reservoir shorelines that lack vegetation cover (Anteau et al., 2012). The amount of ESH available for nesting in a given year is strongly affected by the amount of water entering the Missouri River system through precipitation and the management of water flow from six reservoirs operated by the USACE on the mainstem Missouri River. Prior to the construction of dams, the Missouri River experienced bimodal peak flows in spring and early summer in concordance with the melting of plains and mountain snowpack (Galat and Lipkin, 2000). Flows decreased during summer months, with river stage then dependent upon rainfall. The combination of consistent high flows and occasional extreme high flows, together with the meandering characteristic of the river, regularly reshaped and scoured vegetation from ESH.« less

Forecasting monthly inflow discharge of the Iffezheim reservoir using data-driven models

NASA Astrophysics Data System (ADS)

Zhang, Qing; Aljoumani, Basem; Hillebrand, Gudrun; Hoffmann, Thomas; Hinkelmann, Reinhard

2017-04-01

River stream flow is an essential element in hydrology study fields, especially for reservoir management, since it defines input into reservoirs. Forecasting this stream flow plays an important role in short or long-term planning and management in the reservoir, e.g. optimized reservoir and hydroelectric operation or agricultural irrigation. Highly accurate flow forecasting can significantly reduce economic losses and is always pursued by reservoir operators. Therefore, hydrologic time series forecasting has received tremendous attention of researchers. Many models have been proposed to improve the hydrological forecasting. Due to the fact that most natural phenomena occurring in environmental systems appear to behave in random or probabilistic ways, different cases may need a different methods to forecast the inflow and even a unique treatment to improve the forecast accuracy. The purpose of this study is to determine an appropriate model for forecasting monthly inflow to the Iffezheim reservoir in Germany, which is the last of the barrages in the Upper Rhine. Monthly time series of discharges, measured from 1946 to 2001 at the Plittersdorf station, which is located 6 km downstream of the Iffezheim reservoir, were applied. The accuracies of the used stochastic models - Fiering model and Auto-Regressive Integrated Moving Average models (ARIMA) are compared with Artificial Intelligence (AI) models - single Artificial Neural Network (ANN) and Wavelet ANN models (WANN). The Fiering model is a linear stochastic model and used for generating synthetic monthly data. The basic idea in modeling time series using ARIMA is to identify a simple model with as few model parameters as possible in order to provide a good statistical fit to the data. To identify and fit the ARIMA models, four phase approaches were used: identification, parameter estimation, diagnostic checking, and forecasting. An automatic selection criterion, such as the Akaike information criterion, is utilized to enhance this flexible approach to set up the model. As distinct from both stochastic models, the ANN and its related conjunction methods Wavelet-ANN (WANN) models are effective to handle non-linear systems and have been developed with antecedent flows as inputs to forecast up to 12-months lead-time for the Iffezheim reservoir. In the ANN and WANN models, the Feed Forward Back Propagation method (FFBP) is applied. The sigmoid activity and linear functions were used with several different neurons for the hidden layers and for the output layer, respectively. To compare the accuracy of the different models and identify the most suitable model for reliable forecasting, four quantitative standard statistical performance evaluation measures, the root mean square error (RMSE), the mean bias error (MAE) and the determination correlation coefficient (DC), are employed. The results reveal that the ARIMA (2, 1, 2) performs better than Fiering, ANN and WANN models. Further, the WANN model is found to be slightly better than the ANN model for forecasting monthly inflow of the Iffezheim reservoir. As a result, by using the ARIMA model, the predicted and observed values agree reasonably well.

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.

Characterizing hydroclimatic variability in tributaries of the Upper Colorado River Basin—WY1911-2001

NASA Astrophysics Data System (ADS)

Matter, Margaret A.; Garcia, Luis A.; Fontane, Darrell G.; Bledsoe, Brian

2010-01-01

SummaryMountain snowpack is the main source of water in the semi-arid Colorado River Basin (CRB), and while the demands for water are increasing, competing and often conflicting, the supply is limited and has become increasingly variable over the 20th Century. Greater variability is believed to contribute to lower accuracy in water supply forecasts, plus greater variability violates the assumption of stationarity, a fundamental assumption of many methods used in water resources engineering planning, design and management. Thus, it is essential to understand the underpinnings of hydroclimatic variability in order to accurately predict effects of climate changes and effectively meet future water supply challenges. A new methodology was applied to characterized time series of temperature, precipitation, and streamflow (i.e., historic and reconstructed undepleted flows) according to the three climate regimes that occurred in CRB during the 20th Century. Results for two tributaries in the Upper CRB show that hydroclimatic variability is more deterministic than previously thought because it entails complementary temperature and precipitation patterns associated with wetter or drier conditions on climate regime and annual scales. Complementary temperature and precipitation patterns characterize climate regime type (e.g., cool/wet and warm/dry), and the patterns entail increasing or decreasing temperatures and changes in magnitude and timing of precipitation according to the climate regime type. Accompanying each climate regime on annual scales are complementary temperature ( T) and precipitation ( P) patterns that are associated with upcoming precipitation and annual basin yield (i.e., total annual flow volume at a streamflow gauge). Annual complementary T and P patterns establish by fall, are detectable as early as September, persist to early spring, are related to the relative magnitude of upcoming precipitation and annual basin yield, are unique to climate regime type, and are specific to each river basin. Thus, while most of the water supply in the Upper CRB originates from winter snowpack, statistically significant indictors of relative magnitude of upcoming precipitation and runoff are evident in the fall, well before appreciable snow accumulation. Results of this study suggest strategies that may integrated into existing forecast methods to potentially improve forecast accuracy and advance lead time by as much as six months (i.e., from April 1 to October 1 of the previous year). These techniques also have applications in downscaling climate models and in river restoration and management.

Estimating freshwater productivity, overwinter survival, and migration patterns of Klamath River Coho Salmon

USGS Publications Warehouse

Manhard, Christopher V.; Som, Nicholas A.; Perry, Russell W.; Faukner, Jimmy; Soto, Toz

2018-01-01

An area of great importance to resource management and conservation biology in the Klamath Basin is balancing water usage against the life history requirements of threatened Coho Salmon. One tool for addressing this topic is a freshwater dynamics model to forecast Coho Salmon productivity based on environmental inputs. Constructing such a forecasting tool requires local data to quantify the unique life history processes of Coho Salmon inhabiting this region. Here, we describe analytical methods for estimating a series of sub-models, each capturing a different life history process, which will eventually be synchronized as part of a freshwater dynamics model for Klamath River Coho Salmon. Specifically, we draw upon extensive population monitoring data collected in the basin to estimate models of freshwater productivity, overwinter survival, and migration patterns. Our models of freshwater productivity indicated that high summer temperatures and high winter flows can both adversely affect smolt production and that such relationships are more likely in tributaries with naturally regulated flows due to substantial intraannual environmental variation. Our models of overwinter survival demonstrated extensive variability in survival among years, but not among rearing locations, and demonstrated that a substantial proportion (~ 20%) of age-0+ fish emigrate from some rearing sites in the winter. Our models of migration patterns indicated that many age-0+ fish redistribute in the basin during the summer and winter. Further, we observed that these redistributions can entail long migrations in the mainstem where environmental stressors likely play a role in cueing refuge entry. Finally, our models of migration patterns indicated that changes in discharge are important in cueing the seaward migration of smolts, but that the nature of this behavioral response can differ dramatically between tributaries with naturally and artificially regulated flows. Collectively, these analyses demonstrate that environmental variation interacts with most phases of the freshwater life history of Klamath River Coho Salmon and that anthropogenic environmental variation can have a particularly large bearing on productivity.

Snowmelt and water resources in a changing climate and dustier world

NASA Astrophysics Data System (ADS)

Painter, T. H.

2015-12-01

Snow cover and its melt dominate regional climate and water resources in the world's mountain regions, providing for critical agricultural and sustaining populations in otherwise dry regions. Snowmelt timing and magnitude in mountains tend to be controlled by absorption of solar radiation and snow water equivalent, respectively, and yet both of these are very poorly known even in the best-instrumented mountain regions of the globe. In this talk, we discuss developments in the spaceborne and airborne remote sensing of snow properties, and the assimilation of these products into research water cycle modeling and operational forecasting. Our work with the NWS Colorado Basin River Forecast Center has shown marked improvements in runoff forecasting through inclusion of MODIS and VIIRS fractional snow covered area data. Moreover, the analyses have shown that the CBRFC forecasting errors are strongly sensitive to actual dust radiative forcing in snow with rising limb excursions as large as 40%. With MODIS retrievals of dust radiative forcing, the CBRFC will be implementing modifications to forecasts to reduce those errors to order < 10%. In the last few years, the NASA Airborne Snow Observatory has emerged to provide the first spatially explicit distributions of snow water equivalent and coincident snow albedo products for mountain basins. ASO is an imaging spectrometer and imaging LiDAR system, to quantify snow water equivalent and snow albedo, provide unprecedented knowledge of snow properties, and provide complete, robust inputs to snowmelt runoff models, water management models, and systems of the future. ASO has been flying in the Western US for three snowmelt seasons. In 2015, ASO provided complete basin coverage for the Tuolumne, Merced, Lakes, Rush Creek, and Middle+South Forks of Kings River Basins in the California Sierra Nevada and the Upper Rio Grande, Conejos, and Uncompahgre Basins in the Colorado Rocky Mountains. Analyses show that with ASO data, river flows and reservoir inflows from the ASO acquisition date to 1 July can be estimated with uncertainties of less than 2%. The synergy of the ASO and the satellite retrievals will ultimately allow extension of quantitative knowledge to addressing the snowmelt water resources and availability for agricultural regions in sparsely instrumented regions of the globe.

A first large-scale flood inundation forecasting model

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

Schumann, Guy J-P; Neal, Jeffrey C.; Voisin, Nathalie

2013-11-04

At present continental to global scale flood forecasting focusses on predicting at a point discharge, with little attention to the detail and accuracy of local scale inundation predictions. Yet, inundation is actually the variable of interest and all flood impacts are inherently local in nature. This paper proposes a first large scale flood inundation ensemble forecasting model that uses best available data and modeling approaches in data scarce areas and at continental scales. The model was built for the Lower Zambezi River in southeast Africa to demonstrate current flood inundation forecasting capabilities in large data-scarce regions. The inundation model domainmore » has a surface area of approximately 170k km2. ECMWF meteorological data were used to force the VIC (Variable Infiltration Capacity) macro-scale hydrological model which simulated and routed daily flows to the input boundary locations of the 2-D hydrodynamic model. Efficient hydrodynamic modeling over large areas still requires model grid resolutions that are typically larger than the width of many river channels that play a key a role in flood wave propagation. We therefore employed a novel sub-grid channel scheme to describe the river network in detail whilst at the same time representing the floodplain at an appropriate and efficient scale. The modeling system was first calibrated using water levels on the main channel from the ICESat (Ice, Cloud, and land Elevation Satellite) laser altimeter and then applied to predict the February 2007 Mozambique floods. Model evaluation showed that simulated flood edge cells were within a distance of about 1 km (one model resolution) compared to an observed flood edge of the event. Our study highlights that physically plausible parameter values and satisfactory performance can be achieved at spatial scales ranging from tens to several hundreds of thousands of km2 and at model grid resolutions up to several km2. However, initial model test runs in forecast mode revealed that it is crucial to account for basin-wide hydrological response time when assessing lead time performances notwithstanding structural limitations in the hydrological model and possibly large inaccuracies in precipitation data.« less

Dynamic Statistical Models for Pyroclastic Density Current Generation at Soufrière Hills Volcano

NASA Astrophysics Data System (ADS)

Wolpert, Robert L.; Spiller, Elaine T.; Calder, Eliza S.

2018-05-01

To mitigate volcanic hazards from pyroclastic density currents, volcanologists generate hazard maps that provide long-term forecasts of areas of potential impact. Several recent efforts in the field develop new statistical methods for application of flow models to generate fully probabilistic hazard maps that both account for, and quantify, uncertainty. However a limitation to the use of most statistical hazard models, and a key source of uncertainty within them, is the time-averaged nature of the datasets by which the volcanic activity is statistically characterized. Where the level, or directionality, of volcanic activity frequently changes, e.g. during protracted eruptive episodes, or at volcanoes that are classified as persistently active, it is not appropriate to make short term forecasts based on longer time-averaged metrics of the activity. Thus, here we build, fit and explore dynamic statistical models for the generation of pyroclastic density current from Soufrière Hills Volcano (SHV) on Montserrat including their respective collapse direction and flow volumes based on 1996-2008 flow datasets. The development of this approach allows for short-term behavioral changes to be taken into account in probabilistic volcanic hazard assessments. We show that collapses from the SHV lava dome follow a clear pattern, and that a series of smaller flows in a given direction often culminate in a larger collapse and thereafter directionality of the flows change. Such models enable short term forecasting (weeks to months) that can reflect evolving conditions such as dome and crater morphology changes and non-stationary eruptive behavior such as extrusion rate variations. For example, the probability of inundation of the Belham Valley in the first 180 days of a forecast period is about twice as high for lava domes facing Northwest toward that valley as it is for domes pointing East toward the Tar River Valley. As rich multi-parametric volcano monitoring dataset become increasingly available, eruption forecasting is becoming an increasingly viable and important research field. We demonstrate an approach to utilize such data in order to appropriately 'tune' probabilistic hazard assessments for pyroclastic flows. Our broader objective with development of this method is to help advance time-dependent volcanic hazard assessment, by bridging the

4D Floodplain representation in hydrologic flood forecasting using WRFHydro modeling framework

NASA Astrophysics Data System (ADS)

Gangodagamage, C.; Li, Z.; Adams, T.; Ito, T.; Maitaria, K.; Islam, M.; Dhondia, J.

2015-12-01

Floods claim more lives and damage more property than any other category of natural disaster in the Continental U.S. A system that can demarcate local flood boundaries dynamically could help flood prone communities prepare for and even prevent from catastrophic flood events. Lateral distance from the centerline of the river to the right and left floodplains for the water levels coming out of the models at each grid location have not been properly integrated with the national hydrography dataset (NHDPlus). The NHDPlus dataset represents the stream network with feature classes such as rivers, tributaries, canals, lakes, ponds, dams, coastlines, and stream gages. The NHDPlus dataset consists of approximately 2.7 million river reaches defining how surface water drains to the ocean. These river reaches have upstream and downstream nodes and basic parameters such as flow direction, drainage area, reach slope etc. We modified an existing algorithm (Gangodagamage et al., 2007, 2011) to provide lateral distance from the centerline of the river to the right and left floodplains for the flows simulated by models. Previous work produced floodplain boundaries for static river stages (i.e. 3D metric: distance along the main stem, flow depth, lateral distance from river center line). Our new approach introduces the floodplain boundary for variable water levels with the fourth dimension, time. We use modeled flows from WRFHydro and demarcate the right and left lateral boundaries of inundation dynamically. This approach dynamically integrates with high resolution models (e.g., hourly and ~ 1 km spatial resolution) that are developed from recent advancements in high computational power with ground based measurements (e.g., Fluxnet), lateral inundation vectors (direction and spatial extent) derived from multi-temporal remote sensing data (e.g., LiDAR, WorldView 2, Landsat, ASTER, MODIS), and improved representations of the physical processes through multi-parameterizations. Our approach enhances the normalized (streams are at zero elevations) DEM derived upstream flow routing pathways for stream reaches for given water stages as more and more satellite data become available for various flood inundations. Validation of the inundation boundaries is performed using HEC-RAS hydrodynamic model results for selected streams.

Application of The Rainfall-runoff Model Topkapi For The Entire Basin of The Po River As Part of The European Project Effs

NASA Astrophysics Data System (ADS)

Todini, E.; Bartholmes, J.

The project EFFS (European Flood Forecasting System) aims at developing a flood forecasting system for the major river basins all over Europe. To extend the forecast- ing and thus the warning time in a significant way (up to 10 days) meteorological forecasting data from the ECMWF will be used as input to hydrological models. For this purpose it is fundamental to have a reliable rainfall-runoff model. For the river Po basin we chose the TOPKAPI model (Ciarapica, Todini 1998). TOPKAPI is a physi- cally based rainfall-runoff model that maintains its physical significance passing from hillslope to large basin scale. The aim of the distributed version is to reproduce the spatial variability and to lead to a better understanding of scaling effects on meteo- rological data used as well as of physical phenomena and parameters. By now the TOPKAPI model has been applied successfully to basins of smaller and medium size (up to 8000 km2). The present work also proves that TOPKAPI is a valuable flood forecasting tool for larger basins such as the Po river. An advantage of the TOPKAPI model is its physical basis. It doesn't need a "real" calibration in the common sense of the expression. The calibration work that has to be done is due to the unavoidable averaging and approximation in the input data representing various phenomena. This reduces the calibration work as well as the length of data required. The model was implemented on the Po river at spatial steps of 1km and time steps of 1 hour using available data during the year 1994. After the calibration phase, mesoscale forecasts (from ECMWF) as well as forecasts of LAM models (DWD,DMI) will be used as input to the Po river models and their behaviour will be studied as a function of the prediction quality and of the coarseness of the spatial discretisation.

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.

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.

Real-time flood forecasting by employing artificial neural network based model with zoning matching approach

NASA Astrophysics Data System (ADS)

Sulaiman, M.; El-Shafie, A.; Karim, O.; Basri, H.

2011-10-01

Flood forecasting models are a necessity, as they help in planning for flood events, and thus help prevent loss of lives and minimize damage. At present, artificial neural networks (ANN) have been successfully applied in river flow and water level forecasting studies. ANN requires historical data to develop a forecasting model. However, long-term historical water level data, such as hourly data, poses two crucial problems in data training. First is that the high volume of data slows the computation process. Second is that data training reaches its optimal performance within a few cycles of data training, due to there being a high volume of normal water level data in the data training, while the forecasting performance for high water level events is still poor. In this study, the zoning matching approach (ZMA) is used in ANN to accurately monitor flood events in real time by focusing the development of the forecasting model on high water level zones. ZMA is a trial and error approach, where several training datasets using high water level data are tested to find the best training dataset for forecasting high water level events. The advantage of ZMA is that relevant knowledge of water level patterns in historical records is used. Importantly, the forecasting model developed based on ZMA successfully achieves high accuracy forecasting results at 1 to 3 h ahead and satisfactory performance results at 6 h. Seven performance measures are adopted in this study to describe the accuracy and reliability of the forecasting model developed.

Bayesian flood forecasting methods: A review

NASA Astrophysics Data System (ADS)

Han, Shasha; Coulibaly, Paulin

2017-08-01

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

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).

The Value of Humans in the Operational River Forecasting Enterprise

NASA Astrophysics Data System (ADS)

Pagano, T. C.

2012-04-01

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

Water resources planning for a river basin with recurrent wildfires.

PubMed

Santos, R M B; Sanches Fernandes, L F; Pereira, M G; Cortes, R M V; Pacheco, F A L

2015-09-01

Situated in the north of Portugal, the Beça River basin is subject to recurrent wildfires, which produce serious consequences on soil erosion and nutrient exports, namely by deteriorating the water quality in the basin. In the present study, the ECO Lab tool embedded in the Mike Hydro Basin software was used for the evaluation of river water quality, in particular the dissolved concentration of phosphorus in the period 1990-2013. The phosphorus concentrations are influenced by the burned area and the river flow discharge, but the hydrologic conditions prevail: in a wet year (2000, 16.3 km(2) of burned area) with an average flow of 16.4 m(3)·s(-1) the maximum phosphorus concentration was as low as 0.02 mg·L(-1), while in a dry year (2005, 24.4 km(2) of burned area) with an average flow of 2 m(3)·s(-1) the maximum concentration was as high as 0.57 mg·L(-1). Phosphorus concentrations in the water bodies exceeded the bounds of good ecological status in 2005 and between 2009 and 2012, water for human consumption in 2009 and water for multiple uses in 2010. The River Covas, a right margin tributary of Beça River, is the most appropriate stream as regards the use of water for human consumption, because it presents the biggest water potential with the best water quality. Since wildfires in the basin result essentially from natural causes and climate change forecasts indicate an increase in their frequency and intensity in the near future, forestry measures are proposed to include as a priority the conversion of stands of maritime pine in mixed stands of conifer and hardwood species. Copyright © 2015 Elsevier B.V. All rights reserved.

Application of the Newtonian nudging data assimilation method for the Biebrza River flow model

NASA Astrophysics Data System (ADS)

Miroslaw-Swiatek, Dorota

2010-05-01

Data assimilation provides a tool for integrating observations of spatially distributed environmental variables with model predictions. In this paper a simple data assimilation technique, the Newtonian nudging to individual observations method, has been implemented in the 1D St. Venant equations. The method involves adding a term to the prognostic equation. This term is proportional to the difference between the value calculated in the model at a given point in time and space and the one resulted from observations. Improving the model with available measurement observations is accomplished by adequate weighting functions, that can incorporate prior knowledge about the spatial and temporal variability of the state variables being assimilated. The article contains a description of the numerical model, which employs the finite element method (FEM) to solve the 1D St. Venant equations modified by the ‘nudging' method. The developed model was applied to the Biebrza River, situated in the north-eastern part of Poland, flowing through the last extensive, fairly undisturbed river-marginal peatland in Europe. A 41 km long reach of the Lower Biebrza River described by 68 cross-sections was selected for the study. The observed water stage collected by automatic sensors was the subject of the data assimilation in the Newtonian nudging to individual observations method. The water level observation data were collected in four observation points along a river with time interval 6 hours for one year. The obtained results show a prediction with no nudging and influence of the nudging term on water stages forecast. The developed model enables integrating water stage observation with an unsteady river flow model for improved water level prediction.

Predictability of Western Himalayan river flow: melt seasonal inflow into Bhakra Reservoir in northern India

NASA Astrophysics Data System (ADS)

Pal, I.; Lall, U.; Robertson, A. W.; Cane, M. A.; Bansal, R.

2013-06-01

Snowmelt-dominated streamflow of the Western Himalayan rivers is an important water resource during the dry pre-monsoon spring months to meet the irrigation and hydropower needs in northern India. Here we study the seasonal prediction of melt-dominated total inflow into the Bhakra Dam in northern India based on statistical relationships with meteorological variables during the preceding winter. Total inflow into the Bhakra Dam includes the Satluj River flow together with a flow diversion from its tributary, the Beas River. Both are tributaries of the Indus River that originate from the Western Himalayas, which is an under-studied region. Average measured winter snow volume at the upper-elevation stations and corresponding lower-elevation rainfall and temperature of the Satluj River basin were considered as empirical predictors. Akaike information criteria (AIC) and Bayesian information criteria (BIC) were used to select the best subset of inputs from all the possible combinations of predictors for a multiple linear regression framework. To test for potential issues arising due to multicollinearity of the predictor variables, cross-validated prediction skills of the best subset were also compared with the prediction skills of principal component regression (PCR) and partial least squares regression (PLSR) techniques, which yielded broadly similar results. As a whole, the forecasts of the melt season at the end of winter and as the melt season commences were shown to have potential skill for guiding the development of stochastic optimization models to manage the trade-off between irrigation and hydropower releases versus flood control during the annual fill cycle of the Bhakra Reservoir, a major energy and irrigation source in the region.

Advancing Atmospheric River Forecasts into Subseasonal-to-Seasonal Timescales

NASA Astrophysics Data System (ADS)

Barnes, E. A.; Baggett, C.; Mundhenk, B. D.; Nardi, K.; Maloney, E. D.

2017-12-01

Atmospheric rivers can cause considerable mayhem along the west coast of North America - delivering flooding rains during periods of heightened activity and desiccating droughts during periods of reduced activity. The intrinsic chaos of the atmosphere makes the prediction of atmospheric rivers at subseasonal-to-seasonal (S2S) timescales ( 2 to 6 weeks) an inherently difficult task. We demonstrate here that the potential exists to advance forecast lead times of atmospheric rivers into S2S timescales through knowledge of two of the atmosphere's most prominent oscillations; the Madden-Julian oscillation (MJO) and the Quasi-biennial oscillation (QBO). The dynamical relationship between atmospheric rivers, the MJO and the QBO is hypothesized to occur through modulation of North Pacific blocking. We present an empirical prediction scheme for anomalous atmospheric river activity based solely on the MJO and QBO and demonstrate skillful subseasonal "forecasts of opportunity" 5+ weeks ahead. We conclude with a discussion of the ability of state-of-the-art NWP models to predict atmospheric river characteristics on S2S timescales. With the wide-ranging impacts associated with landfalling atmospheric rivers, even modest gains in the subseasonal prediction of anomalous atmospheric river activity may support early action decision making and benefit numerous sectors of society.

Modeling the Evolution of Riparian Woodlands Facing Climate Change in Three European Rivers with Contrasting Flow Regimes

PubMed Central

Rivaes, Rui P.; Rodríguez-González, Patricia M.; Ferreira, Maria Teresa; Pinheiro, António N.; Politti, Emilio; Egger, Gregory; García-Arias, Alicia; Francés, Felix

2014-01-01

Global circulation models forecasts indicate a future temperature and rainfall pattern modification worldwide. Such phenomena will become particularly evident in Europe where climate modifications could be more severe than the average change at the global level. As such, river flow regimes are expected to change, with resultant impacts on aquatic and riparian ecosystems. Riparian woodlands are among the most endangered ecosystems on earth and provide vital services to interconnected ecosystems and human societies. However, they have not been the object of many studies designed to spatially and temporally quantify how these ecosystems will react to climate change-induced flow regimes. Our goal was to assess the effects of climate-changed flow regimes on the existing riparian vegetation of three different European flow regimes. Cases studies were selected in the light of the most common watershed alimentation modes occurring across European regions, with the objective of appraising expected alterations in the riparian elements of fluvial systems due to climate change. Riparian vegetation modeling was performed using the CASiMiR-vegetation model, which bases its computation on the fluvial disturbance of the riparian patch mosaic. Modeling results show that riparian woodlands may undergo not only at least moderate changes for all flow regimes, but also some dramatic adjustments in specific areas of particular vegetation development stages. There are circumstances in which complete annihilation is feasible. Pluvial flow regimes, like the ones in southern European rivers, are those likely to experience more pronounced changes. Furthermore, regardless of the flow regime, younger and more water-dependent individuals are expected to be the most affected by climate change. PMID:25330151

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

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.

The Devdorak ice-rock avalanche and consequent debris flow from the slope of Mt. Kazbek (Caucasus, Georgia) in 2014

NASA Astrophysics Data System (ADS)

Chernomorets, Sergey; Savernyuk, Elena; Petrakov, Dmitry; Dokukin, Mikhail; Gotsiridze, George; Gavardashvili, Givi; Drobyshev, Valery; Tutubalina, Olga; Zaporozhchenko, Eduard; Kamenev, Nikolay; Kamenev, Vladimir; Kääb, Andreas; Kargel, Jeffrey; Huggel, Christian

2016-04-01

We have studied catastrophic glacial events of 2014 in the Kazbek-Dzhimaray massif, Caucasus Mts., Georgia. The first event is a so called "Kazbek blockage" of the Georgian Military Road, on 17 May 2014, which formed as a result of an ice-rock avalanche onto the Devdorak Glacier, and is similar to blockages which occurred in the same location in the 18th-19th century. The second event is a consequent debris flow on 20 August 2014. In May, June 2014 and September 2015 we conducted three field investigations of the disaster zone, which includes Devdorak Glacier, Amilishka and Kabakhi river valleys, the Terek River valley near the Kabakhi River mouth, and a temporary lake.We analyzed field research data, interpreted SPOT 6, Landsat-8 OLI, Terra ASTER, and Pleiades satellite imagery, as well as post-disaster helicopter imagery. To assess dynamic features of the ice-rock flow on 17 May 2014, we measured valley crossections with Bushnell laser ranger. In 2015 we have marked a 180-m baseline for ground stereosurvey and made a stereopair of the Devdorak glacier terminus from a distance of 700 m. The 17 May 2014 ice-rock avalanche initiated at 4500 m. a.s.l. It collapsed onto the tongue of the Devdorak Glacier which reaches down to 2300 m a.s.l. Downstream of the tongue, the avalanche transformed into an ice-rock "avalanche flow" which blocked the Terek River valley. The traffic on Military Georgian Road (part of E117 highway) which connects Russia with Georgia was stopped. 7 people were killed in their vehicles. The total length of the ice-rock avalanche and the subsequent flow was over 10 km. A temporary lake formed in the Terek river valley, reaching 300 m in length, and over 10 m in depth. For several hours, the lake was threatening another debris flow downstream the Terek river valley. According to field estimates at the Devdorak glacier tongue and in Amilishka, Kabakhi and Terek river valleys, the volume of the transported ice-rock avalanche mass, which deposited in the middle and lower course of the valley below 3000 m a.s.l. was about 2 million cubic metres, while the ice content in the deposits reached 25-30%. It is planned to assess the volume of the trigger mass in the initiation zone later. The flow went along the valley with characteristic superelevations and run-ups, as it moved from one valley side to the other. We identified six superelevaions in fresh deposits, with differences of up to 45 m in flow height on the left and right valley banks. Instrumental measurements of superelevations and subsequent calculations yield the flow velocities of over 200 km/hour. These results lead to a reassessment of similar events which occurred in this valley in 18-19th centuries. Previously the trigger of these events was supposed to be the ice accumulation during surges of Devdorak glacier with subsequent temporary damming of the Amilishka River valley. The analysis of the 2014 event demonstrates that a similar trigger was possible in the past: an ice-rock avalanche onto Devdorak glacier tongue from significantly higher locations. Following the field data analysis, we issued a warning through mass media on 12 August 2014, forecasting a high risk of a new glacial disaster in this site and a new blockage of the Terek River valley and of Military Georgian Road. This forecast came true on 20 August 2014: a glacial debris flow reached the Terek River valley, and partially buried the Dariali hydropower station (under construction), the customs and border control buildings. Three people have been killed. We studied the deposits of this debris flow and morphology of the gully. The deposits entrained by the flow were previously deposited by the ice-rock avalanche of 17 May 2014. The debris flow started after shower rains. The debris flow-gully has a box-like crossection. At the confluence of Amilishka and Chach rivers it reached 30-32 m in width, and eroded the deposits of 17 May 2014 by 7 m. The channel slope at this location was about 7 degrees. Remnant ice in the transit zone has nearly melted by September 2015; however, the ice remains in the deposits near the glacier tongue and in the ice-rock avalanche deposits on the tongue. We have registered the advance of one of the termini of Devdorak Glacier. It moved forward by about 200 m from summer 2014 to September 2015, and became significantly higher. This part of the glacier was overloaded by the ice-rock avalanche deposits which provoked its advance, and should be closely monitored as it can raise the debris flow activity further. The hazard of new ice-rock avalanches and debris flows in the Devdorak gorge remains high. We have developed recommendation on the installation of an early warning system, continuation of glacier hazard monitoring, and suggestions on the construction of a road tunnel to mitigate the risk and avoid casualties in the future.

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.

An index-based framework for assessing patterns and trends in river fragmentation and flow regulation by global dams at multiple scales

NASA Astrophysics Data System (ADS)

Grill, Günther; Lehner, Bernhard; Lumsdon, Alexander E.; MacDonald, Graham K.; Zarfl, Christiane; Reidy Liermann, Catherine

2015-01-01

The global number of dam constructions has increased dramatically over the past six decades and is forecast to continue to rise, particularly in less industrialized regions. Identifying development pathways that can deliver the benefits of new infrastructure while also maintaining healthy and productive river systems is a great challenge that requires understanding the multifaceted impacts of dams at a range of scales. New approaches and advanced methodologies are needed to improve predictions of how future dam construction will affect biodiversity, ecosystem functioning, and fluvial geomorphology worldwide, helping to frame a global strategy to achieve sustainable dam development. Here, we respond to this need by applying a graph-based river routing model to simultaneously assess flow regulation and fragmentation by dams at multiple scales using data at high spatial resolution. We calculated the cumulative impact of a set of 6374 large existing dams and 3377 planned or proposed dams on river connectivity and river flow at basin and subbasin scales by fusing two novel indicators to create a holistic dam impact matrix for the period 1930-2030. Static network descriptors such as basin area or channel length are of limited use in hierarchically nested and dynamic river systems, so we developed the river fragmentation index and the river regulation index, which are based on river volume. These indicators are less sensitive to the effects of network configuration, offering increased comparability among studies with disparate hydrographies as well as across scales. Our results indicate that, on a global basis, 48% of river volume is moderately to severely impacted by either flow regulation, fragmentation, or both. Assuming completion of all dams planned and under construction in our future scenario, this number would nearly double to 93%, largely due to major dam construction in the Amazon Basin. We provide evidence for the importance of considering small to medium sized dams and for the need to include waterfalls to establish a baseline of natural fragmentation. Our versatile framework can serve as a component of river fragmentation and connectivity assessments; as a standardized, easily replicable monitoring framework at global and basin scales; and as part of regional dam planning and management strategies.

Comparison of Machine Learning methods for incipient motion in gravel bed rivers

NASA Astrophysics Data System (ADS)

Valyrakis, Manousos

2013-04-01

Soil erosion and sediment transport of natural gravel bed streams are important processes which affect both the morphology as well as the ecology of earth's surface. For gravel bed rivers at near incipient flow conditions, particle entrainment dynamics are highly intermittent. This contribution reviews the use of modern Machine Learning (ML) methods implemented for short term prediction of entrainment instances of individual grains exposed in fully developed near boundary turbulent flows. Results obtained by network architectures of variable complexity based on two different ML methods namely the Artificial Neural Network (ANN) and the Adaptive Neuro-Fuzzy Inference System (ANFIS) are compared in terms of different error and performance indices, computational efficiency and complexity as well as predictive accuracy and forecast ability. Different model architectures are trained and tested with experimental time series obtained from mobile particle flume experiments. The experimental setup consists of a Laser Doppler Velocimeter (LDV) and a laser optics system, which acquire data for the instantaneous flow and particle response respectively, synchronously. The first is used to record the flow velocity components directly upstream of the test particle, while the later tracks the particle's displacements. The lengthy experimental data sets (millions of data points) are split into the training and validation subsets used to perform the corresponding learning and testing of the models. It is demonstrated that the ANFIS hybrid model, which is based on neural learning and fuzzy inference principles, better predicts the critical flow conditions above which sediment transport is initiated. In addition, it is illustrated that empirical knowledge can be extracted, validating the theoretical assumption that particle ejections occur due to energetic turbulent flow events. Such a tool may find application in management and regulation of stream flows downstream of dams for stream restoration, implementation of sustainable practices in river and estuarine ecosystems and design of stable river bed and banks.

Applications of Experimental Suomi-NPP VIIRS Flood Inundation Maps in Operational Flood Forecasting

NASA Astrophysics Data System (ADS)

Deweese, M. M.

2017-12-01

Flooding is the most costly natural disaster across the globe. In 2016 flooding caused more fatalities than any other natural disaster in the United States. The U.S. National Weather Service (NWS) is mandated to forecast rivers for the protection of life and property and the enhancement of the national economy. Since 2014, the NWS North Central River Forecast Center has utilized experimental near real time flood mapping products from the JPSS Suomi-NPP VIIRS satellite. These products have been demonstrated to provide reliable and high value information for forecasters in ice jam and snowmelt flooding in data sparse regions of the northern plains. In addition, they have proved valuable in rainfall induced flooding within the upper Mississippi River basin. Aerial photography and ground observations have validated the accuracy of the products. Examples are provided from numerous flooding events to demonstrate the operational application of this satellite derived information as a remotely sensed observational data source and it's utility in real time flood forecasting.

Drought Water Right Curtailment

NASA Astrophysics Data System (ADS)

Walker, W.; Tweet, A.; Magnuson-Skeels, B.; Whittington, C.; Arnold, B.; Lund, J. R.

2016-12-01

California's water rights system allocates water based on priority, where lower priority, "junior" rights are curtailed first in a drought. The Drought Water Rights Allocation Tool (DWRAT) was developed to integrate water right allocation models with legal objectives to suggest water rights curtailments during drought. DWRAT incorporates water right use and priorities with a flow-forecasting model to mathematically represent water law and hydrology and suggest water allocations among water rights holders. DWRAT is compiled within an Excel workbook, with an interface and an open-source solver. By implementing California water rights law as an algorithm, DWRAT provides a precise and transparent framework for the complicated and often controversial technical aspects of curtailing water rights use during drought. DWRAT models have been developed for use in the Eel, Russian, and Sacramento river basins. In this study, an initial DWRAT model has been developed for the San Joaquin watershed, which incorporates all water rights holders in the basin and reference gage flows for major tributaries. The San Joaquin DWRAT can assess water allocation reliability by determining probability of rights holders' curtailment for a range of hydrologic conditions. Forecasted flow values can be input to the model to provide decision makers with the ability to make curtailment and water supply strategy decisions. Environmental flow allocations will be further integrated into the model to protect and improve ecosystem water reliability.

Parabolic northern-hemisphere river flow teleconnections to El Niño-Southern Oscillation and the Arctic Oscillation

NASA Astrophysics Data System (ADS)

Fleming, S. W.; Dahlke, H. E.

2014-10-01

It is almost universally assumed in statistical hydroclimatology that relationships between large-scale climate indices and local-scale hydrometeorological responses, though possibly nonlinear, are monotonic. However, recent work suggests that northern-hemisphere atmospheric teleconnections to El Niño-Southern Oscillation (ENSO) and the Arctic Oscillation can be parabolic. The effect has recently been explicitly confirmed in hydrologic responses, though associations are complicated by land surface characteristics and processes, and investigation of water resource implications has been limited to date. Here, we apply an Akaike Information Criterion-based polynomial selection approach to investigate annual flow volume teleconnections for 42 of the northern hemisphere’s largest ocean-reaching rivers. Though we find a rich diversity of responses, parabolic relationships are formally consistent with the data for almost half the rivers, and the optimal model for eight. These highly nonlinear water supply teleconnections could radically alter the standard conceptual model of how water resources respond to climatic variability. For example, the Sacramento river in drought-ridden California exhibits no significant monotonic ENSO teleconnection but a 0.92 probability of a quadratic relationship, reducing mean predictive error by up to 65% and suggesting greater opportunity for climate index-based water supply forecasts than previously appreciated.

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.

Long Range River Discharge Forecasting Using the Gravity Recovery and Climate Experiment (GRACE) Satellite to Predict Conditions for Endemic Cholera

NASA Astrophysics Data System (ADS)

Jutla, A.; Akanda, A. S.; Colwell, R. R.

2014-12-01

Prediction of conditions of an impending disease outbreak remains a challenge but is achievable if the associated and appropriate large scale hydroclimatic process can be estimated in advance. Outbreaks of diarrheal diseases such as cholera, are related to episodic seasonal variability in river discharge in the regions where water and sanitation infrastructure are inadequate and insufficient. However, forecasting river discharge, few months in advance, remains elusive where cholera outbreaks are frequent, probably due to non-availability of geophysical data as well as transboundary water stresses. Here, we show that satellite derived water storage from Gravity Recovery and Climate Experiment Forecasting (GRACE) sensors can provide reliable estimates on river discharge atleast two months in advance over regional scales. Bayesian regression models predicted flooding and drought conditions, a prerequisite for cholera outbreaks, in Bengal Delta with an overall accuracy of 70% for upto 60 days in advance without using any other ancillary ground based data. Forecasting of river discharge will have significant impacts on planning and designing intervention strategies for potential cholera outbreaks in the coastal regions where the disease remain endemic and often fatal.

On operational flood forecasting system involving 1D/2D coupled hydraulic model and data assimilation

NASA Astrophysics Data System (ADS)

Barthélémy, S.; Ricci, S.; Morel, T.; Goutal, N.; Le Pape, E.; Zaoui, F.

2018-07-01

In the context of hydrodynamic modeling, the use of 2D models is adapted in areas where the flow is not mono-dimensional (confluence zones, flood plains). Nonetheless the lack of field data and the computational cost constraints limit the extensive use of 2D models for operational flood forecasting. Multi-dimensional coupling offers a solution with 1D models where the flow is mono-dimensional and with local 2D models where needed. This solution allows for the representation of complex processes in 2D models, while the simulated hydraulic state is significantly better than that of the full 1D model. In this study, coupling is implemented between three 1D sub-models and a local 2D model for a confluence on the Adour river (France). A Schwarz algorithm is implemented to guarantee the continuity of the variables at the 1D/2D interfaces while in situ observations are assimilated in the 1D sub-models to improve results and forecasts in operational mode as carried out by the French flood forecasting services. An implementation of the coupling and data assimilation (DA) solution with domain decomposition and task/data parallelism is proposed so that it is compatible with operational constraints. The coupling with the 2D model improves the simulated hydraulic state compared to a global 1D model, and DA improves results in 1D and 2D areas.

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

DOE PAGES

Yuan, Xing

2016-06-22

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

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

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

Yuan, Xing

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

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.

Detailed predictions of climate induced changes in the thermal and flow regimes in mountain streams of the Iberian Peninsula

NASA Astrophysics Data System (ADS)

Santiago, José M.; Muñoz-Mas, Rafael; García de Jalón, Diego; Solana, Joaquín; Alonso, Carlos; Martínez-Capel, Francisco; Ribalaygua, Jaime; Pórtoles, Javier; Monjo, Robert

2016-04-01

Streamflow and temperature regimes are well-known to influence on the availability of suitable physical habitat for instream biological communities. General Circulation Models (GCMs) have predicted significant changes in timing and geographic distribution of precipitation and atmospheric temperature for the ongoing century. However, differences in these predictions may arise when focusing on different spatial and temporal scales. Therefore, to perform substantiated mitigation and management actions detailed scales are necessary to adequately forecast the consequent thermal and flow regimes. Regional predictions are relatively abundant but detailed ones, both spatially and temporally, are still scarce. The present study aimed at predicting the effects of climate change on the thermal and flow regime in the Iberian Peninsula, refining the resolution of previous studies. For this purpose, the study encompassed 28 sites at eight different mountain rivers and streams in the central part of the Iberian Peninsula (Spain). The daily flow was modelled using different daily, monthly and quarterly lags of the historical precipitation and temperature time series. These precipitation-runoff models were developed by means of M5 model trees. On the other hand water temperature was modelled at similar time scale by means of nonlinear regression from dedicated site-specific data. The developed models were used to simulate the temperature and flow regime under two Representative Concentration Pathway (RCPs) climate change scenarios (RCP 4.5 and RCP 8.5) until the end of the present century by considering nine different GCMs, which were pertinently downscaled. The precipitation-runoff models achieved high accuracy (NSE>0.7), especially in regards of the low flows of the historical series. Results concomitantly forecasted flow reductions between 7 and 17 % (RCP4.5) and between 8 and 49% (RCP8.5) of the annual average in the most cases, being variable the magnitude and timing at each site. The largest predicted changes will occur in summer and the complete depletion of some river segments was forecasted. Winter was the only season predicted flows to remain mostly unaffected by climate change. Mean annual stream temperature was predicted to experience heavy increases, especially during the second half of the century, varying from 0.3 to 1.6°C (RCP4.5), and 0.8 to 4.0°C (RCP8.5). Annual maximum and minimum average temperature increases were predicted to be between 0.1 and 1.5°C (RCP4.5) and between 0.2 and 3.0°C (RCP8.5), and between 0.4 and 1.8°C (RCP4.5) and between 1.1 and 4.5°C (RCP8.5), respectively. The most important increases were predicted to occur in summer while winter will experience the lesser ones. Geology attributable differences on thermal regime were observed between rivers. These results suggested the exacerbation of the principal characteristics of the Mediterranean climate-induced flow regimes with increased summer water temperatures and reduced low flows. Consequently, the synergistic effects of these climate induced changes may significantly impacts instream communities. Predictions of this study will be useful for designing habitat managing strategies for climate change adaptation at the local level. The revealed particularities reinforce the convenience of refining local predictions to design effective management policies.

Forecasting of Average Monthly River Flows in Colombia

NASA Astrophysics Data System (ADS)

Mesa, O. J.; Poveda, G.

2006-05-01

The last two decades have witnessed a marked increase in our knowledge of the causes of interannual hydroclimatic variability and our ability to make predictions. Colombia, located near the seat of the ENSO phenomenon, has been shown to experience negative (positive) anomalies in precipitation in concert with El Niño (La Niña). In general besides the Pacific Ocean, Colombia has climatic influences from the Atlantic Ocean and the Caribbean Sea through the tropical forest of the Amazon basin and the savannas of the Orinoco River, in top of the orographic and hydro-climatic effects introduced by the Andes. As in various other countries of the region, hydro-electric power contributes a large proportion (75 %) of the total electricity generation in Colombia. Also, most agriculture is rain-fed dependant, and domestic water supply relies mainly on surface waters from creeks and rivers. Besides, various vector borne tropical diseases intensify in response to rain and temperature changes. Therefore, there is a direct connection between climatic fluctuations and national and regional economies. This talk specifically presents different forecasts of average monthly stream flows for the inflow into the largest reservoir used for hydropower generation in Colombia, and illustrates the potential economic savings of such forecasts. Because of planning of the reservoir operation, the most appropriated time scale for this application is the annual to interannual. Fortunately, this corresponds to the scale at which hydroclimate variability understanding has improved significantly. Among the different possibilities we have explored: traditional statistical ARIMA models, multiple linear regression, natural and constructed analogue models, the linear inverse model, neural network models, the non-parametric regression splines (MARS) model, regime dependant Markovian models and one we termed PREBEO, which is based on spectral bands decomposition using wavelets. Most of the methods make use of the climatic observations and the general prediction models of ENSO which are routinely reported in various sources (http://www.cpc.ncep.noaa.gov/). We will compare the forecasting skills of the models, depending on lead time and initial month of forecasting. Besides ENSO indices, tropical Atlantic sea surface temperatures and the North Atlantic Oscillation index are relevant for these predictions in Colombia. Clear-cut benefits of these predictions are evident for the operation of the system. Ever since the 1991-1992 ENSO event the government, power companies and big consumers realized on its importance and routinely incorporated it into their operational planning. On the contrary, this new knowledge has not been useful for the expansion of the system to accommodate the increasing demand. Some kind of resonance between the scale of fluctuation of climate and the memory of decision makers produces a hydro-illogical cycle of urgency during El Niño dry times and of unawareness during La Niña abundance.

Novel approach for streamflow forecasting using a hybrid ANFIS-FFA model

NASA Astrophysics Data System (ADS)

Yaseen, Zaher Mundher; Ebtehaj, Isa; Bonakdari, Hossein; Deo, Ravinesh C.; Danandeh Mehr, Ali; Mohtar, Wan Hanna Melini Wan; Diop, Lamine; El-shafie, Ahmed; Singh, Vijay P.

2017-11-01

The present study proposes a new hybrid evolutionary Adaptive Neuro-Fuzzy Inference Systems (ANFIS) approach for monthly streamflow forecasting. The proposed method is a novel combination of the ANFIS model with the firefly algorithm as an optimizer tool to construct a hybrid ANFIS-FFA model. The results of the ANFIS-FFA model is compared with the classical ANFIS model, which utilizes the fuzzy c-means (FCM) clustering method in the Fuzzy Inference Systems (FIS) generation. The historical monthly streamflow data for Pahang River, which is a major river system in Malaysia that characterized by highly stochastic hydrological patterns, is used in the study. Sixteen different input combinations with one to five time-lagged input variables are incorporated into the ANFIS-FFA and ANFIS models to consider the antecedent seasonal variations in historical streamflow data. The mean absolute error (MAE), root mean square error (RMSE) and correlation coefficient (r) are used to evaluate the forecasting performance of ANFIS-FFA model. In conjunction with these metrics, the refined Willmott's Index (Drefined), Nash-Sutcliffe coefficient (ENS) and Legates and McCabes Index (ELM) are also utilized as the normalized goodness-of-fit metrics. Comparison of the results reveals that the FFA is able to improve the forecasting accuracy of the hybrid ANFIS-FFA model (r = 1; RMSE = 0.984; MAE = 0.364; ENS = 1; ELM = 0.988; Drefined = 0.994) applied for the monthly streamflow forecasting in comparison with the traditional ANFIS model (r = 0.998; RMSE = 3.276; MAE = 1.553; ENS = 0.995; ELM = 0.950; Drefined = 0.975). The results also show that the ANFIS-FFA is not only superior to the ANFIS model but also exhibits a parsimonious modelling framework for streamflow forecasting by incorporating a smaller number of input variables required to yield the comparatively better performance. It is construed that the FFA optimizer can thus surpass the accuracy of the traditional ANFIS model in general, and is able to remove the false (inaccurately) forecasted data in the ANFIS model for extremely low flows. The present results have wider implications not only for streamflow forecasting purposes, but also for other hydro-meteorological forecasting variables requiring only the historical data input data, and attaining a greater level of predictive accuracy with the incorporation of the FFA algorithm as an optimization tool in an ANFIS model.

A retrospective streamflow ensemble forecast for an extreme hydrologic event: a case study of Hurricane Irene and on the Hudson River basin

NASA Astrophysics Data System (ADS)

Saleh, Firas; Ramaswamy, Venkatsundar; Georgas, Nickitas; Blumberg, Alan F.; Pullen, Julie

2016-07-01

This paper investigates the uncertainties in hourly streamflow ensemble forecasts for an extreme hydrological event using a hydrological model forced with short-range ensemble weather prediction models. A state-of-the art, automated, short-term hydrologic prediction framework was implemented using GIS and a regional scale hydrological model (HEC-HMS). The hydrologic framework was applied to the Hudson River basin ( ˜ 36 000 km2) in the United States using gridded precipitation data from the National Centers for Environmental Prediction (NCEP) North American Regional Reanalysis (NARR) and was validated against streamflow observations from the United States Geologic Survey (USGS). Finally, 21 precipitation ensemble members of the latest Global Ensemble Forecast System (GEFS/R) were forced into HEC-HMS to generate a retrospective streamflow ensemble forecast for an extreme hydrological event, Hurricane Irene. The work shows that ensemble stream discharge forecasts provide improved predictions and useful information about associated uncertainties, thus improving the assessment of risks when compared with deterministic forecasts. The uncertainties in weather inputs may result in false warnings and missed river flooding events, reducing the potential to effectively mitigate flood damage. The findings demonstrate how errors in the ensemble median streamflow forecast and time of peak, as well as the ensemble spread (uncertainty) are reduced 48 h pre-event by utilizing the ensemble framework. The methodology and implications of this work benefit efforts of short-term streamflow forecasts at regional scales, notably regarding the peak timing of an extreme hydrologic event when combined with a flood threshold exceedance diagram. Although the modeling framework was implemented on the Hudson River basin, it is flexible and applicable in other parts of the world where atmospheric reanalysis products and streamflow data are available.

Improvement of Operational Streamflow Prediction with MODIS-derived Fractional Snow Covered Area Observations

NASA Astrophysics Data System (ADS)

Bender, S.; Burgess, A.; Goodale, C. E.; Mattmann, C. A.; Miller, W. P.; Painter, T. H.; Rittger, K. E.; Stokes, M.; Werner, K.

2013-12-01

Water managers in the western United States depend heavily on the timing and magnitude of snowmelt-driven runoff for municipal supply, irrigation, maintenance of environmental flows, and power generation. The Colorado Basin River Forecast Center (CBRFC) of the National Weather Service issues operational forecasts of snowmelt-driven streamflow for watersheds within the Colorado River Basin (CRB) and eastern Great Basin (EGB), across a wide variety of scales. Therefore, the CBRFC and its stakeholders consider snowpack observations to be highly valuable. Observations of fractional snow covered area (fSCA) from satellite-borne instrumentation can better inform both forecasters and water users with respect to subsequent snowmelt runoff, particularly when combined with observations from ground-based station networks and/or airborne platforms. As part of a multi-year collaborative effort, CBRFC has partnered with the Jet Propulsion Laboratory (JPL) under funding from NASA to incorporate observations of fSCA from NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) into the operational CBRFC hydrologic forecasting and modeling process. In the first year of the collaboration, CBRFC and NASA/JPL integrated snow products into the forecasting and decision making processes of the CBRFC and showed preliminary improvement in operational streamflow forecasts. In late 2012, CBRFC and NASA/JPL began retrospective analysis of relationships between the MODIS Snow Covered Area and Grain size (MODSCAG) fSCA and streamflow patterns for several watersheds within the CRB and the EGB. During the 2013 snowmelt runoff season, CBRFC forecasters used MODIS-derived fSCA semi-quantitatively as a binary indicator of the presence or lack of snow. Indication of the presence or lack of snow by MODIS assisted CBRFC forecasters in determining the cause of divergence between modeled and recently observed streamflow. Several examples of improved forecasts from across the CRB and EGB, informed by MODIS-derived fSCA, are described. Our analysis shows the value of MODIS fSCA to CBRFC and to users of CBRFC's streamflow forecasts. The relationships between the MODIS fSCA and the melt season streamflow vary with the magnitude of runoff, which is important to resource managers. The analysis also emphasizes the importance of the invaluable collaboration between an operational forecasting agency (CBRFC) and a research-oriented agency (NASA/JPL) specializing in remote sensing science. The collaboration is expected to continue over the next several years as CBRFC and JPL work to further improve modeling of snowmelt and prediction of snowmelt-driven streamflow in the CRB and EGB.

A Decision Support System For The Real-Time Allocation Of The Water Resource Of The Tarim River Basin, China

NASA Astrophysics Data System (ADS)

Wei, J.; Wang, G.; Liu, R.

2008-12-01

The Tarim River Basin is the longest inland river in China. Due to water scarcity, ecologically-fragile is becoming a significant constraint to sustainable development in this region. To effectively manage the limited water resources for ecological purposes and for conventional water utilization purposes, a real-time water resources allocation Decision Support System (DSS) has been developed. Based on workflows of the water resources regulations and comprehensive analysis of the efficiency and feasibility of water management strategies, the DSS includes information systems that perform data acquisition, management and visualization, and model systems that perform hydrological forecast, water demand prediction, flow routing simulation and water resources optimization of the hydrological and water utilization process. An optimization and process control strategy is employed to dynamically allocate the water resources among the different stakeholders. The competitive targets and constraints are taken into considered by multi-objective optimization and with different priorities. The DSS of the Tarim River Basin has been developed and been successfully utilized to support the water resources management of the Tarim River Basin since 2005.

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.

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

NASA Astrophysics Data System (ADS)

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

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

Oregon Washington Coastal Ocean Forecast System: Real-time Modeling and Data Assimilation

NASA Astrophysics Data System (ADS)

Erofeeva, S.; Kurapov, A. L.; Pasmans, I.

2016-02-01

Three-day forecasts of ocean currents, temperature and salinity along the Oregon and Washington coasts are produced daily by a numerical ROMS-based ocean circulation model. NAM is used to derive atmospheric forcing for the model. Fresh water discharge from Columbia River, Fraser River, and small rivers in Puget Sound are included. The forecast is constrained by open boundary conditions derived from the global Navy HYCOM model and once in 3 days assimilation of recent data, including HF radar surface currents, sea surface temperature from the GOES satellite, and SSH from several satellite altimetry missions. 4-dimensional variational data assimilation is implemented in 3-day time windows using the tangent linear and adjoint codes developed at OSU. The system is semi-autonomous - all the data, including NAM and HYCOM fields are automatically updated, and daily operational forecast is automatically initiated. The pre-assimilation data quality control and post-assimilation forecast quality control require the operator's involvement. The daily forecast and 60 days of hindcast fields are available for public on opendap. As part of the system model validation plots to various satellites and SEAGLIDER are also automatically updated and available on the web (http://ingria.coas.oregonstate.edu/rtdavow/). Lessons learned in this pilot real-time coastal ocean forecasting project help develop and test metrics for forecast skill assessment for the West Coast Operational Forecast System (WCOFS), currently at testing and development phase at the National Oceanic and Atmospheric Administration (NOAA).

Improving Flood Forecasting in International River Basins

NASA Astrophysics Data System (ADS)

Hossain, Faisal; Katiyar, Nitin

2006-01-01

In flood-prone international river basins (IRBs), many riparian nations that are located close to a basin's outlet face a major problem in effectively forecasting flooding because they are unable to assimilate in situ rainfall data in real time across geopolitical boundaries. NASA's proposed Global Precipitation Measurement (GPM) mission, which is expected to begin in 2010, will comprise high-resolution passive microwave (PM) sensors (at resolution ~3-6 hours, 10 × 10 square kilometers) that may provide new opportunities to improve flood forecasting in these river basins. Research is now needed to realize the potential of GPM. With adequate research in the coming years, it may be possible to identify the specific IRBs that would benefit cost-effectively from a preprogrammed satellite-based forecasting system in anticipation of GPM. Acceleration of such a research initiative is worthwhile because it could reduce the risk of the cancellation of GPM [see Zielinski, 2005].

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.

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.

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

NASA Astrophysics Data System (ADS)

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

2017-12-01

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

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.

The flood event of 10-12 November 2013 on the Tiber River basin (central Italy): real-time flood forecasting with uncertainty supporting risk management and decision-making

NASA Astrophysics Data System (ADS)

Berni, Nicola; Brocca, Luca; Barbetta, Silvia; Pandolfo, Claudia; Stelluti, Marco; Moramarco, Tommaso

2014-05-01

The Italian national hydro-meteorological early warning system is composed by 21 regional offices (Functional Centres, CF). Umbria Region (central Italy) CF provides early warning for floods and landslides, real-time monitoring and decision support systems (DSS) for the Civil Defence Authorities when significant events occur. The alert system is based on hydrometric and rainfall thresholds with detailed procedures for the management of critical events in which different roles of authorities and institutions involved are defined. The real-time flood forecasting system is based also on different hydrological and hydraulic forecasting models. Among these, the MISDc rainfall-runoff model ("Modello Idrologico SemiDistribuito in continuo"; Brocca et al., 2011) and the flood routing model named STAFOM-RCM (STAge Forecasting Model-Rating Curve Model; Barbetta et al., 2014) are continuously operative in real-time providing discharge and stage forecasts, respectively, with lead-times up to 24 hours (when quantitative precipitation forecasts are used) in several gauged river sections in the Upper-Middle Tiber River basin. Models results are published in real-time in the open source CF web platform: www.cfumbria.it. MISDc provides discharge and soil moisture forecasts for different sub-basins while STAFOM-RCM provides stage forecasts at hydrometric sections. Moreover, through STAFOM-RCM the uncertainty of the forecast stage hydrograph is provided in terms of 95% Confidence Interval (CI) assessed by analyzing the statistical properties of model output in terms of lateral. In the period 10th-12th November 2013, a severe flood event occurred in Umbria mainly affecting the north-eastern area and causing significant economic damages, but fortunately no casualties. The territory was interested by intense and persistent rainfall; the hydro-meteorological monitoring network recorded locally rainfall depth over 400 mm in 72 hours. In the most affected area, the recorded rainfall depths correspond approximately to a return period of 200 years. Most rivers in Umbria have been involved, exceeding hydrometric thresholds and causing flooding (e.g. Chiascio river). The flood event was continuously monitored at the Umbria Region CF and the possible evolution predicted and assessed on the basis of the model forecasts. The predictions provided by MISDc and STAFOM-RCM were found useful to support real-time decision-making addressed to flood risk management. Moreover, the quantification of the uncertainty affecting the deterministic forecast stages was found consistent with the level of confidence selected and had practical utility corroborating the need of coupling deterministic forecast and 'uncertainty' when the model output is used to support decisions about flood management. REFERENCES Barbetta, S., Moramarco, T., Brocca, L., Franchini, M., Melone, F. (2014). Confidence interval of real-time forecast stages provided by the STAFOM-RCM model: the case study of the Tiber River (Italy). Hydrological Processes, 28(3), 729-743. Brocca, L., Melone, F., Moramarco, T. (2011). Distributed rainfall-runoff modelling for flood frequency estimation and flood forecasting. Hydrological Processes, 25 (18), 2801-2813

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.

Projected climate-induced habitat loss for salmonids in the John Day River network, Oregon, U.S.A.

USGS Publications Warehouse

Ruesch, Aaron S.; Torgersen, Christian E.; Lawler, Joshua J.; Olden, Julian D.; Peterson, Erin E.; Volk, Carol J.; Lawrence, David J.

2012-01-01

Climate change will likely have profound effects on cold-water species of freshwater fishes. As temperatures rise, cold-water fish distributions may shift and contract in response. Predicting the effects of projected stream warming in stream networks is complicated by the generally poor correlation between water temperature and air temperature. Spatial dependencies in stream networks are complex because the geography of stream processes is governed by dimensions of flow direction and network structure. Therefore, forecasting climate-driven range shifts of stream biota has lagged behind similar terrestrial modeling efforts. We predicted climate-induced changes in summer thermal habitat for 3 cold-water fish species—juvenile Chinook salmon, rainbow trout, and bull trout (Oncorhynchus tshawytscha, O. mykiss, and Salvelinus confluentus, respectively)—in the John Day River basin, northwestern United States. We used a spatially explicit statistical model designed to predict water temperature in stream networks on the basis of flow and spatial connectivity. The spatial distribution of stream temperature extremes during summers from 1993 through 2009 was largely governed by solar radiation and interannual extremes of air temperature. For a moderate climate change scenario, estimated declines by 2100 in the volume of habitat for Chinook salmon, rainbow trout, and bull trout were 69–95%, 51–87%, and 86–100%, respectively. Although some restoration strategies may be able to offset these projected effects, such forecasts point to how and where restoration and management efforts might focus.

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

NASA Astrophysics Data System (ADS)

Kuchment, L.

2012-04-01

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

Ensemble Bayesian forecasting system Part I: Theory and algorithms

NASA Astrophysics Data System (ADS)

Herr, Henry D.; Krzysztofowicz, Roman

2015-05-01

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

Hydrograph simulation models of the Hillsborough and Alafia Rivers, Florida: a preliminary report

USGS Publications Warehouse

Turner, James F.

1972-01-01

Mathematical (digital) models that simulate flood hydrographs from rainfall records have been developed for the following gaging stations in the Hillsborough and Alafia River basins of west-central Florida: Hillsborough River near Tampa, Alafia River at Lithia, and north Prong Alafia River near Keysville. These models, which were developed from historical streamflow and and rainfall records, are based on rainfall-runoff and unit-hydrograph procedures involving an arbitrary separation of the flood hydrograph. These models assume the flood hydrograph to be composed of only two flow components, direct (storm) runoff, and base flow. Expressions describing these two flow components are derived from streamflow and rainfall records and are combined analytically to form algorithms (models), which are programmed for processing on a digital computing system. Most Hillsborough and Alafia River flood discharges can be simulated with expected relative errors less than or equal to 30 percent and flood peaks can be simulated with average relative errors less than 15 percent. Because of the inadequate rainfall network that is used in obtaining input data for the North Prong Alafia River model, simulated peaks are frequently in error by more than 40 percent, particularly for storms having highly variable areal rainfall distribution. Simulation errors are the result of rainfall sample errors and, to a lesser extent, model inadequacy. Data errors associated with the determination of mean basin precipitation are the result of the small number and poor areal distribution of rainfall stations available for use in the study. Model inadequacy, however, is attributed to the basic underlying theory, particularly the rainfall-runoff relation. These models broaden and enhance existing water-management capabilities within these basins by allowing the establishment and implementation of programs providing for continued development in these areas. Specifically, the models serve not only as a basis for forecasting floods, but also for simulating hydrologic information needed in flood-plain mapping and delineating and evaluating alternative flood control and abatement plans.

Multi-parametric variational data assimilation for hydrological forecasting

NASA Astrophysics Data System (ADS)

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

2017-12-01

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

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.

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

NASA Astrophysics Data System (ADS)

Schmitz, G. H.; Cullmann, J.

2008-10-01

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

Assessment of the trophic status of four coastal lagoons and one estuarine delta, eastern Brazil.

PubMed

Cotovicz Junior, Luiz Carlos; Brandini, Nilva; Knoppers, Bastiaan Adriaan; Mizerkowski, Byanka Damian; Sterza, José Mauro; Ovalle, Alvaro Ramon Coelho; Medeiros, Paulo Ricardo Petter

2013-04-01

Anthropogenic eutrophication of aquatic ecosystems continues to be one of the major environmental issues worldwide and also of Brazil. Over the last five decades, several approaches have been proposed to discern the trophic state and the natural and cultural processes involved in eutrophication, including the multi-parameter Assessment of Estuarine Trophic Status (ASSETS) index model. This study applies ASSETS to four Brazilian lagoons (Mundaú, Manguaba, Guarapina, and Piratininga) and one estuarine delta (Paraíba do Sul River), set along the eastern Brazilian coast. The model combines three indices based on the pressure-state-response (PSR) approach to rank the trophic status and forecast the potential eutrophication of a system, to which a final ASSETS grade is established. The lagoons were classified as being eutrophic and highly susceptible to eutrophication, due primarily to their longer residence times but also their high nutrient input index. ASSETS classified the estuary of the Paraíba do Sul river with a low to moderate trophic state (e.g., largely mesotrophic) and low susceptibility to eutrophication. Its nutrient input index was high, but the natural high dilution and flushing potential driven by river flow mitigated the susceptibility to eutrophication. Eutrophication forecasting provided more favorable trends for the Mundaú and Manguaba lagoons and the Paraíba do Sul estuary, in view of the larger investments in wastewater treatment and remediation plans. The final ASSETS ranking system established the lagoons of Mundaú as "moderate," Manguaba as "bad," Guarapina as "poor," and Piratininga as "bad," whereas the Paraíba do Sul River Estuary was "good."

Verifying and Postprocesing the Ensemble Spread-Error Relationship

NASA Astrophysics Data System (ADS)

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

2013-04-01

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

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

NASA Astrophysics Data System (ADS)

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

2016-09-01

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

A study of application of remote sensing to river forecasting. Volume 2: Detailed technical report, NASA-IBM streamflow forecast model user's guide

NASA Technical Reports Server (NTRS)

1975-01-01

The Model is described along with data preparation, determining model parameters, initializing and optimizing parameters (calibration) selecting control options and interpreting results. Some background information is included, and appendices contain a dictionary of variables, a source program listing, and flow charts. The model was operated on an IBM System/360 Model 44, using a model 2250 keyboard/graphics terminal for interactive operation. The model can be set up and operated in a batch processing mode on any System/360 or 370 that has the memory capacity. The model requires 210K bytes of core storage, and the optimization program, OPSET (which was used previous to but not in this study), requires 240K bytes. The data band for one small watershed requires approximately 32 tracks of disk storage.

Putting the "ecology" into environmental flows: ecological dynamics and demographic modelling.

PubMed

Shenton, Will; Bond, Nicholas R; Yen, Jian D L; Mac Nally, Ralph

2012-07-01

There have been significant diversions of water from rivers and streams around the world; natural flow regimes have been perturbed by dams, barriers and excessive extractions. Many aspects of the ecological 'health' of riverine systems have declined due to changes in water flows, which has stimulated the development of thinking about the maintenance and restoration of these systems, which we refer to as environmental flow methodologies (EFMs). Most existing EFMs cannot deliver information on the population viability of species because they: (1) use habitat suitability as a proxy for population status; (2) use historical time series (usually of short duration) to forecast future conditions and flow sequences; (3) cannot, or do not, handle extreme flow events associated with climate variability; and (4) assume process stationarity for flow sequences, which means the past sequences are treated as good indicators of the future. These assumptions undermine the capacity of EFMs to properly represent risks associated with different flow management options; assumption (4) is untenable given most climate-change predictions. We discuss these concerns and advocate the use of demographic modelling as a more appropriate tool for linking population dynamics to flow regime change. A 'meta-species' approach to demographic modelling is discussed as a useful step from habitat based models towards modelling strategies grounded in ecological theory when limited data are available on flow-demographic relationships. Data requirements of demographic models will undoubtedly expose gaps in existing knowledge, but, in so doing, will strengthen future efforts to link changes in river flows with their ecological consequences.

Putting the "Ecology" into Environmental Flows: Ecological Dynamics and Demographic Modelling

NASA Astrophysics Data System (ADS)

Shenton, Will; Bond, Nicholas R.; Yen, Jian D. L.; Mac Nally, Ralph

2012-07-01

There have been significant diversions of water from rivers and streams around the world; natural flow regimes have been perturbed by dams, barriers and excessive extractions. Many aspects of the ecological `health' of riverine systems have declined due to changes in water flows, which has stimulated the development of thinking about the maintenance and restoration of these systems, which we refer to as environmental flow methodologies (EFMs). Most existing EFMs cannot deliver information on the population viability of species because they: (1) use habitat suitability as a proxy for population status; (2) use historical time series (usually of short duration) to forecast future conditions and flow sequences; (3) cannot, or do not, handle extreme flow events associated with climate variability; and (4) assume process stationarity for flow sequences, which means the past sequences are treated as good indicators of the future. These assumptions undermine the capacity of EFMs to properly represent risks associated with different flow management options; assumption (4) is untenable given most climate-change predictions. We discuss these concerns and advocate the use of demographic modelling as a more appropriate tool for linking population dynamics to flow regime change. A `meta-species' approach to demographic modelling is discussed as a useful step from habitat based models towards modelling strategies grounded in ecological theory when limited data are available on flow-demographic relationships. Data requirements of demographic models will undoubtedly expose gaps in existing knowledge, but, in so doing, will strengthen future efforts to link changes in river flows with their ecological consequences.

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

NASA Astrophysics Data System (ADS)

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

2012-04-01

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

Assessment of an ensemble seasonal streamflow forecasting system for Australia

NASA Astrophysics Data System (ADS)

Bennett, James C.; Wang, Quan J.; Robertson, David E.; Schepen, Andrew; Li, Ming; Michael, Kelvin

2017-11-01

Despite an increasing availability of skilful long-range streamflow forecasts, many water agencies still rely on simple resampled historical inflow sequences (stochastic scenarios) to plan operations over the coming year. We assess a recently developed forecasting system called forecast guided stochastic scenarios (FoGSS) as a skilful alternative to standard stochastic scenarios for the Australian continent. FoGSS uses climate forecasts from a coupled ocean-land-atmosphere prediction system, post-processed with the method of calibration, bridging and merging. Ensemble rainfall forecasts force a monthly rainfall-runoff model, while a staged hydrological error model quantifies and propagates hydrological forecast uncertainty through forecast lead times. FoGSS is able to generate ensemble streamflow forecasts in the form of monthly time series to a 12-month forecast horizon. FoGSS is tested on 63 Australian catchments that cover a wide range of climates, including 21 ephemeral rivers. In all perennial and many ephemeral catchments, FoGSS provides an effective alternative to resampled historical inflow sequences. FoGSS generally produces skilful forecasts at shorter lead times ( < 4 months), and transits to climatology-like forecasts at longer lead times. Forecasts are generally reliable and unbiased. However, FoGSS does not perform well in very dry catchments (catchments that experience zero flows more than half the time in some months), sometimes producing strongly negative forecast skill and poor reliability. We attempt to improve forecasts through the use of (i) ESP rainfall forcings, (ii) different rainfall-runoff models, and (iii) a Bayesian prior to encourage the error model to return climatology forecasts in months when the rainfall-runoff model performs poorly. Of these, the use of the prior offers the clearest benefit in very dry catchments, where it moderates strongly negative forecast skill and reduces bias in some instances. However, the prior does not remedy poor reliability in very dry catchments. Overall, FoGSS is an attractive alternative to historical inflow sequences in all but the driest catchments. We discuss ways in which forecast reliability in very dry catchments could be improved in future work.

AIRS Impact on Analysis and Forecast of an Extreme Rainfall Event (Indus River Valley 2010) with a Global Data Assimilation and Forecast System

NASA Technical Reports Server (NTRS)

Reale, O.; Lau, W. K.; Susskind, J.; Rosenberg, R.

2011-01-01

A set of data assimilation and forecast experiments are performed with the NASA Global data assimilation and forecast system GEOS-5, to compare the impact of different approaches towards assimilation of Advanced Infrared Spectrometer (AIRS) data on the precipitation analysis and forecast skill. The event chosen is an extreme rainfall episode which occurred in late July 11 2010 in Pakistan, causing massive floods along the Indus River Valley. Results show that the assimilation of quality-controlled AIRS temperature retrievals obtained under partly cloudy conditions produce better precipitation analyses, and substantially better 7-day forecasts, than assimilation of clear-sky radiances. The improvement of precipitation forecast skill up to 7 day is very significant in the tropics, and is caused by an improved representation, attributed to cloudy retrieval assimilation, of two contributing mechanisms: the low-level moisture advection, and the concentration of moisture over the area in the days preceding the precipitation peak.

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.

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.

Hydrological Predictability for the Peruvian Amazon

NASA Astrophysics Data System (ADS)

Towner, Jamie; Stephens, Elizabeth; Cloke, Hannah; Bazo, Juan; Coughlan, Erin; Zsoter, Ervin

2017-04-01

Population growth in the Peruvian Amazon has prompted the expansion of livelihoods further into the floodplain and thus increasing vulnerability to the annual rise and fall of the river. This growth has coincided with a period of increasing hydrological extremes with more frequent severe flood events. The anticipation and forecasting of these events is crucial for mitigating vulnerability. Forecast-based Financing (FbF) an initiative of the German Red Cross implements risk reducing actions based on threshold exceedance within hydrometeorological forecasts using the Global Flood Awareness System (GloFAS). However, the lead times required to complete certain actions can be long (e.g. several weeks to months ahead to purchase materials and reinforce houses) and are beyond the current capabilities of GloFAS. Therefore, further calibration of the model is required in addition to understanding the climatic drivers and associated hydrological response for specific flood events, such as those observed in 2009, 2012 and 2015. This review sets out to determine the current capabilities of the GloFAS model while exploring the limits of predictability for the Amazon basin. More specifically, how the temporal patterns of flow within the main coinciding tributaries correspond to the overall Amazonian flood wave under various climatic and meteorological influences. Linking the source areas of flow to predictability within the seasonal forecasting system will develop the ability to expand the limit of predictability of the flood wave. This presentation will focus on the Iquitos region of Peru, while providing an overview of the new techniques and current challenges faced within seasonal flood prediction.

Seasonal and daily snowmelt runoff estimates utilizing satellite data. [Wind River Mountains, Wyoming

NASA Technical Reports Server (NTRS)

1980-01-01

Methods using snowcovered area to update seasonal forecasts as snowmelt progresses are also being used in quasi-operational situations. The input of snowcovered area to snowmelt models for short term perdictions was attempted in two ways; namely, the modification of existing hydrologic models and/or the use of models that were specifically designed to use snowcovered area. A daily snowmelt runoff model was used with LANDSAT data to simulate discharge on remote basins in the Wind River Mountains of Wyoming. Daily predicted and actual flows compare closely, and, summarized over the entire snowmelt season (April 1 - September 30), the average difference is only three percent. The model and snowcovered area data are currently being tested on additional watersheds to determine the method's transferability.

Small hydropower spot prediction using SWAT and a diversion algorithm, case study: Upper Citarum Basin

NASA Astrophysics Data System (ADS)

Kardhana, Hadi; Arya, Doni Khaira; Hadihardaja, Iwan K.; Widyaningtyas, Riawan, Edi; Lubis, Atika

2017-11-01

Small-Scale Hydropower (SHP) had been important electric energy power source in Indonesia. Indonesia is vast countries, consists of more than 17.000 islands. It has large fresh water resource about 3 m of rainfall and 2 m of runoff. Much of its topography is mountainous, remote but abundant with potential energy. Millions of people do not have sufficient access to electricity, some live in the remote places. Recently, SHP development was encouraged for energy supply of the places. Development of global hydrology data provides opportunity to predict distribution of hydropower potential. In this paper, we demonstrate run-of-river type SHP spot prediction tool using SWAT and a river diversion algorithm. The use of Soil and Water Assessment Tool (SWAT) with input of CFSR (Climate Forecast System Re-analysis) of 10 years period had been implemented to predict spatially distributed flow cumulative distribution function (CDF). A simple algorithm to maximize potential head of a location by a river diversion expressing head race and penstock had been applied. Firm flow and power of the SHP were estimated from the CDF and the algorithm. The tool applied to Upper Citarum River Basin and three out of four existing hydropower locations had been well predicted. The result implies that this tool is able to support acceleration of SHP development at earlier phase.

An operational real-time flood forecasting system in Southern Italy

NASA Astrophysics Data System (ADS)

Ortiz, Enrique; Coccia, Gabriele; Todini, Ezio

2015-04-01

A real-time flood forecasting system has been operating since year 2012 as a non-structural measure for mitigating the flood risk in Campania Region (Southern Italy), within the Sele river basin (3.240 km2). The Sele Flood Forecasting System (SFFS) has been built within the FEWS (Flood Early Warning System) platform developed by Deltares and it assimilates the numerical weather predictions of the COSMO LAM family: the deterministic COSMO-LAMI I2, the deterministic COSMO-LAMI I7 and the ensemble numerical weather predictions COSMO-LEPS (16 members). Sele FFS is composed by a cascade of three main models. The first model is a fully continuous physically based distributed hydrological model, named TOPKAPI-eXtended (Idrologia&Ambiente s.r.l., Naples, Italy), simulating the dominant processes controlling the soil water dynamics, runoff generation and discharge with a spatial resolution of 250 m. The second module is a set of Neural-Networks (ANN) built for forecasting the river stages at a set of monitored cross-sections. The third component is a Model Conditional Processor (MCP), which provides the predictive uncertainty (i.e., the probability of occurrence of a future flood event) within the framework of a multi-temporal forecast, according to the most recent advancements on this topic (Coccia and Todini, HESS, 2011). The MCP provides information about the probability of exceedance of a maximum river stage within the forecast lead time, by means of a discrete time function representing the variation of cumulative probability of exceeding a river stage during the forecast lead time and the distribution of the time occurrence of the flood peak, starting from one or more model forecasts. This work shows the Sele FFS performance after two years of operation, evidencing the added-values that can provide to a flood early warning and emergency management system.

Forecasting the short-term passenger flow on high-speed railway with neural networks.

PubMed

Xie, Mei-Quan; Li, Xia-Miao; Zhou, Wen-Liang; Fu, Yan-Bing

2014-01-01

Short-term passenger flow forecasting is an important component of transportation systems. The forecasting result can be applied to support transportation system operation and management such as operation planning and revenue management. In this paper, a divide-and-conquer method based on neural network and origin-destination (OD) matrix estimation is developed to forecast the short-term passenger flow in high-speed railway system. There are three steps in the forecasting method. Firstly, the numbers of passengers who arrive at each station or depart from each station are obtained from historical passenger flow data, which are OD matrices in this paper. Secondly, short-term passenger flow forecasting of the numbers of passengers who arrive at each station or depart from each station based on neural network is realized. At last, the OD matrices in short-term time are obtained with an OD matrix estimation method. The experimental results indicate that the proposed divide-and-conquer method performs well in forecasting the short-term passenger flow on high-speed railway.

Enhancing Community Based Early Warning Systems in Nepal with Flood Forecasting Using Local and Global Models

NASA Astrophysics Data System (ADS)

Dugar, Sumit; Smith, Paul; Parajuli, Binod; Khanal, Sonu; Brown, Sarah; Gautam, Dilip; Bhandari, Dinanath; Gurung, Gehendra; Shakya, Puja; Kharbuja, RamGopal; Uprety, Madhab

2017-04-01

Operationalising effective Flood Early Warning Systems (EWS) in developing countries like Nepal poses numerous challenges, with complex topography and geology, sparse network of river and rainfall gauging stations and diverse socio-economic conditions. Despite these challenges, simple real-time monitoring based EWSs have been in place for the past decade. A key constraint of these simple systems is the very limited lead time for response - as little as 2-3 hours, especially for rivers originating from steep mountainous catchments. Efforts to increase lead time for early warning are focusing on imbedding forecasts into the existing early warning systems. In 2016, the Nepal Department of Hydrology and Meteorology (DHM) piloted an operational Probabilistic Flood Forecasting Model in major river basins across Nepal. This comprised a low data approach to forecast water levels, developed jointly through a research/practitioner partnership with Lancaster University and WaterNumbers (UK) and the International NGO Practical Action. Using Data-Based Mechanistic Modelling (DBM) techniques, the model assimilated rainfall and water levels to generate localised hourly flood predictions, which are presented as probabilistic forecasts, increasing lead times from 2-3 hours to 7-8 hours. The Nepal DHM has simultaneously started utilizing forecasts from the Global Flood Awareness System (GLoFAS) that provides streamflow predictions at the global scale based upon distributed hydrological simulations using numerical ensemble weather forecasts from the ECMWF (European Centre for Medium-Range Weather Forecasts). The aforementioned global and local models have already affected the approach to early warning in Nepal, being operational during the 2016 monsoon in the West Rapti basin in Western Nepal. On 24 July 2016, GLoFAS hydrological forecasts for the West Rapti indicated a sharp rise in river discharge above 1500 m3/sec (equivalent to the river warning level at 5 meters) with 53% probability of exceeding the Medium Level Alert in two days. Rainfall stations upstream of the West Rapti catchment recorded heavy rainfall on 26 July, and localized forecasts from the probabilistic model at 8 am suggested that the water level would cross a pre-determined warning level in the next 3 hours. The Flood Forecasting Section at DHM issued a flood advisory, and disseminated SMS flood alerts to more than 13,000 at-risk people residing along the floodplains. Water levels crossed the danger threshold (5.4 meters) at 11 am, peaking at 8.15 meters at 10 pm. Extension of the warning lead time from probabilistic forecasts was significant in minimising the risk to lives and livelihoods as communities gained extra time to prepare, evacuate and respond. Likewise, longer timescale forecasts from GLoFAS could be potentially linked with no-regret early actions leading to improved preparedness and emergency response. These forecasting tools have contributed to enhance the effectiveness and efficiency of existing community based systems, increasing the lead time for response. Nevertheless, extensive work is required on appropriate ways to interpret and disseminate probabilistic forecasts having longer (2-14 days) and shorter (3-5 hours) time horizon for operational deployment as there are numerous uncertainties associated with predictions.

Forecast Mekong: navigating changing waters

USGS Publications Warehouse

Powell, Janine

2011-01-01

The U.S. Geological Survey (USGS) is using research and data from the Mekong River Delta in Southeast Asia to compare restoration, conservation, and management efforts there with those done in other major river deltas, such as the Mississippi River Delta in the United States. The project provides a forum to engage regional partners in the Mekong Basin countries to share data and support local research efforts. Ultimately, Forecast Mekong will lead to more informed decisions about how to make the Mekong and Mississippi Deltas resilient in the face of climate change, economic stresses, and other impacts.

48 CFR 232.072-3 - Cash flow forecasts.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 48 Federal Acquisition Regulations System 3 2010-10-01 2010-10-01 false Cash flow forecasts. 232..., DEPARTMENT OF DEFENSE GENERAL CONTRACTING REQUIREMENTS CONTRACT FINANCING 232.072-3 Cash flow forecasts. (a) A contractor must be able to sustain a sufficient cash flow to perform the contract. When there is...

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

NASA Astrophysics Data System (ADS)

Kasiviswanathan, K.; Sudheer, K.

2013-05-01

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

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

NASA Technical Reports Server (NTRS)

1975-01-01

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

Development and evaluation of a reservoir model for the Chain of Lakes in Illinois

USGS Publications Warehouse

Domanski, Marian M.

2017-01-27

Forecasts of flows entering and leaving the Chain of Lakes reservoir on the Fox River in northeastern Illinois are critical information to water-resource managers who determine the optimal operation of the dam at McHenry, Illinois, to help minimize damages to property and loss of life because of flooding on the Fox River. In 2014, the U.S. Geological Survey; the Illinois Department of Natural Resources, Office of Water Resources; and National Weather Service, North Central River Forecast Center began a cooperative study to develop a system to enable engineers and planners to simulate and communicate flows and to prepare proactively for precipitation events in near real time in the upper Fox River watershed. The purpose of this report is to document the development and evaluation of the Chain of Lakes reservoir model developed in this study.The reservoir model for the Chain of Lakes was developed using the Hydrologic Engineering Center–Reservoir System Simulation program. Because of the complex relation between the dam headwater and reservoir pool elevations, the reservoir model uses a linear regression model that relates dam headwater elevation to reservoir pool elevation. The linear regression model was developed using 17 U.S. Geological Survey streamflow measurements, along with the gage height in the reservoir pool and the gage height at the dam headwater. The Nash-Sutcliffe model efficiency coefficients for all three linear regression model variables ranged from 0.90 to 0.98.The reservoir model performance was evaluated by graphically comparing simulated and observed reservoir pool elevation time series during nine periods of high pool elevation. In addition, the peak elevations during these time periods were graphically compared to the closest-in-time observed pool elevation peak. The mean difference in the simulated and observed peak elevations was -0.03 feet, with a standard deviation of 0.19 feet. The Nash-Sutcliffe coefficient for peak prediction was calculated as 0.94. Evaluation of the model based on accuracy of peak prediction and the ability to simulate an elevation time series showed the performance of the model was satisfactory.

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/.

Recent developments and assessment of a three-dimensional PBL parameterization for improved wind forecasting over complex terrain

NASA Astrophysics Data System (ADS)

Kosovic, B.; Jimenez, P. A.; Haupt, S. E.; Martilli, A.; Olson, J.; Bao, J. W.

2017-12-01

At present, the planetary boundary layer (PBL) parameterizations available in most numerical weather prediction (NWP) models are one-dimensional. One-dimensional parameterizations are based on the assumption of horizontal homogeneity. This homogeneity assumption is appropriate for grid cell sizes greater than 10 km. However, for mesoscale simulations of flows in complex terrain with grid cell sizes below 1 km, the assumption of horizontal homogeneity is violated. Applying a one-dimensional PBL parameterization to high-resolution mesoscale simulations in complex terrain could result in significant error. For high-resolution mesoscale simulations of flows in complex terrain, we have therefore developed and implemented a three-dimensional (3D) PBL parameterization in the Weather Research and Forecasting (WRF) model. The implementation of the 3D PBL scheme is based on the developments outlined by Mellor and Yamada (1974, 1982). Our implementation in the Weather Research and Forecasting (WRF) model uses a pure algebraic model (level 2) to diagnose the turbulent fluxes. To evaluate the performance of the 3D PBL model, we use observations from the Wind Forecast Improvement Project 2 (WFIP2). The WFIP2 field study took place in the Columbia River Gorge area from 2015-2017. We focus on selected cases when physical phenomena of significance for wind energy applications such as mountain waves, topographic wakes, and gap flows were observed. Our assessment of the 3D PBL parameterization also considers a large-eddy simulation (LES). We carried out a nested LES with grid cell sizes of 30 m and 10 m covering a large fraction of the WFIP2 study area. Both LES domains were discretized using 6000 x 3000 x 200 grid cells in zonal, meridional, and vertical direction, respectively. The LES results are used to assess the relative magnitude of horizontal gradients of turbulent stresses and fluxes in comparison to vertical gradients. The presentation will highlight the advantages of the 3D PBL scheme in regions of complex terrain.

Ocean-Atmosphere Coupling Processes Affecting Predictability in the Climate System

NASA Astrophysics Data System (ADS)

Miller, A. J.; Subramanian, A. C.; Seo, H.; Eliashiv, J. D.

2017-12-01

Predictions of the ocean and atmosphere are often sensitive to coupling at the air-sea interface in ways that depend on the temporal and spatial scales of the target fields. We will discuss several aspects of these types of coupled interactions including oceanic and atmospheric forecast applications. For oceanic mesoscale eddies, the coupling can influence the energetics of the oceanic flow itself. For Madden-Julian Oscillation onset, the coupling timestep should resolve the diurnal cycle to properly raise time-mean SST and latent heat flux prior to deep convection. For Atmospheric River events, the evolving SST field can alter the trajectory and intensity of precipitation anomalies along the California coast. Improvements in predictions will also rely on identifying and alleviating sources of biases in the climate states of the coupled system. Surprisingly, forecast skill can also be improved by enhancing stochastic variability in the atmospheric component of coupled models as found in a multiscale ensemble modeling approach.

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.

Impact of AIRS Thermodynamic Profiles on Precipitation Forecasts for Atmospheric River Cases Affecting the Western United States

NASA Technical Reports Server (NTRS)

Zavodsky, Bradley T.; Jedlovec, Gary J.; Blakenship, Clay B.; Wick, Gary A.; Neiman, Paul J.

2013-01-01

This project is a collaborative activity between the NASA Short-term Prediction Research and Transition (SPoRT) Center and the NOAA Hydrometeorology Testbed (HMT) to evaluate a SPoRT Advanced Infrared Sounding Radiometer (AIRS: Aumann et al. 2003) enhanced moisture analysis product. We test the impact of assimilating AIRS temperature and humidity profiles above clouds and in partly cloudy regions, using the three-dimensional variational Gridpoint Statistical Interpolation (GSI) data assimilation (DA) system (Developmental Testbed Center 2012) to produce a new analysis. Forecasts of the Weather Research and Forecasting (WRF) model initialized from the new analysis are compared to control forecasts without the additional AIRS data. We focus on some cases where atmospheric rivers caused heavy precipitation on the US West Coast. We verify the forecasts by comparison with dropsondes and the Cooperative Institute for Research in the Atmosphere (CIRA) Blended Total Precipitable Water product.

Arctic River Discharge and Sediment Loads --- an Overview

NASA Astrophysics Data System (ADS)

Syvitski, J. P.; Overeem, I.; Brakenridge, G. R.; Hudson, B.; Cohen, S.

2014-12-01

Evidence suggests that river discharge has been increasing (+10%) over the last 30 years (1977-2007) for most arctic rivers. The peak melt month occurs earlier in the season in 66% of the studied rivers. Cold season flow is also increasing. Satellite discharge estimates, daily, based on microwave radiometry, are now possible from 1998 onwards. Daily river discharge hindcasts over the last 60 years using the water balance model WBMsed at a 10km spatial resolution are now available. The WBMsed model can be used in forecast mode assuming valid input climatology. The challenge here has been the accuracy of sub-polar precipitation grids. While each of these three methods (gauging, orbital sensing, modeling) has temporal and spatial coverage limitations, the combination of all three methods provides for a realistic way forward for estimating local discharge across the pan arctic. Flood inundation products are routinely produced for the pan-arctic using automated mapping algorithms developed by the Dartmouth Flood Observatory. The determination of artic river sediment loads is less than ideal. Some rivers have only been monitored for a short number of years, and many have not been monitored at all. The WBMsed model is perhaps the best method of estimating the daily sediment flux to the Arctic Ocean, at least for rivers where the mean discharge is greater than 30 m3/s. Additionally there is limited-duration field monitoring by national surveys. New methods are being explored, including back calculating the delivery of sediment to the coastal ocean by plume dimensions observed from space (MODIS, LandSat). These methods have had moderate success when applied to plumes extending in the Greenland fjords. Canada maintains an active circa 7-y satellite program (ArcticNet) to track the Mackenzie discharge during the spring-summer runoff period when turbid river water is apt to flow under and over marginal sea ice in the Beaufort Sea.

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.

Forecasting the Short-Term Passenger Flow on High-Speed Railway with Neural Networks

PubMed Central

Xie, Mei-Quan; Li, Xia-Miao; Zhou, Wen-Liang; Fu, Yan-Bing

2014-01-01

Short-term passenger flow forecasting is an important component of transportation systems. The forecasting result can be applied to support transportation system operation and management such as operation planning and revenue management. In this paper, a divide-and-conquer method based on neural network and origin-destination (OD) matrix estimation is developed to forecast the short-term passenger flow in high-speed railway system. There are three steps in the forecasting method. Firstly, the numbers of passengers who arrive at each station or depart from each station are obtained from historical passenger flow data, which are OD matrices in this paper. Secondly, short-term passenger flow forecasting of the numbers of passengers who arrive at each station or depart from each station based on neural network is realized. At last, the OD matrices in short-term time are obtained with an OD matrix estimation method. The experimental results indicate that the proposed divide-and-conquer method performs well in forecasting the short-term passenger flow on high-speed railway. PMID:25544838

Determining River Ice Displacement Using the Differential Interferometry Synthetic Aperture Radar (D-InSAR) technique

NASA Astrophysics Data System (ADS)

Chu, T.; Lindenschmidt, K. E.

2016-12-01

Monitoring river ice cover dynamics during the course of winter is necessary to comprehend possible negative effects of ice on anthropogenic systems and natural ecosystems to provide a basis to develop mitigation measures. Due to their large scale and limited accessibility to most places along river banks, especially in northern regions, remote sensing techniques are a suitable approach for monitoring river ice regimes. Additionally, determining the vertical displacements of ice covers due to changes in flow provides an indication of vulnerable areas to initial cracking and breakup of the ice cover. Such information is paramount when deciding on suitable locations for winter road crossing along rivers. A number of RADARSAT-2 (RS-2) beam modes (i.e. Wide Fine, Wide Ultra-Fine, Wide Fine Quad Polarization and Spotlight) and D-InSAR methods were examined in this research to characterize slant range and vertical displacement of ice covers along the Slave River in the Northwest Territories, Canada. Our results demonstrate that the RS-2 Spotlight beam mode, processed by the Multiple Aperture InSAR (MAI) method, outperformed other beam modes and conventional InSAR when characterizing spatio-temporal patterns of ice surface fluctuations. For example, the MAI based Spotlight differential interferogram derived from the January and February 2016 images of the Slave River Delta resulted in a slant range displacement of the ice surface between -3.3 and +3.6 cm (vertical displacement between -4.3 and +4.8 cm), due to the changes in river flow and river ice morphology between the two acquisition dates. It is difficult to monitor the ice movement in early and late winter periods due to the loss of phase coherence and error in phase unwrapping. These findings are consistent with our river ice hydraulic modelling and visual interpretation of the river ice processes under different hydrometeorological conditions and river ice morphology. An extension of this study is planned to incorporate the results of ice cover displacement (rise/drop) to locate areas of initial breakup in an ice jam forecasting system. Keywords: D-InSAR, Mutiple Aperture Radar InSAR (MAI), river ice displacement, RADARSAT-2

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.

A high resolution Adriatic-Ionian Sea circulation model for operational forecasting

NASA Astrophysics Data System (ADS)

Ciliberti, Stefania Angela; Pinardi, Nadia; Coppini, Giovanni; Oddo, Paolo; Vukicevic, Tomislava; Lecci, Rita; Verri, Giorgia; Kumkar, Yogesh; Creti', Sergio

2015-04-01

A new numerical regional ocean model for the Italian Seas, with focus on the Adriatic-Ionian basin, has been implemented within the framework of Technologies for Situational Sea Awareness (TESSA) Project. The Adriatic-Ionian regional model (AIREG) represents the core of the new Adriatic-Ionian Forecasting System (AIFS), maintained operational by CMCC since November 2014. The spatial domain covers the Adriatic and the Ionian Seas, extending eastward until the Peloponnesus until the Libyan coasts; it includes also the Tyrrhenian Sea and extends westward, including the Ligurian Sea, the Sardinia Sea and part of the Algerian basin. The model is based on the NEMO-OPA (Nucleus for European Modeling of the Ocean - Ocean PArallelise), version 3.4 (Madec et al. 2008). NEMO has been implemented for AIREG at 1/45° resolution model in horizontal using 121 vertical levels with partial steps. It solves the primitive equations using the time-splitting technique for solving explicitly the external gravity waves. The model is forced by momentum, water and heat fluxes interactively computed by bulk formulae using the 6h-0.25° horizontal-resolution operational analysis and forecast fields from the European Centre for Medium-Range Weather Forecast (ECMWF) (Tonani et al. 2008, Oddo et al. 2009). The atmospheric pressure effect is included as surface forcing for the model hydrodynamics. The evaporation is derived from the latent heat flux, while the precipitation is provided by the Climate Prediction Centre Merged Analysis of Precipitation (CMAP) data. Concerning the runoff contribution, the model considers the estimate of the inflow discharge of 75 rivers that flow into the Adriatic-Ionian basin, collected by using monthly means datasets. Because of its importance as freshwater input in the Adriatic basin, the Po River contribution is provided using daily average observations from ARPA Emilia Romagna observational network. AIREG is one-way nested into the Mediterranean Forecasting System (MFS, http://medforecast.bo.ingv.it/) using daily means fields computed from daily outputs of the 1/16° general circulation model. One-way nesting is done by a novel pre-processing tool for an on-the-fly computation of boundary datasets compatible with BDY module provided by NEMO. It imposes the interpolation constraint and correction as in Pinardi et al. (2003) on the total velocity, ensuring that the total volume transport across boundaries is preserved after the interpolation procedures. In order to compute the lateral open boundary conditions, the model applies the Flow Relaxation Scheme (Engerdhal, 1995) for temperature, salinity and velocities and the Flather's radiation condition (Flather, 1976) for the depth-mean transport. Concerning the forecasting production cycle, AIFS produces 9-days forecast every day, producing hourly and daily means of temperature, salinity, surface currents, heat flux, water flux and shortwave radiation fields. AIREG model performances have been verified by using statistics (root mean square errors and BIAS) with respect to observed data (ARGO and CDT datasets)

Groundwater influence on soil moisture memory and land-atmosphere interactions over the Iberian Peninsula

NASA Astrophysics Data System (ADS)

Martinez-de la Torre, Alberto; Miguez-Macho, Gonzalo

2017-04-01

We investigate the memory introduced in soil moisture fields by groundwater long timescales of variation in the semi-arid regions of the Iberian Peninsula with the LEAFHYDRO soil-vegetation-hydrology model, which includes a dynamic water table fully coupled to soil moisture and river flow via 2-way fluxes. We select a 10-year period (1989-1998) with transitions from wet to dry to again wet long lasting conditions and we carry out simulations at 2.5 km spatial resolution forced by ERA-Interim and a high-resolution precipitation analysis over Spain and Portugal. The model produces a realistic water table that we validate with hundreds of water table depth observation time series (ranging from 4 to 10 years) over the Iberian Peninsula. Modeled river flow is also compared to observations. Over shallow water table regions, results highlight the groundwater buffering effect on soil moisture fields over dry spells and long-term droughts, as well as the slow recovery of pre-drought soil wetness once climatic conditions turn wetter. Groundwater sustains river flow during dry summer periods. The longer lasting wet conditions in the soil when groundwater is considered increase summer evapotranspiration, that is mostly water-limited. Our results suggest that groundwater interaction with soil moisture should be considered for climate seasonal forecasting and climate studies in general over water-limited regions where shallow water tables are significantly present and connected to land surface hydrology.

Medium range flood forecasts at global scale

NASA Astrophysics Data System (ADS)

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

2006-12-01

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

A model-based assessment of the effects of projected climate change on the water resources of Jordan.

PubMed

Wade, A J; Black, E; Brayshaw, D J; El-Bastawesy, M; Holmes, P A C; Butterfield, D; Nuimat, S; Jamjoum, K

2010-11-28

This paper is concerned with the quantification of the likely effect of anthropogenic climate change on the water resources of Jordan by the end of the twenty-first century. Specifically, a suite of hydrological models are used in conjunction with modelled outcomes from a regional climate model, HadRM3, and a weather generator to determine how future flows in the upper River Jordan and in the Wadi Faynan may change. The results indicate that groundwater will play an important role in the water security of the country as irrigation demands increase. Given future projections of reduced winter rainfall and increased near-surface air temperatures, the already low groundwater recharge will decrease further. Interestingly, the modelled discharge at the Wadi Faynan indicates that extreme flood flows will increase in magnitude, despite a decrease in the mean annual rainfall. Simulations projected no increase in flood magnitude in the upper River Jordan. Discussion focuses on the utility of the modelling framework, the problems of making quantitative forecasts and the implications of reduced water availability in Jordan.

Modelling of Sediment Transport of the Mehadica River, Caras Severin County, Romania

NASA Astrophysics Data System (ADS)

Grozav, Adia; Beilicci, Robert; Beilicci, Erika

2017-10-01

Study case is situated in Caras-Severin County. Every sediment transport model application is different both in terms of time and space scale, study objectives, required accuracy, allocated resources, background of the study team etc. For sediment transport modelling, it is necessary to know the characteristics of the sediment in the river bed. Therefore, it is recommended to collect a number of bed sediment grap samples. These samples should be analysing in terms of grain size distribution. To solve theoretical problems of movement of water in the river Mehadica, it requires modelling of water flow in this case. Numerical modelling was performed using the program MIKE11. MIKE 11 is a user-friendly, fully dynamic, one-dimensional modelling tool for the detailed analysis, design, management and operation of both simple and complex river and channel systems. With its exceptional flexibility, speed and user friendly environment, MIKE 11 provides a complete and effective design environment for engineering, water resources, water quality management and planning applications. The Hydrodynamic (HD) module is the nucleus of the MIKE 11 modelling system and forms the basis for most modules including Flood Forecasting, Advection- Dispersion, Water Quality and Non-cohesive sediment transport modules. The MIKE 11 HD module solves the vertically integrated equations for the conservation of mass and momentum, i.e. the Saint-Venant equations. The input data are: area plan with location of cross sections; cross sections topographical data and roughness of river bed; flood discharge hydrograph. Advanced computational modules are included for description of flow over hydraulic structures, including possibilities to describe structure operation.

Hydrological and sedimentation implications of landscape changes in a Himalayan catchment due to bioenergy cropping

NASA Astrophysics Data System (ADS)

Remesan, Renji; Holman, Ian; Janes, Victoria

2015-04-01

There is a global effort to focus on the development of bioenergy and energy cropping, due to the generally increasing demand for crude oil, high oil price volatility and climate change mitigation challenges. Second generation energy cropping is expected to increase greatly in India as the Government of India has recently approved a national policy of 20 % biofuel blending by 2017; furthermore, the country's biomass based power generation potential is estimated as around ~24GW and large investments are expected in coming years to increase installed capacity. In this study, we have modelled the environmental influences (e.g.: hydrology and sediment) of scenarios of increased biodiesel cropping (Jatropha curcas) using the Soil and Water Assessment Tool (SWAT) in a northern Indian river basin. SWAT has been applied to the River Beas basin, using daily Tropical Rainfall Measuring Mission (TRMM) precipitation and NCEP Climate Forecast System Reanalysis (CFSR) meteorological data to simulate the river regime and crop yields. We have applied Sequential Uncertainty Fitting Ver. 2 (SUFI-2) to quantify the parameter uncertainty of the stream flow modelling. The model evaluation statistics for daily river flows at the Jwalamukhi and Pong gauges show good agreement with measured flows (Nash Sutcliffe efficiency of 0.70 and PBIAS of 7.54 %). The study has applied two land use change scenarios of (1) increased bioenergy cropping in marginal (grazing) lands in the lower and middle regions of catchment (2) increased bioenergy cropping in low yielding areas of row crops in the lower and middle regions of the catchment. The presentation will describe the improved understanding of the hydrological, erosion and sediment delivery and food production impacts arising from the introduction of a new cropping variety to a marginal area; and illustrate the potential prospects of bioenergy production in Himalayan valleys.

Processing of next generation weather radar-multisensor precipitation estimates and quantitative precipitation forecast data for the DuPage County streamflow simulation system

USGS Publications Warehouse

Bera, Maitreyee; Ortel, Terry W.

2018-01-12

The U.S. Geological Survey, in cooperation with DuPage County Stormwater Management Department, is testing a near real-time streamflow simulation system that assists in the management and operation of reservoirs and other flood-control structures in the Salt Creek and West Branch DuPage River drainage basins in DuPage County, Illinois. As part of this effort, the U.S. Geological Survey maintains a database of hourly meteorological and hydrologic data for use in this near real-time streamflow simulation system. Among these data are next generation weather radar-multisensor precipitation estimates and quantitative precipitation forecast data, which are retrieved from the North Central River Forecasting Center of the National Weather Service. The DuPage County streamflow simulation system uses these quantitative precipitation forecast data to create streamflow predictions for the two simulated drainage basins. This report discusses in detail how these data are processed for inclusion in the Watershed Data Management files used in the streamflow simulation system for the Salt Creek and West Branch DuPage River drainage basins.

OHD/HL: Presentations

Science.gov Websites

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

Recent advances in the multimodel hydrologic ensemble forecasting using the HydroProg system in the Nysa Klodzka river basin (southwestern Poland)

NASA Astrophysics Data System (ADS)

Niedzielski, Tomasz; Mizinski, Bartlomiej; Swierczynska-Chlasciak, Malgorzata

2017-04-01

The HydroProg system, the real-time multimodel hydrologic ensemble system elaborated at the University of Wroclaw (Poland) in frame of the research grant no. 2011/01/D/ST10/04171 financed by National Science Centre of Poland, has been experimentally launched in 2013 in the Nysa Klodzka river basin (southwestern Poland). Since that time the system has been working operationally to provide water level predictions in real time. At present, depending on a hydrologic gauge, up to eight hydrologic models are run. They are data- and physically-based solutions, with the majority of them being the data-based ones. The paper aims to report on the performance of the implementation of the HydroProg system for the basin in question. We focus on several high flows episodes and discuss the skills of the individual models in forecasting them. In addition, we present the performance of the multimodel ensemble solution. We also introduce a new prognosis which is determined in the following way: for a given lead time we select the most skillful prediction (from the set of all individual models running at a given gauge and their multimodel ensemble) using the performance statistics computed operationally in real time as a function of lead time.

A review and update of the Virginia Department of Transportation's cash flow forecasting model : interim report.

DOT National Transportation Integrated Search

1995-01-01

The Virginia Department of Transportation uses a cash flow forecasting model to predict operations expenditures by month. Components of this general forecasting model estimate line items in the VDOT budget. The cash flow model was developed in the ea...

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.

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.

Discharge prediction in the Upper Senegal River using remote sensing data

NASA Astrophysics Data System (ADS)

Ceccarini, Iacopo; Raso, Luciano; Steele-Dunne, Susan; Hrachowitz, Markus; Nijzink, Remko; Bodian, Ansoumana; Claps, Pierluigi

2017-04-01

The Upper Senegal River, West Africa, is a poorly gauged basin. Nevertheless, discharge predictions are required in this river for the optimal operation of the downstream Manantali reservoir, flood forecasting, development plans for the entire basin and studies for adaptation to climate change. Despite the need for reliable discharge predictions, currently available rainfall-runoff models for this basin provide only poor performances, particularly during extreme regimes, both low-flow and high-flow. In this research we develop a rainfall-runoff model that combines remote-sensing input data and a-priori knowledge on catchment physical characteristics. This semi-distributed model, is based on conceptual numerical descriptions of hydrological processes at the catchment scale. Because of the lack of reliable input data from ground observations, we use the Tropical Rainfall Measuring Mission (TRMM) remote-sensing data for precipitation and the Global Land Evaporation Amsterdam Model (GLEAM) for the terrestrial potential evaporation. The model parameters are selected by a combination of calibration, by match of observed output and considering a large set of hydrological signatures, as well as a-priori knowledge on the catchment. The Generalized Likelihood Uncertainty Estimation (GLUE) method was used to choose the most likely range in which the parameter sets belong. Analysis of different experiments enhances our understanding on the added value of distributed remote-sensing data and a-priori information in rainfall-runoff modelling. Results of this research will be used for decision making at different scales, contributing to a rational use of water resources in this river.

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.

Eco-morphological Real-time Forecasting tool to predict hydrodynamic, sediment and nutrient dynamic in Coastal Louisiana

NASA Astrophysics Data System (ADS)

Messina, F.; Meselhe, E. A.; Buckman, L.; Twight, D.

2017-12-01

Louisiana coastal zone is one of the most productive and dynamic eco-geomorphic systems in the world. This unique natural environment has been alternated by human activities and natural processes such as sea level rise, subsidence, dredging of canals for oil and gas production, the Mississippi River levees which don't allow the natural river sediment. As a result of these alterations land loss, erosion and flood risk are becoming real issues for Louisiana. Costal authorities have been studying the benefits and effects of several restoration projects, e.g. freshwater and sediment diversions. The protection of communities, wildlife and of the unique environments is a high priority in this region. The Water Institute of the Gulf, together with Deltares, has developed a forecasting and information system for a pilot location in Coastal Louisiana, specifically for Barataria Bay and Breton Sound Basins in the Mississippi River Deltaic Plain. The system provides a 7-day forecast of water level, salinity, and temperature, under atmospheric and coastal forecasted conditions, such as freshwater riverine inflow, rainfall, evaporation, wind, and tide. The system also forecasts nutrient distribution (e.g., Chla and dissolved oxygen) and sediment transport. The Flood Early Warning System FEWS is used as a platform to import multivariate data from several sources, use them to monitor the pilot location and to provide boundary conditions to the model. A hindcast model is applied to compare the model results to the observed data, and to provide the initial condition to the forecast model. This system represents a unique tool which provides valuable information regarding the overall conditions of the basins. It offers the opportunity to adaptively manage existing and planned diversions to meet certain salinity and water level targets or thresholds while maximizing land-building goals. Moreover, water quality predictions provide valuable information on the current ecological conditions of the area. Real time observations and model predictions can be used as guidance to decision makers regarding the operation of control structures in response to forecasted weather or river flood events. Coastal communities can benefit from water level, salinity and water quality forecast to manage their activities.

Ensemble Streamflow Prediction in Korea: Past and Future 5 Years

NASA Astrophysics Data System (ADS)

Jeong, D.; Kim, Y.; Lee, J.

2005-05-01

The Ensemble Streamflow Prediction (ESP) approach was first introduced in 2000 by the Hydrology Research Group (HRG) at Seoul National University as an alternative probabilistic forecasting technique for improving the 'Water Supply Outlook' That is issued every month by the Ministry of Construction and Transportation in Korea. That study motivated the Korea Water Resources Corporation (KOWACO) to establish their seasonal probabilistic forecasting system for the 5 major river basins using the ESP approach. In cooperation with the HRG, the KOWACO developed monthly optimal multi-reservoir operating systems for the Geum river basin in 2004, which coupled the ESP forecasts with an optimization model using sampling stochastic dynamic programming. The user interfaces for both ESP and SSDP have also been designed for the developed computer systems to become more practical. More projects for developing ESP systems to the other 3 major river basins (i.e. the Nakdong, Han and Seomjin river basins) was also completed by the HRG and KOWACO at the end of December 2004. Therefore, the ESP system has become the most important mid- and long-term streamflow forecast technique in Korea. In addition to the practical aspects, resent research experience on ESP has raised some concerns into ways of improving the accuracy of ESP in Korea. Jeong and Kim (2002) performed an error analysis on its resulting probabilistic forecasts and found that the modeling error is dominant in the dry season, while the meteorological error is dominant in the flood season. To address the first issue, Kim et al. (2004) tested various combinations and/or combining techniques and showed that the ESP probabilistic accuracy could be improved considerably during the dry season when the hydrologic models were combined and/or corrected. In addition, an attempt was also made to improve the ESP accuracy for the flood season using climate forecast information. This ongoing project handles three types of climate forecast information: (1) the Monthly Industrial Meteorology Information Magazine (MIMIM) of the Korea Meteorological Administration (2) the Global Data Assimilation Prediction System (GDAPS), and (3) the US National Centers for Environmental Prediction (NCEP). Each of these forecasts is issued in a unique format: (1) MIMIM is a most-probable-event forecast, (2) GDAPS is a single series of deterministic forecasts, and (3) NCEP is an ensemble of deterministic forecasts. Other minor issues include how long the initial conditions influences the ESP accuracy, and how many ESP scenarios are needed to obtain the best accuracy. This presentation also addresses some future research that is needed for ESP in Korea.

Evaluation of the Wind Flow Variability Using Scanning Doppler Lidar Measurements

NASA Astrophysics Data System (ADS)

Sand, S. C.; Pichugina, Y. L.; Brewer, A.

2016-12-01

Better understanding of the wind flow variability at the heights of the modern turbines is essential to accurately assess of generated wind power and efficient turbine operations. Nowadays the wind energy industry often utilizes scanning Doppler lidar to measure wind-speed profiles at high spatial and temporal resolution.The study presents wind flow features captured by scanning Doppler lidars during the second Wind Forecast and Improvement Project (WFIP 2) sponsored by the Department of Energy (DOE) and National Oceanic and Atmospheric Administration (NOAA). This 18-month long experiment in the Columbia River Basin aims to improve model wind forecasts complicated by mountain terrain, coastal effects, and numerous wind farms.To provide a comprehensive dataset to use for characterizing and predicting meteorological phenomena important to Wind Energy, NOAA deployed scanning, pulsed Doppler lidars to two sites in Oregon, one at Wasco, located upstream of all wind farms relative to the predominant westerly flow in the region, and one at Arlington, located in the middle of several wind farms.In this presentation we will describe lidar scanning patterns capable of providing data in conical, or vertical-slice modes. These individual scans were processed to obtain 15-min averaged profiles of wind speed and direction in real time. Visualization of these profiles as time-height cross sections allows us to analyze variability of these parameters with height, time and location, and reveal periods of rapid changes (ramp events). Examples of wind flow variability between two sites of lidar measurements along with examples of reduced wind velocity downwind of operating turbines (wakes) will be presented.

Practical Application of Modern Forecasting and Decision Tools at an Operational River Management Agency

NASA Astrophysics Data System (ADS)

Jawdy, C. M.; Carney, S.; Barber, N. M.; Balk, B. C.; Miller, G. A.

2017-12-01

The Tennessee Valley Authority (TVA) recently completed a complete overhaul of our River Forecast System (RFS). This modernization effort encompassed: uplift or addition of 89 data feeds calibration of a 140 subbasin rainfall-runoff model calibration of over 650 miles of hydraulic routings implementation of a decision optimization routine for 29 reservoirs implementation of hydrothermal forecast models for five river-cooled thermal plants creation of decision-friendly displays creation of a user-friendly wiki creation of a robust reporting system This talk will walk attendees through how a 24x7 river and grid management agency made decisions around how to operationalize the latest technologies in hydrology, hydraulics, decision science and information technology. The tradeoffs inherent in such an endeavor will be discussed so that research-oriented attendees can understand how best to align their research if they desire adoption within industry. More industry-oriented attendees can learn about the mechanics of how to succeed at such a large and complex project. Following the description of the modernization project, I can discuss TVA's plans for future growth of the system. We plan to add the following capabilities in the coming years: forecast verification tools to communicate floodplain risk tools to choose the best possible model forcings ensemble inflow modelling a river policy that allows for more reasonable tradeoff of benefits river decisions based on ensembles The iterative staging of such improvements is highly fraught with technical, political and operational risks. I will discuss how TVA's is using what we learned in the RFS modernization effort to grow further into delivering on the promise of these additional technologies.

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.

Numerical Analysis of Flood modeling of upper Citarum River under Extreme Flood Condition

NASA Astrophysics Data System (ADS)

Siregar, R. I.

2018-02-01

This paper focuses on how to approach the numerical method and computation to analyse flood parameters. Water level and flood discharge are the flood parameters solved by numerical methods approach. Numerical method performed on this paper for unsteady flow conditions have strengths and weaknesses, among others easily applied to the following cases in which the boundary irregular flow. The study area is in upper Citarum Watershed, Bandung, West Java. This paper uses computation approach with Force2 programming and HEC-RAS to solve the flow problem in upper Citarum River, to investigate and forecast extreme flood condition. Numerical analysis based on extreme flood events that have occurred in the upper Citarum watershed. The result of water level parameter modeling and extreme flood discharge compared with measurement data to analyse validation. The inundation area about flood that happened in 2010 is about 75.26 square kilometres. Comparing two-method show that the FEM analysis with Force2 programs has the best approach to validation data with Nash Index is 0.84 and HEC-RAS that is 0.76 for water level. For discharge data Nash Index obtained the result analysis use Force2 is 0.80 and with use HEC-RAS is 0.79.

Conditional Monthly Weather Resampling Procedure for Operational Seasonal Water Resources Forecasting

NASA Astrophysics Data System (ADS)

Beckers, J.; Weerts, A.; Tijdeman, E.; Welles, E.; McManamon, A.

2013-12-01

To provide reliable and accurate seasonal streamflow forecasts for water resources management several operational hydrologic agencies and hydropower companies around the world use the Extended Streamflow Prediction (ESP) procedure. The ESP in its original implementation does not accommodate for any additional information that the forecaster may have about expected deviations from climatology in the near future. Several attempts have been conducted to improve the skill of the ESP forecast, especially for areas which are affected by teleconnetions (e,g. ENSO, PDO) via selection (Hamlet and Lettenmaier, 1999) or weighting schemes (Werner et al., 2004; Wood and Lettenmaier, 2006; Najafi et al., 2012). A disadvantage of such schemes is that they lead to a reduction of the signal to noise ratio of the probabilistic forecast. To overcome this, we propose a resampling method conditional on climate indices to generate meteorological time series to be used in the ESP. The method can be used to generate a large number of meteorological ensemble members in order to improve the statistical properties of the ensemble. The effectiveness of the method was demonstrated in a real-time operational hydrologic seasonal forecasts system for the Columbia River basin operated by the Bonneville Power Administration. The forecast skill of the k-nn resampler was tested against the original ESP for three basins at the long-range seasonal time scale. The BSS and CRPSS were used to compare the results to those of the original ESP method. Positive forecast skill scores were found for the resampler method conditioned on different indices for the prediction of spring peak flows in the Dworshak and Hungry Horse basin. For the Libby Dam basin however, no improvement of skill was found. The proposed resampling method is a promising practical approach that can add skill to ESP forecasts at the seasonal time scale. Further improvement is possible by fine tuning the method and selecting the most informative climate indices for the region of interest.

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

NASA Astrophysics Data System (ADS)

Clark, Elizabeth; Wood, Andy; Nijssen, Bart; Mendoza, Pablo; Newman, Andy; Nowak, Kenneth; Arnold, Jeffrey

2017-04-01

In an automated forecast system, hydrologic data assimilation (DA) performs the valuable function of correcting raw simulated watershed model states to better represent external observations, including measurements of streamflow, snow, soil moisture, and the like. Yet the incorporation of automated DA into operational forecasting systems has been a long-standing challenge due to the complexities of the hydrologic system, which include numerous lags between state and output variations. To help demonstrate that such methods can succeed in operational automated implementations, we present results from the real-time application of an ensemble particle filter (PF) for short-range (7 day lead) ensemble flow forecasts in western US river basins. We use the System for Hydromet Applications, Research and Prediction (SHARP), developed by the National Center for Atmospheric Research (NCAR) in collaboration with the University of Washington, U.S. Army Corps of Engineers, and U.S. Bureau of Reclamation. SHARP is a fully automated platform for short-term to seasonal hydrologic forecasting applications, incorporating uncertainty in initial hydrologic conditions (IHCs) and in hydrometeorological predictions through ensemble methods. In this implementation, IHC uncertainty is estimated by propagating an ensemble of 100 temperature and precipitation time series through conceptual and physically-oriented models. The resulting ensemble of derived IHCs exhibits a broad range of possible soil moisture and snow water equivalent (SWE) states. The PF selects and/or weights and resamples the IHCs that are most consistent with external streamflow observations, and uses the particles to initialize a streamflow forecast ensemble driven by ensemble precipitation and temperature forecasts downscaled from the Global Ensemble Forecast System (GEFS). We apply this method in real-time for several basins in the western US that are important for water resources management, and perform a hindcast experiment to evaluate the utility of PF-based data assimilation on streamflow forecasts skill. This presentation describes findings, including a comparison of sequential and non-sequential particle weighting methods.

48 CFR 232.072-3 - Cash flow forecasts.

Code of Federal Regulations, 2013 CFR

2013-10-01

... forecasts is a strong indicator of serious managerial deficiencies or potential contract cost or performance... the causes of any differences. (d) Cash flow forecasts must— (1) Show the origin and use of all...

48 CFR 232.072-3 - Cash flow forecasts.

Code of Federal Regulations, 2011 CFR

2011-10-01

... forecasts is a strong indicator of serious managerial deficiencies or potential contract cost or performance... the causes of any differences. (d) Cash flow forecasts must— (1) Show the origin and use of all...

48 CFR 232.072-3 - Cash flow forecasts.

Code of Federal Regulations, 2014 CFR

2014-10-01

... forecasts is a strong indicator of serious managerial deficiencies or potential contract cost or performance... the causes of any differences. (d) Cash flow forecasts must— (1) Show the origin and use of all...

48 CFR 232.072-3 - Cash flow forecasts.

Code of Federal Regulations, 2012 CFR

2012-10-01

... forecasts is a strong indicator of serious managerial deficiencies or potential contract cost or performance... the causes of any differences. (d) Cash flow forecasts must— (1) Show the origin and use of all...

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.

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.

Neural network river forecasting through baseflow separation and binary-coded swarm optimization

NASA Astrophysics Data System (ADS)

Taormina, Riccardo; Chau, Kwok-Wing; Sivakumar, Bellie

2015-10-01

The inclusion of expert knowledge in data-driven streamflow modeling is expected to yield more accurate estimates of river quantities. Modular models (MMs) designed to work on different parts of the hydrograph are preferred ways to implement such approach. Previous studies have suggested that better predictions of total streamflow could be obtained via modular Artificial Neural Networks (ANNs) trained to perform an implicit baseflow separation. These MMs fit separately the baseflow and excess flow components as produced by a digital filter, and reconstruct the total flow by adding these two signals at the output. The optimization of the filter parameters and ANN architectures is carried out through global search techniques. Despite the favorable premises, the real effectiveness of such MMs has been tested only on a few case studies, and the quality of the baseflow separation they perform has never been thoroughly assessed. In this work, we compare the performance of MM against global models (GMs) for nine different gaging stations in the northern United States. Binary-coded swarm optimization is employed for the identification of filter parameters and model structure, while Extreme Learning Machines, instead of ANN, are used to drastically reduce the large computational times required to perform the experiments. The results show that there is no evidence that MM outperform global GM for predicting the total flow. In addition, the baseflow produced by the MM largely underestimates the actual baseflow component expected for most of the considered gages. This occurs because the values of the filter parameters maximizing overall accuracy do not reflect the geological characteristics of the river basins. The results indeed show that setting the filter parameters according to expert knowledge results in accurate baseflow separation but lower accuracy of total flow predictions, suggesting that these two objectives are intrinsically conflicting rather than compatible.

Flood Frequency Analysis using different flood descriptors - the Warsaw reach of the river Vistula case study

NASA Astrophysics Data System (ADS)

Karamuz, Emilia; Kochanek, Krzysztof; Romanowicz, Renata

2014-05-01

Flood frequency analysis (FFA) is customarily performed using annual maximum flows. However, there is a number of different flood descriptors that could be used. Among them are water levels, peaks over the threshold, flood-wave duration, flood volume, etc. In this study we compare different approaches to FFA for their suitability for flood risk assessment. The main goal is to obtain the FFA curve with the smallest possible uncertainty limits, in particular for the distribution tail. The extrapolation of FFA curves is crucial in future flood risk assessment in a changing climate. We compare the FFA curves together with their uncertainty limits obtained using flows, water levels, flood inundation area and volumes for the Warsaw reach of the river Vistula. Moreover, we derive the FFA curves obtained using simulated flows. The results are used to derive the error distribution for the maximum simulated and observed values under different modelling techniques and assess its influence on flood risk predictions for ungauged catchments. MIKE11, HEC-RAS and transfer function model are applied in average and extreme conditions to model flow propagation in the Warsaw Vistula reach. The additional questions we want to answer are what is the range of application of different modelling tools under various flow conditions and how can the uncertainty of flood risk assessment be decreased. This work was partly supported by the projects "Stochastic flood forecasting system (The River Vistula reach from Zawichost to Warsaw)" and "Modern statistical models for analysis of flood frequency and features of flood waves", carried by the Institute of Geophysics, Polish Academy of Sciences on the order of the National Science Centre (contracts Nos. 2011/01/B/ST10/06866 and 2012/05/B/ST10/00482, respectively). The water level and flow data were provided by the Institute of Meteorology and Water Management (IMGW), Poland.

Climate and Salmon Restoration in the Columbia River Basin: The Role and Usability of Seasonal Forecasts.

NASA Astrophysics Data System (ADS)

Pulwarty, Roger S.; Redmond, Kelly T.

1997-03-01

The Pacific Northwest is dependent on the vast and complex Columbia River system for power production, irrigation, navigation, flood control, recreation, municipal and industrial water supplies, and fish and wildlife habitat. In recent years Pacific salmon populations in this region, a highly valued cultural and economic resource, have declined precipitously. Since 1980, regional entities have embarked on the largest effort at ecosystem management undertaken to date in the United States, primarily aimed at balancing hydropower demands with salmon restoration activities. It has become increasingly clear that climatically driven fluctuations in the freshwater and marine environments occupied by these fish are an important influence on population variability. It is also clear that there are significant prospects of climate predictability that may prove advantageous in managing the water resources shared by the long cast of regional interests. The main thrusts of this study are 1) to describe the climate and management environments of the Columbia River basin, 2) to assess the present degree of use and benefits of available climate information, 3) to identify new roles and applications made possible by recent advances in climate forecasting, and 4) to understand, from the point of view of present and potential users in specific contexts of salmon management, what information might be needed, for what uses, and when, where, and how it should be provided. Interviews were carried out with 32 individuals in 19 organizations involved in salmon management decisions. Primary needs were in forecasting runoff volume and timing, river transit times, and stream temperatures, as much as a year or more in advance. Most respondents desired an accuracy of 75% for a seasonal forecast. Despite the significant influence of precipitation and its subsequent hydrologic impacts on the regional economy, no specific use of the present climate forecasts was uncovered. Understanding the limitations to information use forms a major component of this study. The complexity of the management environment, the lack of well-defined linkages among potential users and forecasters, and the lack of supplementary background information relating to the forecasts pose substantial barriers to future use of forecasts. Recommendations to address these problems are offered. The use of climate information and forecasts to reduce the uncertainty inherent in managing large systems for diverse needs bears significant promise.

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.

Weather Research and Forecasting Model Sensitivity Comparisons for Warm Season Convective Initiation

NASA Technical Reports Server (NTRS)

Watson, Leela R.; Hoeth, Brian; Blottman, Peter F.

2007-01-01

Mesoscale weather conditions can significantly affect the space launch and landing operations at Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS). During the summer months, land-sea interactions that occur across KSC and CCAFS lead to the formation of a sea breeze, which can then spawn deep convection. These convective processes often last 60 minutes or less and pose a significant challenge to the forecasters at the National Weather Service (NWS) Spaceflight Meteorology Group (SMG). The main challenge is that a "GO" forecast for thunderstorms and precipitation at the Shuttle Landing Facility is required at the 90 minute deorbit decision for End Of Mission (EOM) and at the 30 minute Return To Launch Site (RTLS) decision. Convective initiation, timing, and mode also present a forecast challenge for the NWS in Melbourne, FL (MLB). The NWS MLB issues such tactical forecast information as Terminal Aerodrome Forecasts (TAF5), Spot Forecasts for fire weather and hazardous materials incident support, and severe/hazardous weather Watches, Warnings, and Advisories. Lastly, these forecasting challenges can also affect the 45th Weather Squadron (45 WS), which provides comprehensive weather forecasts for shuttle launch, as well as ground operations, at KSC and CCAFS. The need for accurate mesoscale model forecasts to aid in their decision making is crucial. This study specifically addresses the skill of different model configurations in forecasting warm season convective initiation. Numerous factors influence the development of convection over the Florida peninsula. These factors include sea breezes, river and lake breezes, the prevailing low-level flow, and convergent flow due to convex coastlines that enhance the sea breeze. The interaction of these processes produces the warm season convective patterns seen over the Florida peninsula. However, warm season convection remains one of the most poorly forecast meteorological parameters. To determine which configuration options are best to address this specific forecast concern, the Weather Research and Forecasting (WRF) model, which has two dynamical cores - the Advanced Research WRF (ARW) and the Non-hydrostatic Mesoscale Model (NMM) was employed. In addition to the two dynamical cores, there are also two options for a "hot-start" initialization of the WRF model - the Local Analysis and Prediction System (LAPS; McGinley 1995) and the Advanced Regional Prediction System (ARPS) Data Analysis System (ADAS; Brewster 1996). Both LAPS and ADAS are 3- dimensional weather analysis systems that integrate multiple meteorological data sources into one consistent analysis over the user's domain of interest. This allows mesoscale models to benefit from the addition of highresolution data sources. Having a series of initialization options and WRF cores, as well as many options within each core, provides SMG and MLB with considerable flexibility as well as challenges. It is the goal of this study to assess the different configurations available and to determine which configuration will best predict warm season convective initiation.

Salinity trends in the Ebro River (Spain)

NASA Astrophysics Data System (ADS)

Lorenzo-Gonzalez, M.° Angeles; Isidoro, Daniel; Quilez, Dolores

2016-04-01

In the Ebro River Basin (Spain), the increase in water diversion for irrigation (following the increase in irrigated area) and the recovery of natural vegetation in the upper reaches, along with climate change have induced changes in the river flow and its associated salt loads. This study was supported by the Ebro River Basin Administration (CHE) and aimed to establish the trends in the salt concentrations and loads of the Ebro River at Tortosa (no 027, the extreme downstream gauging station). The CHE databases from 1972-73 to 2011-12, including mean monthly flows (Q) and concentration readings (electrical conductivity converted to total dissolved solids -TDS- by regression) from monthly grab samples, have been used. The trends were established by (i) harmonic regression analysis; (ii) linear regression by month; and (iii) the non-parametric Mann-Kendall method. Additionally, (iv) the regressions of TDS on Q in the current and previous months were established, allowing for analyzing separately the trends in TDS linked to- (TDSq) and independent of- (TDSaj) the observed changes in flow. In all cases, the trends were analyzed for different periods within the full span 1973-2012 (1973 to 2012, 1981 to 2012, 1990-2012 and 2001-2012), trying to account for periods with sensibly similar patterns of land use change. An increase in TDS was found for all the periods analyzed that was lower as shorter periods were used, suggesting that lower salinity changes might be taking place in the last years, possibly due to the reduction in the rate of irrigation development and to the on-going irrigation modernization process. The higher seasonal TDS increases were found in autumn and winter months and the increase in TDS was linked both to intrinsic changes in salinity (TDSaj) and to the observed decrease in flow (TDSq). On the other hand, the salt loads decreased, especially in autumn, as a result of the observed flow decrease. These results are based on the observed evolution of flows and salinity in 1973-2012 and can only be extrapolated into the future if the drivers of this evolution (climate and land use changes) remain unchanged in the following years, what is uncertain. A more comprehensive methodology to estimate the effects of irrigation on water salinity has been developed based on a mass balance approach. Using actual data on volumes and concentrations of return flows observed in the basin (dependent on the actual salinity of soils and waters and the irrigation systems, among other factors), the return flows of the irrigated areas are aggregated to match the actual flows and loads observed in the Ebro River. Once this balance is satisfied, the effect of new irrigated areas, drainage water reuse, irrigation modernization, or climate change would be incorporated to the balance yielding salinity forecasts based on planned irrigation developments and modernization or climate change predictions. A priori, irrigation modernization would produce lower, more concentrated volumes of return flows with lower salt loads that would result in lower TDS concentrations in the Ebro River.

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

NASA Astrophysics Data System (ADS)

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

2017-04-01

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

Development of a monthly to seasonal forecast framework tailored to inland waterway transport in central Europe

NASA Astrophysics Data System (ADS)

Meißner, Dennis; Klein, Bastian; Ionita, Monica

2017-12-01

Traditionally, navigation-related forecasts in central Europe cover short- to medium-range lead times linked to the travel times of vessels to pass the main waterway bottlenecks leaving the loading ports. Without doubt, this aspect is still essential for navigational users, but in light of the growing political intention to use the free capacity of the inland waterway transport in Europe, additional lead time supporting strategic decisions is more and more in demand. However, no such predictions offering extended lead times of several weeks up to several months currently exist for considerable parts of the European waterway network. This paper describes the set-up of a monthly to seasonal forecasting system for the German stretches of the international waterways of the Rhine, Danube and Elbe rivers. Two competitive forecast approaches have been implemented: the dynamical set-up forces a hydrological model with post-processed outputs from ECMWF general circulation model System 4, whereas the statistical approach is based on the empirical relationship (teleconnection) of global oceanic, climate and regional hydro-meteorological data with river flows. The performance of both forecast methods is evaluated in relation to the climatological forecast (ensemble of historical streamflow) and the well-known ensemble streamflow prediction approach (ESP, ensemble based on historical meteorology) using common performance indicators (correlation coefficient; mean absolute error, skill score; mean squared error, skill score; and continuous ranked probability, skill score) and an impact-based evaluation quantifying the potential economic gain. The following four key findings result from this study: (1) as former studies for other regions of central Europe indicate, the accuracy and/or skill of the meteorological forcing used has a larger effect than the quality of initial hydrological conditions for relevant stations along the German waterways. (2) Despite the predictive limitations on longer lead times in central Europe, this study reveals the existence of a valuable predictability of streamflow on monthly up to seasonal timescales along the Rhine, upper Danube and Elbe waterways, and the Elbe achieves the highest skill and economic value. (3) The more physically based and the statistical approach are able to improve the predictive skills and economic value compared to climatology and the ESP approach. The specific forecast skill highly depends on the forecast location, the lead time and the season. (4) Currently, the statistical approach seems to be most skilful for the three waterways investigated. The lagged relationship between the monthly and/or seasonal streamflow and the climatic and/or oceanic variables vary between 1 month (e.g. local precipitation, temperature and soil moisture) up to 6 months (e.g. sea surface temperature). Besides focusing on improving the forecast methodology, especially by combining the individual approaches, the focus is on developing useful forecast products on monthly to seasonal timescales for waterway transport and to operationalize the related forecasting service.

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.

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.

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.

Computer model of Raritan River Basin water-supply system in central New Jersey

USGS Publications Warehouse

Dunne, Paul; Tasker, Gary D.

1996-01-01

This report describes a computer model of the Raritan River Basin water-supply system in central New Jersey. The computer model provides a technical basis for evaluating the effects of alternative patterns of operation of the Raritan River Basin water-supply system during extended periods of below-average precipitation. The computer model is a continuity-accounting model consisting of a series of interconnected nodes. At each node, the inflow volume, outflow volume, and change in storage are determined and recorded for each month. The model runs with a given set of operating rules and water-use requirements including releases, pumpages, and diversions. The model can be used to assess the hypothetical performance of the Raritan River Basin water- supply system in past years under alternative sets of operating rules. It also can be used to forecast the likelihood of specified outcomes, such as the depletion of reservoir contents below a specified threshold or of streamflows below statutory minimum passing flows, for a period of up to 12 months. The model was constructed on the basis of current reservoir capacities and the natural, unregulated monthly runoff values recorded at U.S. Geological Survey streamflow- gaging stations in the basin.

Development and Application of a Process-based River System Model at a Continental Scale

NASA Astrophysics Data System (ADS)

Kim, S. S. H.; Dutta, D.; Vaze, J.; Hughes, J. D.; Yang, A.; Teng, J.

2014-12-01

Existing global and continental scale river models, mainly designed for integrating with global climate model, are of very course spatial resolutions and they lack many important hydrological processes, such as overbank flow, irrigation diversion, groundwater seepage/recharge, which operate at a much finer resolution. Thus, these models are not suitable for producing streamflow forecast at fine spatial resolution and water accounts at sub-catchment levels, which are important for water resources planning and management at regional and national scale. A large-scale river system model has been developed and implemented for water accounting in Australia as part of the Water Information Research and Development Alliance between Australia's Bureau of Meteorology (BoM) and CSIRO. The model, developed using node-link architecture, includes all major hydrological processes, anthropogenic water utilisation and storage routing that influence the streamflow in both regulated and unregulated river systems. It includes an irrigation model to compute water diversion for irrigation use and associated fluxes and stores and a storage-based floodplain inundation model to compute overbank flow from river to floodplain and associated floodplain fluxes and stores. An auto-calibration tool has been built within the modelling system to automatically calibrate the model in large river systems using Shuffled Complex Evolution optimiser and user-defined objective functions. The auto-calibration tool makes the model computationally efficient and practical for large basin applications. The model has been implemented in several large basins in Australia including the Murray-Darling Basin, covering more than 2 million km2. The results of calibration and validation of the model shows highly satisfactory performance. The model has been operalisationalised in BoM for producing various fluxes and stores for national water accounting. This paper introduces this newly developed river system model describing the conceptual hydrological framework, methods used for representing different hydrological processes in the model and the results and evaluation of the model performance. The operational implementation of the model for water accounting is discussed.

Global SWOT Data Assimilation of River Hydrodynamic Model; the Twin Simulation Test of CaMa-Flood

NASA Astrophysics Data System (ADS)

Ikeshima, D.; Yamazaki, D.; Kanae, S.

2016-12-01

CaMa-Flood is a global scale model for simulating hydrodynamics in large scale rivers. It can simulate river hydrodynamics such as river discharge, flooded area, water depth and so on by inputting water runoff derived from land surface model. Recently many improvements at parameters or terrestrial data are under process to enhance the reproducibility of true natural phenomena. However, there are still some errors between nature and simulated result due to uncertainties in each model. SWOT (Surface water and Ocean Topography) is a satellite, which is going to be launched in 2021, can measure open water surface elevation. SWOT observed data can be used to calibrate hydrodynamics model at river flow forecasting and is expected to improve model's accuracy. Combining observation data into model to calibrate is called data assimilation. In this research, we developed data-assimilated river flow simulation system in global scale, using CaMa-Flood as river hydrodynamics model and simulated SWOT as observation data. Generally at data assimilation, calibrating "model value" with "observation value" makes "assimilated value". However, the observed data of SWOT satellite will not be available until its launch in 2021. Instead, we simulated the SWOT observed data using CaMa-Flood. Putting "pure input" into CaMa-Flood produce "true water storage". Extracting actual daily swath of SWOT from "true water storage" made simulated observation. For "model value", we made "disturbed water storage" by putting "noise disturbed input" to CaMa-Flood. Since both "model value" and "observation value" are made by same model, we named this twin simulation. At twin simulation, simulated observation of "true water storage" is combined with "disturbed water storage" to make "assimilated value". As the data assimilation method, we used ensemble Kalman filter. If "assimilated value" is closer to "true water storage" than "disturbed water storage", the data assimilation can be marked effective. Also by changing the input disturbance of "disturbed water storage", acceptable rate of uncertainty at the input may be discussed.

Snow cover trend and hydrological characteristics of the Astore River basin (Western Himalayas) and its comparison to the Hunza basin (Karakoram region).

PubMed

Tahir, Adnan Ahmad; Chevallier, Pierre; Arnaud, Yves; Ashraf, Muhammad; Bhatti, Muhammad Tousif

2015-02-01

A large proportion of Pakistan's irrigation water supply is taken from the Upper Indus River Basin (UIB) in the Himalaya-Karakoram-Hindukush range. More than half of the annual flow in the UIB is contributed by five of its snow and glacier-fed sub-basins including the Astore (Western Himalaya - south latitude of the UIB) and Hunza (Central Karakoram - north latitude of the UIB) River basins. Studying the snow cover, its spatio-temporal change and the hydrological response of these sub-basins is important so as to better manage water resources. This paper compares new data from the Astore River basin (mean catchment elevation, 4100 m above sea level; m asl afterwards), obtained using MODIS satellite snow cover images, with data from a previously-studied high-altitude basin, the Hunza (mean catchment elevation, 4650 m asl). The hydrological regime of this sub-catchment was analyzed using the hydrological and climate data available at different altitudes from the basin area. The results suggest that the UIB is a region undergoing a stable or slightly increasing trend of snow cover in the southern (Western Himalayas) and northern (Central Karakoram) parts. Discharge from the UIB is a combination of snow and glacier melt with rainfall-runoff at southern part, but snow and glacier melt are dominant at the northern part of the catchment. Similar snow cover trends (stable or slightly increasing) but different river flow trends (increasing in Astore and decreasing in Hunza) suggest a sub-catchment level study of the UIB to understand thoroughly its hydrological behavior for better flood forecasting and water resources management. Copyright © 2014 Elsevier B.V. All rights reserved.

Regional scale flood modeling using NEXRAD rainfall, GIS, and HEC-HMS/RAS: a case study for the San Antonio River Basin Summer 2002 storm event.

PubMed

Knebl, M R; Yang, Z-L; Hutchison, K; Maidment, D R

2005-06-01

This paper develops a framework for regional scale flood modeling that integrates NEXRAD Level III rainfall, GIS, and a hydrological model (HEC-HMS/RAS). The San Antonio River Basin (about 4000 square miles, 10,000 km2) in Central Texas, USA, is the domain of the study because it is a region subject to frequent occurrences of severe flash flooding. A major flood in the summer of 2002 is chosen as a case to examine the modeling framework. The model consists of a rainfall-runoff model (HEC-HMS) that converts precipitation excess to overland flow and channel runoff, as well as a hydraulic model (HEC-RAS) that models unsteady state flow through the river channel network based on the HEC-HMS-derived hydrographs. HEC-HMS is run on a 4 x 4 km grid in the domain, a resolution consistent with the resolution of NEXRAD rainfall taken from the local river authority. Watershed parameters are calibrated manually to produce a good simulation of discharge at 12 subbasins. With the calibrated discharge, HEC-RAS is capable of producing floodplain polygons that are comparable to the satellite imagery. The modeling framework presented in this study incorporates a portion of the recently developed GIS tool named Map to Map that has been created on a local scale and extends it to a regional scale. The results of this research will benefit future modeling efforts by providing a tool for hydrological forecasts of flooding on a regional scale. While designed for the San Antonio River Basin, this regional scale model may be used as a prototype for model applications in other areas of the country.

Climate change poised to threaten hydrologic connectivity and endemic fishes in dryland streams

PubMed Central

Jaeger, Kristin L.; Olden, Julian D.; Pelland, Noel A.

2014-01-01

Protecting hydrologic connectivity of freshwater ecosystems is fundamental to ensuring species persistence, ecosystem integrity, and human well-being. More frequent and severe droughts associated with climate change are poised to significantly alter flow intermittence patterns and hydrologic connectivity in dryland streams of the American Southwest, with deleterious effects on highly endangered fishes. By integrating local-scale hydrologic modeling with emerging approaches in landscape ecology, we quantify fine-resolution, watershed-scale changes in habitat size, spacing, and connectance under forecasted climate change in the Verde River Basin, United States. Model simulations project annual zero-flow day frequency to increase by 27% by midcentury, with differential seasonal consequences on continuity (temporal continuity at discrete locations) and connectivity (spatial continuity within the network). A 17% increase in the frequency of stream drying events is expected throughout the network with associated increases in the duration of these events. Flowing portions of the river network will diminish between 8% and 20% in spring and early summer and become increasingly isolated by more frequent and longer stretches of dry channel fragments, thus limiting the opportunity for native fishes to access spawning habitats and seasonally available refuges. Model predictions suggest that midcentury and late century climate will reduce network-wide hydrologic connectivity for native fishes by 6–9% over the course of a year and up to 12–18% during spring spawning months. Our work quantifies climate-induced shifts in stream drying and connectivity across a large river network and demonstrates their implications for the persistence of a globally endemic fish fauna. PMID:25136090

Inference of Stream Network Fragmentation Patterns from Ground Water - Surface Water Interactions on the High Plains Aquifer

NASA Astrophysics Data System (ADS)

Chandler, D. G.; Yang, X.; Steward, D. R.; Gido, K.

2007-12-01

Stream networks in the Great Plains integrate fluxes from precipitation as surface runoff in discrete events and groundwater as base flow. Changes in land cover and agronomic practices and development of ground water resources to support irrigated agriculture have resulted in profound changes in the occurrence and magnitude of stream flows, especially near the Ogallala aquifer, where precipitation is low. These changes have demonstrably altered the aquatic habitat of western Kansas, with documented changes in fish populations, riparian communities and groundwater quality due to stream transmission losses. Forecasting future changes in aquatic and riparian ecology and groundwater quality requires a large scale spatially explicit model of groundwater- surface water interaction. In this study, we combine historical data on land use, stream flow, production well development and groundwater level observations with groundwater elevation modeling to support a geospatial framework for assessing changes in refugia for aquatic species in four rivers in western Kansas between 1965 and 2005. Decreased frequency and duration of streamflow occurred in all rivers, but the extent of change depended on the geomorphology of the river basin and the extent of groundwater development. In the absence of streamflow, refugia for aquatic species were defined as the stream reaches below the phreatic surface of the regional aquifer. Changes in extent, location and degree of fragmentation of gaining reaches was found to be a strong predictor of surface water occurrence during drought and a robust hydrological template for the analysis of changes in recharge to alluvial and regional aquifers and riparian and aquatic habitat.

Climate change poised to threaten hydrologic connectivity and endemic fishes in dryland streams.

PubMed

Jaeger, Kristin L; Olden, Julian D; Pelland, Noel A

2014-09-23

Protecting hydrologic connectivity of freshwater ecosystems is fundamental to ensuring species persistence, ecosystem integrity, and human well-being. More frequent and severe droughts associated with climate change are poised to significantly alter flow intermittence patterns and hydrologic connectivity in dryland streams of the American Southwest, with deleterious effects on highly endangered fishes. By integrating local-scale hydrologic modeling with emerging approaches in landscape ecology, we quantify fine-resolution, watershed-scale changes in habitat size, spacing, and connectance under forecasted climate change in the Verde River Basin, United States. Model simulations project annual zero-flow day frequency to increase by 27% by midcentury, with differential seasonal consequences on continuity (temporal continuity at discrete locations) and connectivity (spatial continuity within the network). A 17% increase in the frequency of stream drying events is expected throughout the network with associated increases in the duration of these events. Flowing portions of the river network will diminish between 8% and 20% in spring and early summer and become increasingly isolated by more frequent and longer stretches of dry channel fragments, thus limiting the opportunity for native fishes to access spawning habitats and seasonally available refuges. Model predictions suggest that midcentury and late century climate will reduce network-wide hydrologic connectivity for native fishes by 6-9% over the course of a year and up to 12-18% during spring spawning months. Our work quantifies climate-induced shifts in stream drying and connectivity across a large river network and demonstrates their implications for the persistence of a globally endemic fish fauna.

A New Multivariate Approach in Generating Ensemble Meteorological Forcings for Hydrological Forecasting

NASA Astrophysics Data System (ADS)

Khajehei, Sepideh; Moradkhani, Hamid

2015-04-01

Producing reliable and accurate hydrologic ensemble forecasts are subject to various sources of uncertainty, including meteorological forcing, initial conditions, model structure, and model parameters. Producing reliable and skillful precipitation ensemble forecasts is one approach to reduce the total uncertainty in hydrological applications. Currently, National Weather Prediction (NWP) models are developing ensemble forecasts for various temporal ranges. It is proven that raw products from NWP models are biased in mean and spread. Given the above state, there is a need for methods that are able to generate reliable ensemble forecasts for hydrological applications. One of the common techniques is to apply statistical procedures in order to generate ensemble forecast from NWP-generated single-value forecasts. The procedure is based on the bivariate probability distribution between the observation and single-value precipitation forecast. However, one of the assumptions of the current method is fitting Gaussian distribution to the marginal distributions of observed and modeled climate variable. Here, we have described and evaluated a Bayesian approach based on Copula functions to develop an ensemble precipitation forecast from the conditional distribution of single-value precipitation forecasts. Copula functions are known as the multivariate joint distribution of univariate marginal distributions, which are presented as an alternative procedure in capturing the uncertainties related to meteorological forcing. Copulas are capable of modeling the joint distribution of two variables with any level of correlation and dependency. This study is conducted over a sub-basin in the Columbia River Basin in USA using the monthly precipitation forecasts from Climate Forecast System (CFS) with 0.5x0.5 Deg. spatial resolution to reproduce the observations. The verification is conducted on a different period and the superiority of the procedure is compared with Ensemble Pre-Processor approach currently used by National Weather Service River Forecast Centers in USA.

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.

Floods in Bangladesh and Northeast India

NASA Technical Reports Server (NTRS)

2002-01-01

For the past month heavy monsoon rains have led to massive flooding in eastern India, Nepal, and Bangladesh, which have killed over 500 people and left millions homeless. This false-color image acquired on August 5, 2002, by the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard NASA's Terra spacecraft shows the extent of this flooding. In the upper right-hand corner of the image, the swollen Brahmaputra River runs east to west through the Indian state of Assam. Normally, the river and its tributaries would resemble a tangle of thin lines. Moving to the upper left-hand corner, flooding can be seen along the Ganges River in the state of Bihar, India. Both of these rivers flow into Bangladesh along with many others from India and Nepal. Heavy monsoon rains from all across the region have inundated the small country with water this year. Floodwaters have all but covered northeastern Bangladesh, which is usually dry. The Jamuna River, which runs down the center of the country off of the Brahmaputra River, now resembles a narrow lake. Millions of dollars in crops have been destroyed and thousands have been left stranded in their villages or on rafts. Forecasters are warning that flooding could get worse. In the false-color image, land is green, and water is black and dark brown. Clouds appear pink, red and white. Credit: Jacques Descloitres, MODIS Land Rapid Response Team, NASA/GSFC

Towards Optimal Operation of the Reservoir System in Upper Yellow River: Incorporating Long- and Short-term Operations and Using Rolling Updated Hydrologic Forecast Information

NASA Astrophysics Data System (ADS)

Si, Y.; Li, X.; Li, T.; Huang, Y.; Yin, D.

2016-12-01

The cascade reservoirs in Upper Yellow River (UYR), one of the largest hydropower bases in China, play a vital role in peak load and frequency regulation for Northwest China Power Grid. The joint operation of this system has been put forward for years whereas has not come into effect due to management difficulties and inflow uncertainties, and thus there is still considerable improvement room for hydropower production. This study presents a decision support framework incorporating long- and short-term operation of the reservoir system. For long-term operation, we maximize hydropower production of the reservoir system using historical hydrological data of multiple years, and derive operating rule curves for storage reservoirs. For short-term operation, we develop a program consisting of three modules, namely hydrologic forecast module, reservoir operation module and coordination module. The coordination module is responsible for calling the hydrologic forecast module to acquire predicted inflow within a short-term horizon, and transferring the information to the reservoir operation module to generate optimal release decision. With the hydrologic forecast information updated, the rolling short-term optimization is iterated until the end of operation period, where the long-term operating curves serve as the ending storage target. As an application, the Digital Yellow River Integrated Model (referred to as "DYRIM", which is specially designed for runoff-sediment simulation in the Yellow River basin by Tsinghua University) is used in the hydrologic forecast module, and the successive linear programming (SLP) in the reservoir operation module. The application in the reservoir system of UYR demonstrates that the framework can effectively support real-time decision making, and ensure both computational accuracy and speed. Furthermore, it is worth noting that the general framework can be extended to any other reservoir system with any or combination of hydrological model(s) to forecast and any solver to optimize the operation of reservoir system.

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

NASA Astrophysics Data System (ADS)

Li, J.

2017-12-01

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

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

NASA Astrophysics Data System (ADS)

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

2011-12-01

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

Near-real-time simulation and internet-based delivery of forecast-flood inundation maps using two-dimensional hydraulic modeling--A pilot study for the Snoqualmie River, Washington

USGS Publications Warehouse

Jones, Joseph L.; Fulford, Janice M.; Voss, Frank D.

2002-01-01

A system of numerical hydraulic modeling, geographic information system processing, and Internet map serving, supported by new data sources and application automation, was developed that generates inundation maps for forecast floods in near real time and makes them available through the Internet. Forecasts for flooding are generated by the National Weather Service (NWS) River Forecast Center (RFC); these forecasts are retrieved automatically by the system and prepared for input to a hydraulic model. The model, TrimR2D, is a new, robust, two-dimensional model capable of simulating wide varieties of discharge hydrographs and relatively long stream reaches. TrimR2D was calibrated for a 28-kilometer reach of the Snoqualmie River in Washington State, and is used to estimate flood extent, depth, arrival time, and peak time for the RFC forecast. The results of the model are processed automatically by a Geographic Information System (GIS) into maps of flood extent, depth, and arrival and peak times. These maps subsequently are processed into formats acceptable by an Internet map server (IMS). The IMS application is a user-friendly interface to access the maps over the Internet; it allows users to select what information they wish to see presented and allows the authors to define scale-dependent availability of map layers and their symbology (appearance of map features). For example, the IMS presents a background of a digital USGS 1:100,000-scale quadrangle at smaller scales, and automatically switches to an ortho-rectified aerial photograph (a digital photograph that has camera angle and tilt distortions removed) at larger scales so viewers can see ground features that help them identify their area of interest more effectively. For the user, the option exists to select either background at any scale. Similar options are provided for both the map creator and the viewer for the various flood maps. This combination of a robust model, emerging IMS software, and application interface programming should allow the technology developed in the pilot study to be applied to other river systems where NWS forecasts are provided routinely.

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

NASA Astrophysics Data System (ADS)

Chen, L.

2017-12-01

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

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.

Definition of Pluviometric Thresholds For A Real Time Flood Forecasting System In The Arno Watershed

NASA Astrophysics Data System (ADS)

Amadio, P.; Mancini, M.; Mazzetti, P.; Menduni, G.; Nativi, S.; Rabuffetti, D.; Ravazzani, G.; Rosso, R.

The pluviometric flood forecasting thresholds are an easy method that helps river flood emergency management collecting data from limited area meteorologic model or telemetric raingauges. The thresholds represent the cumulated rainfall depth which generate critic discharge for a particular section. The thresholds were calculated for different sections of Arno river and for different antecedent moisture condition using the flood event distributed hydrologic model FEST. The model inputs were syntethic hietographs with different shape and duration. The system realibility has been verified by generating 500 year syntethic rainfall for 3 important subwatersheds of the studied area. A new technique to consider spatial variability of rainfall and soil properties effects on hydrograph has been investigated. The "Geomorphologic Weights" were so calculated. The alarm system has been implemented in a dedicated software (MIMI) that gets measured and forecast rainfall data from Autorità di Bacino and defines the state of the alert of the river sections.

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.

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

NASA Astrophysics Data System (ADS)

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

2017-12-01

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

Solutions Network Formulation Report. The Potential Contribution of the Ocean Surface Topography Mission to the General NOAA Oil Monitoring Environment

NASA Technical Reports Server (NTRS)

Hilbert, Kent; Anderson, Daniel; Lewis, David

2007-01-01

Data collected by the OSTM could be used to provide a solution for the GNOME DST. GNOME, developed by NOAA?s Office of Response and Restoration Hazardous Materials Response Division, geospatially models oil spill trajectories using wind, current, river flow, and tidal data. Data collected by the OSTM would supply information about ocean currents and wind speeds. This Candidate Solution is in alignment with the Coastal Management, Water Management, Disaster Management, Public Health, Ecological Forecasting, and Homeland Security National Applications and will benefit society by improving the capabilities of emergency responders who evaluate an oil spill?s probable threat.

Contribution of lateral terrestrial water flows to the regional hydrological cycle: A joint soil-atmospheric moisture tagging procedure with WRF-Hydro

NASA Astrophysics Data System (ADS)

Arnault, Joel; Wei, Jianhui; Zhang, Zhenyu; Wagner, Sven; Kunstmann, Harald

2017-04-01

Water resources management requires an accurate knowledge of the behavior of the regional hydrological cycle components, including precipitation, evapotranspiration, river discharge and soil water storage. Atmospheric models such as the Weather Research and Forecasting (WRF) model provide a tool to evaluate these components. The main drawback of these atmospheric models, however, is that the terrestrial segment of the hydrological cycle is reduced to vertical infiltration, and that lateral terrestrial water flows are neglected. Recent model developments have focused on coupled atmospheric-hydrological modeling systems, such as WRF-hydro, in order to take into account subsurface, overland and river flow. The aim of this study is to investigate the contribution of lateral terrestrial water flows to the regional hydrological cycle, with the help of a joint soil-atmospheric moisture tagging procedure. This procedure is the extended version of an existing atmospheric moisture tagging method developed in WRF and WRF-Hydro (Arnault et al. 2017). It is used to quantify the partitioning of precipitation into water stored in the soil, runoff, evapotranspiration, and potentially subsequent precipitation through regional recycling. An application to a high precipitation event on 23 June 2009 in the upper Danube river basin, Germany and Austria, is presented. Precipitating water during this day is tagged for the period 2009-2011. Its contribution to runoff and evapotranspiration decreases with time, but is still not negligible in the summer 2011. At the end of the study period, less than 5 % of the precipitating water on 23 June 2009 remains in the soil. The additionally resolved lateral terrestrial water flows in WRF-Hydro modify the partitioning between surface and underground runoff, in association with a slight increase of evapotranspiration and recycled precipitation. Reference: Arnault, J., R. Knoche, J. Wei, and H. Kunstmann (2016), Evaporation tagging and atmospheric water budget analysis with WRF: A regional precipitation recycling study for West Africa, Water Resour. Res., 52, 1544-1567, doi:10.1002/2015WR017704.

Analysis on the adaptive countermeasures to ecological management under changing environment in the Tarim River Basin, China

NASA Astrophysics Data System (ADS)

Yang, Fan; Xue, Lianqing; Zhang, Luochen; Chen, Xinfang; Chi, Yixia

2017-12-01

This article aims to explore the adaptive utilization strategies of flow regime versus traditional practices in the context of climate change and human activities in the arid area. The study presents quantitative analysis of climatic and anthropogenic factors to streamflow alteration in the Tarim River Basin (TRB) using the Budyko method and adaptive utilization strategies to eco-hydrological regime by comparing the applicability between autoregressive moving average model (ARMA) model and combined regression model. Our results suggest that human activities played a dominant role in streamflow deduction in the mainstream with contribution of 120.7%~190.1%. While in the headstreams, climatic variables were the primary determinant of streamflow by 56.5~152.6% of the increase. The comparison revealed that combined regression model performed better than ARMA model with the qualified rate of 80.49~90.24%. Based on the forecasts of streamflow for different purposes, the adaptive utilization scheme of water flow is established from the perspective of time and space. Our study presents an effective water resources scheduling scheme for the ecological environment and provides references for ecological protection and water allocation in the arid area.

Forecast Informed Reservoir Operations - An Opportunity to Improve Water Supply Reliability Lake Mendocino Demonstration Project

NASA Astrophysics Data System (ADS)

Jasperse, J.; Ralph, F. M.

2016-12-01

Forecast Informed Reservoir Operations (FIRO) is a management strategy that is gaining interest in the western United States as a means to improve the performance of reservoirs to provide more reliable water supply for municipal, agricultural, and environmental water needs as well as enhancing the flood protection capacity of reservoirs. Many surface water reservoirs were built decades ago and are operated in accordance with rules that were developed based on the best information at the time of construction. Over time there have been increasing stressors that impact effective reservoir operations including: increasing water demand; providing in-stream flows for habitat of aquatic species; and climate change. Few new reservoirs are being constructed, therefore there is motivation by water managers to more effectively operate existing reservoirs by optimizing operational rules under a decision framework that considers forecasting. The viability of FIRO is being investigated at Lake Mendocino in northern California. This facility is managed for flood protection by the U.S. Army Corps of Engineers (USACE) while the Sonoma County Water Agency (SCWA) is responsible for the management of the water supply pool. Reductions of reservoir inflow due to recent operational license conditions of an upstream hydroelectric facility coupled with highly variable precipitation (due to the significance of atmospheric rivers in the region) has led to difficulties in maintaining reservoir storage to meet stream flows for agricultural and municipal water users and to meet in-stream flow requirements for three salmonid species listed under the Endangered Species Act. The reduced water supply reliability of the reservoir has motivated water managers and scientists from local, state, and federal agencies to investigate whether FIRO could help address this challenge. This effort is led by a Steering Committee comprised of members from SCWA, Scripps-UC San Diego, USACE, NOAA, California Department of Water Resources, USGS, and Bureau of Reclamation. The partnership is involved with: conducting a preliminary viability assessment; evaluating operational strategies and decision support tools should FIRO be found to be viable; and coordinating research to support advances in new technology and improved forecasting skill.

Rainfall forecast in the Upper Mahaweli basin in Sri Lanka using RegCM model

NASA Astrophysics Data System (ADS)

Muhammadh, K. M.; Mafas, M. M. M.; Weerakoon, S. B.

2017-04-01

The Upper Mahaweli basin is the upper most sub basin of 788 km2 in size above Polgolla barrage in the Mahaweli River, the longest river in Sri Lanka which starts from the central hills of the island and drains to the sea at the North-east coast. Rainfall forecast in the Upper Mahaweli basin is important for issuing flood warning in the river downstream of the reservoirs, landslide warning in the settlements in hilly areas. Anticipatory water management in the basin including reservoir operations, barrage gate operation for releasing water for irrigation and flood control also require reliable rainfall and runoff prediction in the sub basin. In this study, the Regional Climate Model (RegCM V4.4.5.11) is calibrated for the basin to dynamically downscale reanalysis weather data of Global Climate Model (GCM) to forecast the rainfall in the basin. Observed rainfalls at gauging stations within the basin were used for model calibration and validation. The observed rainfall data was analysed using ARC GIS and the output of RegCM was analysed using GrADS tool. The output of the model and the observed precipitation were obtained on grids of size 0.1 degrees and the accuracy of the predictions were analysed using RMSE and Mean Model Absolute Error percentage (MAME %). The predictions by the calibrated RegCM model for the basin is shown to be satisfactory. The model is a useful tool for rainfall forecast in the Upper Mahaweli River basin.

The total probabilities from high-resolution ensemble forecasting of floods

NASA Astrophysics Data System (ADS)

Olav Skøien, Jon; Bogner, Konrad; Salamon, Peter; Smith, Paul; Pappenberger, Florian

2015-04-01

Ensemble forecasting has for a long time been used in meteorological modelling, to give an indication of the uncertainty of the forecasts. As meteorological ensemble forecasts often show some bias and dispersion errors, there is a need for calibration and post-processing of the ensembles. Typical methods for this are Bayesian Model Averaging (Raftery et al., 2005) and Ensemble Model Output Statistics (EMOS) (Gneiting et al., 2005). There are also methods for regionalizing these methods (Berrocal et al., 2007) and for incorporating the correlation between lead times (Hemri et al., 2013). To make optimal predictions of floods along the stream network in hydrology, we can easily use the ensemble members as input to the hydrological models. However, some of the post-processing methods will need modifications when regionalizing the forecasts outside the calibration locations, as done by Hemri et al. (2013). We present a method for spatial regionalization of the post-processed forecasts based on EMOS and top-kriging (Skøien et al., 2006). We will also look into different methods for handling the non-normality of runoff and the effect on forecasts skills in general and for floods in particular. Berrocal, V. J., Raftery, A. E. and Gneiting, T.: Combining Spatial Statistical and Ensemble Information in Probabilistic Weather Forecasts, Mon. Weather Rev., 135(4), 1386-1402, doi:10.1175/MWR3341.1, 2007. Gneiting, T., Raftery, A. E., Westveld, A. H. and Goldman, T.: Calibrated Probabilistic Forecasting Using Ensemble Model Output Statistics and Minimum CRPS Estimation, Mon. Weather Rev., 133(5), 1098-1118, doi:10.1175/MWR2904.1, 2005. Hemri, S., Fundel, F. and Zappa, M.: Simultaneous calibration of ensemble river flow predictions over an entire range of lead times, Water Resour. Res., 49(10), 6744-6755, doi:10.1002/wrcr.20542, 2013. Raftery, A. E., Gneiting, T., Balabdaoui, F. and Polakowski, M.: Using Bayesian Model Averaging to Calibrate Forecast Ensembles, Mon. Weather Rev., 133(5), 1155-1174, doi:10.1175/MWR2906.1, 2005. Skøien, J. O., Merz, R. and Blöschl, G.: Top-kriging - Geostatistics on stream networks, Hydrol. Earth Syst. Sci., 10(2), 277-287, 2006.

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

NASA Astrophysics Data System (ADS)

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

2011-01-01

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

Development of flood-inundation maps for the West Branch Susquehanna River near the Borough of Jersey Shore, Lycoming County, Pennsylvania

USGS Publications Warehouse

Roland, Mark A.; Hoffman, Scott A.

2011-01-01

Streamflow data, water-surface-elevation profiles derived from a Hydrologic Engineering Center River Analysis System hydraulic model, and geographical information system digital elevation models were used to develop a set of 18 flood-inundation maps for an approximately 5-mile reach of the West Branch Susquehanna River near the Borough of Jersey Shore, Pa. The inundation maps were created by the U.S. Geological Survey in cooperation with the Susquehanna River Basin Commission and Lycoming County as part of an ongoing effort by the National Oceanic and Atmospheric Administration's National Weather Service to focus on continued improvements to the flood forecasting and warning abilities in the Susquehanna River Basin and to modernize flood-forecasting methodologies. The maps, ranging from 23.0 to 40.0 feet in 1-foot increments, correspond to river stage at the U.S. Geological Survey streamgage 01549760 at Jersey Shore. The electronic files used to develop the maps were provided to the National Weather Service for incorporation into their Advanced Hydrologic Prediction Service website. The maps are displayed on this website, which serves as a web-based floodwarning system, and can be used to identify areas of predicted flood inundation associated with forecasted flood-peak stages. During times of flooding or predicted flooding, these maps can be used by emergency managers and the public to take proactive steps to protect life and reduce property damage caused by floods.

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

Seasonal forecast of St. Louis encephalitis virus transmission, Florida.

PubMed

Shaman, Jeffrey; Day, Jonathan F; Stieglitz, Marc; Zebiak, Stephen; Cane, Mark

2004-05-01

Disease transmission forecasts can help minimize human and domestic animal health risks by indicating where disease control and prevention efforts should be focused. For disease systems in which weather-related variables affect pathogen proliferation, dispersal, or transmission, the potential for disease forecasting exists. We present a seasonal forecast of St. Louis encephalitis virus transmission in Indian River County, Florida. We derive an empiric relationship between modeled land surface wetness and levels of SLEV transmission in humans. We then use these data to forecast SLEV transmission with a seasonal lead. Forecast skill is demonstrated, and a real-time seasonal forecast of epidemic SLEV transmission is presented. This study demonstrates how weather and climate forecast skill-verification analyses may be applied to test the predictability of an empiric disease forecast model.

Seasonal Forecast of St. Louis Encephalitis Virus Transmission, Florida

PubMed Central

Day, Jonathan F.; Stieglitz, Marc; Zebiak, Stephen; Cane, Mark

2004-01-01

Disease transmission forecasts can help minimize human and domestic animal health risks by indicating where disease control and prevention efforts should be focused. For disease systems in which weather-related variables affect pathogen proliferation, dispersal, or transmission, the potential for disease forecasting exists. We present a seasonal forecast of St. Louis encephalitis virus transmission in Indian River County, Florida. We derive an empirical relationship between modeled land surface wetness and levels of SLEV transmission in humans. We then use these data to forecast SLEV transmission with a seasonal lead. Forecast skill is demonstrated, and a real-time seasonal forecast of epidemic SLEV transmission is presented. This study demonstrates how weather and climate forecast skill verification analyses may be applied to test the predictability of an empirical disease forecast model. PMID:15200812

A River Discharge Model for Coastal Taiwan during Typhoon Morakot

DTIC Science & Technology

2012-08-01

Multidisciplinary Simulation, Estimation, and Assimilation Systems Reports in Ocean Science and Engineering MSEAS-13 A River Discharge...in this region. The island’s major rivers have correspondingly large drainage basins, and outflow from these river mouths can substantially reduce the...Multidisciplinary Simulation, Estimation, and Assimilation System (MSEAS) has been used to simulate the ocean dynamics and forecast the uncertainty

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

NASA Astrophysics Data System (ADS)

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

2012-12-01

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

A hybrid spatiotemporal drought forecasting model for operational use

NASA Astrophysics Data System (ADS)

Vasiliades, L.; Loukas, A.

2010-09-01

Drought forecasting plays an important role in the planning and management of natural resources and water resource systems in a river basin. Early and timelines forecasting of a drought event can help to take proactive measures and set out drought mitigation strategies to alleviate the impacts of drought. Spatiotemporal data mining is the extraction of unknown and implicit knowledge, structures, spatiotemporal relationships, or patterns not explicitly stored in spatiotemporal databases. As one of data mining techniques, forecasting is widely used to predict the unknown future based upon the patterns hidden in the current and past data. This study develops a hybrid spatiotemporal scheme for integrated spatial and temporal forecasting. Temporal forecasting is achieved using feed-forward neural networks and the temporal forecasts are extended to the spatial dimension using a spatial recurrent neural network model. The methodology is demonstrated for an operational meteorological drought index the Standardized Precipitation Index (SPI) calculated at multiple timescales. 48 precipitation stations and 18 independent precipitation stations, located at Pinios river basin in Thessaly region, Greece, were used for the development and spatiotemporal validation of the hybrid spatiotemporal scheme. Several quantitative temporal and spatial statistical indices were considered for the performance evaluation of the models. Furthermore, qualitative statistical criteria based on contingency tables between observed and forecasted drought episodes were calculated. The results show that the lead time of forecasting for operational use depends on the SPI timescale. The hybrid spatiotemporal drought forecasting model could be operationally used for forecasting up to three months ahead for SPI short timescales (e.g. 3-6 months) up to six months ahead for large SPI timescales (e.g. 24 months). The above findings could be useful in developing a drought preparedness plan in the region.

Linked Hydrologic-Hydrodynamic Model Framework to Forecast Impacts of Rivers on Beach Water Quality

NASA Astrophysics Data System (ADS)

Anderson, E. J.; Fry, L. M.; Kramer, E.; Ritzenthaler, A.

2014-12-01

The goal of NOAA's beach quality forecasting program is to use a multi-faceted approach to aid in detection and prediction of bacteria in recreational waters. In particular, our focus has been on the connection between tributary loads and bacteria concentrations at nearby beaches. While there is a clear link between stormwater runoff and beach water quality, quantifying the contribution of river loadings to nearshore bacterial concentrations is complicated due to multiple processes that drive bacterial concentrations in rivers as well as those processes affecting the fate and transport of bacteria upon exiting the rivers. In order to forecast potential impacts of rivers on beach water quality, we developed a linked hydrologic-hydrodynamic water quality framework that simulates accumulation and washoff of bacteria from the landscape, and then predicts the fate and transport of washed off bacteria from the watershed to the coastal zone. The framework includes a watershed model (IHACRES) to predict fecal indicator bacteria (FIB) loadings to the coastal environment (accumulation, wash-off, die-off) as a function of effective rainfall. These loadings are input into a coastal hydrodynamic model (FVCOM), including a bacteria transport model (Lagrangian particle), to simulate 3D bacteria transport within the coastal environment. This modeling system provides predictive tools to assist local managers in decision-making to reduce human health threats.

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.

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

NASA Astrophysics Data System (ADS)

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

2016-12-01

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

Effect of irrigation pumpage during drought on karst aquifer systems in highly agricultural watersheds: example of the Apalachicola-Chattahoochee-Flint river basin, southeastern USA

NASA Astrophysics Data System (ADS)

Mitra, Subhasis; Srivastava, Puneet; Singh, Sarmistha

2016-09-01

In the Apalachicola-Chattahoochee-Flint (ACF) river basin in Alabama, Georgia, and Florida (USA), population growth in the city of Atlanta and increased groundwater withdrawal for irrigation in southwest Georgia are greatly affecting the supply of freshwater to downstream regions. This study was conducted to understand and quantify the effect of irrigation pumpage on the karst Upper Floridan Aquifer and river-aquifer interactions in the lower ACF river basin in southwest Georgia. The groundwater MODular Finite-Element model (MODFE) was used for this study. The effect of two drought years, a moderate and a severe drought year, were simulated. Comparison of the results of the irrigated and non-irrigated scenarios showed that groundwater discharge to streams is a major outflow from the aquifer, and irrigation can cause as much as 10 % change in river-aquifer flux. The results also show that during months with high irrigation (e.g., June 2011), storage loss (34 %), the recharge and discharge from the upper semi-confining unit (30 %), and the river-aquifer flux (31 %) are the major water components contributing towards the impact of irrigation pumpage in the study area. A similar scenario plays out in many river basins throughout the world, especially in basins in which underlying karst aquifers are directly connected to a nearby stream. The study suggests that improved groundwater withdrawal strategies using climate forecasts needs to be developed in such a way that excessive withdrawals during droughts can be reduced to protect streams and river flows.

The value of long-term monitoring in the development of ground-water-flow models

USGS Publications Warehouse

Feinstein, Daniel T.; Hart, David J.; Krohelski, James T.

2004-01-01

As environmental issues have come to the forefront of public concern, so has the awareness of the importance of ground water in the overall water cycle and as a source of the Nation’s drinking water. Heightened interest has spawned a host of scientific enterprises (Taylor and Alley, 2001). Some activities are directed toward collection of water-level data and related information to monitor the physical and chemical state of the resource. Other activities are directed at interpretive studies undertaken, for example, to optimize the location of new water-supply wells or to protect rivers and lakes fed by ground water. An important type of interpretive study is the computer ground-water-flow model that inte- grates field data in a mathematical framework. Long-term, systematic collection of hydro- logic data is crucial to the construction and testing of ground-water models so that they can reproduce the evolution of flow systems and forecast future conditions. 

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.

A system for forecasting and monitoring cash flow : phase I : forecasting payments on construction contracts.

DOT National Transportation Integrated Search

1983-01-01

The research on which this paper is based was performed as part of a study to develop a system for generating a one-to-two year forecast of monthly cash flows for the Virginia Department of Highways and Transportation. It revealed that presently used...

The utility of satellite precipitation products for hydrologic prediction in topographically complex regions: The Chehalis River Basin, WA as a case study

NASA Astrophysics Data System (ADS)

Cao, Q.; Mehran, A.; Lettenmaier, D. P.; Mass, C.; Johnson, N.

2015-12-01

Accurate measurements of precipitation are of great importance in hydrologic predictions especially for floods, which are a pervasive natural hazard. One of the primary objectives of Global Precipitation Measurement (GPM) mission is to provide a basis for hydrologic predictions using satellite sensors. A major advance in GPM relative to the Tropical Rainfall Measuring Mission (TRMM) is that it observes atmospheric river (AR) events, most of which have landfall too far north to be tracked by TRMM. These events are responsible for most major floods along the U.S. West Coast. We address the question of whether, for hydrologic modeling purposes, it is better to use precipitation products derived directly from GPM and/or other precipitation fields from weather models that have assimilated satellite data. Our overall strategy is to compare different methods for prediction of flood and/or high flow events by different forcings on the hydrologic model. We examine four different configurations of the Distroibute Hydrology Soil Vegetation Model (DHSVM) over the Chehalis River Basin that use a) precipitation forcings based on gridded station data; b) precipitation forcings based on NWS WSR-88D data, c) forcings based from short-term precipitation forecasts using the Weather Research and Forecasting (WRF) mesoscale atmospheric model, and d) satellite-based precipitation estimates (TMPA and IMERG). We find that in general, biases in the radar and satellite products result in much larger errors than with either gridded station data or WRF forcings, but if these biases are removed, comparable performance in flood predictions can be achieved by Satellite-based precipitation estimates (TMPA and IMERG).

Linking the pacific decadal oscillation to seasonal stream discharge patterns in Southeast Alaska

USGS Publications Warehouse

Neal, E.G.; Todd, Walter M.; Coffeen, C.

2002-01-01

This study identified and examined differences in Southeast Alaskan streamflow patterns between the two most recent modes of the Pacific decadal oscillation (PDO). Identifying relationships between the PDO and specific regional phenomena is important for understanding climate variability, interpreting historical hydrological variability, and improving water-resources forecasting. Stream discharge data from six watersheds in Southeast Alaska were divided into cold-PDO (1947-1976) and warm-PDO (1977-1998) subsets. For all watersheds, the average annual streamflows during cold-PDO years were not significantly different from warm-PDO years. Monthly and seasonal discharges, however, did differ significantly between the two subsets, with the warm-PDO winter flows being typically higher than the cold-PDO winter flows and the warm-PDO summer flows being typically lower than the cold-PDO flows. These results were consistent with and driven by observed temperature and snowfall patterns for the region. During warm-PDO winters, precipitation fell as rain and ran-off immediately, causing higher than normal winter streamflow. During cold-PDO winters, precipitation was stored as snow and ran off during the summer snowmelt, creating greater summer streamflows. The Mendenhall River was unique in that it experienced higher flows for all seasons during the warm-PDO relative to the cold-PDO. The large amount of Mendenhall River discharge caused by glacial melt during warm-PDO summers offset any flow reduction caused by lack of snow accumulation during warm-PDO winters. The effect of the PDO on Southeast Alaskan watersheds differs from other regions of the Pacific Coast of North America in that monthly/seasonal discharge patterns changed dramatically with the switch in PDO modes but annual discharge did not. ?? 2002 Elsevier Science B.V. All rights reserved.

Fuzzy Temporal Logic Based Railway Passenger Flow Forecast Model

PubMed Central

Dou, Fei; Jia, Limin; Wang, Li; Xu, Jie; Huang, Yakun

2014-01-01

Passenger flow forecast is of essential importance to the organization of railway transportation and is one of the most important basics for the decision-making on transportation pattern and train operation planning. Passenger flow of high-speed railway features the quasi-periodic variations in a short time and complex nonlinear fluctuation because of existence of many influencing factors. In this study, a fuzzy temporal logic based passenger flow forecast model (FTLPFFM) is presented based on fuzzy logic relationship recognition techniques that predicts the short-term passenger flow for high-speed railway, and the forecast accuracy is also significantly improved. An applied case that uses the real-world data illustrates the precision and accuracy of FTLPFFM. For this applied case, the proposed model performs better than the k-nearest neighbor (KNN) and autoregressive integrated moving average (ARIMA) models. PMID:25431586

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

NASA Astrophysics Data System (ADS)

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

2012-04-01

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

Effects of Changing Climate During the Snow Ablation Season on Seasonal Streamflow Forecasts

NASA Astrophysics Data System (ADS)

Gutzler, D. S.; Chavarria, S. B.

2017-12-01

Seasonal forecasts of total surface runoff (Q) in snowmelt-dominated watersheds derive most of their prediction skill from the historical relationship between late winter snowpack (SWE) and subsequent snowmelt runoff. Across the western US, however, the relationship between SWE and Q is weakening as temperatures rise. We describe the effects of climate variability and change during the springtime snow ablation season on water supply outlooks (forecasts of Q) for southwestern rivers. As snow melts earlier, the importance of post-snow rainfall increases: interannual variability of spring season precipitation accounts for an increasing fraction of the variability of Q in recent decades. The results indicate that improvements to the skill of S2S forecasts of spring season temperature and precipitation would contribute very significantly to water supply outlooks that are now based largely on observed SWE. We assess this hypothesis using historical data from several snowpack-dominated basins in the American Southwest (Rio Grande, Pecos, and Gila Rivers) which are undergoing rapid climate change.

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

NASA Astrophysics Data System (ADS)

Wanders, Niko; Wada, Yoshihide

2015-12-01

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

The effects of large-scale afforestation and climate change on water allocation in the Macquarie River catchment, NSW, Australia.

PubMed

Herron, Natasha; Davis, Richard; Jones, Roger

2002-08-01

Widespread afforestation has been proposed as one means of addressing the increasing dryland and stream salinity problem in Australia. However, modelling results presented here suggest that large-scale tree planting will substantially reduce river flows and impose costs on downstream water users if planted in areas of high runoff yield. Streamflow reductions in the Macquarie River, NSW, Australia are estimated for a number of tree planting scenarios and global warming forecasts. The modelling framework includes the Sacramento rainfall-runoff model and IQQM, a streamflow routing tool, as well as various global climate model outputs from which daily rainfall and potential evaporation data files have been generated in OzClim, a climate scenario generator. For a 10% increase in tree cover in the headwaters of the Macquarie, we estimate a 17% reduction in inflows to Burrendong Dam. The drying trend for a mid-range scenario of regional rainfall and potential evaporation caused by a global warming of 0.5 degree C may cause an additional 5% reduction in 2030. These flow reductions will decrease the frequency of bird-breeding events in Macquarie Marshes (a RAMSAR protected wetland) and reduce the security of supply to irrigation areas downstream. Inter-decadal climate variability is predicted to have a very significant influence on catchment hydrologic behaviour. A further 20% reduction in flows from the long-term historical mean is possible, should we move into an extended period of below average rainfall years, such as occurred in eastern Australia between 1890 and 1948. Because current consumptive water use is largely adapted to the wetter conditions of post 1949, a return to prolonged dry periods would cause significant environmental stress given the agricultural and domestic water developments that have been instituted.

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

NASA Astrophysics Data System (ADS)

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

2016-12-01

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

Forecasting the Emergency Department Patients Flow.

PubMed

Afilal, Mohamed; Yalaoui, Farouk; Dugardin, Frédéric; Amodeo, Lionel; Laplanche, David; Blua, Philippe

2016-07-01

Emergency department (ED) have become the patient's main point of entrance in modern hospitals causing it frequent overcrowding, thus hospital managers are increasingly paying attention to the ED in order to provide better quality service for patients. One of the key elements for a good management strategy is demand forecasting. In this case, forecasting patients flow, which will help decision makers to optimize human (doctors, nurses…) and material(beds, boxs…) resources allocation. The main interest of this research is forecasting daily attendance at an emergency department. The study was conducted on the Emergency Department of Troyes city hospital center, France, in which we propose a new practical ED patients classification that consolidate the CCMU and GEMSA categories into one category and innovative time-series based models to forecast long and short term daily attendance. The models we developed for this case study shows very good performances (up to 91,24 % for the annual Total flow forecast) and robustness to epidemic periods.

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

NASA Astrophysics Data System (ADS)

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

2015-04-01

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

A data assimilation system combining CryoSat-2 data and hydrodynamic river models

NASA Astrophysics Data System (ADS)

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

2018-02-01

There are numerous hydrologic studies using satellite altimetry data from repeat-orbit missions such as Envisat or Jason over rivers. This study is one of the first examples for the combination of altimetry from drifting-ground track satellite missions, namely CryoSat-2, with a river model. CryoSat-2 SARIn Level 2 data is used to improve a 1D hydrodynamic model of the Brahmaputra River in South Asia, which is based on the Saint-Venant equations for unsteady flow and set up in the MIKE HYDRO River software. After calibration of discharge and water level the hydrodynamic model can accurately and bias-free represent the spatio-temporal variations of water levels. A data assimilation framework has been developed and linked with the model. It is a flexible framework that can assimilate water level data which are arbitrarily distributed in time and space. The setup has been used to assimilate CryoSat-2 water level observations over the Assam valley for the years 2010-2015, using an Ensemble Transform Kalman Filter (ETKF). Performance improvement in terms of discharge forecasting skill was then evaluated. For experiments with synthetic CryoSat-2 data the continuous ranked probability score (CRPS) was improved by up to 32%, whilst for experiments assimilating real data it could be improved by up to 10%. The developed methods are expected to be transferable to other rivers and altimeter missions. The model setup and calibration is based almost entirely on globally available remote sensing data.

A Bayesian joint probability modeling approach for seasonal forecasting of streamflows at multiple sites

NASA Astrophysics Data System (ADS)

Wang, Q. J.; Robertson, D. E.; Chiew, F. H. S.

2009-05-01

Seasonal forecasting of streamflows can be highly valuable for water resources management. In this paper, a Bayesian joint probability (BJP) modeling approach for seasonal forecasting of streamflows at multiple sites is presented. A Box-Cox transformed multivariate normal distribution is proposed to model the joint distribution of future streamflows and their predictors such as antecedent streamflows and El Niño-Southern Oscillation indices and other climate indicators. Bayesian inference of model parameters and uncertainties is implemented using Markov chain Monte Carlo sampling, leading to joint probabilistic forecasts of streamflows at multiple sites. The model provides a parametric structure for quantifying relationships between variables, including intersite correlations. The Box-Cox transformed multivariate normal distribution has considerable flexibility for modeling a wide range of predictors and predictands. The Bayesian inference formulated allows the use of data that contain nonconcurrent and missing records. The model flexibility and data-handling ability means that the BJP modeling approach is potentially of wide practical application. The paper also presents a number of statistical measures and graphical methods for verification of probabilistic forecasts of continuous variables. Results for streamflows at three river gauges in the Murrumbidgee River catchment in southeast Australia show that the BJP modeling approach has good forecast quality and that the fitted model is consistent with observed data.

Fine-temporal forecasting of outbreak probability and severity: Ross River virus in Western Australia.

PubMed

Koolhof, I S; Bettiol, S; Carver, S

2017-10-01

Health warnings of mosquito-borne disease risk require forecasts that are accurate at fine-temporal resolutions (weekly scales); however, most forecasting is coarse (monthly). We use environmental and Ross River virus (RRV) surveillance to predict weekly outbreak probabilities and incidence spanning tropical, semi-arid, and Mediterranean regions of Western Australia (1991-2014). Hurdle and linear models were used to predict outbreak probabilities and incidence respectively, using time-lagged environmental variables. Forecast accuracy was assessed by model fit and cross-validation. Residual RRV notification data were also examined against mitigation expenditure for one site, Mandurah 2007-2014. Models were predictive of RRV activity, except at one site (Capel). Minimum temperature was an important predictor of RRV outbreaks and incidence at all predicted sites. Precipitation was more likely to cause outbreaks and greater incidence among tropical and semi-arid sites. While variable, mitigation expenditure coincided positively with increased RRV incidence (r 2 = 0·21). Our research demonstrates capacity to accurately predict mosquito-borne disease outbreaks and incidence at fine-temporal resolutions. We apply our findings, developing a user-friendly tool enabling managers to easily adopt this research to forecast region-specific RRV outbreaks and incidence. Approaches here may be of value to fine-scale forecasting of RRV in other areas of Australia, and other mosquito-borne diseases.

The challenge of forecasting impacts of flash floods: test of a simplified hydraulic approach and validation based on insurance claim data

NASA Astrophysics Data System (ADS)

Le Bihan, Guillaume; Payrastre, Olivier; Gaume, Eric; Moncoulon, David; Pons, Frédéric

2017-11-01

Up to now, flash flood monitoring and forecasting systems, based on rainfall radar measurements and distributed rainfall-runoff models, generally aimed at estimating flood magnitudes - typically discharges or return periods - at selected river cross sections. The approach presented here goes one step further by proposing an integrated forecasting chain for the direct assessment of flash flood possible impacts on inhabited areas (number of buildings at risk in the presented case studies). The proposed approach includes, in addition to a distributed rainfall-runoff model, an automatic hydraulic method suited for the computation of flood extent maps on a dense river network and over large territories. The resulting catalogue of flood extent maps is then combined with land use data to build a flood impact curve for each considered river reach, i.e. the number of inundated buildings versus discharge. These curves are finally used to compute estimated impacts based on forecasted discharges. The approach has been extensively tested in the regions of Alès and Draguignan, located in the south of France, where well-documented major flash floods recently occurred. The article presents two types of validation results. First, the automatically computed flood extent maps and corresponding water levels are tested against rating curves at available river gauging stations as well as against local reference or observed flood extent maps. Second, a rich and comprehensive insurance claim database is used to evaluate the relevance of the estimated impacts for some recent major floods.

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.

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).

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.

National Weather Service Forecast Office Guam Home

Science.gov Websites

National Alerts Text Current Conditions Observations Satellite Hydrology River & Lake AHPS Radar Imagery AAFB (Guam) AAFB (Guam) Dial up CONUS Radar Forecasts Activity Planner Guam Public Marine Aviation ; Weather Topics: Local Alerts, Current Conditions, Radar, Satellite, Climate, W-GUM.Webmaster@noaa.gov

Lower Granite Dam Smolt Monitoring Program, 2005-2006 Annual Report.

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

Mensik, Fred; Rapp, Shawn; Ross, Doug

2007-01-01

The 2005 fish collection season at Lower Granite Dam (LGR) was characterized by average water temperatures, below average flows, above average spill, low levels of debris and the record number of smolts collected compared to the previous five years. With the continued release of unclipped supplementation chinook and steelhead above LGR, we cannot accurately distinguish wild chinook, steelhead, and sockeye/kokanee in the sample. For the purposes of this report we will designate fish as clipped and unclipped. This season a total of 13,030,967 juvenile salmonids were collected at LGR. Of these, 12,099,019 were transported to release sites below Bonneville Dam,more » 12,032,623 by barge and 66,396 by truck. An additional 898,235 fish were bypassed to the river due to over-capacity of the raceways, barges or trucks and for research purposes. This was the first season of summer spill at LGR. Spill was initiated at 12:01am June 20 as directed by the ruling set forth by Judge James Redden of the United States District Court (Order CV 01-640-RE). In addition, the Lower Granite project also conducted a summer spill test alternating spill and spill patterns between spill to the gas cap without the removable spillway weir (RSW) and spill with up to 20 kcfs utilizing the RSW. Because of the forecast low flow this year, most hatchery reared subyearling fall chinook were released up to three weeks early. With the unexpected high flows in late May and early June, more than 90% of the subyearling chinook were collected prior to the initiation of the court ordered summer spill program. Collection number fluctuations reflect river flow and project operations for any given year. For example, low flow years (2001, 2004 and 2005) result in higher collection numbers. Court ordered spill throughout the summer migration will directly affect collection of fall subyearling chinook collection numbers. The editors of this report urge the reader to use caution when comparing fish collection numbers between years, considering both annual river flows and annual project operations, because both affect fish migration and collection.« less

Lower Granite Dam Smolt Monitoring Program, Annual Report 2005-2006.

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

Menski, Fred

2007-01-01

The 2005 fish collection season at Lower Granite Dam (LGR) was characterized by average water temperatures, below average flows, above average spill, low levels of debris and the record number of smolts collected compared to the previous five years. With the continued release of unclipped supplementation chinook and steelhead above LGR, we cannot accurately distinguish wild chinook, steelhead, and sockeye/kokanee in the sample. For the purposes of this report we will designate fish as clipped and unclipped. This season a total of 13,030,967 juvenile salmonids were collected at LGR. Of these, 12,099,019 were transported to release sites below Bonneville Dam,more » 12,032,623 by barge and 66,396 by truck. An additional 898,235 fish were bypassed to the river due to over-capacity of the raceways, barges or trucks and for research purposes. This was the first season of summer spill at LGR. Spill was initiated at 12:01am June 20 as directed by the ruling set forth by Judge James Redden of the United States District Court (Order CV 01-640-RE). In addition, the Lower Granite project also conducted a summer spill test alternating spill and spill patterns between spill to the gas cap without the removable spillway weir (RSW) and spill with up to 20 kcfs utilizing the RSW. Because of the forecast low flow this year, most hatchery reared subyearling fall chinook were released up to three weeks early. With the unexpected high flows in late May and early June, more than 90% of the subyearling chinook were collected prior to the initiation of the court ordered summer spill program. Collection number fluctuations reflect river flow and project operations for any given year. For example, low flow years (2001, 2004 and 2005) result in higher collection numbers. Court ordered spill throughout the summer migration will directly affect collection of fall subyearling chinook collection numbers. The editors of this report urge the reader to use caution when comparing fish collection numbers between years, considering both annual river flows and annual project operations, because both affect fish migration and collection.« less

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

NASA Astrophysics Data System (ADS)

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

2011-07-01

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

LiDAR-Derived Flood-Inundation Maps for Real-Time Flood-Mapping Applications, Tar River Basin, North Carolina

USGS Publications Warehouse

Bales, Jerad D.; Wagner, Chad R.; Tighe, Kirsten C.; Terziotti, Silvia

2007-01-01

Flood-inundation maps were created for selected streamgage sites in the North Carolina Tar River basin. Light detection and ranging (LiDAR) data with a vertical accuracy of about 20 centimeters, provided by the Floodplain Mapping Information System of the North Carolina Floodplain Mapping Program, were processed to produce topographic data for the inundation maps. Bare-earth mass point LiDAR data were reprocessed into a digital elevation model with regularly spaced 1.5-meter by 1.5-meter cells. A tool was developed as part of this project to connect flow paths, or streams, that were inappropriately disconnected in the digital elevation model by such features as a bridge or road crossing. The Hydraulic Engineering Center-River Analysis System (HEC-RAS) model, developed by the U.S. Army Corps of Engineers, was used for hydraulic modeling at each of the study sites. Eleven individual hydraulic models were developed for the Tar River basin sites. Seven models were developed for reaches with a single gage, and four models were developed for reaches of the Tar River main stem that receive flow from major gaged tributaries, or reaches in which multiple gages were near one another. Combined, the Tar River hydraulic models included 272 kilometers of streams in the basin, including about 162 kilometers on the Tar River main stem. The hydraulic models were calibrated to the most current stage-discharge relations at 11 long-term streamgages where rating curves were available. Medium- to high-flow discharge measurements were made at some of the sites without rating curves, and high-water marks from Hurricanes Fran and Floyd were available for high-stage calibration. Simulated rating curves matched measured curves over the full range of flows. Differences between measured and simulated water levels for a specified flow were no more than 0.44 meter and typically were less. The calibrated models were used to generate a set of water-surface profiles for each of the 11 modeled reaches at 0.305-meter increments for water levels ranging from bankfull to approximately the highest recorded water level at the downstream-most gage in each modeled reach. Inundated areas were identified by subtracting the water-surface elevation in each 1.5-meter by 1.5-meter grid cell from the land-surface elevation in the cell through an automated routine that was developed to identify all inundated cells hydraulically connected to the cell at the downstream-most gage in the model domain. Inundation maps showing transportation networks and orthoimagery were prepared for display on the Internet. These maps also are linked to the U.S. Geological Survey North Carolina Water Science Center real-time streamflow website. Hence, a user can determine the near real-time stage and water-surface elevation at a U.S. Geological Survey streamgage site in the Tar River basin and link directly to the flood-inundation maps for a depiction of the estimated inundated area at the current water level. Although the flood-inundation maps represent distinct boundaries of inundated areas, some uncertainties are associated with these maps. These are uncertainties in the topographic data for the hydraulic model computational grid and inundation maps, effective friction values (Manning's n), model-validation data, and forecast hydrographs, if used. The Tar River flood-inundation maps were developed by using a steady-flow hydraulic model. This assumption clearly has less of an effect on inundation maps produced for low flows than for high flows when it typically takes more time to inundate areas. A flood in which water levels peak and fall slowly most likely will result in more inundation than a similar flood in which water levels peak and fall quickly. Limitations associated with the steady-flow assumption for hydraulic modeling vary from site to site. The one-dimensional modeling approach used in this study resulted in good agreement between measurements and simulations. T

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

NASA Astrophysics Data System (ADS)

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

2010-09-01

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

Management Options During the 2011-2012 Drought on the Apalachicola River: A Systems Dynamic Model Evaluation.

PubMed

Leitman, S; Pine, W E; Kiker, G

2016-08-01

The Apalachicola-Chattahoochee-Flint River basin (ACF) is a large watershed in the southeastern United States. In 2012, the basin experienced the second year of a severe drought and the third multi-year drought in the last 15 years. During severe droughts, low reservoir and river levels can cause economic and ecological impacts to the reservoir, river, and estuarine ecosystems. During drought, augmenting Apalachicola River discharge through upstream reservoir releases and demand management are intuitive and often-suggested solutions to minimizing downstream effects. We assessed whether the existing reservoir system could be operated to minimize drought impacts on downstream water users and ecosystems through flow augmentation. Our analysis finds that in extreme drought such as observed during 2012, increases in water releases from reservoir storage are insufficient to even increase Apalachicola River discharge to levels observed in the 2007 drought. This suggests that there is simply not enough water available in managed storage to offset extreme drought events. Because drought frequency and intensity is predicted to increase under a variety of climate forecasts, our results demonstrate the need for a critical assessment of how water managers will meet increasing water demands in the ACF. Key uncertainties that should be addressed include (1) identifying the factors that led to extremely low Flint River discharge in 2012, and (2) determining how water "saved" via demand management is allocated to storage or passed to downstream ecosystem needs as part of the ongoing revisions to the ACF Water Control Manual by the US Army Corps of Engineers.

Management Options During the 2011-2012 Drought on the Apalachicola River: A Systems Dynamic Model Evaluation

NASA Astrophysics Data System (ADS)

Leitman, S.; Pine, W. E.; Kiker, G.

2016-08-01

The Apalachicola-Chattahoochee-Flint River basin (ACF) is a large watershed in the southeastern United States. In 2012, the basin experienced the second year of a severe drought and the third multi-year drought in the last 15 years. During severe droughts, low reservoir and river levels can cause economic and ecological impacts to the reservoir, river, and estuarine ecosystems. During drought, augmenting Apalachicola River discharge through upstream reservoir releases and demand management are intuitive and often-suggested solutions to minimizing downstream effects. We assessed whether the existing reservoir system could be operated to minimize drought impacts on downstream water users and ecosystems through flow augmentation. Our analysis finds that in extreme drought such as observed during 2012, increases in water releases from reservoir storage are insufficient to even increase Apalachicola River discharge to levels observed in the 2007 drought. This suggests that there is simply not enough water available in managed storage to offset extreme drought events. Because drought frequency and intensity is predicted to increase under a variety of climate forecasts, our results demonstrate the need for a critical assessment of how water managers will meet increasing water demands in the ACF. Key uncertainties that should be addressed include (1) identifying the factors that led to extremely low Flint River discharge in 2012, and (2) determining how water "saved" via demand management is allocated to storage or passed to downstream ecosystem needs as part of the ongoing revisions to the ACF Water Control Manual by the US Army Corps of Engineers.

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

NASA Astrophysics Data System (ADS)

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

2014-06-01

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

River catchment rainfall series analysis using additive Holt-Winters method

NASA Astrophysics Data System (ADS)

Puah, Yan Jun; Huang, Yuk Feng; Chua, Kuan Chin; Lee, Teang Shui

2016-03-01

Climate change is receiving more attention from researchers as the frequency of occurrence of severe natural disasters is getting higher. Tropical countries like Malaysia have no distinct four seasons; rainfall has become the popular parameter to assess climate change. Conventional ways that determine rainfall trends can only provide a general result in single direction for the whole study period. In this study, rainfall series were modelled using additive Holt-Winters method to examine the rainfall pattern in Langat River Basin, Malaysia. Nine homogeneous series of more than 25 years data and less than 10% missing data were selected. Goodness of fit of the forecasted models was measured. It was found that seasonal rainfall model forecasts are generally better than the monthly rainfall model forecasts. Three stations in the western region exhibited increasing trend. Rainfall in southern region showed fluctuation. Increasing trends were discovered at stations in the south-eastern region except the seasonal analysis at station 45253. Decreasing trend was found at station 2818110 in the east, while increasing trend was shown at station 44320 that represents the north-eastern region. The accuracies of both rainfall model forecasts were tested using the recorded data of years 2010-2012. Most of the forecasts are acceptable.

A system for forecasting and monitoring cash flow : phase II, forecasting federal and state revenues, maintenance contracts, other expenditures, and cash balances.

DOT National Transportation Integrated Search

1985-01-01

The research on which this report is based was performed as part of a study to develop an improved system for generating a two-year forecast of monthly cash flows for the Virginia Department of Highways and Transportation. It revealed that current te...

Experiences in regional landslide forecasting from Piemonte region (North-western Italy) and South-Eastern Norway between the 15th and the 23rd of May 2013

NASA Astrophysics Data System (ADS)

Tiranti, Davide; Boje, Søren; Cremonini, Roberto; Devoli, Graziella; Sund, Monica

2017-04-01

Although Italy and Norway belongs to different climates, they can be influenced by the same large low pressure systems. On May 2013, ARPA in Piemonte region and NVE in Norway issued warning for flood and landslides due to the arriving of a deep and large low pressure (known as Vb-tief). This type of weather is well known to produce the largest floods in Europe. Recent studies in Norway confirm that similar systems are also responsible of triggering landslide events. In this contribution we present how the existing forecasting systems in Piemonte region and in Norway react and we summarize our experiences. Regional early warning systems (EWS) are operational both in Piemonte region (Italy) and nationally in Norway to forecast shallow landslides, debris flows and debris avalanches. Both EWSs provides daily landslide hazard assessments based on quantitative thresholds and daily rainfall forecasts coupled with qualitative expert analysis. The ARPA Piemonte warning system has been operational since 1994 while the NVE one since 2013: daily bulletins are published respectively by http://www.arpa.piemonte.gov.it/rischinaturali and www.varsom.no. From 15th May to 19nd June 2013, ARPA Piemonte rain gauges recorded more that 200mm in Piemonte and 60-90cm fresh snow over the Alps above 2000m asl. Several rivers were flooded and diffuse landslides were occurred over all the region. In Norway the same weather type lasts a bit longer from 15th May to 2nd June 2013. South-Eastern Norway received a lot of rain distributed in 2 major events, the 15th - 16th of May and between the 22nd and 23rd of May. In addition, high temperatures produced intense snow melting over a large area. Snow depth was less than normal but the snow melted within two weeks while the frost in the area was deeper than normal. From 21st to 23rd May heavy rainfall, over 70 mm in a few hours, fell over the Glomma river basin, especially over Gudbrandsdalen, causing extensive flood along Glomma river and hundreds of landslides. The large floods and landslides caused extensive damages to roads and railways as well as buildings and other infrastructure in both countries. In Norway, the Oppland and Hedmark counties suffered most of the damages, as well as railway lines and road line estimated at over 175000 Euro.

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

Dynamic coupling of three hydrodynamic models

NASA Astrophysics Data System (ADS)

Hartnack, J. N.; Philip, G. T.; Rungoe, M.; Smith, G.; Johann, G.; Larsen, O.; Gregersen, J.; Butts, M. B.

2008-12-01

The need for integrated modelling is evidently present within the field of flood management and flood forecasting. Engineers, modellers and managers are faced with flood problems which transcend the classical hydrodynamic fields of urban, river and coastal flooding. Historically the modeller has been faced with having to select one hydrodynamic model to cover all the aspects of the potentially complex dynamics occurring in a flooding situation. Such a single hydrodynamic model does not cover all dynamics of flood modelling equally well. Thus the ideal choice may in fact be a combination of models. Models combining two numerical/hydrodynamic models are becoming more standard, typically these models combine a 1D river model with a 2D overland flow model or alternatively a 1D sewer/collection system model with a 2D overland solver. In complex coastal/urban areas the flood dynamics may include rivers/streams, collection/storm water systems along with the overland flow. The dynamics within all three areas is of the same time scale and there is feedback in the system across the couplings. These two aspects dictate a fully dynamic three way coupling as opposed to running the models sequentially. It will be shown that the main challenges of the three way coupling are time step issues related to the difference in numerical schemes used in the three model components and numerical instabilities caused by the linking of the model components. MIKE FLOOD combines the models MIKE 11, MIKE 21 and MOUSE into one modelling framework which makes it possible to couple any combination of river, urban and overland flow fully dynamically. The MIKE FLOOD framework will be presented with an overview of the coupling possibilities. The flood modelling concept will be illustrated through real life cases in Australia and in Germany. The real life cases reflect dynamics and interactions across all three model components which are not possible to reproduce using a two-way coupling alone. The models comprise 2D inundation modelling, river networks with multiple structures (pumps, weirs, culverts), urban drainage networks as well as dam break modelling. The models were used to quantify the results of storm events or failures (dam break, pumping failures etc) coinciding with high discharge in river system and heavy rainfall. The detailed representation of the flow path through the city allowed a direct assessment of flood risk Thus it is found that the three-way coupled model is a practical and useful tool for integrated flood management.

Stochastic Model of Seasonal Runoff Forecasts

NASA Astrophysics Data System (ADS)

Krzysztofowicz, Roman; Watada, Leslie M.

1986-03-01

Each year the National Weather Service and the Soil Conservation Service issue a monthly sequence of five (or six) categorical forecasts of the seasonal snowmelt runoff volume. To describe uncertainties in these forecasts for the purposes of optimal decision making, a stochastic model is formulated. It is a discrete-time, finite, continuous-space, nonstationary Markov process. Posterior densities of the actual runoff conditional upon a forecast, and transition densities of forecasts are obtained from a Bayesian information processor. Parametric densities are derived for the process with a normal prior density of the runoff and a linear model of the forecast error. The structure of the model and the estimation procedure are motivated by analyses of forecast records from five stations in the Snake River basin, from the period 1971-1983. The advantages of supplementing the current forecasting scheme with a Bayesian analysis are discussed.

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

NASA Astrophysics Data System (ADS)

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

2015-11-01

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

Optimized Structure of the Traffic Flow Forecasting Model With a Deep Learning Approach.

PubMed

Yang, Hao-Fan; Dillon, Tharam S; Chen, Yi-Ping Phoebe

2017-10-01

Forecasting accuracy is an important issue for successful intelligent traffic management, especially in the domain of traffic efficiency and congestion reduction. The dawning of the big data era brings opportunities to greatly improve prediction accuracy. In this paper, we propose a novel model, stacked autoencoder Levenberg-Marquardt model, which is a type of deep architecture of neural network approach aiming to improve forecasting accuracy. The proposed model is designed using the Taguchi method to develop an optimized structure and to learn traffic flow features through layer-by-layer feature granulation with a greedy layerwise unsupervised learning algorithm. It is applied to real-world data collected from the M6 freeway in the U.K. and is compared with three existing traffic predictors. To the best of our knowledge, this is the first time that an optimized structure of the traffic flow forecasting model with a deep learning approach is presented. The evaluation results demonstrate that the proposed model with an optimized structure has superior performance in traffic flow forecasting.

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.

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.

Home | Sonoma County Water Agency

Science.gov Websites

Precipitation Information (AQPI) Fluoridation Atmospheric Rivers Urban Water Management Plan Flood Protection Advanced Quantitative Precipitation Information (AQPI) Fluoridation Atmospheric Rivers Urban Water Management Plan Flood Protection Flood Forecast/Emergency Info Stream Maintenance Program Flood Protection

An optimization based sampling approach for multiple metrics uncertainty analysis using generalized likelihood uncertainty estimation

NASA Astrophysics Data System (ADS)

Zhou, Rurui; Li, Yu; Lu, Di; Liu, Haixing; Zhou, Huicheng

2016-09-01

This paper investigates the use of an epsilon-dominance non-dominated sorted genetic algorithm II (ɛ-NSGAII) as a sampling approach with an aim to improving sampling efficiency for multiple metrics uncertainty analysis using Generalized Likelihood Uncertainty Estimation (GLUE). The effectiveness of ɛ-NSGAII based sampling is demonstrated compared with Latin hypercube sampling (LHS) through analyzing sampling efficiency, multiple metrics performance, parameter uncertainty and flood forecasting uncertainty with a case study of flood forecasting uncertainty evaluation based on Xinanjiang model (XAJ) for Qing River reservoir, China. Results obtained demonstrate the following advantages of the ɛ-NSGAII based sampling approach in comparison to LHS: (1) The former performs more effective and efficient than LHS, for example the simulation time required to generate 1000 behavioral parameter sets is shorter by 9 times; (2) The Pareto tradeoffs between metrics are demonstrated clearly with the solutions from ɛ-NSGAII based sampling, also their Pareto optimal values are better than those of LHS, which means better forecasting accuracy of ɛ-NSGAII parameter sets; (3) The parameter posterior distributions from ɛ-NSGAII based sampling are concentrated in the appropriate ranges rather than uniform, which accords with their physical significance, also parameter uncertainties are reduced significantly; (4) The forecasted floods are close to the observations as evaluated by three measures: the normalized total flow outside the uncertainty intervals (FOUI), average relative band-width (RB) and average deviation amplitude (D). The flood forecasting uncertainty is also reduced a lot with ɛ-NSGAII based sampling. This study provides a new sampling approach to improve multiple metrics uncertainty analysis under the framework of GLUE, and could be used to reveal the underlying mechanisms of parameter sets under multiple conflicting metrics in the uncertainty analysis process.

Does model structure limit the use of satellite data as hydrologic forcing for distributed operational models?

NASA Astrophysics Data System (ADS)

Bowman, A. L.; Franz, K.; Hogue, T. S.

2015-12-01

We are investigating the implications for use of satellite data in operational streamflow prediction. Specifically, the consequence of potential hydrologic model structure deficiencies on the ability to achieve improved forecast accuracy through the use of satellite data. We want to understand why advanced data do not lead to improved streamflow simulations by exploring how various fluxes and states differ among models of increasing complexity. In a series of prior studies, we investigated the use of a daily satellite-derived potential evapotranspiration (PET) estimate as input to the National Weather Service (NWS) streamflow forecast models for watersheds in the Upper Mississippi and Red river basins. Although the spatial PET product appears to represent the day-to-day variability in PET more realistically than current climatological methods used by the NWS, the impact of the satellite data on streamflow simulations results in slightly poorer model efficiency overall. Analysis of the model states indicates the model progresses differently between simulations with baseline PET and the satellite-derived PET input, though variation in streamflow simulations overall is negligible. For instance, the upper zone states, responsible for the high flows of a hydrograph, show a profound difference, while simulation of the peak flows tend to show little variation in the timing and magnitude. Using the spatial PET input, the lower zone states show improvement with simulating the recession limb and baseflow portion of the hydrograph. We anticipate that through a better understanding of the relationship between model structure, model states, and simulated streamflow we will be able to diagnose why simulations of discharge from the forecast model have failed to improve when provided seemingly more representative input data. Identifying model limitations are critical to demonstrating the full benefit of a satellite data for operational use.

1997 flood tracking chart for the Red River of the North basin

USGS Publications Warehouse

Wiche, G.J.; Martin, C.R.; Albright, L.L.; Wald, Geraldine B.

1997-01-01

The flood tracking chart for the Red River of the North Basin can be used by local citizens and emergency response personnel to determine the latest river stage. By comparing the current stage (water-surface elevation above some datum) and predicted flood crest to the recorded peak stages of previous floods, emergency response personnel and residents can make informed decisions concerning the threat to life and property. The flood tracking chart shows a map of the basin with the location of major real-time streamflow-gaging stations in the basin. Click on a station in the map or in the list below the map. Streamflow and stage information for the last 7 days, current stage relative to recorded peak stages, and streamflow for the previous 18 months are provided in graphic form, along with information such as station location and length of record. The National Weather Service has direct access to all information collected by the USGS for use in their forecasting models and routinely broadcasts the forecast information to the news media and on shortwave radio. The radio frequencies are 162.400 MHz (megahertz) in Petersburg, N. Dak., and Detroit Lakes, Minn.; 162.425 MHz in Webster, N. Dak., and Bemidji, Minn.; 162.450 MHz in Roosevelt, Minn.; 162.475 MHz in Grand Forks and Amenia, N. Dak.; and 162.550 MHz in Thief River Falls, Minn. To use the flood tracking chart for a particular property, determine the approximate elevation of the threatened property and the elevation of the gaging station that is closest to the threatened property. For example, most people in Grand Forks, N. Dak., probably will use the Red River of the North at Grand Forks station. Record the flood elevation for the gaging station. Compare the flood elevation to the elevation of the property to immediately know if the property has an impending threat of flooding. One must be cautioned by the fact that the surface of flowing water is not flat but has a slope. Therefore, the water-surface elevation near a threatened property might not be the same as the river stages at the gaging stations. The network of river-gaging stations in the Red River of the North Basin is operated by the USGS in cooperation with the U.S. Army Corps of Engineers, the North Dakota State Water Commission, the Minnesota Department of Natural Resources, the Southeast Cass Water Resources District, the Cass County Joint Water Resource District, the Red River Joint Water Resource Board, and the Red River Watershed Management Board. For more information about USGS programs in North Dakota, contact the District Chief, U.S. Geological Survey, North Dakota District, at (701) 250-7400.

Monitoring and Evaluation of Smolt Migration in the Columbia River Basin : Volume VI : Evaluation of the 2000 Predictions of the Run-Timing of Wild Migrant Chinook Salmon and Steelhead Trout, and Hatchery Sockeye Salmon in the Snake River Basin, and Combined Wild Hatchery Salminids Migrating to Rock Island and McNary Dams using Program RealTime.

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

Burgess, Caitlin

1998-07-01

Program RealTime provided tracking and forecasting of the 2000 in season outmigration via the internet for stocks of wild PIT-tagged spring/summer chinook salmon. These stocks were ESUs from nineteen release sites above Lower Granite dam, including Bear Valley Creek, Big Creek, Camas Creek (new), Cape Horn Creek, Catherine Creek, Elk Creek, Herd Creek, Imnaha River, Johnson Creek (new), Lake Creek, Loon Creek, Lostine River, Marsh Creek, Minam River, East Fork Salmon River (new), South Fork Salmon River, Secesh River, Sulfur Creek and Valley Creek. Forecasts were also provided for two stocks of hatchery-reared PIT-tagged summer-run sockeye salmon, from Redfish Lakemore » and Alturas Lake (new); for a subpopulation of the PIT-tagged wild Snake River fall subyearling chinook salmon; for all wild Snake River PIT-tagged spring/summer yearling chinook salmon (new) and steelhead trout (new)detected at Lower Granite Dam during the 2000 outmigration. The 2000 RealTime project began making forecasts for combined wild- and hatchery-reared runs-at-large of subyearling and yearling chinook, coho, and sockeye salmon, and steelhead trout migrating to Rock Island and McNary Dams on the mid-Columbia River and the mainstem Columbia River. Due to the new (in 1999-2000) Snake River basin hatchery protocol of releasing unmarked hatchery-reared fish, the RealTime forecasting project no longer makes run-timing forecasts for wild Snake River runs-at-large using FPC passage indices, as it has done for the previous three years (1997-1999). The season-wide measure of Program RealTime performance, the mean absolute difference (MAD) between in-season predictions and true (observed) passage percentiles, improved relative to previous years for nearly all stocks. The average season-wide MAD of all (nineteen) spring/summer yearling chinook salmon ESUs dropped from 5.7% in 1999 to 4.5% in 2000. The 2000 MAD for the hatchery-reared Redfish Lake sockeye salmon ESU was the lowest recorded, at 6.0%, down from 6.7% in 1999. The MAD for the PIT-tagged ESU of wild Snake River fall sub-yearling chinook salmon, after its second season of run-timing forecasting, was 4.7% in 2000 compared to 5.5% in 1999. The high accuracy of season-wide performance in 2000 was largely due to exceptional Program RealTime performance in the last half of the season. Passage predictions from fifteen of the sixteen spring/summer yearling chinook salmon ESUs available for comparison improved in 2000 compared to 1999. The last-half average MAD over all the yearling chinook salmon ESUs was 4.3% in 2000, compared to 6.5% in 1999. Program RealTime 2000 first-half forecasting performance was slightly worse than that of 1999 (MAD = 4.5%), but still comparable to previous years with a MAD equal to 5.1%. Three yearling chinook ESUs showed moderately large (> 10%) MADs. These stocks had larger-than-average recapture percentages in 2000, producing over-predictions early in the season, in a dynamic reminiscent of migration year 1998 (Burgess et al., 1999). The passage distribution of the new stock of hatchery-reared sockeye salmon from Alturas Lake was well-predicted by Program RealTime, based on only two years of historical data (whole-season MAD = 4.3%). The two new run-of-the-river PIT-tagged stocks of wild yearling chinook salmon and steelhead trout were predicted with very good accuracy (whole-season MADs were 4.8% for steelhead trout and 1.7% for yearling chinook salmon), particularly during the last half of the outmigration. First-half steelhead predictions were among the season's worst (MAD = 10.8%), with over-predictions attributable to the largest passage on record of wild PIT-tagged steelhead trout to Lower Granite Dam. The results of RealTime predictions of passage percentiles of combined wild and hatchery-reared salmonids to Rock Island and McNary were mixed. Some of these passage-indexed runs-at-large were predicted with exceptional accuracy (whole-season MADs for coho salmon outmigrating to Rock Island Dam and McNary Dam were, respectively, 0.58% and 1.24%; for yearling chinook to McNary, 0.59%) while others were not forecast well at all (first-half MADs of sockeye salmon migrating to Rock Island and McNary Dams, respectively, were 19.25% and 12.78%). The worst performances for these mid- and mainstem-Columbia River runs-at-large were probably due to large hatchery release disturbing the smoothly accumulating percentages of normal fish passage. The RealTime project used a stock-specific method of upwardly adjusting PIT-tagged smolt counts at Lower Granite Dam. For chinook and sockeye salmon, the project continued using the 1999 formulation for spill-adjustment. For the new stock of wild PIT-tagged steelhead trout, a formula derived for steelhead trout only was used.« less

Predictability and possible earlier awareness of extreme precipitation across Europe

NASA Astrophysics Data System (ADS)

Lavers, David; Pappenberger, Florian; Richardson, David; Zsoter, Ervin

2017-04-01

Extreme hydrological events can cause large socioeconomic damages in Europe. In winter, a large proportion of these flood episodes are associated with atmospheric rivers, a region of intense water vapour transport within the warm sector of extratropical cyclones. When preparing for such extreme events, forecasts of precipitation from numerical weather prediction models or river discharge forecasts from hydrological models are generally used. Given the strong link between water vapour transport (integrated vapour transport IVT) and heavy precipitation, it is possible that IVT could be used to warn of extreme events. Furthermore, as IVT is located in extratropical cyclones, it is hypothesized to be a more predictable variable due to its link with synoptic-scale atmospheric dynamics. In this research, we firstly provide an overview of the predictability of IVT and precipitation forecasts, and secondly introduce and evaluate the ECMWF Extreme Forecast Index (EFI) for IVT. The EFI is a tool that has been developed to evaluate how ensemble forecasts differ from the model climate, thus revealing the extremeness of the forecast. The ability of the IVT EFI to capture extreme precipitation across Europe during winter 2013/14, 2014/15, and 2015/16 is presented. The results show that the IVT EFI is more capable than the precipitation EFI of identifying extreme precipitation in forecast week 2 during forecasts initialized in a positive North Atlantic Oscillation (NAO) phase. However, the precipitation EFI is superior during the negative NAO phase and at shorter lead times. An IVT EFI example is shown for storm Desmond in December 2015 highlighting its potential to identify upcoming hydrometeorological extremes.

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

NASA Astrophysics Data System (ADS)

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

2013-02-01

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

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.

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.

Comparison of Adaline and Multiple Linear Regression Methods for Rainfall Forecasting

NASA Astrophysics Data System (ADS)

Sutawinaya, IP; Astawa, INGA; Hariyanti, NKD

2018-01-01

Heavy rainfall can cause disaster, therefore need a forecast to predict rainfall intensity. Main factor that cause flooding is there is a high rainfall intensity and it makes the river become overcapacity. This will cause flooding around the area. Rainfall factor is a dynamic factor, so rainfall is very interesting to be studied. In order to support the rainfall forecasting, there are methods that can be used from Artificial Intelligence (AI) to statistic. In this research, we used Adaline for AI method and Regression for statistic method. The more accurate forecast result shows the method that used is good for forecasting the rainfall. Through those methods, we expected which is the best method for rainfall forecasting here.

Dynamic rating curve assessment in hydrometric stations and calculation of the associated uncertainties : Quality and monitoring indicators

NASA Astrophysics Data System (ADS)

Morlot, Thomas; Perret, Christian; Favre, Anne-Catherine

2013-04-01

Whether we talk about safety reasons, energy production or regulation, water resources management is one of EDF's (French hydropower company) main concerns. To meet these needs, since the fifties EDF-DTG operates a hydrometric network that includes more than 350 hydrometric stations. The data collected allow real time monitoring of rivers (hydro meteorological forecasts at points of interests), as well as hydrological studies and the sizing of structures. Ensuring the quality of stream flow data is a priority. A rating curve is an indirect method of estimating the discharge in rivers based on water level measurements. The value of discharge obtained thanks to the rating curve is not entirely accurate due to the constant changes of the river bed morphology, to the precision of the gaugings (direct and punctual discharge measurements) and to the quality of the tracing. As time goes on, the uncertainty of the estimated discharge from a rating curve « gets older » and increases: therefore the final level of uncertainty remains particularly difficult to assess. Moreover, the current EDF capacity to produce a rating curve is not suited to the frequency of change of the stage-discharge relationship. The actual method does not take into consideration the variation of the flow conditions and the modifications of the river bed which occur due to natural processes such as erosion, sedimentation and seasonal vegetation growth. In order to get the most accurate stream flow data and to improve their reliability, this study undertakes an original « dynamic» method to compute rating curves based on historical gaugings from a hydrometric station. A curve is computed for each new gauging and a model of uncertainty is adjusted for each of them. The model of uncertainty takes into account the inaccuracies in the measurement of the water height, the quality of the tracing, the uncertainty of the gaugings and the aging of the confidence intervals calculated with a variographic analysis. These rating curves enable to provide values of stream flow taking into account the variability of flow conditions, while providing a model of uncertainties resulting from the aging of the rating curves. By taking into account the variability of the flow conditions and the life of the hydrometric station, this original dynamic method can answer important questions in the field of hydrometry such as « How many gaugings a year have to be made so as to produce stream flow data with an average uncertainty of X% ? » and « When and in which range of water flow do we have to realize those gaugings ? ». KEY WORDS : Uncertainty, Rating curve, Hydrometric station, Gauging, Variogram, Stream Flow

Chemical weather forecasting for the Yangtze River Delta

NASA Astrophysics Data System (ADS)

Xie, Y.; Xu, J.; Zhou, G.; Chang, L.; Chen, B.

2016-12-01

Shanghai is one of the largest megacities in the world. With rapid economic growth of the city and its surrounding areas in recent years, air pollution has posed adverse effects on public health and ecosystem. In winter heavy pollution episodes are often associated with PM exceedances under stagnant conditions or transport events, whereas in summer the region frequently experiences elevated O3 levels. Chemical weather prediction systems with the WRF-Chem and CMAQ models are being developed to support air quality and haze forecasting for Shanghai and the Yangtze River Delta region. We will present main components of the modeling system, forecasting products, as well as evaluation results. Evaluation of the WRF-Chem forecasts show the model has generally good ability to capture the temporal variations of O3 and PM2.5. Substantial regional differences exist, with the best performance in Shanghai. Meanwhile, the forecasts tend to degrade during highly polluted episodes and transitional time periods, which highlights the need to improve model representation of key process (e.g. meteorological fields and formation of secondary pollutants). Recent work includes using the ECMWF global model forecasts as chemical boundary conditions for our regional model. We investigate the impact of chemical downscaling, and also compare the results from different models participated in the PANDA (PArtnership with chiNa on space Data) project. Results from ongoing efforts (e.g. chemical weather forecasting driven by SMS regional high resolution NWP) will also be presented.

The forecasting research of early warning systems for atmospheric pollutants: A case in Yangtze River Delta region

NASA Astrophysics Data System (ADS)

Song, Yiliao; Qin, Shanshan; Qu, Jiansheng; Liu, Feng

2015-10-01

The issue of air quality regarding PM pollution levels in China is a focus of public attention. To address that issue, to date, a series of studies is in progress, including PM monitoring programs, PM source apportionment, and the enactment of new ambient air quality index standards. However, related research concerning computer modeling for PM future trends estimation is rare, despite its significance to forecasting and early warning systems. Thereby, a study regarding deterministic and interval forecasts of PM is performed. In this study, data on hourly and 12 h-averaged air pollutants are applied to forecast PM concentrations within the Yangtze River Delta (YRD) region of China. The characteristics of PM emissions have been primarily examined and analyzed using different distribution functions. To improve the distribution fitting that is crucial for estimating PM levels, an artificial intelligence algorithm is incorporated to select the optimal parameters. Following that step, an ANF model is used to conduct deterministic forecasts of PM. With the identified distributions and deterministic forecasts, different levels of PM intervals are estimated. The results indicate that the lognormal or gamma distributions are highly representative of the recorded PM data with a goodness-of-fit R2 of approximately 0.998. Furthermore, the results of the evaluation metrics (MSE, MAPE and CP, AW) also show high accuracy within the deterministic and interval forecasts of PM, indicating that this method enables the informative and effective quantification of future PM trends.

Forecasting snowmelt flooding over Britain using the Grid-to-Grid model: a review and assessment of methods

NASA Astrophysics Data System (ADS)

Dey, Seonaid R. A.; Moore, Robert J.; Cole, Steven J.; Wells, Steven C.

2017-04-01

In many regions of high annual snowfall, snowmelt modelling can prove to be a vital component of operational flood forecasting and warning systems. Although Britain as a whole does not experience prolonged periods of lying snow, with the exception of the Scottish Highlands, the inclusion of snowmelt modelling can still have a significant impact on the skill of flood forecasts. Countrywide operational flood forecasts over Britain are produced using the national Grid-to-Grid (G2G) distributed hydrological model. For Scotland, snowmelt is included in these forecasts through a G2G snow hydrology module involving temperature-based snowfall/rainfall partitioning and functions for temperature-excess snowmelt, snowpack storage and drainage. Over England and Wales, the contribution of snowmelt is included by pre-processing the precipitation prior to input into G2G. This removes snowfall diagnosed from weather model outputs and adds snowmelt from an energy budget land surface scheme to form an effective liquid water gridded input to G2G. To review the operational options for including snowmelt modelling in G2G over Britain, a project was commissioned by the Environment Agency through the Flood Forecasting Centre (FFC) for England and Wales and in partnership with the Scottish Environment Protection Agency (SEPA) and Natural Resources Wales (NRW). Results obtained from this snowmelt review project will be reported on here. The operational methods used by the FFC and SEPA are compared on past snowmelt floods, alongside new alternative methods of treating snowmelt. Both case study and longer-term analyses are considered, covering periods selected from the winters 2009-2010, 2012-2013, 2013-2014 and 2014-2015. Over Scotland, both of the snowmelt methods used operationally by FFC and SEPA provided a clear improvement to the river flow simulations. Over England and Wales, fewer and less significant snowfall events occurred, leading to less distinction in the results between the methods. It is noted that, for all methods considered, large uncertainties remain in flood forecasts influenced by snowmelt. Understanding and quantifying these uncertainties should lead to more informed flood forecasts and associated guidance information.

Dynamic Management of Releases for the Delaware River Basin using NYC's Operations Support Tool

NASA Astrophysics Data System (ADS)

Weiss, W.; Wang, L.; Murphy, T.; Muralidhar, D.; Tarrier, B.

2011-12-01

The New York City Department of Environmental Protection (DEP) has initiated design of an Operations Support Tool (OST), a state-of-the-art decision support system to provide computational and predictive support for water supply operations and planning. Using an interim version of OST, DEP and the New York State Department of Environmental Conservation (DEC) have developed a provisional, one-year Delaware River Basin reservoir release program to succeed the existing Flexible Flow Management Program (FFMP) which expired on May 31, 2011. The FFMP grew out of the Good Faith Agreement of 1983 among the four Basin states (New York, New Jersey, Pennsylvania, and Delaware) that established modified diversions and flow targets during drought conditions. It provided a set of release schedules as a framework for managing diversions and releases from New York City's Delaware Basin reservoirs in order to support multiple objectives, including water supply, drought mitigation, flood mitigation, tailwaters fisheries, main stem habitat, recreation, and salinity repulsion. The provisional program (OST-FFMP) defines available water based on current Upper Delaware reservoir conditions and probabilistic forecasts of reservoir inflow. Releases are then set based on a set of release schedules keyed to the water availability. Additionally, OST-FFMP attempts to provide enhanced downstream flood protection by making spill mitigation releases to keep the Delaware System reservoirs at a seasonally varying conditional storage objective. The OST-FFMP approach represents a more robust way of managing downstream releases, accounting for predicted future hydrologic conditions by making more water available for release when conditions are forecasted to be wet and protecting water supply reliability when conditions are forecasted to be dry. Further, the dynamic nature of the program allows the release decision to be adjusted as hydrologic conditions change. OST simulations predict that this program can provide substantial benefits for downstream stakeholders while protecting DEP's ability to ensure a reliable water supply for 9 million customers in NYC and the surrounding communities. The one-year nature of the program will allow for DEP and the Decree Parties to evaluate and improve the program in the future. This paper will describe the OST-FFMP program and discuss preliminary observations on its performance based on key NYC and downstream stakeholder performance metrics.

Using Fluvial Geomorphology as a Physical Template in Process-Based and Recovery Enhancement Approaches to River Management

NASA Astrophysics Data System (ADS)

Fryirs, K.

2016-12-01

In an `era of river repair' fluvial geomorphology has emerged as a key science in river management practice. Geomorphologists are ideally placed to use their science in an applied manner to provide guidance on the impact of floods and droughts, landuse and climate change, and water use on river forms, processes and evolution. Increasingly, fluvial geomorphologists are also asked to make forecasts about how systems might adjust in the future, and to work with managers to implement strategies on-the-ground. Using case study material from Eastern Australia (Bega, Hunter, Wollombi and Lockyer catchments) I will focus on how process-based understanding of rivers has developed and evolved to provide a coherent physical template for effective and proactive, river management practice. I will focus on four key principles and demonstrate how geomorphology has been, and should continue to be, used in process-based, recovery enhancement approaches to river management. How understanding the difference between river behaviour and river change is used to determine how a river is `expected' to function, and how to identify anomalous processes requiring a treatment response. How understanding evolutionary trajectory is used to make future forecasts on river condition and recovery potential, and how working with processes can enhance river recovery. How geomorphic information can be used as a physical template atop which to analyse a range of biotic processes and habitat outcomes. How geomorphic information is used to effectively prioritise and plan river conservation and rehabilitation activities as part of catchment and region-scale action plans.

Iowa Flood Information System: Towards Integrated Data Management, Analysis and Visualization

NASA Astrophysics Data System (ADS)

Demir, I.; Krajewski, W. F.; Goska, R.; Mantilla, R.; Weber, L. J.; Young, N.

2012-04-01

The Iowa Flood Information System (IFIS) is a web-based platform developed by the Iowa Flood Center (IFC) to provide access to flood inundation maps, real-time flood conditions, flood forecasts both short-term and seasonal, flood-related data, information and interactive visualizations for communities in Iowa. The key element of the system's architecture is the notion of community. Locations of the communities, those near streams and rivers, define basin boundaries. The IFIS provides community-centric watershed and river characteristics, weather (rainfall) conditions, and streamflow data and visualization tools. Interactive interfaces allow access to inundation maps for different stage and return period values, and flooding scenarios with contributions from multiple rivers. Real-time and historical data of water levels, gauge heights, and rainfall conditions are available in the IFIS by streaming data from automated IFC bridge sensors, USGS stream gauges, NEXRAD radars, and NWS forecasts. Simple 2D and 3D interactive visualizations in the IFIS make the data more understandable to general public. Users are able to filter data sources for their communities and selected rivers. The data and information on IFIS is also accessible through web services and mobile applications. The IFIS is optimized for various browsers and screen sizes to provide access through multiple platforms including tablets and mobile devices. The IFIS includes a rainfall-runoff forecast model to provide a five-day flood risk estimate for around 500 communities in Iowa. Multiple view modes in the IFIS accommodate different user types from general public to researchers and decision makers by providing different level of tools and details. River view mode allows users to visualize data from multiple IFC bridge sensors and USGS stream gauges to follow flooding condition along a river. The IFIS will help communities make better-informed decisions on the occurrence of floods, and will alert communities in advance to help minimize damage of floods. This presentation provides an overview and live demonstration of the tools and interfaces in the IFIS developed to date to provide a platform for one-stop access to flood related data, visualizations, flood conditions, and forecast.

Iowa Flood Information System

NASA Astrophysics Data System (ADS)

Demir, I.; Krajewski, W. F.; Goska, R.; Mantilla, R.; Weber, L. J.; Young, N.

2011-12-01

The Iowa Flood Information System (IFIS) is a web-based platform developed by the Iowa Flood Center (IFC) to provide access to flood inundation maps, real-time flood conditions, flood forecasts both short-term and seasonal, flood-related data, information and interactive visualizations for communities in Iowa. The key element of the system's architecture is the notion of community. Locations of the communities, those near streams and rivers, define basin boundaries. The IFIS provides community-centric watershed and river characteristics, weather (rainfall) conditions, and streamflow data and visualization tools. Interactive interfaces allow access to inundation maps for different stage and return period values, and flooding scenarios with contributions from multiple rivers. Real-time and historical data of water levels, gauge heights, and rainfall conditions are available in the IFIS by streaming data from automated IFC bridge sensors, USGS stream gauges, NEXRAD radars, and NWS forecasts. Simple 2D and 3D interactive visualizations in the IFIS make the data more understandable to general public. Users are able to filter data sources for their communities and selected rivers. The data and information on IFIS is also accessible through web services and mobile applications. The IFIS is optimized for various browsers and screen sizes to provide access through multiple platforms including tablets and mobile devices. The IFIS includes a rainfall-runoff forecast model to provide a five-day flood risk estimate for around 500 communities in Iowa. Multiple view modes in the IFIS accommodate different user types from general public to researchers and decision makers by providing different level of tools and details. River view mode allows users to visualize data from multiple IFC bridge sensors and USGS stream gauges to follow flooding condition along a river. The IFIS will help communities make better-informed decisions on the occurrence of floods, and will alert communities in advance to help minimize damage of floods. This presentation provides an overview of the tools and interfaces in the IFIS developed to date to provide a platform for one-stop access to flood related data, visualizations, flood conditions, and forecast.

Flood Risk Management in Iowa through an Integrated Flood Information System

NASA Astrophysics Data System (ADS)

Demir, Ibrahim; Krajewski, Witold

2013-04-01

The Iowa Flood Information System (IFIS) is a web-based platform developed by the Iowa Flood Center (IFC) to provide access to flood inundation maps, real-time flood conditions, flood forecasts both short-term and seasonal, flood-related data, information and interactive visualizations for communities in Iowa. The key element of the system's architecture is the notion of community. Locations of the communities, those near streams and rivers, define basin boundaries. The IFIS provides community-centric watershed and river characteristics, weather (rainfall) conditions, and streamflow data and visualization tools. Interactive interfaces allow access to inundation maps for different stage and return period values, and flooding scenarios with contributions from multiple rivers. Real-time and historical data of water levels, gauge heights, and rainfall conditions are available in the IFIS by streaming data from automated IFC bridge sensors, USGS stream gauges, NEXRAD radars, and NWS forecasts. Simple 2D and 3D interactive visualizations in the IFIS make the data more understandable to general public. Users are able to filter data sources for their communities and selected rivers. The data and information on IFIS is also accessible through web services and mobile applications. The IFIS is optimized for various browsers and screen sizes to provide access through multiple platforms including tablets and mobile devices. The IFIS includes a rainfall-runoff forecast model to provide a five-day flood risk estimate for around 1100 communities in Iowa. Multiple view modes in the IFIS accommodate different user types from general public to researchers and decision makers by providing different level of tools and details. River view mode allows users to visualize data from multiple IFC bridge sensors and USGS stream gauges to follow flooding condition along a river. The IFIS will help communities make better-informed decisions on the occurrence of floods, and will alert communities in advance to help minimize damage of floods. This presentation provides an overview and live demonstration of the tools and interfaces in the IFIS developed to date to provide a platform for one-stop access to flood related data, visualizations, flood conditions, and forecast.

A statistical forecast model using the time-scale decomposition technique to predict rainfall during flood period over the middle and lower reaches of the Yangtze River Valley

NASA Astrophysics Data System (ADS)

Hu, Yijia; Zhong, Zhong; Zhu, Yimin; Ha, Yao

2018-04-01

In this paper, a statistical forecast model using the time-scale decomposition method is established to do the seasonal prediction of the rainfall during flood period (FPR) over the middle and lower reaches of the Yangtze River Valley (MLYRV). This method decomposites the rainfall over the MLYRV into three time-scale components, namely, the interannual component with the period less than 8 years, the interdecadal component with the period from 8 to 30 years, and the interdecadal component with the period larger than 30 years. Then, the predictors are selected for the three time-scale components of FPR through the correlation analysis. At last, a statistical forecast model is established using the multiple linear regression technique to predict the three time-scale components of the FPR, respectively. The results show that this forecast model can capture the interannual and interdecadal variation of FPR. The hindcast of FPR during 14 years from 2001 to 2014 shows that the FPR can be predicted successfully in 11 out of the 14 years. This forecast model performs better than the model using traditional scheme without time-scale decomposition. Therefore, the statistical forecast model using the time-scale decomposition technique has good skills and application value in the operational prediction of FPR over the MLYRV.

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.

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

NASA Astrophysics Data System (ADS)

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

2015-04-01

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

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

Scenario Analysis: Evaluating Biodiversity Response to Forecasted Land-Use Change in the San Pedro River Basin (U.S.-Mexico)

EPA Science Inventory

Envisioning and evaluating future scenarios has emerged as a critical component of both science and social decision-making. The ability to assess, report, map, and forecast the life support functions of ecosystems is absolutely critical to our capacity to make informed decisions...

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.

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

NASA Astrophysics Data System (ADS)

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

2012-09-01

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

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.

Forecast Informed Reservoir Operations: Bringing Science and Decision-Makers Together to Explore Use of Hydrometeorological Forecasts to Support Future Reservoir Operations

NASA Astrophysics Data System (ADS)

Ralph, F. M.; Jasperse, J.

2017-12-01

Forecast Informed Reservoir Operations (FIRO) is a proposed strategy that is exploring inorporation of improved hydrometeorological forecasts of land-falling atmospheric rivers on the U.S. West Coast into reservoir operations. The first testbed for this strategy is Lake Mendocino, which is located in the East Fork of the 1485 mi2 Russian River Watershed in northern California. This project is guided by the Lake Mendocino FIRO Steering Committee (SC). The SC is an ad hoc committee that consists of water managers and scientists from several federal, state, and local agencies, and universities who have teamed to evaluate whether current or improved technology and scientific understanding can be utilized to improve water supply reliability, enhance flood mitigation and support recovery of listed salmon for the Russian River of northern California. In 2015, the SC created a detailed work plan, which included a Preliminary Viability Assessment, which has now been completed. The SC developed a vision that operational efficiency would be improved by using forecasts to inform decisions about releasing or storing water. FIRO would use available reservoir storage in an efficient manner by (1) better forecasting inflow (or lack of inflow) with enhanced technology, and (2) adapting operation in real time to meet the need for storage, rather than making storage available just in case it is needed. The envisioned FIRO strategy has the potential to simultaneously improve water supply reliability, flood protection, and ecosystem outcomes through a more efficient use of existing infrastructure while requiring minimal capital improvements in the physical structure of the dam. This presentation will provide an overview of the creation of the FIRO SC and how it operates, and describes the lessons learned through this partnership. Results in the FIRO Preliminary Viability Assessment will be summarized and next steps described.

Forecast Mekong

USGS Publications Warehouse

Turnipseed, D. Phil

2011-01-01

Forecast Mekong is part of the U.S. Department of State's Lower Mekong Initiative, which was launched in 2009 by Secretary Hillary Clinton and the Foreign Ministers of Cambodia, Laos, Thailand, and Vietnam to enhance partnerships between the U.S. and the Lower Mekong River countries in the areas of environment, health, education, and infrastructure. The U.S. Geological Survey (USGS) is working in close cooperation with the U.S. Department of State to use research and data from the Lower Mekong Basin to provide hands-on results that will help decision makers in Lower Mekong River countries in the planning and design for restoration, conservation, and management efforts in the basin.

Development of Hydrological Model of Klang River Valley for flood forecasting

NASA Astrophysics Data System (ADS)

Mohammad, M.; Andras, B.

2012-12-01

This study is to review the impact of climate change and land used on flooding through the Klang River and to compare the changes in the existing river system in Klang River Basin with the Storm water Management and Road Tunnel (SMART) which is now already operating in the city centre of Kuala Lumpur. Klang River Basin is the most urbanized region in Malaysia. More than half of the basin has been urbanized on the land that is prone to flooding. Numerous flood mitigation projects and studies have been carried out to enhance the existing flood forecasting and mitigation project. The objective of this study is to develop a hydrological model for flood forecasting in Klang Basin Malaysia. Hydrological modelling generally requires large set of input data and this is more often a challenge for a developing country. Due to this limitation, the Tropical Rainfall Measuring Mission (TRMM) rainfall measurement, initiated by the US space agency NASA and Japanese space agency JAXA was used in this study. TRMM data was transformed and corrected by quantile to quantile transformation. However, transforming the data based on ground measurement doesn't make any significant improvement and the statistical comparison shows only 10% difference. The conceptual HYMOD model was used in this study and calibrated using ROPE algorithm. But, using the whole time series of the observation period in this area resulted in insufficient performance. The depth function which used in ROPE algorithm are then used to identified and calibrated using only unusual event to observed the improvement and efficiency of the model.

Dynamic modelling of five different phytoplankton groups in the River Thames (UK)

NASA Astrophysics Data System (ADS)

Bussi, Gianbattista; Whitehead, Paul; Bowes, Michael; Read, Daniel; Dadson, Simon

2015-04-01

Phytoplankton play a vital role in fluvial ecosystems, being a major producer of organic carbon, a food source for primary consumers and a relevant source of oxygen for many low-gradient rivers, but also a producer of potentially harmful toxins (e.g. cyanobacteria). For these reasons, the forecast and prevention of algal blooms is fundamental for the safe management of river systems. In this study, we developed a new process-based phytoplankton model for operational management and forecast of algal and cyanobacteria blooms subject to environmental change. The model is based on a mass-balance and it reproduces phytoplankton growth and death, taking into account the controlling effect played by water temperature, solar radiation, self-shading and dissolved phosphorus and silicon concentrations. The model was implemented in five reaches of the River Thames (UK) with a daily time step over a period of three years, and its results were compared to a novel dataset of cytometric data which includes community cell abundance of chlorophytes, diatoms, cyanobacteria, microcystis-like cyanobacteria and picoalgae. The model results were satisfactory in terms of fitting the observed data. A Multi-Objective General Sensitivity Analysis was also carried out in order to quantify model sensitivity to its parameters. It showed that the most influential parameters are phytoplankton growth and death rates, while phosphorus concentration showed little influence on phytoplankton growth, due to the high levels of phosphorus in the River Thames. The model was demonstrated to be a reliable tool to be used in algal bloom forecasting and management.

Using Terrain Analysis and Remote Sensing to Improve Snow Mass Balance and Runoff Prediction

NASA Astrophysics Data System (ADS)

Venteris, E. R.; Coleman, A. M.; Wigmosta, M. S.

2010-12-01

Approximately 70-80% of the water in the international Columbia River basin is sourced from snowmelt. The demand for this water has competing needs, as it is used for agricultural irrigation, municipal, hydro and nuclear power generation, and environmental in-stream flow requirements. Accurate forecasting of water supply is essential for planning current needs and prediction of future demands due to growth and climate change. A significant limitation on current forecasting is spatial and temporal uncertainty in snowpack characteristics, particularly snow water equivalent. Currently, point measurements of snow mass balance are provided by the NRCS SNOTEL network. Each site consists of a snow mass sensor and meteorology station that monitors snow water equivalent, snow depth, precipitation, and temperature. There are currently 152 sites in the mountains of Oregon and Washington. An important step in improving forecasts is determining how representative each SNOTEL site is of the total mass balance of the watershed through a full accounting of the spatiotemporal variability in snowpack processes. This variation is driven by the interaction between meteorological processes, land cover, and landform. Statistical and geostatistical spatial models relate the state of the snowpack (characterized through SNOTEL, snow course measurements, and multispectral remote sensing) to terrain attributes derived from digital elevation models (elevation, aspect, slope, compound topographic index, topographic shading, etc.) and land cover. Time steps representing the progression of the snow season for several meteorologically distinct water years are investigated to identify and quantify dominant physical processes. The spatially distributed snow balance data can be used directly as model inputs to improve short- and long-range hydrologic forecasts.

Evaluating the effects of monthly river flow trends on Environmental Flow allocation

NASA Astrophysics Data System (ADS)

Torabi Haghighi, Ali; Klove, Bjorn

2010-05-01

The Natural river flow regime can be changed by the construction of hydraulic structures such as dams, hydropower plants, pump stations and so on. Due to the new river flow regime, some parts of water resources must be allocated to environmental flow (EF). There are more than 62 hydrological methods which have been proposed for calculating EF, although these methods don't have enough acceptability to be used in practical cases and The so other methods are preferred such as holistic,….. Most hydrological methods do not take basin physiography, climate, location of hydraulic structures, monthly river flow regime, historical trend of river (annually regime), purpose of hydraulic structures and so on, into consideration. In the present work, data from more than 180 rivers from Asia (71 rivers and 16 countries), Europe (79 Rivers and 23 countries), Americas (23 rivers and 10 countries) and Africa (12 rivers and 6 countries) were used to assess EF. The rivers were divided into 5 main groups of regular permanent rivers, semi regular permanent rivers, irregular permanent rivers, seasonal rivers and dry rivers, for each groups EF calculated by some hydrological methods and compared with the natural flow regime. The results showed that besides the amount of EF, the monthly distribution of flow is very important and should be considered in reservoir operation. In seasonal rivers and dry rivers, hydraulic structure construction can be useful for conserving aquatic ecosystems

Mount St. Helens Future Expected Deposition Scenario (FEDS)

DTIC Science & Technology

2011-04-14

Gradation in HEC - RAS Sediment Transport Model of the Lower Cowlitz River ...Cowlitz 1-D/2-D modeling. Will also be used to test proposed measures where appropriate. Cowlitz River Toutle to Columbia 1-D HEC - RAS Aug 2004...Sep 2008 (6 years) Calibration Model Cowlitz River Toutle to Columbia 1-D HEC - RAS Oct 2007 – Sep 2035 (28 years) Forecast to predict trends in

Accuracy enhancement for forecasting water levels of reservoirs and river streams using a multiple-input-pattern fuzzification approach.

PubMed

Valizadeh, Nariman; El-Shafie, Ahmed; Mirzaei, Majid; Galavi, Hadi; Mukhlisin, Muhammad; Jaafar, Othman

2014-01-01

Water level forecasting is an essential topic in water management affecting reservoir operations and decision making. Recently, modern methods utilizing artificial intelligence, fuzzy logic, and combinations of these techniques have been used in hydrological applications because of their considerable ability to map an input-output pattern without requiring prior knowledge of the criteria influencing the forecasting procedure. The artificial neurofuzzy interface system (ANFIS) is one of the most accurate models used in water resource management. Because the membership functions (MFs) possess the characteristics of smoothness and mathematical components, each set of input data is able to yield the best result using a certain type of MF in the ANFIS models. The objective of this study is to define the different ANFIS model by applying different types of MFs for each type of input to forecast the water level in two case studies, the Klang Gates Dam and Rantau Panjang station on the Johor river in Malaysia, to compare the traditional ANFIS model with the new introduced one in two different situations, reservoir and stream, showing the new approach outweigh rather than the traditional one in both case studies. This objective is accomplished by evaluating the model fitness and performance in daily forecasting.

Accuracy Enhancement for Forecasting Water Levels of Reservoirs and River Streams Using a Multiple-Input-Pattern Fuzzification Approach

PubMed Central

Mirzaei, Majid; Jaafar, Othman

2014-01-01

Water level forecasting is an essential topic in water management affecting reservoir operations and decision making. Recently, modern methods utilizing artificial intelligence, fuzzy logic, and combinations of these techniques have been used in hydrological applications because of their considerable ability to map an input-output pattern without requiring prior knowledge of the criteria influencing the forecasting procedure. The artificial neurofuzzy interface system (ANFIS) is one of the most accurate models used in water resource management. Because the membership functions (MFs) possess the characteristics of smoothness and mathematical components, each set of input data is able to yield the best result using a certain type of MF in the ANFIS models. The objective of this study is to define the different ANFIS model by applying different types of MFs for each type of input to forecast the water level in two case studies, the Klang Gates Dam and Rantau Panjang station on the Johor river in Malaysia, to compare the traditional ANFIS model with the new introduced one in two different situations, reservoir and stream, showing the new approach outweigh rather than the traditional one in both case studies. This objective is accomplished by evaluating the model fitness and performance in daily forecasting. PMID:24790567

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

NASA Astrophysics Data System (ADS)

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

2014-12-01

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

2014 Gulf of Mexico Hypoxia Forecast

USGS Publications Warehouse

Scavia, Donald; Evans, Mary Anne; Obenour, Dan

2014-01-01

The Gulf of Mexico annual summer hypoxia forecasts are based on average May total nitrogen loads from the Mississippi River basin for that year. The load estimate, recently released by USGS, is 4,761 metric tons per day. Based on that estimate, we predict the area of this summer’s hypoxic zone to be 14,000 square kilometers (95% credible interval, 8,000 to 20,000) – an “average year”. Our forecast hypoxic volume is 50 km3 (95% credible interval, 20 to 77).

PREDICTION OF CONTAMINATED SEDIMENT TRANSPORT IN THE MAURICE RIVER-UNION LAKE, NEW JERSEY, USA

EPA Science Inventory

This paper describes a sediment and contaminant transport model and its application to the Maurice River-Union Lake system in southern New Jersey, USA for the purpose of characterizing and forecasting sediment and arsenic distributions before and after proposed dredging activitie...

Multivariate postprocessing techniques for probabilistic hydrological forecasting

NASA Astrophysics Data System (ADS)

Hemri, Stephan; Lisniak, Dmytro; Klein, Bastian

2016-04-01

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

An Open-Book Modular Watershed Modeling Framework for Rapid Prototyping of GPM- based Flood Forecasting in International River Basins

NASA Astrophysics Data System (ADS)

Katiyar, N.; Hossain, F.

2006-05-01

Floods have always been disastrous for human life. It accounts for about 15 % of the total death related to natural disasters. There are around 263 transboundary river basins listed by UNESCO, wherein at least 30 countries have more than 95% of their territory locked in one or more such transboundary basins. For flood forecasting in the lower riparian nations of these International River Basins (IRBs), real-time rainfall data from upstream nations is naturally the most critical factor governing the forecasting effectiveness. However, many upstream nations fail to provide data to the lower riparian nations due to a lack of in-situ rainfall measurement infrastructure or a lack of a treaty for real-time sharing of rainfall data. A potential solution is therefore to use satellites that inherently measure rainfall across political boundaries. NASA's proposed Global Precipitation Measurement (GPM) mission appears very promising in providing this vital rainfall information under the data- limited scenario that will continue to prevail in most IRBs. However, satellite rainfall is associated with uncertainty and hence, proper characterization of the satellite rainfall error propagation in hydrologic models for flood forecasting is a critical priority that should be resolved in the coming years in anticipation of GPM. In this study, we assess an open book modular watershed modeling approach for estimating the expected error in flood forecasting related to GPM rainfall data. Our motivation stems from the critical challenge in identifying the specific IRBs that would benefit from a pre-programmed satellite-based forecasting system in anticipation of GPM. As the number of flood-prone IRBs is large, conventional data-intensive implementation of existing physically-based distributed hydrologic models on case-by-case IRBs is considered time-consuming for completing such a global assessment. A more parsimonious approach is justified at the expense of a tolerable loss of detail and accuracy. Through assessment of our proposed modular modeling framework, we present our initial understanding in resolving the fundamental question - Can a parsimonious open-book watershed modeling framework be a physically consistent proxy for rapid and global identification of IRBs in greater need of a GPM-based flood forecasting system?

Development and Implementation of an Optimization Model for Hydropower and Total Dissolved Gas in the Mid-Columbia River System

DOE PAGES

Witt, Adam; Magee, Timothy; Stewart, Kevin; ...

2017-08-10

Managing energy, water, and environmental priorities and constraints within a cascade hydropower system is a challenging multiobjective optimization effort that requires advanced modeling and forecasting tools. Within the mid-Columbia River system, there is currently a lack of specific solutions for predicting how coordinated operational decisions can mitigate the impacts of total dissolved gas (TDG) supersaturation while satisfying multiple additional policy and hydropower generation objectives. In this study, a reduced-order TDG uptake equation is developed that predicts tailrace TDG at seven hydropower facilities on the mid-Columbia River. The equation is incorporated into a general multiobjective river, reservoir, and hydropower optimization toolmore » as a prioritized operating goal within a broader set of system-level objectives and constraints. A test case is presented to assess the response of TDG and hydropower generation when TDG supersaturation is optimized to remain under state water-quality standards. Satisfaction of TDG as an operating goal is highly dependent on whether constraints that limit TDG uptake are implemented at a higher priority than generation requests. According to the model, an opportunity exists to reduce TDG supersaturation and meet hydropower generation requirements by shifting spillway flows to different time periods. In conclusion, a coordinated effort between all project owners is required to implement systemwide optimized solutions that satisfy the operating policies of all stakeholders.« less

Development and Implementation of an Optimization Model for Hydropower and Total Dissolved Gas in the Mid-Columbia River System

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

Witt, Adam; Magee, Timothy; Stewart, Kevin

Managing energy, water, and environmental priorities and constraints within a cascade hydropower system is a challenging multiobjective optimization effort that requires advanced modeling and forecasting tools. Within the mid-Columbia River system, there is currently a lack of specific solutions for predicting how coordinated operational decisions can mitigate the impacts of total dissolved gas (TDG) supersaturation while satisfying multiple additional policy and hydropower generation objectives. In this study, a reduced-order TDG uptake equation is developed that predicts tailrace TDG at seven hydropower facilities on the mid-Columbia River. The equation is incorporated into a general multiobjective river, reservoir, and hydropower optimization toolmore » as a prioritized operating goal within a broader set of system-level objectives and constraints. A test case is presented to assess the response of TDG and hydropower generation when TDG supersaturation is optimized to remain under state water-quality standards. Satisfaction of TDG as an operating goal is highly dependent on whether constraints that limit TDG uptake are implemented at a higher priority than generation requests. According to the model, an opportunity exists to reduce TDG supersaturation and meet hydropower generation requirements by shifting spillway flows to different time periods. In conclusion, a coordinated effort between all project owners is required to implement systemwide optimized solutions that satisfy the operating policies of all stakeholders.« less

Changes in river water temperature between 1980 and 2012 in Yongan watershed, eastern China: Magnitude, drivers and models

NASA Astrophysics Data System (ADS)

Chen, Dingjiang; Hu, Minpeng; Guo, Yi; Dahlgren, Randy A.

2016-02-01

Climate warming is expected to have major impacts on river water quality, water column/hyporheic zone biogeochemistry and aquatic ecosystems. A quantitative understanding of spatio-temporal air (Ta) and water (Tw) temperature dynamics is required to guide river management and to facilitate adaptations to climate change. This study determined the magnitude, drivers and models for increasing Tw in three river segments of the Yongan watershed in eastern China. Over the 1980-2012 period, Tw in the watershed increased by 0.029-0.046 °C yr-1 due to a ∼0.050 °C yr-1 increase of Ta and changes in local human activities (e.g., increasing developed land and population density and decreasing forest area). A standardized multiple regression model was developed for predicting annual Tw (R2 = 0.88-0.91) and identifying/partitioning the impact of the principal drivers on increasing Tw:Ta (76 ± 1%), local human activities (14 ± 2%), and water discharge (10 ± 1%). After normalizing water discharge, climate warming and local human activities were estimated to contribute 81-95% and 5-19% of the observed rising Tw, respectively. Models forecast a 0.32-1.76 °C increase in Tw by 2050 compared with the 2000-2012 baseline condition based on four future scenarios. Heterogeneity of warming rates existed across seasons and river segments, with the lower flow river and dry season demonstrating a more pronounced response to climate warming and human activities. Rising Tw due to changes in climate, local human activities and hydrology has a considerable potential to aggravate river water quality degradation and coastal water eutrophication in summer. Thus it should be carefully considered in developing watershed management strategies in response to climate change.

The Role of Forests in Regulating the River Flow Regime of Large Basins of the World

NASA Astrophysics Data System (ADS)

Salazar, J. F.; Villegas, J. C.; Mercado-Bettin, D. A.; Rodríguez, E.

2016-12-01

Many natural and social phenomena depend on river flow regimes that are being altered by global change. Understanding the mechanisms behind such alterations is crucial for predicting river flow regimes in a changing environment. Here we explore potential linkages between the presence of forests and the capacity of river basins for regulating river flows. Regulation is defined here as the capacity of river basins to attenuate the amplitude of the river flow regime, that is to reduce the difference between high and low flows. We first use scaling theory to show how scaling properties of observed river flows can be used to classify river basins as regulated or unregulated. This parsimonious classification is based on a physical interpretation of the scaling properties (particularly the scaling exponents) that is novel (most previous studies have focused on the interpretation of the scaling exponents for floods only), and widely-applicable to different basins (the only assumption is that river flows in a given river basin exhibit scaling properties through well-known power laws). Then we show how this scaling framework can be used to explore global-change-induced temporal variations in the regulation capacity of river basins. Finally, we propose a conceptual hypothesis (the "Forest reservoir concept") to explain how large-scale forests can exert important effects on the long-term water balance partitioning and regulation capacity of large basins of the world. Our quantitative results are based on data analysis (river flows and land cover features) from 22 large basins of the world, with emphasis in the Amazon river and its main tributaries. Collectively, our findings support the hypothesis that forest cover enhances the capacity of large river basins to maintain relatively high mean river flows, as well as to regulate (ameliorate) extreme river flows. Advancing towards this quantitative understanding of the relation between forest cover and river flow regimes is crucial for water management- and land cover-related decisions.

The Role of Forests in Regulating the River Flow Regime of Large Basins of the World

NASA Astrophysics Data System (ADS)

Salazar, J. F.; Villegas, J. C.; Mercado-Bettin, D. A.; Rodríguez, E.

2017-12-01

Many natural and social phenomena depend on river flow regimes that are being altered by global change. Understanding the mechanisms behind such alterations is crucial for predicting river flow regimes in a changing environment. Here we explore potential linkages between the presence of forests and the capacity of river basins for regulating river flows. Regulation is defined here as the capacity of river basins to attenuate the amplitude of the river flow regime, that is to reduce the difference between high and low flows. We first use scaling theory to show how scaling properties of observed river flows can be used to classify river basins as regulated or unregulated. This parsimonious classification is based on a physical interpretation of the scaling properties (particularly the scaling exponents) that is novel (most previous studies have focused on the interpretation of the scaling exponents for floods only), and widely-applicable to different basins (the only assumption is that river flows in a given river basin exhibit scaling properties through well-known power laws). Then we show how this scaling framework can be used to explore global-change-induced temporal variations in the regulation capacity of river basins. Finally, we propose a conceptual hypothesis (the "Forest reservoir concept") to explain how large-scale forests can exert important effects on the long-term water balance partitioning and regulation capacity of large basins of the world. Our quantitative results are based on data analysis (river flows and land cover features) from 22 large basins of the world, with emphasis in the Amazon river and its main tributaries. Collectively, our findings support the hypothesis that forest cover enhances the capacity of large river basins to maintain relatively high mean river flows, as well as to regulate (ameliorate) extreme river flows. Advancing towards this quantitative understanding of the relation between forest cover and river flow regimes is crucial for water management- and land cover-related decisions.

Forecasting production in Liquid Rich Shale plays

NASA Astrophysics Data System (ADS)

Nikfarman, Hanieh

Production from Liquid Rich Shale (LRS) reservoirs is taking center stage in the exploration and production of unconventional reservoirs. Production from the low and ultra-low permeability LRS plays is possible only through multi-fractured horizontal wells (MFHW's). There is no existing workflow that is applicable to forecasting multi-phase production from MFHW's in LRS plays. This project presents a practical and rigorous workflow for forecasting multiphase production from MFHW's in LRS reservoirs. There has been much effort in developing workflows and methodology for forecasting in tight/shale plays in recent years. The existing workflows, however, are applicable only to single phase flow, and are primarily used in shale gas plays. These methodologies do not apply to the multi-phase flow that is inevitable in LRS plays. To account for complexities of multiphase flow in MFHW's the only available technique is dynamic modeling in compositional numerical simulators. These are time consuming and not practical when it comes to forecasting production and estimating reserves for a large number of producers. A workflow was developed, and validated by compositional numerical simulation. The workflow honors physics of flow, and is sufficiently accurate while practical so that an analyst can readily apply it to forecast production and estimate reserves in a large number of producers in a short period of time. To simplify the complex multiphase flow in MFHW, the workflow divides production periods into an initial period where large production and pressure declines are expected, and the subsequent period where production decline may converge into a common trend for a number of producers across an area of interest in the field. Initial period assumes the production is dominated by single-phase flow of oil and uses the tri-linear flow model of Erdal Ozkan to estimate the production history. Commercial software readily available can simulate flow and forecast production in this period. In the subsequent Period, dimensionless rate and dimensionless time functions are introduced that help identify transition from initial period into subsequent period. The production trends in terms of the dimensionless parameters converge for a range of rock permeability and stimulation intensity. This helps forecast production beyond transition to the end of life of well. This workflow is applicable to single fluid system.

Using Flow Charts to Visualize the Decision-Making Process in Space Weather Forecasting

NASA Astrophysics Data System (ADS)

Aung, M. T. Y.; Myat, T.; Zheng, Y.; Mays, M. L.; Ngwira, C.; Damas, M. C.

2016-12-01

Our society today relies heavily on technological systems such as satellites, navigation systems, power grids and aviation. These systems are very sensitive to space weather disturbances. When Earth-directed space weather driven by the Sun arrives at the Earth, it causes changes to the Earth's radiation environment and the magnetosphere. Strong disturbances in the magnetosphere of the Earth are responsible for geomagnetic storms that can last from hours to days depending on strength of storms. Geomagnetic storms can severely impact critical infrastructure on Earth, such as the electric power grid, and Solar Energetic Particles that can endanger life in outer space. How can we lessen these adverse effects? They can be lessened through the early warning signals sent by space weather forecasters before CME or high-speed stream arrives. A space weather forecaster's duty is to send predicted notifications to high-tech industries and NASA missions so that they could take extra measures for protection. NASA space weather forecasters make prediction decisions by following certain steps and processes from the time an event occurs at the sun all the way to the impact locations. However, there has never been a tool that helps these forecasters visualize the decision process until now. A flow chart is created to help forecasters visualize the decision process. This flow chart provides basic knowledge of space weather and can be used to train future space weather forecasters. It also helps to cut down the training period and increase consistency in forecasting. The flow chart is also a great reference for people who are already familiar with space weather.

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

NASA Astrophysics Data System (ADS)

Grossi, Giovanna; Caronna, Paolo; Ranzi, Roberto

2014-05-01

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

Using 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.

Towards spatially distributed flood forecasts in flash flood prone areas: application to the supervision of a road network in the South of France

NASA Astrophysics Data System (ADS)

Naulin, Jean-Philippe; Payrastre, Olivier; Gaume, Eric; Delrieu, Guy

2013-04-01

Accurate flood forecasts are crucial for an efficient flood event management. Until now, hydro-meteorological forecasts have been mainly used for early-warnings in France (Meteorological and flood vigilance maps) or over the world (Flash-flood guidances). These forecasts are generally limited to the main streams covered by the flood forecasting services or to specific watersheds with particular assets like check dams which are in most cases well gauged river sections, leaving aside large parts of the territory. A distributed hydro-meteorological forecasting approach will be presented, able to take advantage of the high spatial and temporal resolution rainfall estimates that are now available to provide information at ungauged sites. The proposed system aiming at detecting road inundation risks had been initially developed and tested in areas of limited size. Its extension to a whole region (the Gard region in the South of France) will be presented, including over 2000 crossing points between rivers and roads and its validation against a large data set of actually reported road inundations observed during recent flash-flood events. These first validation results appear promising. Such a tool would provide the necessary information for flood event management services to identify the areas at risk and to take the appropriate safety and rescue measures: pre-positioning of rescue means, stopping of the traffic on exposed roads, determination of safe accesses or evacuation routes. Moreover, beyond the specific application to the supervision of a road network, this work provides also results concerning the performances of hydro-meteorological forecasts for ungauged headwaters.

Fish Passage Center; Columbia Basin Fish and Wildlife Authority, 2000 Annual Report.

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

DeHart, Michele

2001-06-01

The year 2000 hydrosystem operations illustrated two main points: (1) that the NMFS Biological Opinion on the operations of the Federal Columbia River Power System (FCRPS) fish migration measures could not be met in a slightly below average water year, and; (2) the impacts and relationships of energy deregulation and volatile wholesale energy prices on the ability of the FCRPS to provide the Biological Opinion fish migration measures. In 2000, a slightly below average water year, the flow targets were not met and, when energy ''emergencies'' were declared, salmon protection measures were reduced. The 2000 migration year was a belowmore » average runoff volume year with an actual run off volume of 61.1 MAF or 96% of average. This year illustrated the ability of the hydro system to meet the migration protection measures established by the NMFS Biological Opinion. The winter operation of storage reservoirs was based upon inaccurate runoff volume forecasts which predicted a January-July runoff volume forecast at The Dalles of 102 to 105% of average, from January through June. Reservoir flood control drafts during the winter months occurred according to these forecasts. This caused an over-draft of reservoirs that resulted in less volume of water available for fish flow augmentation in the spring and the summer. The season Biological Opinion flow targets for spring and summer migrants at Lower Granite and McNary dams were not met. Several power emergencies were declared by BPA in the summer of 2000. The first in June was caused by loss of resources (WNP2 went off-line). The second and third emergencies were declared in August as a result of power emergencies in California and in the Northwest. The unanticipated effects of energy deregulation, power market volatility and rising wholesale electricity prices, and Californian energy deregulation reduced the ability of the FCRPS to implement fish protection measures. A Spill Plan Agreement was implemented in the FCRPS. Under this plan, spill hours were increased at Lower Monumental Dam. Spill volume at The Dalles was reduced and daytime spill tests were conducted at John Day and Bonneville Dams. Although provided for fish, most spill that occurred in 2000 was either in excess of project hydraulic capacity or excess generation. This effectively reduced the actual cost of the spill program. For the most part, spill in 2000 was managed to the waiver limits for total dissolved gas levels and the NMFS action criteria for dissolved gas signs were not exceeded. Hatchery spring chinook returns comprised an estimated 81.4% of the total spring chinook adult return to Lower Granite Dam. Smolt travel time and survival were similar to past years for most Smolt Monitoring Program groups. The notable exceptions were Snake River hatchery steelhead groups and mid-Columbia hatchery sub-yearling groups from Wells and Ringold hatcheries, which had significantly lower survival than previous years. Yearling chinook travel time showed variation from past years, reflecting the atypical flow shape in 2000 which had high flows in April, declining through May.« less

Fuzzy State Transition and Kalman Filter Applied in Short-Term Traffic Flow Forecasting

PubMed Central

Ming-jun, Deng; Shi-ru, Qu

2015-01-01

Traffic flow is widely recognized as an important parameter for road traffic state forecasting. Fuzzy state transform and Kalman filter (KF) have been applied in this field separately. But the studies show that the former method has good performance on the trend forecasting of traffic state variation but always involves several numerical errors. The latter model is good at numerical forecasting but is deficient in the expression of time hysteretically. This paper proposed an approach that combining fuzzy state transform and KF forecasting model. In considering the advantage of the two models, a weight combination model is proposed. The minimum of the sum forecasting error squared is regarded as a goal in optimizing the combined weight dynamically. Real detection data are used to test the efficiency. Results indicate that the method has a good performance in terms of short-term traffic forecasting. PMID:26779258

Fuzzy State Transition and Kalman Filter Applied in Short-Term Traffic Flow Forecasting.

PubMed

Deng, Ming-jun; Qu, Shi-ru

2015-01-01

Traffic flow is widely recognized as an important parameter for road traffic state forecasting. Fuzzy state transform and Kalman filter (KF) have been applied in this field separately. But the studies show that the former method has good performance on the trend forecasting of traffic state variation but always involves several numerical errors. The latter model is good at numerical forecasting but is deficient in the expression of time hysteretically. This paper proposed an approach that combining fuzzy state transform and KF forecasting model. In considering the advantage of the two models, a weight combination model is proposed. The minimum of the sum forecasting error squared is regarded as a goal in optimizing the combined weight dynamically. Real detection data are used to test the efficiency. Results indicate that the method has a good performance in terms of short-term traffic forecasting.

Tropical Peatland Geomorphology and Hydrology

NASA Astrophysics Data System (ADS)

Cobb, A.; Harvey, C. F.

2017-12-01

Tropical peatlands cover many low-lying areas in the tropics. In tropical peatlands, a feedback between hydrology, landscape morphology, and carbon storage causes waterlogged organic matter to accumulate into gently mounded land forms called peat domes over thousands of years. Peat domes have a stable morphology in which peat production is balanced by loss and net precipitation is balanced by lateral flow, creating a link between peatland morphology, rainfall patterns and drainage networks. We show how landscape morphology can be used to make inferences about hydrologic processes in tropical peatlands. In particular, we show that approaches using simple storage-discharge relationships for catchments are especially well suited to tropical peatlands, allowing river forecasting based on peatland morphology in catchments with tropical peatland subcatchments.

MULTI-TEMPORAL LAND USE GENERATION FOR THE OHIO RIVER BASIN

EPA Science Inventory

A set of backcast and forecast land use maps of the Ohio River Basin (ORB) was developed that could be used to assess the spatial-temporal patterns of land use/land cover (LULC) change in this important basin. This approach was taken to facilitate assessment of integrated sustain...

PREDICTION OF CONTAMINATED SEDIMENT TRANSPORT IN THE MAURICE RIVER-UNION LAKE, NEW JERSEY, USA

EPA Science Inventory

A sediment and contaminant transport model and its application to the Maurice River-Union Lake system in southern New Jersey, USA is described. The application is meant to characterize and forecast sediment and arsenic (As) distributions before and after proposed dredging activit...

Analog-Based Postprocessing of Navigation-Related Hydrological Ensemble Forecasts

NASA Astrophysics Data System (ADS)

Hemri, S.; Klein, B.

2017-11-01

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

Nowcasting, forecasting and hindcasting Harvey and Irma inundation in near-real time using a continental 2D hydrodynamic model

NASA Astrophysics Data System (ADS)

Sampson, C. C.; Wing, O.; Quinn, N.; Smith, A.; Neal, J. C.; Schumann, G.; Bates, P.

2017-12-01

During an ongoing natural disaster data are required on: (1) the current situation (nowcast); (2) its likely immediate evolution (forecast); and (3) a consistent view post-event of what actually happened (hindcast or reanalysis). We describe methods used to achieve all three tasks for flood inundation during the Harvey and Irma events using a continental scale 2D hydrodynamic model (Wing et al., 2017). The model solves the local inertial form of the Shallow Water equations over a regular grid of 1 arcsecond ( 30m). Terrain data are taken from the USGS National Elevation Dataset with known flood defences represented using the U.S. Army Corps of Engineers National Levee Dataset. Channels are treated as sub-grid scale features using the HydroSHEDS global hydrography data set. The model is driven using river flows, rainfall and coastal water levels. It simulates river flooding in basins > 50 km2, and fluvial and coastal flooding everywhere. Previous wide area validation tests show this model to be capable of matching FEMA maps and USGS local models built with bespoke data with hit rates of 86% and 92% respectively (Wing et al., 2017). Boundary conditions were taken from NOAA QPS data to produce nowcast and forecast simulations in near real time, before updating with NOAA observations to produce the hindcast. During the event simulation results were supplied to major insurers and multi-nationals who used them to estimate their likely capital exposure and to mitigate flood damage to their infrastructure whilst the event was underway. Simulations were validated against modelled flood footprints computed by FEMA and USACE, and composite satellite imagery produced by the Dartmouth Flood Observatory. For the Harvey event, hit rates ranged from 60-84% against these data sources, but a lack of metadata meant it was difficult to perform like-for-like comparisons. The satellite data also appeared to miss known flooding in urban areas that was picked up in the models. Despite these limitations, the validation was able to pick our areas, notably along the Colorado River near Houston, where our model under-performed and identify areas for future development. The study shows that high resolution near real-time inundation predictions over very large areas during complex events with multiple flood drivers are now a reality.

Development of web-based services for an ensemble flood forecasting and risk assessment system

NASA Astrophysics Data System (ADS)

Yaw Manful, Desmond; He, Yi; Cloke, Hannah; Pappenberger, Florian; Li, Zhijia; Wetterhall, Fredrik; Huang, Yingchun; Hu, Yuzhong

2010-05-01

Flooding is a wide spread and devastating natural disaster worldwide. Floods that took place in the last decade in China were ranked the worst amongst recorded floods worldwide in terms of the number of human fatalities and economic losses (Munich Re-Insurance). Rapid economic development and population expansion into low lying flood plains has worsened the situation. Current conventional flood prediction systems in China are neither suited to the perceptible climate variability nor the rapid pace of urbanization sweeping the country. Flood prediction, from short-term (a few hours) to medium-term (a few days), needs to be revisited and adapted to changing socio-economic and hydro-climatic realities. The latest technology requires implementation of multiple numerical weather prediction systems. The availability of twelve global ensemble weather prediction systems through the ‘THORPEX Interactive Grand Global Ensemble' (TIGGE) offers a good opportunity for an effective state-of-the-art early forecasting system. A prototype of a Novel Flood Early Warning System (NEWS) using the TIGGE database is tested in the Huai River basin in east-central China. It is the first early flood warning system in China that uses the massive TIGGE database cascaded with river catchment models, the Xinanjiang hydrologic model and a 1-D hydraulic model, to predict river discharge and flood inundation. The NEWS algorithm is also designed to provide web-based services to a broad spectrum of end-users. The latter presents challenges as both databases and proprietary codes reside in different locations and converge at dissimilar times. NEWS will thus make use of a ready-to-run grid system that makes distributed computing and data resources available in a seamless and secure way. An ability to run or function on different operating systems and provide an interface or front that is accessible to broad spectrum of end-users is additional requirement. The aim is to achieve robust interoperability through strong security and workflow capabilities. A physical network diagram and a work flow scheme of all the models, codes and databases used to achieve the NEWS algorithm are presented. They constitute a first step in the development of a platform for providing real time flood forecasting services on the web to mitigate 21st century weather phenomena.

Multi-time scale Climate Informed Stochastic Hybrid Simulation-Optimization Model (McISH model) for Multi-Purpose Reservoir System

NASA Astrophysics Data System (ADS)

Lu, M.; Lall, U.

2013-12-01

In order to mitigate the impacts of climate change, proactive management strategies to operate reservoirs and dams are needed. A multi-time scale climate informed stochastic model is developed to optimize the operations for a multi-purpose single reservoir by simulating decadal, interannual, seasonal and sub-seasonal variability. We apply the model to a setting motivated by the largest multi-purpose dam in N. India, the Bhakhra reservoir on the Sutlej River, a tributary of the Indus. This leads to a focus on timing and amplitude of the flows for the monsoon and snowmelt periods. The flow simulations are constrained by multiple sources of historical data and GCM future projections, that are being developed through a NSF funded project titled 'Decadal Prediction and Stochastic Simulation of Hydroclimate Over Monsoon Asia'. The model presented is a multilevel, nonlinear programming model that aims to optimize the reservoir operating policy on a decadal horizon and the operation strategy on an updated annual basis. The model is hierarchical, in terms of having a structure that two optimization models designated for different time scales are nested as a matryoshka doll. The two optimization models have similar mathematical formulations with some modifications to meet the constraints within that time frame. The first level of the model is designated to provide optimization solution for policy makers to determine contracted annual releases to different uses with a prescribed reliability; the second level is a within-the-period (e.g., year) operation optimization scheme that allocates the contracted annual releases on a subperiod (e.g. monthly) basis, with additional benefit for extra release and penalty for failure. The model maximizes the net benefit of irrigation, hydropower generation and flood control in each of the periods. The model design thus facilitates the consistent application of weather and climate forecasts to improve operations of reservoir systems. The decadal flow simulations are re-initialized every year with updated climate projections to improve the reliability of the operation rules for the next year, within which the seasonal operation strategies are nested. The multi-level structure can be repeated for monthly operation with weekly subperiods to take advantage of evolving weather forecasts and seasonal climate forecasts. As a result of the hierarchical structure, sub-seasonal even weather time scale updates and adjustment can be achieved. Given an ensemble of these scenarios, the McISH reservoir simulation-optimization model is able to derive the desired reservoir storage levels, including minimum and maximum, as a function of calendar date, and the associated release patterns. The multi-time scale approach allows adaptive management of water supplies acknowledging the changing risks, meeting both the objectives over the decade in expected value and controlling the near term and planning period risk through probabilistic reliability constraints. For the applications presented, the target season is the monsoon season from June to September. The model also includes a monthly flood volume forecast model, based on a Copula density fit to the monthly flow and the flood volume flow. This is used to guide dynamic allocation of the flood control volume given the forecasts.

Evaluating sub-seasonal skill in probabilistic forecasts of Atmospheric Rivers and associated extreme events

NASA Astrophysics Data System (ADS)

Subramanian, A. C.; Lavers, D.; Matsueda, M.; Shukla, S.; Cayan, D. R.; Ralph, M.

2017-12-01

Atmospheric rivers (ARs) - elongated plumes of intense moisture transport - are a primary source of hydrological extremes, water resources and impactful weather along the West Coast of North America and Europe. There is strong demand in the water management, societal infrastructure and humanitarian sectors for reliable sub-seasonal forecasts, particularly of extreme events, such as floods and droughts so that actions to mitigate disastrous impacts can be taken with sufficient lead-time. Many recent studies have shown that ARs in the Pacific and the Atlantic are modulated by large-scale modes of climate variability. Leveraging the improved understanding of how these large-scale climate modes modulate the ARs in these two basins, we use the state-of-the-art multi-model forecast systems such as the North American Multi-Model Ensemble (NMME) and the Subseasonal-to-Seasonal (S2S) database to help inform and assess the probabilistic prediction of ARs and related extreme weather events over the North American and European West Coasts. We will present results from evaluating probabilistic forecasts of extreme precipitation and AR activity at the sub-seasonal scale. In particular, results from the comparison of two winters (2015-16 and 2016-17) will be shown, winters which defied canonical El Niño teleconnection patterns over North America and Europe. We further extend this study to analyze probabilistic forecast skill of AR events in these two basins and the variability in forecast skill during certain regimes of large-scale climate modes.

Effective Integration of Earth Observation Data and Flood Modeling for Rapid Disaster Response: The Texas 2015 Case

NASA Astrophysics Data System (ADS)

Schumann, G.

2016-12-01

Routinely obtaining real-time 2-D inundation patterns of a flood event at a meaningful spatial resolution and over large scales is at the moment only feasible with either operational aircraft flights or satellite imagery. Of course having model simulations of floodplain inundation available to complement the remote sensing data is highly desirable, for both event re-analysis and forecasting event inundation. Using the Texas 2015 flood disaster, we demonstrate the value of multi-scale EO data for large scale 2-D floodplain inundation modeling and forecasting. A dynamic re-analysis of the Texas 2015 flood disaster was run using a 2-D flood model developed for accurate large scale simulations. We simulated the major rivers entering the Gulf of Mexico and used flood maps produced from both optical and SAR satellite imagery to examine regional model sensitivities and assess associated performance. It was demonstrated that satellite flood maps can complement model simulations and add value, although this is largely dependent on a number of important factors, such as image availability, regional landscape topology, and model uncertainty. In the preferred case where model uncertainty is high, landscape topology is complex (i.e. urbanized coastal area) and satellite flood maps are available (in case of SAR for instance), satellite data can significantly reduce model uncertainty by identifying the "best possible" model parameter set. However, most often the situation is occurring where model uncertainty is low and spatially contiguous flooding can be mapped from satellites easily enough, such as in rural large inland river floodplains. Consequently, not much value from satellites can be added. Nevertheless, where a large number of flood maps are available, model credibility can be increased substantially. In the case presented here this was true for at least 60% of the many thousands of kilometers of river flow length simulated, where satellite flood maps existed. The next steps of this project is to employ a technique termed "targeted observation" approach, which is an assimilation based procedure that allows quantifying the impact observations have on model predictions at the local scale and also along the entire river system, when assimilated with the model at specific "overpass" locations.

Estimation and prediction of origin-destination matrices for I-66.

DOT National Transportation Integrated Search

2011-09-01

This project uses the Box-Jenkins time-series technique to model and forecast the traffic flows and then : uses the flow forecasts to predict the origin-destination matrices. First, a detailed analysis was conducted : to investigate the best data cor...

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

NASA Astrophysics Data System (ADS)

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

2017-11-01

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

Integral assessment of floodplains as a basis for spatially-explicit flood loss forecasts

NASA Astrophysics Data System (ADS)

Zischg, Andreas Paul; Mosimann, Markus; Weingartner, Rolf

2016-04-01

A key aspect of disaster prevention is flood discharge forecasting which is used for early warning and therefore as a decision support for intervention forces. Hereby, the phase between the issued forecast and the time when the expected flood occurs is crucial for an optimal planning of the intervention. Typically, river discharge forecasts cover the regional level only, i.e. larger catchments. However, it is important to note that these forecasts are not useable directly for specific target groups on local level because these forecasts say nothing about the consequences of the predicted flood in terms of affected areas, number of exposed residents and houses. For this, on one hand simulations of the flooding processes and on the other hand data of vulnerable objects are needed. Furthermore, flood modelling in a high spatial and temporal resolution is required for robust flood loss estimation. This is a resource-intensive task from a computing time point of view. Therefore, in real-time applications flood modelling in 2D is not suited. Thus, forecasting flood losses in the short-term (6h-24h in advance) requires a different approach. Here, we propose a method to downscale the river discharge forecast to a spatially-explicit flood loss forecast. The principal procedure is to generate as many flood scenarios as needed in advance to represent the flooded areas for all possible flood hydrographs, e.g. very high peak discharges of short duration vs. high peak discharges with high volumes. For this, synthetic flood hydrographs were derived from the hydrologic time series. Then, the flooded areas of each scenario were modelled with a 2D flood simulation model. All scenarios were intersected with the dataset of vulnerable objects, in our case residential, agricultural and industrial buildings with information about the number of residents, the object-specific vulnerability, and the monetary value of the objects. This dataset was prepared by a data-mining approach. For each flood scenario, the resulting number of affected residents, houses and therefore the losses are computed. This integral assessment leads to a hydro-economical characterisation of each floodplain. Based on that, a transfer function between discharge forecast and damages can be elaborated. This transfer function describes the relationship between predicted peak discharge, flood volume and the number of exposed houses, residents and the related losses. It also can be used to downscale the regional discharge forecast to a local level loss forecast. In addition, a dynamic map delimiting the probable flooded areas on the basis of the forecasted discharge can be prepared. The predicted losses and the delimited flooded areas provide a complementary information for assessing the need of preventive measures on one hand on the long-term timescale and on the other hand 6h-24h in advance of a predicted flood. To conclude, we can state that the transfer function offers the possibility for an integral assessment of floodplains as a basis for spatially-explicit flood loss forecasts. The procedure has been developed and tested in the alpine and pre-alpine environment of the Aare river catchment upstream of Bern, Switzerland.

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.

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

NASA Astrophysics Data System (ADS)

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

2013-12-01

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

Development Of An Open System For Integration Of Heterogeneous Models For Flood Forecasting And Hazard Mitigation

NASA Astrophysics Data System (ADS)

Chang, W.; Tsai, W.; Lin, F.; Lin, S.; Lien, H.; Chung, T.; Huang, L.; Lee, K.; Chang, C.

2008-12-01

During a typhoon or a heavy storm event, using various forecasting models to predict rainfall intensity, and water level variation in rivers and flood situation in the urban area is able to reveal its capability technically. However, in practice, the following two causes tend to restrain the further application of these models as a decision support system (DSS) for the hazard mitigation. The first one is due to the difficulty of integration of heterogeneous models. One has to take into consideration the different using format of models, such as input files, output files, computational requirements, and so on. The second one is that the development of DSS requires, due to the heterogeneity of models and systems, a friendly user interface or platform to hide the complexity of various tools from users. It is expected that users can be governmental officials rather than professional experts, therefore the complicated interface of DSS is not acceptable. Based on the above considerations, in the present study, we develop an open system for integration of several simulation models for flood forecasting by adopting the FEWS (Flood Early Warning System) platform developed by WL | Delft Hydraulics. It allows us to link heterogeneous models effectively and provides suitable display modules. In addition, FEWS also has been adopted by Water Resource Agency (WRA), Taiwan as the standard operational system for river flooding management. That means this work can be much easily integrated with the use of practical cases. In the present study, based on FEWS platform, the basin rainfall-runoff model, SOBEK channel-routing model, and estuary tide forecasting model are linked and integrated through the physical connection of model initial and boundary definitions. The work flow of the integrated processes of models is shown in Fig. 1. This differs from the typical single model linking used in FEWS, which only aims at data exchange but without much physical consideration. So it really makes the tighter collaboration work among these hydrological models. In addition, in order to make communication between system users and decision makers efficient and effective, a real-time and multi-user communication platform, designated as Co-life, is incorporated in the present study. Through its application sharing function, the flood forecasting results can be displayed for all attendees situated at different locations to help the processes of decision making for hazard mitigation. Fig. 2 shows the cyber-conference of WRA officials with the Co-life system for hazard mitigation during the typhoon event.

Space-Time Variability in River Flow Regimes of Northeast Turkey

NASA Astrophysics Data System (ADS)

Saris, F.; Hannah, D. M.; Eastwood, W. J.

2011-12-01

The northeast region of Turkey is characterised by relatively high annual precipitation totals and river flow. It is a mountainous region with high ecological status and also it is of prime interest to the energy sector. These characteristics make this region an important area for a hydroclimatology research in terms of future availability and management of water resources. However, there is not any previous research identifying hydroclimatological variability across the region. This study provides first comprehensive and detailed information on river flow regimes of northeast Turkey which is delimited by two major river basins namely East Black Sea (EBS) and Çoruh River (ÇRB) basins. A novel river flow classification is used that yields a large-scale perspective on hydroclimatology patterns of the region and allows interpretations regarding the controlling factors on river flow variability. River flow regimes are classified (with respect to timing and magnitude of flow) to examine spatial variability based on long-term average regimes, and also by grouping annual regimes for each station-year to identify temporal (between-year) variability. Results indicate that rivers in northeast Turkey are characterised by marked seasonal flow variation with an April-May-June maximum flow period. Spatial variability in flow regime seasonality is dependent largely on the topography of the study area. The EBS Basin, for which the North Anatolian Mountains cover the eastern part, is characterised by a May-June peak; whereas the ÇRB is defined by an April-May flow peak. The timing of river flows indicates that snowmelt is an important process and contributor of river flow maxima for both basins. The low flow season is January and February. Intermediate and low regime magnitude classes dominate in ÇRB and EBS basins, respectively, while high flow magnitude class is observed for one station only across the region. Result of regime stability analysis (year-to-year variation) shows that April-May and May-June peak shape classes together with low and intermediate magnitude classes are the most frequent and persistent flow regimes. This research has advanced understanding of hydroclimatological processes in northeast Turkey by identifying river flow regimes and together with explanations regarding the controlling factors on river flow variability.

Utilizing Climate Forecasts for Improving Water and Power Systems Coordination

NASA Astrophysics Data System (ADS)

Arumugam, S.; Queiroz, A.; Patskoski, J.; Mahinthakumar, K.; DeCarolis, J.

2016-12-01

Climate forecasts, typically monthly-to-seasonal precipitation forecasts, are commonly used to develop streamflow forecasts for improving reservoir management. Irrespective of their high skill in forecasting, temperature forecasts in developing power demand forecasts are not often considered along with streamflow forecasts for improving water and power systems coordination. In this study, we consider a prototype system to analyze the utility of climate forecasts, both precipitation and temperature, for improving water and power systems coordination. The prototype system, a unit-commitment model that schedules power generation from various sources, is considered and its performance is compared with an energy system model having an equivalent reservoir representation. Different skill sets of streamflow forecasts and power demand forecasts are forced on both water and power systems representations for understanding the level of model complexity required for utilizing monthly-to-seasonal climate forecasts to improve coordination between these two systems. The analyses also identify various decision-making strategies - forward purchasing of fuel stocks, scheduled maintenance of various power systems and tradeoff on water appropriation between hydropower and other uses - in the context of various water and power systems configurations. Potential application of such analyses for integrating large power systems with multiple river basins is also discussed.

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

NASA Astrophysics Data System (ADS)

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

2016-12-01

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

Spatiotemporal drought forecasting using nonlinear models

NASA Astrophysics Data System (ADS)

Vasiliades, Lampros; Loukas, Athanasios

2010-05-01

Spatiotemporal data mining is the extraction of unknown and implicit knowledge, structures, spatiotemporal relationships, or patterns not explicitly stored in spatiotemporal databases. As one of data mining techniques, forecasting is widely used to predict the unknown future based upon the patterns hidden in the current and past data. In order to achieve spatiotemporal forecasting, some mature analysis tools, e.g., time series and spatial statistics are extended to the spatial dimension and the temporal dimension, respectively. Drought forecasting plays an important role in the planning and management of natural resources and water resource systems in a river basin. Early and timelines forecasting of a drought event can help to take proactive measures and set out drought mitigation strategies to alleviate the impacts of drought. Despite the widespread application of nonlinear mathematical models, comparative studies on spatiotemporal drought forecasting using different models are still a huge task for modellers. This study uses a promising approach, the Gamma Test (GT), to select the input variables and the training data length, so that the trial and error workload could be greatly reduced. The GT enables to quickly evaluate and estimate the best mean squared error that can be achieved by a smooth model on any unseen data for a given selection of inputs, prior to model construction. The GT is applied to forecast droughts using monthly Standardized Precipitation Index (SPI) timeseries at multiple timescales in several precipitation stations at Pinios river basin in Thessaly region, Greece. Several nonlinear models have been developed efficiently, with the aid of the GT, for 1-month up to 12-month ahead forecasting. Several temporal and spatial statistical indices were considered for the performance evaluation of the models. The predicted results show reasonably good agreement with the actual data for short lead times, whereas the forecasting accuracy decreases with increase in lead time. Finally, the developed nonlinear models could be used in an early warning system for risk and decision analyses at the study area.

Drought forecasting in Luanhe River basin involving climatic indices

NASA Astrophysics Data System (ADS)

Ren, Weinan; Wang, Yixuan; Li, Jianzhu; Feng, Ping; Smith, Ronald J.

2017-11-01

Drought is regarded as one of the most severe natural disasters globally. This is especially the case in Tianjin City, Northern China, where drought can affect economic development and people's livelihoods. Drought forecasting, the basis of drought management, is an important mitigation strategy. In this paper, we evolve a probabilistic forecasting model, which forecasts transition probabilities from a current Standardized Precipitation Index (SPI) value to a future SPI class, based on conditional distribution of multivariate normal distribution to involve two large-scale climatic indices at the same time, and apply the forecasting model to 26 rain gauges in the Luanhe River basin in North China. The establishment of the model and the derivation of the SPI are based on the hypothesis of aggregated monthly precipitation that is normally distributed. Pearson correlation and Shapiro-Wilk normality tests are used to select appropriate SPI time scale and large-scale climatic indices. Findings indicated that longer-term aggregated monthly precipitation, in general, was more likely to be considered normally distributed and forecasting models should be applied to each gauge, respectively, rather than to the whole basin. Taking Liying Gauge as an example, we illustrate the impact of the SPI time scale and lead time on transition probabilities. Then, the controlled climatic indices of every gauge are selected by Pearson correlation test and the multivariate normality of SPI, corresponding climatic indices for current month and SPI 1, 2, and 3 months later are demonstrated using Shapiro-Wilk normality test. Subsequently, we illustrate the impact of large-scale oceanic-atmospheric circulation patterns on transition probabilities. Finally, we use a score method to evaluate and compare the performance of the three forecasting models and compare them with two traditional models which forecast transition probabilities from a current to a future SPI class. The results show that the three proposed models outperform the two traditional models and involving large-scale climatic indices can improve the forecasting accuracy.

Monthly streamflow forecasting in the Rhine basin

NASA Astrophysics Data System (ADS)

Schick, Simon; Rössler, Ole; Weingartner, Rolf

2017-04-01

Forecasting seasonal streamflow of the Rhine river is of societal relevance as the Rhine is an important water way and water resource in Western Europe. The present study investigates the predictability of monthly mean streamflow at lead times of zero, one, and two months with the focus on potential benefits by the integration of seasonal climate predictions. Specifically, we use seasonal predictions of precipitation and surface air temperature released by the European Centre for Medium-Range Weather Forecasts (ECMWF) for a regression analysis. In order to disentangle forecast uncertainty, the 'Reverse Ensemble Streamflow Prediction' framework is adapted here to the context of regression: By using appropriate subsets of predictors the regression model is constrained to either the initial conditions, the meteorological forcing, or both. An operational application is mimicked by equipping the model with the seasonal climate predictions provided by ECMWF. Finally, to mitigate the spatial aggregation of the meteorological fields the model is also applied at the subcatchment scale, and the resulting predictions are combined afterwards. The hindcast experiment is carried out for the period 1982-2011 in cross validation mode at two gauging stations, namely the Rhine at Lobith and Basel. The results show that monthly forecasts are skillful with respect to climatology only at zero lead time. In addition, at zero lead time the integration of seasonal climate predictions decreases the mean absolute error by 5 to 10 percentage compared to forecasts which are solely based on initial conditions. This reduction most likely is induced by the seasonal prediction of precipitation and not air temperature. The study is completed by bench marking the regression model with runoff simulations from ECMWFs seasonal forecast system. By simply using basin averages followed by a linear bias correction, these runoff simulations translate well to monthly streamflow. Though the regression model is only slightly outperformed, we argue that runoff out of the land surface component of seasonal climate forecasting systems is an interesting option when it comes to seasonal streamflow forecasting in large river basins.

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

NASA Astrophysics Data System (ADS)

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

2018-06-01

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

Using Collaborative Modeling to Inform Policy Decisions in the Bow River Basin

NASA Astrophysics Data System (ADS)

Sheer, A. S.; Sheer, D. P.; Hill, D.

2011-12-01

The Bow River in Alberta, Canada serves a wide range of municipal, agricultural, recreational, and industrial purposes in the province. In 2006, the basin was deemed over-allocation and closed to new licenses. The Calgary region, however, continues to expand. In the next 60 to 70 years, population levels are expected to reach 2.8 million (more than double the current 1.2 million) with 800,000 new jobs. This increasing pressure led several stakeholders to work together in the development of a new management model to improve the management of the system as an integrated watershed. The major previous model of the system allowed only limited flexibility in management, focusing instead on strict license based operations. Over a 6 month period, with numerous multi-party meetings, the group developed the Bow River Operations Model (BROM). Based in OASIS software, this model integrated input from the major water users in the region to emulate the real-life decisions that are actually made on a daily bases, even when technically in violation of the strict license agreements (e.x. junior licensees receiving water despite senior priority due to exceedingly low volume requirements). Using historical records as forecasts, and performance measures developed with the best available science through expert opinion, participants jointly developed a new reservoir operation strategy. Even with such a short timeframe, the BROM exercise showed that there was substantial room for improvement. Utilizing a "Water Bank" of purchased storage, downstream flows could be significantly improved without affecting quality. Additional benefits included fishery improvement, new recreation opportunities, dissolved oxygen improvement during critical periods, and the ability to accommodate long-term demand forecasts for surrounding municipalities. This strategy has been presented to, and favorably received by, the Alberta Minister of Environment for guidance during negotiations with the local hydropower utility. Work continues on implementation and new areas of the basin are being considered for similar processes.

The Experimental Regional Ensemble Forecast System (ExREF): Its Use in NWS Forecast Operations and Preliminary Verification

NASA Technical Reports Server (NTRS)

Reynolds, David; Rasch, William; Kozlowski, Daniel; Burks, Jason; Zavodsky, Bradley; Bernardet, Ligia; Jankov, Isidora; Albers, Steve

2014-01-01

The Experimental Regional Ensemble Forecast (ExREF) system is a tool for the development and testing of new Numerical Weather Prediction (NWP) methodologies. ExREF is run in near-realtime by the Global Systems Division (GSD) of the NOAA Earth System Research Laboratory (ESRL) and its products are made available through a website, an ftp site, and via the Unidata Local Data Manager (LDM). The ExREF domain covers most of North America and has 9-km horizontal grid spacing. The ensemble has eight members, all employing WRF-ARW. The ensemble uses a variety of initial conditions from LAPS and the Global Forecasting System (GFS) and multiple boundary conditions from the GFS ensemble. Additionally, a diversity of physical parameterizations is used to increase ensemble spread and to account for the uncertainty in forecasting extreme precipitation events. ExREF has been a component of the Hydrometeorology Testbed (HMT) NWP suite in the 2012-2013 and 2013-2014 winters. A smaller domain covering just the West Coast was created to minimize band-width consumption for the NWS. This smaller domain has and is being distributed to the National Weather Service (NWS) Weather Forecast Office and California Nevada River Forecast Center in Sacramento, California, where it is ingested into the Advanced Weather Interactive Processing System (AWIPS I and II) to provide guidance on the forecasting of extreme precipitation events. This paper will review the cooperative effort employed by NOAA ESRL, NASA SPoRT (Short-term Prediction Research and Transition Center), and the NWS to facilitate the ingest and display of ExREF data utilizing the AWIPS I and II D2D and GFE (Graphical Software Editor) software. Within GFE is a very useful verification software package called BoiVer that allows the NWS to utilize the River Forecast Center's 4 km gridded QPE to compare with all operational NWP models 6-hr QPF along with the ExREF mean 6-hr QPF so the forecasters can build confidence in the use of the ExREF in preparing their rainfall forecasts. Preliminary results will be presented.

A review and update of the Virginia Department of Transportation cash flow forecasting model.

DOT National Transportation Integrated Search

1996-01-01

This report details the research done to review and update components of the VDOT cash flow forecasting model. Specifically, the study updated the monthly factors submodel used to predict payments on construction contracts. For the other submodel rev...

Evaluation of uncertainty in capturing the spatial variability and magnitudes of extreme hydrological events for the uMngeni catchment, South Africa

NASA Astrophysics Data System (ADS)

Kusangaya, Samuel; Warburton Toucher, Michele L.; van Garderen, Emma Archer

2018-02-01

Downscaled General Circulation Models (GCMs) output are used to forecast climate change and provide information used as input for hydrological modelling. Given that our understanding of climate change points towards an increasing frequency, timing and intensity of extreme hydrological events, there is therefore the need to assess the ability of downscaled GCMs to capture these extreme hydrological events. Extreme hydrological events play a significant role in regulating the structure and function of rivers and associated ecosystems. In this study, the Indicators of Hydrologic Alteration (IHA) method was adapted to assess the ability of simulated streamflow (using downscaled GCMs (dGCMs)) in capturing extreme river dynamics (high and low flows), as compared to streamflow simulated using historical climate data from 1960 to 2000. The ACRU hydrological model was used for simulating streamflow for the 13 water management units of the uMngeni Catchment, South Africa. Statistically downscaled climate models obtained from the Climate System Analysis Group at the University of Cape Town were used as input for the ACRU Model. Results indicated that, high flows and extreme high flows (one in ten year high flows/large flood events) were poorly represented both in terms of timing, frequency and magnitude. Simulated streamflow using dGCMs data also captures more low flows and extreme low flows (one in ten year lowest flows) than that captured in streamflow simulated using historical climate data. The overall conclusion was that although dGCMs output can reasonably be used to simulate overall streamflow, it performs poorly when simulating extreme high and low flows. Streamflow simulation from dGCMs must thus be used with caution in hydrological applications, particularly for design hydrology, as extreme high and low flows are still poorly represented. This, arguably calls for the further improvement of downscaling techniques in order to generate climate data more relevant and useful for hydrological applications such as in design hydrology. Nevertheless, the availability of downscaled climatic output provide the potential of exploring climate model uncertainties in different hydro climatic regions at local scales where forcing data is often less accessible but more accurate at finer spatial scales and with adequate spatial detail.

Exploratory studies into seasonal flow forecasting potential for large lakes

NASA Astrophysics Data System (ADS)

Sene, Kevin; Tych, Wlodek; Beven, Keith

2018-01-01

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

Traffic flow forecasting for intelligent transportation systems.

DOT National Transportation Integrated Search

1995-01-01

The capability to forecast traffic volume in an operational setting has been identified as a critical need for intelligent transportation systems (ITS). In particular, traffic volume forecasts will directly support proactive traffic control and accur...

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.

A gain-loss framework based on ensemble flow forecasts to switch the urban drainage-wastewater system management towards energy optimization during dry periods

NASA Astrophysics Data System (ADS)

Courdent, Vianney; Grum, Morten; Munk-Nielsen, Thomas; Mikkelsen, Peter S.

2017-05-01

Precipitation is the cause of major perturbation to the flow in urban drainage and wastewater systems. Flow forecasts, generated by coupling rainfall predictions with a hydrologic runoff model, can potentially be used to optimize the operation of integrated urban drainage-wastewater systems (IUDWSs) during both wet and dry weather periods. Numerical weather prediction (NWP) models have significantly improved in recent years, having increased their spatial and temporal resolution. Finer resolution NWP are suitable for urban-catchment-scale applications, providing longer lead time than radar extrapolation. However, forecasts are inevitably uncertain, and fine resolution is especially challenging for NWP. This uncertainty is commonly addressed in meteorology with ensemble prediction systems (EPSs). Handling uncertainty is challenging for decision makers and hence tools are necessary to provide insight on ensemble forecast usage and to support the rationality of decisions (i.e. forecasts are uncertain and therefore errors will be made; decision makers need tools to justify their choices, demonstrating that these choices are beneficial in the long run). This study presents an economic framework to support the decision-making process by providing information on when acting on the forecast is beneficial and how to handle the EPS. The relative economic value (REV) approach associates economic values with the potential outcomes and determines the preferential use of the EPS forecast. The envelope curve of the REV diagram combines the results from each probability forecast to provide the highest relative economic value for a given gain-loss ratio. This approach is traditionally used at larger scales to assess mitigation measures for adverse events (i.e. the actions are taken when events are forecast). The specificity of this study is to optimize the energy consumption in IUDWS during low-flow periods by exploiting the electrical smart grid market (i.e. the actions are taken when no events are forecast). Furthermore, the results demonstrate the benefit of NWP neighbourhood post-processing methods to enhance the forecast skill and increase the range of beneficial uses.

A Watershed-based spatially-explicit demonstration of an Integrated Environmental Modeling Framework for Ecosystem Services in the Coal River Basin (WV, USA)

EPA Science Inventory

We demonstrate a spatially-explicit regional assessment of current condition of aquatic ecoservices in the Coal River Basin (CRB), with limited sensitivity analysis for the atmospheric contaminant mercury. The integrated modeling framework (IMF) forecasts water quality and quant...

Flood-inundation maps for Grand River, Red Cedar River, and Sycamore Creek near Lansing, Michigan

USGS Publications Warehouse

Whitehead, Matthew; Ostheimer, Chad J.

2015-08-26

These maps, used in conjunction with real-time USGS streamgage data and NWS forecasting, provide critical information to emergency management personnel and the public. This information is used to plan flood response actions, such as evacuations and road closures, as well as aid in postflood recovery efforts.

A Riparian Approach to Dendrochronological Flow Reconstruction, Yellowstone River, Montana

NASA Astrophysics Data System (ADS)

Schook, D. M.; Rathburn, S. L.; Friedman, J. M.

2015-12-01

Tree ring-based flow reconstructions can reveal river discharge variability over durations far exceeding the gauged record, building perspective for both the measured record and future flows. We use plains cottonwood (Populus deltoides subsp. monilifera) tree rings collected from four rivers to reconstruct flow history of the Yellowstone River near its confluence with the Missouri River. Upland trees in dry regions are typically used in flow reconstruction because their annual growth is controlled by the same precipitation that drives downstream flow, but our study improves flow reconstruction by including floodplain trees that are directly affected by the river. Cores from over 1000 cottonwoods along the Yellowstone, Powder, Little Missouri, and Redwater Rivers were collected from within a 170 km radius to reconstruct flows using the Age Curve Standardization technique in a multiple regression analysis. The large sample from trees spanning many age classes allows us to use only the rings that were produced when each tree was less than 50 years old and growth was most strongly correlated to river discharge. Using trees from a range of rivers improves our ability to differentiate between growth resulting from local precipitation and river flow, and we show that cottonwood growth differs across these neighboring rivers having different watersheds. Using the program Seascorr, tree growth is found to better correlated to seasonal river discharge (R = 0.69) than to local precipitation (R = 0.45). Our flow reconstruction reveals that the most extreme multi-year or multi-decade drought periods of the last 250 years on either the Yellowstone (1817-1821) or Powder (1846-1865) Rivers are missed by the gauged discharge record. Across all sites, we document increased growth in the 20th century compared to the 19th, a finding unattainable with conventional methods but having important implications for flow management.

The Relative Performance of High Resolution Quantitative Precipitation Estimates in the Russian River Basin

NASA Astrophysics Data System (ADS)

Bytheway, J. L.; Biswas, S.; Cifelli, R.; Hughes, M.

2017-12-01

The Russian River carves a 110 mile path through Mendocino and Sonoma counties in western California, providing water for thousands of residents and acres of agriculture as well as a home for several species of endangered fish. The Russian River basin receives almost all of its precipitation during the October through March wet season, and the systems bringing this precipitation are often impacted by atmospheric river events as well as the complex topography of the region. This study will examine the performance of several high resolution (hourly, < 5km) estimates of precipitation from observational products and forecasts over the 2015-2016 and 2016-2017 wet seasons. Comparisons of event total rainfall as well as hourly rainfall will be performed using 1) rain gauges operated by the National Oceanic and Atmospheric Administration (NOAA) Physical Sciences Division (PSD), 2) products from the Multi-Radar/Multi-Sensor (MRMS) QPE dataset, and 3) quantitative precipitation forecasts from the High Resolution Rapid Refresh (HRRR) model at 1, 3, 6, and 12 hour lead times. Further attention will be given to cases or locations representing large disparities between the estimates.

The economic value of Trinity River water

USGS Publications Warehouse

Douglas, A.J.; Taylor, J.G.

1999-01-01

The Trinity River, largest tributary of the Klamath River, has its head-waters in the Trinity Alps of north-central California. After the construction of Trinity Dam in 1963, 90% of the Trinity River flow at Lewiston was moved to the Sacramento River via the Clear Creek Tunnel, a manmade conduit. Hydropower is produced at four installations along the route of Trinity River water that is diverted to the Sacramento River, and power production at three of these installations would diminish if no Trinity River water were diverted to the Sacramento River. After Trinity River water reaches the Sacramento River, it flows toward the Sacramento-San Joaquin Delta and San Francisco Bay. Trinity River water is pumped via Bureau of Reclamation canals and pumps to the northern San Joaquin Valley, where it is used for irrigated agriculture. The social cost of putting more water down the Trinity River is the sum of the value of the foregone consumer surplus from hydropower production as well as the value of the foregone irrigation water. Sharply diminished instream flows have also severely affected the size and robustness of Trinity River salmon, steelhead, shad and sturgeon runs. Survey data were used to estimate the non-market benefits of augmenting Trinity River instream flows by letting more water flow down the Trinity and moving less water to the Sacramento River. Preservation benefits for Trinity River instream flows and fish runs are $803 million per annum for the scenario that returns the most water down the Trinity River, a value that greatly exceeds the social cost estimate.The Trinity River, largest tributary of the Klamath River, has its headwaters in the Trinity Alps of north-central California. After the construction of Trinity Dam in 1963, 90% of the Trinity River flow at Lewiston was moved to the Sacramento River via the Clear Creek Tunnel, a manmade conduit. Hydropower is produced at four installations along the route of Trinity River water that is diverted to the Sacramento River, and power production at three of these installations would diminish if no Trinity River water were diverted to the Sacramento River. After Trinity River water reaches the Sacramento River, it flows toward the Sacramento-San Joaquin Delta and San Francisco Bay. Trinity River water is pumped via Bureau of Reclamation canals and pumps to the northern San Joaquin Valley, where it is used for irrigated agriculture. The social cost of putting more water down the Trinity River is the sum of the value of the foregone consumer surplus from hydropower production as well as the value of the foregone irrigation water. Sharply diminished instream flows have also severely affected the size and robustness of Trinity River salmon, steelhead, shad and sturgeon runs. Survey data were used to estimate the non-market benefits of augmenting Trinity River instream flows by letting more water flow down the Trinity and moving less water to the Sacramento River. Preservation benefits for Trinity River instream flows and fish runs are $803 million per annum for the scenario that returns the most water down the Trinity River, a value that greatly exceeds the social cost estimate.

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.

Development and application of a modified wireless tracer for disaster prevention

NASA Astrophysics Data System (ADS)

Chung Yang, Han; Su, Chih Chiang

2016-04-01

Typhoon-induced flooding causes water overflow in a river channel, which results in general and bridge scour and soil erosion, thus leading to bridge failure, debris flow and landslide collapse. Therefore, dynamic measurement technology should be developed to assess scour in channels and landslide as a disaster-prevention measure against bridge failure and debris flow. This paper presents a wireless tracer that enables monitoring general scour in river channels and soil erosion in hillsides. The wireless tracer comprises a wireless high-power radio modem, various electronic components, and a self-designed printed circuit board that are all combined with a 9-V battery pack and an auto switch. The entire device is sealed in a jar by silicon. After it was modified, the wireless tracer underwent the following tests for practical applications: power continuation and durability, water penetration, and signal transmission during floating. A regression correlation between the wireless tracer's transmission signal and distance was also established. This device can be embedded at any location where scouring is monitored, and, in contrast to its counterparts that detect scour depth by identifying and analyzing received signals, it enables real-time observation of the scouring process. In summary, the wireless tracer developed in this study provides a dynamic technology for real-time monitoring of scouring (or erosion) and forecasting of landslide hazards. Keywords: wireless tracer; scour; real-time monitoring; landslide hazard.

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.

Spatially distributed flood forecasting in flash flood prone areas: Application to road network supervision in Southern France

NASA Astrophysics Data System (ADS)

Naulin, J.-P.; Payrastre, O.; Gaume, E.

2013-04-01

SummaryAccurate flood forecasts are critical to an efficient flood event management strategy. Until now, hydro-meteorological forecasts have mainly been used to establish early-warnings in France (meteorological and flood vigilance maps) or over the world (flash-flood guidances). These forecasts are typically limited either to the main streams covered by the flood forecasting services or to watersheds with specific assets like check dams, which in most cases are well gauged river sections, thus leaving aside large parts of the territory. This paper presents a distributed hydro-meteorological forecasting approach, which makes use of the high spatial and temporal resolution rainfall estimates that are now available, to provide information at ungauged sites. The proposed system intended to detect road inundation risks had initially been developed and tested in areas of limited size. This paper presents the extension of such a system to an entire region (i.e. the Gard region in Southern France), including over 2000 crossing points between rivers and roads and its validation with respect to a large data set of actual reported road inundations observed during recent flash flood events. These initial validation results appear to be most promising. The eventual proposed tool would provide the necessary information for flood event management services to identify the areas at risk and adopt appropriate safety and rescue measures: i.e. pre-positioning of rescue equipment, interruption of the traffic on the exposed roads and determination of safe access or evacuation routes. Moreover, beyond the specific application to the supervision of a road network, the research undertaken herein also provides results for the performance of hydro-meteorological forecasts on ungauged headwaters.

Predictability and Prediction of Low-Frequency Rainfall Over the Lower Reaches of the Yangtze River Valley on the Time Scale of 20 to 30 days

NASA Astrophysics Data System (ADS)

Yang, Qiuming

2018-01-01

This paper presents a predictability study of the 20-30-day low-frequency rainfall over the lower reaches of the Yangtze River valley (LYRV). This study relies on an extended complex autoregressive (ECAR) model method, which is based on the principal components of the global 850 hPa low-frequency meridional wind. ECAR is a recently advanced climate forecast method, based on data-driven models. It not only reflects the lagged variations information between the leading low-frequency components of the global circulation and rainfall in a complex space, but also displays the ability to describe the synergy variations of low-frequency components of a climate system in a low dimensional space. A 6-year forecast experiment is conducted on the low-frequency rainfall over the LYRV for the extended-range daily forecasts during 2009-2014, based on the time-varying high-order ECAR. These experimental results demonstrate that the useful skills of the real-time forecasts are achieved for an extended lead-time up to 28 days with a fifth-order model, and are also shown to be 27-day lead for forecasts which are initiated from weak intraseasonal oscillation (ISO). This high-order ECAR displays the ability to significantly improve the predictions of the ISO. The analysis of the 20-30-day ISO predictability reveals a predictability limit of about 28-40 days. Therefore, the forecast framework used in this study is determined to have the potential to assist in improving the real-time forecasts for the 20-30-day oscillations related to the heavy rainfall over the LYRV in summer.

Forecasting the Movement of Educational Administrators Through Vacancy Flows

ERIC Educational Resources Information Center

Brown, Daniel J.

1976-01-01

Discusses the problem of forecasting manpower flows in administrative hierarchies of educational organizations, reviews groups of manpower models, discusses characteristics of administrative hierarchies and the vacancy model as it relates to those characteristics, and carries out validation and projective tests of the model. (Author/IRT)

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.

Low Streamflow Forcasting using Minimum Relative Entropy

NASA Astrophysics Data System (ADS)

Cui, H.; Singh, V. P.

2013-12-01

Minimum relative entropy spectral analysis is derived in this study, and applied to forecast streamflow time series. Proposed method extends the autocorrelation in the manner that the relative entropy of underlying process is minimized so that time series data can be forecasted. Different prior estimation, such as uniform, exponential and Gaussian assumption, is taken to estimate the spectral density depending on the autocorrelation structure. Seasonal and nonseasonal low streamflow series obtained from Colorado River (Texas) under draught condition is successfully forecasted using proposed method. Minimum relative entropy determines spectral of low streamflow series with higher resolution than conventional method. Forecasted streamflow is compared to the prediction using Burg's maximum entropy spectral analysis (MESA) and Configurational entropy. The advantage and disadvantage of each method in forecasting low streamflow is discussed.

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

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

Yan, Hongxiang; Moradkhani, Hamid; Zarekarizi, Mahkameh

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

Parametrisation of initial conditions for seasonal stream flow forecasting in the Swiss Rhine basin

NASA Astrophysics Data System (ADS)

Schick, Simon; Rössler, Ole; Weingartner, Rolf

2016-04-01

Current climate forecast models show - to the best of our knowledge - low skill in forecasting climate variability in Central Europe at seasonal lead times. When it comes to seasonal stream flow forecasting, initial conditions thus play an important role. Here, initial conditions refer to the catchments moisture at the date of forecast, i.e. snow depth, stream flow and lake level, soil moisture content, and groundwater level. The parametrisation of these initial conditions can take place at various spatial and temporal scales. Examples are the grid size of a distributed model or the time aggregation of predictors in statistical models. Therefore, the present study aims to investigate the extent to which the parametrisation of initial conditions at different spatial scales leads to differences in forecast errors. To do so, we conduct a forecast experiment for the Swiss Rhine at Basel, which covers parts of Germany, Austria, and Switzerland and is southerly bounded by the Alps. Seasonal mean stream flow is defined for the time aggregation of 30, 60, and 90 days and forecasted at 24 dates within the calendar year, i.e. at the 1st and 16th day of each month. A regression model is employed due to the various anthropogenic effects on the basins hydrology, which often are not quantifiable but might be grasped by a simple black box model. Furthermore, the pool of candidate predictors consists of antecedent temperature, precipitation, and stream flow only. This pragmatic approach follows the fact that observations of variables relevant for hydrological storages are either scarce in space or time (soil moisture, groundwater level), restricted to certain seasons (snow depth), or regions (lake levels, snow depth). For a systematic evaluation, we therefore focus on the comprehensive archives of meteorological observations and reanalyses to estimate the initial conditions via climate variability prior to the date of forecast. The experiment itself is based on four different approaches, whose differences in model skill were estimated within a rigorous cross-validation framework for the period 1982-2013: The predictands are regressed on antecedent temperature, precipitation, and stream flow. Here, temperature and precipitation constitute basin averages out of the E-OBS gridded data set. As in 1., but temperature and precipitation are used at the E-OBS grid scale (0.25 degree in longitude and latitude) without spatial averaging. As in 1., but the regression model is applied to 66 gauged subcatchments of the Rhine basin. Forecasts for these subcatchments are then simply summed and upscaled to the area of the Rhine basin. As in 3., but the forecasts at the subcatchment scale are additionally weighted in terms of hydrological representativeness of the corresponding subcatchment.

Food Web Structure in a Harsh Glacier-Fed River

PubMed Central

Clitherow, Leonie R.; Carrivick, Jonathan L.; Brown, Lee E.

2013-01-01

Glacier retreat is occurring across the world, and associated river ecosystems are expected to respond more rapidly than those in flowing waters in other regions. The river environment directly downstream of a glacier snout is characterised by extreme low water temperature and unstable channel sediments but these habitats may become rarer with widespread glacier retreat. In these extreme environments food web dynamics have been little studied, yet they could offer opportunities to test food web theories using highly resolved food webs owing to their low taxonomic richness. This study examined the interactions of macroinvertebrate and diatom taxa in the Ödenwinkelkees river, Austrian central Alps between 2006 and 2011. The webs were characterised by low taxon richness (13–22), highly connected individuals (directed connectance up to 0.19) and short mean food chain length (2.00–2.36). The dominant macroinvertebrates were members of the Chironomidae genus Diamesa and had an omnivorous diet rich in detritus and diatoms as well as other Chironomidae. Simuliidae (typically detritivorous filterers) had a diet rich in diatoms but also showed evidence of predation on Chironomidae larvae. Food webs showed strong species-averaged and individual size structuring but mass-abundance scaling coefficients were larger than those predicted by metabolic theory, perhaps due to a combination of spatial averaging effects of patchily distributed consumers and resources, and/or consumers deriving unquantified resources from microorganisms attached to the large amounts of ingested rock fragments. Comparison of food web structural metrics with those from 62 published river webs suggest these glacier-fed river food web properties were extreme but in line with general food web scaling predictions, a finding which could prove useful to forecast the effects of anticipated future glacier retreat on the structure of aquatic food webs. PMID:23613751

Hyporheic Zone Residence Time Distributions in Regulated River Corridors

NASA Astrophysics Data System (ADS)

Song, X.; Chen, X.; Shuai, P.; Gomez-Velez, J. D.; Ren, H.; Hammond, G. E.

2017-12-01

Regulated rivers exhibit stage fluctuations at multiple frequencies due to both natural processes (e.g., seasonal cycle) and anthropogenic activities (e.g., dam operation). The interaction between the dynamic river flow conditions and the heterogeneous aquifer properties results in complex hydrologic exchange pathways that are ubiquitous in free-flowing and regulated river corridors. The dynamic nature of the exchange flow is reflected in the residence time distribution (RTD) of river water within the groundwater system, which is a key metric that links river corridor biogeochemical processes with the hydrologic exchange. Understanding the dynamics of RTDs is critical to gain the mechanistic understanding of hydrologic exchange fluxes and propose new parsimonious models for river corridors, yet it is understudied primarily due to the high computational demands. In this study, we developed parallel particle tracking algorithms to reveal how river flow variations affect the RTD of river water in the alluvial aquifer. Particle tracking was conducted using the velocity outputs generated by three-dimensional groundwater flow simulations of PFLOTRAN in a 1600 x 800 x 20m model domain within the DOE Hanford Site. Long-term monitoring data of inland well water levels and river stage were used for eight years of flow simulation. Nearly a half million particles were continually released along the river boundary to calculate the RTDs. Spectral analysis of the river stage data revealed high-frequency (sub-daily to weekly) river stage fluctuations caused by dam operations. The higher frequencies of stage variation were progressively filtered to generate multiple sets of flow boundary conditions. A series of flow simulations were performed by using the filtered flow boundary conditions and various degrees of subsurface heterogeneity to study the relative contribution of flow dynamics and physical heterogeneity on river water RTD. Our results revealed multimodal RTDs of river water as a result of the highly variable exchange pathways driven by interactions between dynamic flow and aquifer heterogeneity. A relationship between the RTD and frequency of flow variation was built for each heterogeneity structure, which can be used to assess the potential ecological consequences of dam operations in regulated rivers.