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Hapuarachchi, Hapu Arachchige Prasantha, Mohammed Abdul Bari, Aynul Kabir, Mohammad Mahadi Hasan, Fitsum Markos Woldemeskel, Nilantha Gamage, Patrick Daniel Sunter, et al. "Development of a national 7-day ensemble streamflow forecasting service for Australia." Hydrology and Earth System Sciences 26, no. 18 (September 29, 2022): 4801–21. http://dx.doi.org/10.5194/hess-26-4801-2022.
Повний текст джерелаАнотація:
Abstract. Reliable streamflow forecasts with associated uncertainty estimates are essential to manage and make better use of Australia's scarce surface water resources. Here we present the development of an operational 7 d ensemble streamflow forecasting service for Australia to meet the growing needs of users, primarily water and river managers, for probabilistic forecasts to support their decision making. We test the modelling methodology for 100 catchments to learn the characteristics of different rainfall forecasts from Numerical Weather Prediction (NWP) models, the effect of statistical processing on streamflow forecasts, the optimal ensemble size, and parameters of a bootstrapping technique for calculating forecast skill. A conceptual rainfall–runoff model, GR4H (hourly), and lag and route channel routing model that are in-built in the Short-term Water Information Forecasting Tools (SWIFT) hydrologic modelling package are used to simulate streamflow from input rainfall and potential evaporation. The statistical catchment hydrologic pre-processor (CHyPP) is used for calibrating rainfall forecasts, and the error reduction and representation in stages (ERRIS) model is used to reduce hydrological errors and quantify hydrological uncertainty. Calibrating raw forecast rainfall with CHyPP is an efficient method to significantly reduce bias and improve reliability for up to 7 lead days. We demonstrate that ERRIS significantly improves forecast skill up to 7 lead days. Forecast skills are highest in temperate perennially flowing rivers, while it is lowest in intermittently flowing rivers. A sensitivity analysis for optimising the number of streamflow ensemble members for the operational service shows that more than 200 members are needed to represent the forecast uncertainty. We show that the bootstrapping block size is sensitive to the forecast skill calculation. A bootstrapping block size of 1 month is recommended to capture maximum possible uncertainty. We present benchmark criteria for accepting forecast locations for the public service. Based on the criteria, 209 forecast locations out of a possible 283 are selected in different hydro-climatic regions across Australia for the public service. The service, which has been operational since 2019, provides daily updates of graphical and tabular products of ensemble streamflow forecasts along with performance information, for up to 7 lead days.
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Brown, James D., and Dong-Jun Seo. "A Nonparametric Postprocessor for Bias Correction of Hydrometeorological and Hydrologic Ensemble Forecasts." Journal of Hydrometeorology 11, no. 3 (June 1, 2010): 642–65. http://dx.doi.org/10.1175/2009jhm1188.1.
Повний текст джерелаАнотація:
Abstract This paper describes a technique for quantifying and removing biases from ensemble forecasts of hydrometeorological and hydrologic variables. The technique makes no a priori assumptions about the distributional form of the variables, which is often unknown or difficult to model parametrically. The aim is to estimate the conditional cumulative distribution function (ccdf) of the observed variable given a (possibly biased) real-time ensemble forecast. This ccdf represents the “true” probability distribution of the forecast variable, subject to sampling uncertainties. In the absence of a known distributional form, the ccdf should be estimated nonparametrically. It is noted that the probability of exceeding a threshold of the observed variable, such as flood stage, is equivalent to the expectation of an indicator variable defined for that threshold. The ccdf is then modeled through a linear combination of the indicator variables of the forecast ensemble members. The technique is based on Bayesian optimal linear estimation of indicator variables and is analogous to indicator cokriging (ICK) in geostatistics. By developing linear estimators for the conditional expectation of the observed variable at many thresholds, ICK provides a discrete approximation of the full ccdf. Since ICK minimizes the conditional error variance of the indicator variable at each threshold, it effectively minimizes the continuous ranked probability score (CRPS) when infinitely many thresholds are employed. The technique is used to bias-correct precipitation ensemble forecasts from the NCEP Global Ensemble Forecast System (GEFS) and streamflow ensemble forecasts from the National Weather Service (NWS) River Forecast Centers (RFCs). Split-sample validation results are presented for several attributes of ensemble forecast quality, including reliability and discrimination. In general, the forecast biases were substantially reduced following ICK. Overall, the technique shows significant potential for bias-correcting ensemble forecasts whose distributional form is unknown or nonparametric.
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Yuan, Xing, Joshua K. Roundy, Eric F. Wood, and Justin Sheffield. "Seasonal Forecasting of Global Hydrologic Extremes: System Development and Evaluation over GEWEX Basins." Bulletin of the American Meteorological Society 96, no. 11 (November 1, 2015): 1895–912. http://dx.doi.org/10.1175/bams-d-14-00003.1.
Повний текст джерелаАнотація:
Abstract Seasonal hydrologic extremes in the form of droughts and wet spells have devastating impacts on human and natural systems. Improving understanding and predictive capability of hydrologic extremes, and facilitating adaptations through establishing climate service systems at regional to global scales are among the grand challenges proposed by the World Climate Research Programme (WCRP) and are the core themes of the Regional Hydroclimate Projects (RHP) under the Global Energy and Water Cycle Experiment (GEWEX). An experimental global seasonal hydrologic forecasting system has been developed that is based on coupled climate forecast models participating in the North American Multimodel Ensemble (NMME) project and an advanced land surface hydrologic model. The system is evaluated over major GEWEX RHP river basins by comparing with ensemble streamflow prediction (ESP). The multimodel seasonal forecast system provides higher detectability for soil moisture droughts, more reliable low and high f low ensemble forecasts, and better “real time” prediction for the 2012 North American extreme drought. The association of the onset of extreme hydrologic events with oceanic and land precursors is also investigated based on the joint distribution of forecasts and observations. Climate models have a higher probability of missing the onset of hydrologic extremes when there is no oceanic precursor. But oceanic precursor alone is insufficient to guarantee a correct forecast—a land precursor is also critical in avoiding a false alarm for forecasting extremes. This study is targeted at providing the scientific underpinning for the predictability of hydrologic extremes over GEWEX RHP basins and serves as a prototype for seasonal hydrologic forecasts within the Global Framework for Climate Services (GFCS).
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Schaake, J., J. Demargne, R. Hartman, M. Mullusky, E. Welles, L. Wu, H. Herr, X. Fan, and D. J. Seo. "Precipitation and temperature ensemble forecasts from single-value forecasts." Hydrology and Earth System Sciences Discussions 4, no. 2 (April 2, 2007): 655–717. http://dx.doi.org/10.5194/hessd-4-655-2007.
Повний текст джерелаАнотація:
Abstract. A procedure is presented to construct ensemble forecasts from single-value forecasts of precipitation and temperature. This involves dividing the spatial forecast domain and total forecast period into a number of parts that are treated as separate forecast events. The spatial domain is divided into hydrologic sub-basins. The total forecast period is divided into time periods, one for each model time step. For each event archived values of forecasts and corresponding observations are used to model the joint distribution of forecasts and observations. The conditional distribution of observations for a given single-value forecast is used to represent the corresponding probability distribution of events that may occur for that forecast. This conditional forecast distribution subsequently is used to create ensemble members that vary in space and time using the "Schaake Shuffle" (Clark et al, 2004). The resulting ensemble members have the same space-time patterns as historical observations so that space-time joint relationships between events that have a significant effect on hydrological response tend to be preserved. Forecast uncertainty is space and time-scale dependent. For a given lead time to the beginning of the valid period of an event, forecast uncertainty depends on the length of the forecast valid time period and the spatial area to which the forecast applies. Although the "Schaake Shuffle" procedure, when applied to construct ensemble members from a time-series of single value forecasts, may preserve some of this scale dependency, it may not be sufficient without additional constraint. To account more fully for the time-dependent structure of forecast uncertainty, events for additional "aggregate" forecast periods are defined as accumulations of different "base" forecast periods. The generated ensemble members can be ingested by an Ensemble Streamflow Prediction system to produce ensemble forecasts of streamflow and other hydrological variables that reflect the meteorological uncertainty. The methodology is illustrated by an application to generate temperature and precipitation ensemble forecasts for the American River in California. Parameter estimation and dependent validation results are presented based on operational single-value forecasts archives of short-range River Forecast Center (RFC) forecasts and medium-range ensemble mean forecasts from the National Weather Service (NWS) Global Forecast System (GFS).
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Demargne, Julie, Limin Wu, Satish K. Regonda, James D. Brown, Haksu Lee, Minxue He, Dong-Jun Seo, et al. "The Science of NOAA's Operational Hydrologic Ensemble Forecast Service." Bulletin of the American Meteorological Society 95, no. 1 (January 2014): 79–98. http://dx.doi.org/10.1175/bams-d-12-00081.1.
Повний текст джерела
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Kim, Sunghee, Hossein Sadeghi, Reza Ahmad Limon, Manabendra Saharia, Dong-Jun Seo, Andrew Philpott, Frank Bell, James Brown, and Minxue He. "Assessing the Skill of Medium-Range Ensemble Precipitation and Streamflow Forecasts from the Hydrologic Ensemble Forecast Service (HEFS) for the Upper Trinity River Basin in North Texas." Journal of Hydrometeorology 19, no. 9 (September 1, 2018): 1467–83. http://dx.doi.org/10.1175/jhm-d-18-0027.1.
Повний текст джерелаАнотація:
Abstract To issue early warnings for the public to act, for emergency managers to take preventive actions, and for water managers to operate their systems cost-effectively, it is necessary to maximize the time horizon over which streamflow forecasts are skillful. In this work, we assess the value of medium-range ensemble precipitation forecasts generated with the Hydrologic Ensemble Forecast Service (HEFS) of the U.S. National Weather Service (NWS) in increasing the lead time and skill of streamflow forecasts for five headwater basins in the upper Trinity River basin in north-central Texas. The HEFS uses ensemble mean precipitation forecasts from the Global Ensemble Forecast System (GEFS) of the National Centers for Environment Prediction (NCEP). For comparative evaluation, we verify ensemble streamflow forecasts generated with the HEFS forced by the GEFS forecast with those forced by the short-range quantitative precipitation forecasts (QPFs) from the NWS West Gulf River Forecast Center (WGRFC) based on guidance from the NCEP’s Weather Prediction Center. We also assess the benefits of postprocessing the raw ensemble streamflow forecasts and evaluate the impact of selected parameters within the HEFS on forecast quality. The results show that the use of medium-range precipitation forecasts from the GEFS with the HEFS extends the time horizon for skillful forecasting of mean daily streamflow by 1–3 days for significant events when compared with using only the 72-h River Forecast Center (RFC) QPF with the HEFS. The HEFS forced by the GEFS also improves the skill of two-week-ahead biweekly streamflow forecast by about 20% over climatological forecast for the largest 1% of the observed biweekly flow.
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Porter, James H., Adão H. Matonse, and Allan Frei. "The New York City Operations Support Tool (OST): Managing Water for Millions of People in an Era of Changing Climate and Extreme Hydrological Events." Journal of Extreme Events 02, no. 02 (December 2015): 1550008. http://dx.doi.org/10.1142/s2345737615500086.
Повний текст джерелаАнотація:
With an average daily delivery of 1.1 billion gallons ([Formula: see text]) of drinking water to approximately nine million people in New York City (NYC) and four upstate counties, the NYC Water Supply is among the world’s largest unfiltered systems. In addition to reliably supplying water in terms of quantity and quality, the city has to fulfill other flow objectives to serve downstream communities. At times, such as during extreme hydrological events, water quality issues may restrict water usage from parts of the system; the city is proactively implementing a number of programs to monitor and minimize the impact. To help guide operations and planning, NYC has developed the Operations Support Tool (OST), a decision support system that utilizes ensemble forecasts provided by the National Weather Service (NWS) Hydrologic Ensemble Forecast Service (HEFS). This paper provides an overview of OST and shows two operations case studies to illustrate how OST is used to support risk-based water supply management. As the modeling uncertainty is strongly impacted by the forecast skill, we also discuss how changes in patterns of hydrological extreme events elevate the challenge faced by water supply managers and the role of the scientific community to integrate non-stationarity approaches in hydrologic forecasting.
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Franz, K. J., and T. S. Hogue. "Evaluating uncertainty estimates in hydrologic models: borrowing measures from the forecast verification community." Hydrology and Earth System Sciences 15, no. 11 (November 15, 2011): 3367–82. http://dx.doi.org/10.5194/hess-15-3367-2011.
Повний текст джерелаАнотація:
Abstract. The hydrologic community is generally moving towards the use of probabilistic estimates of streamflow, primarily through the implementation of Ensemble Streamflow Prediction (ESP) systems, ensemble data assimilation methods, or multi-modeling platforms. However, evaluation of probabilistic outputs has not necessarily kept pace with ensemble generation. Much of the modeling community is still performing model evaluation using standard deterministic measures, such as error, correlation, or bias, typically applied to the ensemble mean or median. Probabilistic forecast verification methods have been well developed, particularly in the atmospheric sciences, yet few have been adopted for evaluating uncertainty estimates in hydrologic model simulations. In the current paper, we overview existing probabilistic forecast verification methods and apply the methods to evaluate and compare model ensembles produced from two different parameter uncertainty estimation methods: the Generalized Uncertainty Likelihood Estimator (GLUE), and the Shuffle Complex Evolution Metropolis (SCEM). Model ensembles are generated for the National Weather Service SACramento Soil Moisture Accounting (SAC-SMA) model for 12 forecast basins located in the Southeastern United States. We evaluate the model ensembles using relevant metrics in the following categories: distribution, correlation, accuracy, conditional statistics, and categorical statistics. We show that the presented probabilistic metrics are easily adapted to model simulation ensembles and provide a robust analysis of model performance associated with parameter uncertainty. Application of these methods requires no information in addition to what is already available as part of traditional model validation methodology and considers the entire ensemble or uncertainty range in the approach.
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Carlberg, Bradley, Kristie Franz, and William Gallus. "A Method to Account for QPF Spatial Displacement Errors in Short-Term Ensemble Streamflow Forecasting." Water 12, no. 12 (December 13, 2020): 3505. http://dx.doi.org/10.3390/w12123505.
Повний текст джерелаАнотація:
To account for spatial displacement errors common in quantitative precipitation forecasts (QPFs), a method using systematic shifting of QPF fields was tested to create ensemble streamflow forecasts. While previous studies addressed spatial displacement using neighborhood approaches, shifting of QPF accounts for those errors while maintaining the structure of predicted systems, a feature important in hydrologic forecasts. QPFs from the nine-member High-Resolution Rapid Refresh Ensemble were analyzed for 46 forecasts from 6 cases covering 17 basins within the National Weather Service North Central River Forecast Center forecasting region. Shifts of 55.5 and 111 km were made in the four cardinal and intermediate directions, increasing the ensemble size to 81 members. These members were input into a distributed hydrologic model to create an ensemble streamflow prediction. Overall, the ensemble using the shifted QPFs had an improved frequency of non-exceedance and probability of detection, and thus better predicted flood occurrence. However, false alarm ratio did not improve, likely because shifting multiple QPF ensembles increases the potential to place heavy precipitation in a basin where none actually occurred. A weighting scheme based on a climatology of displacements was tested, improving overall performance slightly compared to the approach using non-weighted members.
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Franz, K. J., and T. S. Hogue. "Evaluating uncertainty estimates in hydrologic models: borrowing measures from the forecast verification community." Hydrology and Earth System Sciences Discussions 8, no. 2 (March 30, 2011): 3085–131. http://dx.doi.org/10.5194/hessd-8-3085-2011.
Повний текст джерелаАнотація:
Abstract. The hydrologic community is generally moving towards the use of probabilistic estimates of streamflow, primarily through the implementation of Ensemble Streamflow Prediction (ESP) systems, ensemble data assimilation methods, or multi-modeling platforms. However, evaluation of probabilistic outputs has not necessarily kept pace with ensemble generation. Much of the modeling community is still performing model evaluation using standard deterministic measures, such as error, correlation, or bias, typically applied to the ensemble mean or median. Probabilistic forecast verification methods have been well developed, particularly in the atmospheric sciences yet, few have been adopted for evaluating uncertainty estimates in hydrologic model simulations. In the current paper, we overview existing probabilistic forecast verification methods and apply the methods to evaluate and compare model ensembles produced from different parameter uncertainty estimation methods. The Generalized Uncertainty Likelihood Estimator (GLUE), a modified version of GLUE, and the Shuffle Complex Evolution Metropolis (SCEM) are used to generate model ensembles for the National Weather Service SACramento Soil Moisture Accounting (SAC-SMA) model for 12 forecast basins located in the Southeastern United States. We evaluate the model ensembles using relevant metrics in the following categories: distribution, correlation, accuracy, conditional statistics, and categorical statistics. We show that the probabilistic metrics are easily adapted to model simulation ensembles and provide a robust analysis of parameter uncertainty, one that is commensurate with the dimension of the ensembles themselves. Application of these methods requires no information in addition to what is already available as part of traditional model validation methodology and considers the entire ensemble or uncertainty range in the approach.
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Werner, Kevin, David Brandon, Martyn Clark, and Subhrendu Gangopadhyay. "Climate Index Weighting Schemes for NWS ESP-Based Seasonal Volume Forecasts." Journal of Hydrometeorology 5, no. 6 (December 1, 2004): 1076–90. http://dx.doi.org/10.1175/jhm-381.1.
Повний текст джерелаАнотація:
Abstract This study compares methods to incorporate climate information into the National Weather Service River Forecast System (NWSRFS). Three small-to-medium river subbasins following roughly along a longitude in the Colorado River basin with different El Niño–Southern Oscillation signals were chosen as test basins. Historical ensemble forecasts of the spring runoff for each basin were generated using modeled hydrologic states and historical precipitation and temperature observations using the Ensemble Streamflow Prediction (ESP) component of the NWSRFS. Two general methods for using a climate index (e.g., Niño-3.4) are presented. The first method, post-ESP, uses the climate index to weight ensemble members from ESP. Four different post-ESP weighting schemes are presented. The second method, preadjustment, uses the climate index to modify the temperature and precipitation ensembles used in ESP. Two preadjustment methods are presented. This study shows the distance-sensitive nearest-neighbor post-ESP to be superior to the other post-ESP weighting schemes. Further, for the basins studied, forecasts based on post-ESP techniques outperformed those based on preadjustment techniques.
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Wanders, Niko, Stephan Thober, Rohini Kumar, Ming Pan, Justin Sheffield, Luis Samaniego, and Eric F. Wood. "Development and Evaluation of a Pan-European Multimodel Seasonal Hydrological Forecasting System." Journal of Hydrometeorology 20, no. 1 (January 1, 2019): 99–115. http://dx.doi.org/10.1175/jhm-d-18-0040.1.
Повний текст джерелаАнотація:
Abstract Hydrological forecasts with a high temporal and spatial resolution are required to provide the level of information needed by end users. So far high-resolution multimodel seasonal hydrological forecasts have been unavailable due to 1) lack of availability of high-resolution meteorological seasonal forecasts, requiring temporal and spatial downscaling; 2) a mismatch between the provided seasonal forecast information and the user needs; and 3) lack of consistency between the hydrological model outputs to generate multimodel seasonal hydrological forecasts. As part of the End-to-End Demonstrator for Improved Decision Making in the Water Sector in Europe (EDgE) project commissioned by the Copernicus Climate Change Service (ECMWF), this study provides a unique dataset of seasonal hydrological forecasts derived from four general circulation models [CanCM4, GFDL Forecast-Oriented Low Ocean Resolution version of CM2.5 (GFDL-FLOR), ECMWF Season Forecast System 4 (ECMWF-S4), and Météo-France LFPW] in combination with four hydrological models [mesoscale hydrologic model (mHM), Noah-MP, PCRaster Global Water Balance (PCR-GLOBWB), and VIC]. The forecasts are provided at daily resolution, 6-month lead time, and 5-km spatial resolution over the historical period from 1993 to 2012. Consistency in hydrological model parameterization ensures an increased consistency in the hydrological forecasts. Results show that skillful discharge forecasts can be made throughout Europe up to 3 months in advance, with predictability up to 6 months for northern Europe resulting from the improved predictability of the spring snowmelt. The new system provides an unprecedented ensemble of seasonal hydrological forecasts with significant skill over Europe to support water management. This study highlights the potential advantages of multimodel based forecasting system in providing skillful hydrological forecasts.
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Alizadeh, Babak, Reza Ahmad Limon, Dong-Jun Seo, Haksu Lee, and James Brown. "Multiscale Postprocessor for Ensemble Streamflow Prediction for Short to Long Ranges." Journal of Hydrometeorology 21, no. 2 (February 2020): 265–85. http://dx.doi.org/10.1175/jhm-d-19-0164.1.
Повний текст джерелаАнотація:
AbstractA novel multiscale postprocessor for ensemble streamflow prediction, MS-EnsPost, is described and comparatively evaluated with the existing postprocessor in the National Weather Service’s Hydrologic Ensemble Forecast Service, EnsPost. MS-EnsPost uses data-driven correction of magnitude-dependent bias in simulated flow, multiscale regression using observed and simulated flows over a range of temporal aggregation scales, and ensemble generation using parsimonious error modeling. For comparative evaluation, 139 basins in eight River Forecast Centers in the United States were used. Streamflow predictability in different hydroclimatological regions is assessed and characterized, and gains by MS-EnsPost over EnsPost are attributed. The ensemble mean and ensemble prediction results indicate that, compared to EnsPost, MS-EnsPost reduces the root-mean-square error and mean continuous ranked probability score of day-1 to day-7 predictions of mean daily flow by 5%–68% and by 2%–62%, respectively. The deterministic and probabilistic results indicate that for most basins the improvement by MS-EnsPost is due to both magnitude-dependent bias correction and full utilization of hydrologic memory through multiscale regression. Comparison of the continuous ranked probability skill score results with hydroclimatic indices indicates that the skill of ensemble streamflow prediction with post processing is modulated largely by the fraction of precipitation as snowfall and, for non-snow-driven basins, mean annual precipitation.
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Velázquez, J. A., T. Petit, A. Lavoie, M. A. Boucher, R. Turcotte, V. Fortin, and F. Anctil. "An evaluation of the canadian global meteorological ensemble prediction system for short-term hydrological forecasting." Hydrology and Earth System Sciences Discussions 6, no. 4 (July 7, 2009): 4891–917. http://dx.doi.org/10.5194/hessd-6-4891-2009.
Повний текст джерелаАнотація:
Abstract. Hydrological forecasting consists in the assessment of future streamflow. Current deterministic forecasts do not give any information concerning the uncertainty, which might be limiting in a decision-making process. Ensemble forecasts are expected to fill this gap. In July 2007, the Meteorological Service of Canada has improved its ensemble prediction system, which has been operational since 1998. It uses the GEM model to generate a 20-member ensemble on a 100 km grid, at mid-latitudes. This improved system is used for the first time for hydrological ensemble predictions. Five watersheds in Quebec (Canada) are studied: Chaudière, Châteauguay, Du Nord, Kénogami and Du Lièvre. An interesting 17-day rainfall event has been selected in October 2007. Forecasts are produced in a 3 h time step for a 3-day forecast horizon. The deterministic forecast is also available and it is compared with the ensemble ones. In order to correct the bias of the ensemble, an updating procedure has been applied to the output data. Results showed that ensemble forecasts are more skilful than the deterministic ones, as measured by the Continuous Ranked Probability Score (CRPS), especially for 72 h forecasts. However, the hydrological ensemble forecasts are under dispersed: a situation that improves with the increasing length of the prediction horizons. We conjecture that this is due in part to the fact that uncertainty in the initial conditions of the hydrological model is not taken into account.
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Velázquez, J. A., T. Petit, A. Lavoie, M. A. Boucher, R. Turcotte, V. Fortin, and F. Anctil. "An evaluation of the Canadian global meteorological ensemble prediction system for short-term hydrological forecasting." Hydrology and Earth System Sciences 13, no. 11 (November 25, 2009): 2221–31. http://dx.doi.org/10.5194/hess-13-2221-2009.
Повний текст джерелаАнотація:
Abstract. Hydrological forecasting consists in the assessment of future streamflow. Current deterministic forecasts do not give any information concerning the uncertainty, which might be limiting in a decision-making process. Ensemble forecasts are expected to fill this gap. In July 2007, the Meteorological Service of Canada has improved its ensemble prediction system, which has been operational since 1998. It uses the GEM model to generate a 20-member ensemble on a 100 km grid, at mid-latitudes. This improved system is used for the first time for hydrological ensemble predictions. Five watersheds in Quebec (Canada) are studied: Chaudière, Châteauguay, Du Nord, Kénogami and Du Lièvre. An interesting 17-day rainfall event has been selected in October 2007. Forecasts are produced in a 3 h time step for a 3-day forecast horizon. The deterministic forecast is also available and it is compared with the ensemble ones. In order to correct the bias of the ensemble, an updating procedure has been applied to the output data. Results showed that ensemble forecasts are more skilful than the deterministic ones, as measured by the Continuous Ranked Probability Score (CRPS), especially for 72 h forecasts. However, the hydrological ensemble forecasts are under dispersed: a situation that improves with the increasing length of the prediction horizons. We conjecture that this is due in part to the fact that uncertainty in the initial conditions of the hydrological model is not taken into account.
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Rosenberg, Eric A., Andrew W. Wood, and Anne C. Steinemann. "Informing Hydrometric Network Design for Statistical Seasonal Streamflow Forecasts." Journal of Hydrometeorology 14, no. 5 (October 1, 2013): 1587–604. http://dx.doi.org/10.1175/jhm-d-12-0136.1.
Повний текст джерелаАнотація:
Abstract A hydrometric network design approach is developed for enhancing statistical seasonal streamflow forecasts. The approach employs gridded, model-simulated water balance variables as predictors in equations generated via principal components regression in order to identify locations for additional observations that most improve forecast skill. The approach is applied toward the expansion of the Natural Resources Conservation Service (NRCS) Snowpack Telemetry (SNOTEL) network in 24 western U.S. basins using two forecasting scenarios: one that assumes the currently standard predictors of snow water equivalent and water year-to-date precipitation and one that considers soil moisture as an additional predictor variable. Resulting improvements are spatially and temporally analyzed, attributed to dominant predictor contributions, and evaluated in the context of operational NRCS forecasts, ensemble-based National Weather Service (NWS) forecasts, and historical as-issued NRCS/NWS coordinated forecasts. Findings indicate that, except for basins with sparse existing networks, substantial improvements in forecast skill are only possible through the addition of soil moisture variables. Furthermore, locations identified as optimal for soil moisture sensor installation are primarily found in regions of low to mid elevation, in contrast to the higher elevations where SNOTEL stations are traditionally situated. The study corroborates prior research while demonstrating that soil moisture data can explicitly improve operational water supply forecasts (particularly during the accumulation season), that statistical forecasts are comparable in skill to ensemble-based forecasts, and that simulated hydrologic data can be combined with observations to improve statistical forecasts. The approach can be generalized to other settings and applications involving the use of point observations for statistical prediction models.
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Brown, James D., Minxue He, Satish Regonda, Limin Wu, Haksu Lee, and Dong-Jun Seo. "Verification of temperature, precipitation, and streamflow forecasts from the NOAA/NWS Hydrologic Ensemble Forecast Service (HEFS): 2. Streamflow verification." Journal of Hydrology 519 (November 2014): 2847–68. http://dx.doi.org/10.1016/j.jhydrol.2014.05.030.
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Girons Lopez, Marc, Louise Crochemore, and Ilias G. Pechlivanidis. "Benchmarking an operational hydrological model for providing seasonal forecasts in Sweden." Hydrology and Earth System Sciences 25, no. 3 (March 8, 2021): 1189–209. http://dx.doi.org/10.5194/hess-25-1189-2021.
Повний текст джерелаАнотація:
Abstract. Probabilistic seasonal forecasts are important for many water-intensive activities requiring long-term planning. Among the different techniques used for seasonal forecasting, the ensemble streamflow prediction (ESP) approach has long been employed due to the singular dependence on past meteorological records. The Swedish Meteorological and Hydrological Institute is currently extending the use of long-range forecasts within its operational warning service, which requires a thorough analysis of the suitability and applicability of different methods with the national S-HYPE hydrological model. To this end, we aim to evaluate the skill of ESP forecasts over 39 493 catchments in Sweden, understand their spatio-temporal patterns, and explore the main hydrological processes driving forecast skill. We found that ESP forecasts are generally skilful for most of the country up to 3 months into the future but that large spatio-temporal variations exist. Forecasts are most skilful during the winter months in northern Sweden, except for the highly regulated hydropower-producing rivers. The relationships between forecast skill and 15 different hydrological signatures show that forecasts are most skilful for slow-reacting, baseflow-dominated catchments and least skilful for flashy catchments. Finally, we show that forecast skill patterns can be spatially clustered in seven unique regions with similar hydrological behaviour. Overall, these results contribute to identifying in which areas and seasons and how long into the future ESP hydrological forecasts provide an added value, not only for the national forecasting and warning service, but also, most importantly, for guiding decision-making in critical services such as hydropower management and risk reduction.
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Harrigan, Shaun, Ervin Zsoter, Hannah Cloke, Peter Salamon, and Christel Prudhomme. "Daily ensemble river discharge reforecasts and real-time forecasts from the operational Global Flood Awareness System." Hydrology and Earth System Sciences 27, no. 1 (January 2, 2023): 1–19. http://dx.doi.org/10.5194/hess-27-1-2023.
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Abstract. Operational global-scale hydrological forecasting systems are used to help manage hydrological extremes such as floods and droughts. The vast amounts of raw data that underpin forecast systems and the ability to generate information on forecast skill have, until now, not been publicly available. As part of the Global Flood Awareness System (GloFAS; https://www.globalfloods.eu/, last access: 3 December 2022) service evolution, in this paper daily ensemble river discharge reforecasts and real-time forecast datasets are made free and openly available through the Copernicus Climate Change Service (C3S) Climate Data Store (CDS). They include real-time forecast data starting on 1 January 2020 updated operationally every day and a 20-year set of reforecasts and associated metadata. This paper describes the model components and configuration used to generate the real-time river discharge forecasts and the reforecasts. An evaluation of ensemble forecast skill using the continuous ranked probability skill score (CRPSS) was also undertaken for river points around the globe. Results show that GloFAS is skilful in over 93 % of catchments in the short (1 to 3 d) and medium range (5 to 15 d) against a persistence benchmark forecast and skilful in over 80 % of catchments out to the extended range (16 to 30 d) against a climatological benchmark forecast. However, the strength of skill varies considerably by location with GloFAS found to have no or negative skill at longer lead times in broad hydroclimatic regions in tropical Africa, western coast of South America, and catchments dominated by snow and ice in high northern latitudes. Forecast skill is summarised as a new headline skill score available as a new layer on the GloFAS forecast Web Map Viewer to aid user interpretation and understanding of forecast quality.
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Candogan Yossef, Naze, Rens van Beek, Albrecht Weerts, Hessel Winsemius, and Marc F. P. Bierkens. "Skill of a global forecasting system in seasonal ensemble streamflow prediction." Hydrology and Earth System Sciences 21, no. 8 (August 14, 2017): 4103–14. http://dx.doi.org/10.5194/hess-21-4103-2017.
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Abstract. In this study we assess the skill of seasonal streamflow forecasts with the global hydrological forecasting system Flood Early Warning System (FEWS)-World, which has been set up within the European Commission 7th Framework Programme Project Global Water Scarcity Information Service (GLOWASIS). FEWS-World incorporates the distributed global hydrological model PCR-GLOBWB (PCRaster Global Water Balance). We produce ensemble forecasts of monthly discharges for 20 large rivers of the world, with lead times of up to 6 months, forcing the system with bias-corrected seasonal meteorological forecast ensembles from the European Centre for Medium-range Weather Forecasts (ECMWF) and with probabilistic meteorological ensembles obtained following the ESP procedure. Here, the ESP ensembles, which contain no actual information on weather, serve as a benchmark to assess the additional skill that may be obtained using ECMWF seasonal forecasts. We use the Brier skill score (BSS) to quantify the skill of the system in forecasting high and low flows, defined as discharges higher than the 75th and lower than the 25th percentiles for a given month, respectively. We determine the theoretical skill by comparing the results against model simulations and the actual skill in comparison to discharge observations. We calculate the ratios of actual-to-theoretical skill in order to quantify the percentage of the potential skill that is achieved. The results suggest that the performance of ECMWF S3 forecasts is close to that of the ESP forecasts. While better meteorological forecasts could potentially lead to an improvement in hydrological forecasts, this cannot be achieved yet using the ECMWF S3 dataset.
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Brown, James D., Limin Wu, Minxue He, Satish Regonda, Haksu Lee, and Dong-Jun Seo. "Verification of temperature, precipitation, and streamflow forecasts from the NOAA/NWS Hydrologic Ensemble Forecast Service (HEFS): 1. Experimental design and forcing verification." Journal of Hydrology 519 (November 2014): 2869–89. http://dx.doi.org/10.1016/j.jhydrol.2014.05.028.
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Pagano, Thomas C., Andrew W. Wood, Maria-Helena Ramos, Hannah L. Cloke, Florian Pappenberger, Martyn P. Clark, Michael Cranston, et al. "Challenges of Operational River Forecasting." Journal of Hydrometeorology 15, no. 4 (July 30, 2014): 1692–707. http://dx.doi.org/10.1175/jhm-d-13-0188.1.
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Abstract Skillful and timely streamflow forecasts are critically important to water managers and emergency protection services. To provide these forecasts, hydrologists must predict the behavior of complex coupled human–natural systems using incomplete and uncertain information and imperfect models. Moreover, operational predictions often integrate anecdotal information and unmodeled factors. Forecasting agencies face four key challenges: 1) making the most of available data, 2) making accurate predictions using models, 3) turning hydrometeorological forecasts into effective warnings, and 4) administering an operational service. Each challenge presents a variety of research opportunities, including the development of automated quality-control algorithms for the myriad of data used in operational streamflow forecasts, data assimilation, and ensemble forecasting techniques that allow for forecaster input, methods for using human-generated weather forecasts quantitatively, and quantification of human interference in the hydrologic cycle. Furthermore, much can be done to improve the communication of probabilistic forecasts and to design a forecasting paradigm that effectively combines increasingly sophisticated forecasting technology with subjective forecaster expertise. These areas are described in detail to share a real-world perspective and focus for ongoing research endeavors.
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Wang, Han, Ping-an Zhong, Ervin Zsoter, Christel Prudhomme, Florian Pappenberger, and Bin Xu. "Regional Adaptability of Global and Regional Hydrological Forecast System." Water 15, no. 2 (January 14, 2023): 347. http://dx.doi.org/10.3390/w15020347.
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Our paper aims to improve flood forecasting by establishing whether a global hydrological forecast system could be used as an alternative to a regional system, or whether it could provide additional information. This paper was based on the operational Global Flood Awareness System (GloFAS) of the European Commission Copernicus Emergency Management Service, as well as on a regional hydrological forecast system named RHFS, which was created with observations recorded in the Wangjiaba river basin in China. We compared the discharge simulations of the two systems, and tested the influence of input. Then the discharge ensemble forecasts were evaluated for lead times of 1–7 d, and the impact on the forecasts of errors in initialization and modelling were considered. We also used quantile mapping (QM) to post-process the discharge simulations and forecasts. The results showed: (1) GloFAS (KGE of 0.54) had a worse discharge simulation than RHFS (KGE of 0.88), mainly because of the poor quality of the input; (2) the average forecast skill of GloFAS (CRPSS about 0.2) was inferior to that of RHFS (CRPSS about 0.6), because of the errors in the initialization and the model, however, GloFAS had a higher forecast quality than RHFS at high flow with longer lead times; (3) QM performed well at eliminating errors in input, the model, and the initialization.
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Donegan, Seán, Conor Murphy, Shaun Harrigan, Ciaran Broderick, Dáire Foran Quinn, Saeed Golian, Jeff Knight, et al. "Conditioning ensemble streamflow prediction with the North Atlantic Oscillation improves skill at longer lead times." Hydrology and Earth System Sciences 25, no. 7 (July 22, 2021): 4159–83. http://dx.doi.org/10.5194/hess-25-4159-2021.
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Abstract. Skilful hydrological forecasts can benefit decision-making in water resources management and other water-related sectors that require long-term planning. In Ireland, no such service exists to deliver forecasts at the catchment scale. In order to understand the potential for hydrological forecasting in Ireland, we benchmark the skill of ensemble streamflow prediction (ESP) for a diverse sample of 46 catchments using the GR4J (Génie Rural à 4 paramètres Journalier) hydrological model. Skill is evaluated within a 52-year hindcast study design over lead times of 1 d to 12 months for each of the 12 initialisation months, January to December. Our results show that ESP is skilful against a probabilistic climatology benchmark in the majority of catchments up to several months ahead. However, the level of skill was strongly dependent on lead time, initialisation month, and individual catchment location and storage properties. Mean ESP skill was found to decay rapidly as a function of lead time, with a continuous ranked probability skill score (CRPSS) of 0.8 (1 d), 0.32 (2-week), 0.18 (1-month), 0.05 (3-month), and 0.01 (12-month). Forecasts were generally more skilful when initialised in summer than other seasons. A strong correlation (ρ=0.94) was observed between forecast skill and catchment storage capacity (baseflow index), with the most skilful regions, the Midlands and the East, being those where slowly responding, high-storage catchments are located. Forecast reliability and discrimination were also assessed with respect to low- and high-flow events. In addition to our benchmarking experiment, we conditioned ESP with the winter North Atlantic Oscillation (NAO) using adjusted hindcasts from the Met Office's Global Seasonal Forecasting System version 5. We found gains in winter forecast skill (CRPSS) of 7 %–18 % were possible over lead times of 1 to 3 months and that improved reliability and discrimination make NAO-conditioned ESP particularly effective at forecasting dry winters, a critical season for water resources management. We conclude that ESP is skilful in a number of different contexts and thus should be operationalised in Ireland given its potential benefits for water managers and other stakeholders.
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Voces-Aboy, Jose, Inmaculada Abia-Llera, Eroteida Sánchez-García, Beatriz Navascués, Ernesto Rodríguez-Camino, María Nieves Garrido-del-Pozo, María Concepción García-Gómez, José Adolfo Álvarez-González, and Fernando Pastor-Argüello. "Web-based decision support toolbox for Spanish reservoirs." Advances in Science and Research 16 (August 8, 2019): 157–63. http://dx.doi.org/10.5194/asr-16-157-2019.
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Abstract. Under the S-ClimWaRe (Seasonal Climate prediction in support of Water Reservoirs management) initiative, a climate service to support decision-making process by water managers in Spanish reservoirs has been developed. It consists in a web-based toolbox jointly designed with stakeholders. The website is organized in two main areas. The first one allows the user to explore, for any water reservoir or grid point over continental Spain, the existing hydrological variability and risk linked to climate variability. This is performed through a set of indicators obtained from time series of hydrological and meteorological observations and North Atlantic Oscillation (NAO) index, identified as main climate driver in this geographical region. The second main area provides seasonal forecasts of NAO and both reservoir inflow and precipitation, complemented by information on probabilistic forecasts skill. Currently the NAO index is the only driver implemented for display, and forecasts come from a statistical forecasting system developed only for the extended winter NDJFM period. Through the MEDSCOPE (MEDiterranean Services Chain based On climate PrEdictions) project new sources of predictability and relationships with different climate drivers will be explored. Forecast skill improvement is expected after the combination and weighting of ensemble members of the Copernicus seasonal forecasting systems. These forecasts will feed more sophisticated hydrological models. The toolbox has been flexible designed with respect to sources of seasonal forecasts and extension to additional drivers, variables and seasons. In this way, user requirements and scientific progress will be easily incorporated to new versions of this climate service.
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Flamig, Zachary L., Humberto Vergara, and Jonathan J. Gourley. "The Ensemble Framework For Flash Flood Forecasting (EF5) v1.2: description and case study." Geoscientific Model Development 13, no. 10 (October 16, 2020): 4943–58. http://dx.doi.org/10.5194/gmd-13-4943-2020.
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Abstract. The Ensemble Framework For Flash Flood Forecasting (EF5) was developed specifically for improving hydrologic predictions to aid in the issuance of flash flood warnings by the US National Weather Service. EF5 features multiple water balance models and two routing schemes which can be used to generate ensemble forecasts of streamflow, streamflow normalized by upstream basin area (i.e., unit streamflow), and soil saturation. EF5 is designed to utilize high-resolution precipitation forcing datasets now available in real time. A study on flash-flood-scale basins was conducted over the conterminous United States using gauged basins with catchment areas less than 1000 km2. The results of the study show that the three uncalibrated water balance models linked to kinematic wave routing are skillful in simulating streamflow.
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Meißner, Dennis, Bastian Klein, and Monica Ionita. "Development of a monthly to seasonal forecast framework tailored to inland waterway transport in central Europe." Hydrology and Earth System Sciences 21, no. 12 (December 15, 2017): 6401–23. http://dx.doi.org/10.5194/hess-21-6401-2017.
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Abstract. 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.
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Ostermöller, Jennifer, Philip Lorenz, Kristina Fröhlich, Frank Kreienkamp, and Barbara Früh. "Downscaling and Evaluation of Seasonal Climate Data for the European Power Sector." Atmosphere 12, no. 3 (February 26, 2021): 304. http://dx.doi.org/10.3390/atmos12030304.
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Within the Clim2Power project, two case studies focus on seasonal variations of the hydropower production in the river basins of the Danube (Germany/Austria) and the Douro (Portugal). To deliver spatially highly resolved climate data as an input for the hydrological models, the forecasts of the German Climate Forecast System (GCFS2.0) need to be downscaled. The statistical-empirical method EPISODES is used in this approach. It is adapted to the seasonal data, which consists of ensemble hindcasts and forecasts. Beside this, the two case study regions need specific configurations of the statistical model, providing appropriate predictors for the meteorological variables. This paper describes the technical details of the adaptation of the EPISODES method for the needs of Clim2Power. We analyse the hindcast skill of the downscaled hindcasts of all four seasons for the two variables near-surface (2 m) temperature and precipitation, and conclude that on the average the skill is conserved compared to the global model. This means that the seasonal information is available at a higher spatial resolution without losing skill. Furthermore, the output of the statistical downscaling is nearly bias-free, which is, beside the higher spatial resolution, an added value for the climate service.
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Sparrow, Sarah, Andrew Bowery, Glenn D. Carver, Marcus O. Köhler, Pirkka Ollinaho, Florian Pappenberger, David Wallom, and Antje Weisheimer. "OpenIFS@home version 1: a citizen science project for ensemble weather and climate forecasting." Geoscientific Model Development 14, no. 6 (June 9, 2021): 3473–86. http://dx.doi.org/10.5194/gmd-14-3473-2021.
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Abstract. Weather forecasts rely heavily on general circulation models of the atmosphere and other components of the Earth system. National meteorological and hydrological services and intergovernmental organizations, such as the European Centre for Medium-Range Weather Forecasts (ECMWF), provide routine operational forecasts on a range of spatio-temporal scales by running these models at high resolution on state-of-the-art high-performance computing systems. Such operational forecasts are very demanding in terms of computing resources. To facilitate the use of a weather forecast model for research and training purposes outside the operational environment, ECMWF provides a portable version of its numerical weather forecast model, OpenIFS, for use by universities and other research institutes on their own computing systems. In this paper, we describe a new project (OpenIFS@home) that combines OpenIFS with a citizen science approach to involve the general public in helping conduct scientific experiments. Volunteers from across the world can run OpenIFS@home on their computers at home, and the results of these simulations can be combined into large forecast ensembles. The infrastructure of such distributed computing experiments is based on our experience and expertise with the climateprediction.net (https://www.climateprediction.net/, last access: 1 June 2021) and weather@home systems. In order to validate this first use of OpenIFS in a volunteer computing framework, we present results from ensembles of forecast simulations of Tropical Cyclone Karl from September 2016 studied during the NAWDEX field campaign. This cyclone underwent extratropical transition and intensified in mid-latitudes to give rise to an intense jet streak near Scotland and heavy rainfall over Norway. For the validation we use a 2000-member ensemble of OpenIFS run on the OpenIFS@home volunteer framework and a smaller ensemble of the size of operational forecasts using ECMWF's forecast model in 2016 run on the ECMWF supercomputer with the same horizontal resolution as OpenIFS@home. We present ensemble statistics that illustrate the reliability and accuracy of the OpenIFS@home forecasts and discuss the use of large ensembles in the context of forecasting extreme events.
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Liang, Xin-Zhong, Min Xu, Xing Yuan, Tiejun Ling, Hyun I. Choi, Feng Zhang, Ligang Chen, et al. "Regional Climate–Weather Research and Forecasting Model." Bulletin of the American Meteorological Society 93, no. 9 (September 1, 2012): 1363–87. http://dx.doi.org/10.1175/bams-d-11-00180.1.
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The CWRF is developed as a climate extension of the Weather Research and Forecasting model (WRF) by incorporating numerous improvements in the representation of physical processes and integration of external (top, surface, lateral) forcings that are crucial to climate scales, including interactions between land, atmosphere, and ocean; convection and microphysics; and cloud, aerosol, and radiation; and system consistency throughout all process modules. This extension inherits all WRF functionalities for numerical weather prediction while enhancing the capability for climate modeling. As such, CWRF can be applied seamlessly to weather forecast and climate prediction. The CWRF is built with a comprehensive ensemble of alternative parameterization schemes for each of the key physical processes, including surface (land, ocean), planetary boundary layer, cumulus (deep, shallow), microphysics, cloud, aerosol, and radiation, and their interactions. This facilitates the use of an optimized physics ensemble approach to improve weather or climate prediction along with a reliable uncertainty estimate. The CWRF also emphasizes the societal service capability to provide impactrelevant information by coupling with detailed models of terrestrial hydrology, coastal ocean, crop growth, air quality, and a recently expanded interactive water quality and ecosystem model. This study provides a general CWRF description and basic skill evaluation based on a continuous integration for the period 1979– 2009 as compared with that of WRF, using a 30-km grid spacing over a domain that includes the contiguous United States plus southern Canada and northern Mexico. In addition to advantages of greater application capability, CWRF improves performance in radiation and terrestrial hydrology over WRF and other regional models. Precipitation simulation, however, remains a challenge for all of the tested models.
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Jasper-Tönnies, Alrun, Sandra Hellmers, Thomas Einfalt, Alexander Strehz, and Peter Fröhle. "Ensembles of radar nowcasts and COSMO-DE-EPS for urban flood management." Water Science and Technology 2017, no. 1 (February 23, 2018): 27–35. http://dx.doi.org/10.2166/wst.2018.079.
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Abstract Sophisticated strategies are required for flood warning in urban areas regarding convective heavy rainfall events. An approach is presented to improve short-term precipitation forecasts by combining ensembles of radar nowcasts with the high-resolution numerical weather predictions COSMO-DE-EPS of the German Weather Service. The combined ensemble forecasts are evaluated and compared to deterministic precipitation forecasts of COSMO-DE. The results show a significantly improved quality of the short-term precipitation forecasts and great potential to improve flood warnings for urban catchments. The combined ensemble forecasts are produced operationally every 5 min. Applications involve the Flood Warning Service Hamburg (WaBiHa) and real-time hydrological simulations with the model KalypsoHydrology.
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Shukla, S., and D. P. Lettenmaier. "Seasonal hydrologic prediction in the United States: understanding the role of initial hydrologic conditions and seasonal climate forecast skill." Hydrology and Earth System Sciences 15, no. 11 (November 22, 2011): 3529–38. http://dx.doi.org/10.5194/hess-15-3529-2011.
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Abstract. Seasonal hydrologic forecasts derive their skill from knowledge of initial hydrologic conditions and climate forecast skill associated with seasonal climate outlooks. Depending on the type of hydrological regime and the season, the relative contributions of initial hydrologic conditions and climate forecast skill to seasonal hydrologic forecast skill vary. We seek to quantify these contributions on a relative basis across the Conterminous United States. We constructed two experiments – Ensemble Streamflow Prediction and reverse-Ensemble Streamflow Prediction – to partition the contributions of the initial hydrologic conditions and climate forecast skill to overall forecast skill. In ensemble streamflow prediction (first experiment) hydrologic forecast skill is derived solely from knowledge of initial hydrologic conditions, whereas in reverse-ensemble streamflow prediction (second experiment), it is derived solely from atmospheric forcings (i.e. perfect climate forecast skill). Using the ratios of root mean square error in predicting cumulative runoff and mean monthly soil moisture of each experiment, we identify the variability of the relative contributions of the initial hydrologic conditions and climate forecast skill spatially throughout the year. We conclude that the initial hydrologic conditions generally have the strongest influence on the prediction of cumulative runoff and soil moisture at lead-1 (first month of the forecast period), beyond which climate forecast skill starts to have greater influence. Improvement in climate forecast skill alone will lead to better seasonal hydrologic forecast skill in most parts of the Northeastern and Southeastern US throughout the year and in the Western US mainly during fall and winter months; whereas improvement in knowledge of the initial hydrologic conditions can potentially improve skill most in the Western US during spring and summer months. We also observed that at a short lead time (i.e. lead-1) contribution of the initial hydrologic conditions in soil moisture forecasts is more extensive than in cumulative runoff forecasts across the Conterminous US.
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Belluš, Martin, Florian Weidle, Christoph Wittmann, Yong Wang, Simona Taşku, and Martina Tudor. "Aire Limitée Adaptation dynamique Développement InterNational – Limited Area Ensemble Forecasting (ALADIN-LAEF)." Advances in Science and Research 16 (May 21, 2019): 63–68. http://dx.doi.org/10.5194/asr-16-63-2019.
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Abstract. A meso-scale ensemble system Aire Limitée Adaptation dynamique Développement InterNational – Limited Area Ensemble Forecasting (ALADIN-LAEF) based on the limited area model ALADIN has been developed in the framework of Regional Cooperation for Limited Area modelling in Central Europe (RC LACE) consortium, focusing on short range probabilistic forecasts and profiting from advanced multi-scale ALARO physics. Its main purpose is to provide probabilistic forecast on daily basis for the national weather services of RC LACE partners. It also serves as a reliable source of probabilistic information applied to downstream hydrology and energy industry.
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Thirel, G., E. Martin, J. F. Mahfouf, S. Massart, S. Ricci, F. Regimbeau, and F. Habets. "A past discharge assimilation system for ensemble streamflow forecasts over France – Part 2: Impact on the ensemble streamflow forecasts." Hydrology and Earth System Sciences 14, no. 8 (August 24, 2010): 1639–53. http://dx.doi.org/10.5194/hess-14-1639-2010.
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Abstract. The use of ensemble streamflow forecasts is developing in the international flood forecasting services. Ensemble streamflow forecast systems can provide more accurate forecasts and useful information about the uncertainty of the forecasts, thus improving the assessment of risks. Nevertheless, these systems, like all hydrological forecasts, suffer from errors on initialization or on meteorological data, which lead to hydrological prediction errors. This article, which is the second part of a 2-part article, concerns the impacts of initial states, improved by a streamflow assimilation system, on an ensemble streamflow prediction system over France. An assimilation system was implemented to improve the streamflow analysis of the SAFRAN-ISBA-MODCOU (SIM) hydro-meteorological suite, which initializes the ensemble streamflow forecasts at Météo-France. This assimilation system, using the Best Linear Unbiased Estimator (BLUE) and modifying the initial soil moisture states, showed an improvement of the streamflow analysis with low soil moisture increments. The final states of this suite were used to initialize the ensemble streamflow forecasts of Météo-France, which are based on the SIM model and use the European Centre for Medium-range Weather Forecasts (ECMWF) 10-day Ensemble Prediction System (EPS). Two different configurations of the assimilation system were used in this study: the first with the classical SIM model and the second using improved soil physics in ISBA. The effects of the assimilation system on the ensemble streamflow forecasts were assessed for these two configurations, and a comparison was made with the original (i.e. without data assimilation and without the improved physics) ensemble streamflow forecasts. It is shown that the assimilation system improved most of the statistical scores usually computed for the validation of ensemble predictions (RMSE, Brier Skill Score and its decomposition, Ranked Probability Skill Score, False Alarm Rate, etc.), especially for the first few days of the time range. The assimilation was slightly more efficient for small basins than for large ones.
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Adams III, Thomas E., and Randel Dymond. "Evaluation and Benchmarking of Operational Short-Range Ensemble Mean and Median Streamflow Forecasts for the Ohio River Basin." Journal of Hydrometeorology 19, no. 10 (October 1, 2018): 1689–706. http://dx.doi.org/10.1175/jhm-d-18-0102.1.
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Abstract This study presents findings from a real-time forecast experiment that compares legacy deterministic hydrologic stage forecasts to ensemble mean and median stage forecasts from the NOAA/NWS Meteorological Model-Based Ensemble Forecast System (MMEFS). The NOAA/NWS Ohio River Forecast Center (OHRFC) area of responsibility defines the experimental region. Real-time forecasts from subbasins at 54 forecast point locations, ranging in drainage area, geographic location within the Ohio River valley, and watershed response time serve as the basis for analyses. In the experiment, operational hydrologic forecasts, with a 24-h quantitative precipitation forecast (QPF) and forecast temperatures, are compared to MMEFS hydrologic ensemble mean and median forecasts, with model forcings from the NOAA/NWS National Centers for Environmental Prediction (NCEP) North American Ensemble Forecast System (NAEFS), over the period from 30 November 2010 through 24 May 2012. Experiments indicate that MMEFS ensemble mean and median forecasts exhibit lower errors beginning at about lead time 90 h when forecasts at all locations are aggregated. With fast response basins that peak at ≤24 h, ensemble mean and median forecasts exhibit lower errors much earlier, beginning at about lead time 36 h, which suggests the viability of using MMEFS ensemble forecasts as an alternative to OHRFC legacy forecasts. Analyses show that ensemble median forecasts generally exhibit smaller errors than ensemble mean forecasts for all stage ranges. Verification results suggest that OHRFC MMEFS NAEFS ensemble forecasts are reasonable, but needed improvements are identified.
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Zhao, L., Q. Duan, J. Schaake, A. Ye, and J. Xia. "A hydrologic post-processor for ensemble streamflow predictions." Advances in Geosciences 29 (February 28, 2011): 51–59. http://dx.doi.org/10.5194/adgeo-29-51-2011.
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Abstract. This paper evaluates the performance of a statistical post-processor for imperfect hydrologic model forecasts. Assuming that the meteorological forecasts are well-calibrated, we employ a "General Linear Model (GLM)" to post-process simulations produced by a hydrologic model. For a particular forecast date, the observations and simulations from an "analysis window" and hydrologic model forecasts for a "forecast window", the GLM Post-Processor (GLMPP) is used to produce an ensemble of predictions of the streamflow observations that will occur during the "forecast window". The objectives of the GLMPP are to: (1) preserve any skill in the original hydrologic ensemble forecast; (2) correct systematic model biases; (3) retain the equal-likelihood assumption for the ensemble; (4) preserve temporal scale dependency relationships in streamflow hydrographs and the uncertainty in the predictions; and, (5) produce reliable ensemble predictions. Observed and simulated daily streamflow data from the Second Workshop on Model Parameter Estimation Experiment (MOPEX) are used to test how well these objectives are met when the GLMPP is applied to ensemble hydrologic forecasts driven by well calibrated meteorological forecasts. A 39-year hydrologic dataset from the French Broad basin is split into calibration and verification periods. The results show that the GLMPP built using data from the calibration period removes the mean bias when applied to hydrologic model simulations from both the calibration and verification periods. Probability distributions of the post-processed model simulations are shown to be closer to the climatological probability distributions of observed streamflow than the distributions of the unadjusted simulated flows. A number of experiments with different GLMPP configurations were also conducted to examine the effects of different configurations for forecast and analysis window lengths on the robustness of the results.
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Ye, Jinyin, Yuehong Shao, and Zhijia Li. "Flood Forecasting Based on TIGGE Precipitation Ensemble Forecast." Advances in Meteorology 2016 (2016): 1–9. http://dx.doi.org/10.1155/2016/9129734.
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TIGGE (THORPEX International Grand Global Ensemble) was a major part of the THORPEX (Observing System Research and Predictability Experiment). It integrates ensemble precipitation products from all the major forecast centers in the world and provides systematic evaluation on the multimodel ensemble prediction system. Development of meteorologic-hydrologic coupled flood forecasting model and early warning model based on the TIGGE precipitation ensemble forecast can provide flood probability forecast, extend the lead time of the flood forecast, and gain more time for decision-makers to make the right decision. In this study, precipitation ensemble forecast products from ECMWF, NCEP, and CMA are used to drive distributed hydrologic model TOPX. We focus on Yi River catchment and aim to build a flood forecast and early warning system. The results show that the meteorologic-hydrologic coupled model can satisfactorily predict the flow-process of four flood events. The predicted occurrence time of peak discharges is close to the observations. However, the magnitude of the peak discharges is significantly different due to various performances of the ensemble prediction systems. The coupled forecasting model can accurately predict occurrence of the peak time and the corresponding risk probability of peak discharge based on the probability distribution of peak time and flood warning, which can provide users a strong theoretical foundation and valuable information as a promising new approach.
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Samaniego, Luis, Stephan Thober, Niko Wanders, Ming Pan, Oldrich Rakovec, Justin Sheffield, Eric F. Wood, et al. "Hydrological Forecasts and Projections for Improved Decision-Making in the Water Sector in Europe." Bulletin of the American Meteorological Society 100, no. 12 (December 1, 2019): 2451–72. http://dx.doi.org/10.1175/bams-d-17-0274.1.
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Abstract Simulations of water fluxes at high spatial resolution that consistently cover historical observations, seasonal forecasts, and future climate projections are key to providing climate services aimed at supporting operational and strategic planning, and developing mitigation and adaptation policies. The End-to-end Demonstrator for improved decision-making in the water sector in Europe (EDgE) is a proof-of-concept project funded by the Copernicus Climate Change Service program that addresses these requirements by combining a multimodel ensemble of state-of-the-art climate model outputs and hydrological models to deliver sectoral climate impact indicators (SCIIs) codesigned with private and public water sector stakeholders from three contrasting European countries. The final product of EDgE is a water-oriented information system implemented through a web application. Here, we present the underlying structure of the EDgE modeling chain, which is composed of four phases: 1) climate data processing, 2) hydrological modeling, 3) stakeholder codesign and SCII estimation, and 4) uncertainty and skill assessments. Daily temperature and precipitation from observational datasets, four climate models for seasonal forecasts, and five climate models under two emission scenarios are consistently downscaled to 5-km spatial resolution to ensure locally relevant simulations based on four hydrological models. The consistency of the hydrological models is guaranteed by using identical input data for land surface parameterizations. The multimodel outputs are composed of 65 years of historical observations, a 19-yr ensemble of seasonal hindcasts, and a century-long ensemble of climate impact projections. These unique, high-resolution hydroclimatic simulations and SCIIs provide an unprecedented information system for decision-making over Europe and can serve as a template for water-related climate services in other regions.
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Wu, Limin, Dong-Jun Seo, Julie Demargne, James D. Brown, Shuzheng Cong, and John Schaake. "Generation of ensemble precipitation forecast from single-valued quantitative precipitation forecast for hydrologic ensemble prediction." Journal of Hydrology 399, no. 3-4 (March 2011): 281–98. http://dx.doi.org/10.1016/j.jhydrol.2011.01.013.
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Luo, Lifeng, and Eric F. Wood. "Use of Bayesian Merging Techniques in a Multimodel Seasonal Hydrologic Ensemble Prediction System for the Eastern United States." Journal of Hydrometeorology 9, no. 5 (October 1, 2008): 866–84. http://dx.doi.org/10.1175/2008jhm980.1.
Повний текст джерелаАнотація:
Abstract Skillful seasonal hydrologic predictions are useful in managing water resources, preparing for droughts and their impacts, energy planning, and many other related sectors. In this study, a seasonal hydrologic ensemble prediction system is developed and evaluated over the eastern United States, with a focus on the Ohio River basin. The system uses a hydrologic model (i.e., the Variable Infiltration Capacity model) as the central element for producing ensemble predictions of soil moisture, snow, and streamflow with lead times up to six months. One unique feature of this system is in the method for generating ensemble atmospheric forcings for the forecast period. It merges seasonal climate forecasts from multiple climate models with observed climatology in a Bayesian framework, such that the uncertainties related to the atmospheric forcings can be better quantified while the signals from individual models are combined. Simultaneously, climate model forecasts are downscaled to an appropriate spatial scale for hydrologic predictions. When generating daily meteorological forcing, the system uses the rank structures of selected historical forcing records to ensure reasonable weather patterns in space and time. Seasonal hydrologic predictions are made with this system, using seasonal climate forecast from the NCEP Climate Forecast System (CFS), and from a combination of the NCEP CFS and seven climate models in the European Union’s Development of a European Multimodel Ensemble System for Seasonal-to-Interannual Prediction (CFS+DEMETER). Forecasts of these two types are made for the summer periods (May to October) of 1981–99 and are compared to forecasts produced with the traditional Ensemble Streamflow Prediction (ESP) approach used in operational seasonal streamflow predictions. The forecasts from this system for the summer of 1988 show very promising skill in precipitation, soil moisture, and streamflow over the Ohio River basin, especially the multimodel CFS+DEMETER forecast. The evaluation with all 19 summer forecasts shows that the multimodel CFS+DEMETER forecast is significantly better than the ESP forecast during the first two months of the forecasts. The advantage is marginal to moderate when only the CFS forecast is used. This study validates the approach of using seasonal climate predictions from dynamic climate models in hydrological predictions, and it also emphasizes the need for international collaborations to develop multimodel seasonal predictions.
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Matus, Sean A., Francina Dominguez, Daniel R. Gambill, and Heidi R. Howard. "Embracing Uncertainty: Using Probabilistic Weather Forecasts to Make Ensemble Hydraulic Predictions at Remote Low-Water Crossings." Journal of Hydrometeorology 21, no. 5 (May 2020): 953–69. http://dx.doi.org/10.1175/jhm-d-19-0238.1.
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AbstractLow-water crossings are structures designed to be overtopped during high river flows. These structures are usually constructed in remote locations, making timely emergency response difficult in case of flooding. In this work, five historical flooding events were hindcasted at a remote low-water crossing in central Texas. An ensemble of model-simulated precipitation forcing cascades uncertainty through hydrologic and hydraulic models. Each precipitation ensemble member corresponds to an independent model run, resulting in an ensemble 24-h streamflow forecast initialized at 0000 UTC. In addition to the hydrologic conditions, the forecast is expanded to predict river hydraulics, through flow velocity and depth. Analysis of the five hindcast events indicates that cascading probabilistic precipitation through hydrologic and hydraulic models adds robustness to river forecasts compared to deterministic methods. The approach provides a means to communicate the uncertainty of predictions through the ensemble spread. Analysis of deterministic hazard thresholds suggest that a hydraulic stability threshold, calculated as the multiplication of flow velocity and depth, is a useful alternative approach to NWS high-water categories for communicating hydrologic/hydraulic risk, as well as associated model uncertainty in the simplest manner possible.
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Han, Shasha, and Paulin Coulibaly. "Probabilistic Flood Forecasting Using Hydrologic Uncertainty Processor with Ensemble Weather Forecasts." Journal of Hydrometeorology 20, no. 7 (July 2019): 1379–98. http://dx.doi.org/10.1175/jhm-d-18-0251.1.
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Recent advances in the field of flood forecasting have shown increased interests in probabilistic forecasting as it provides not only the point forecast but also the assessment of associated uncertainty. Here, an investigation of a hydrologic uncertainty processor (HUP) as a postprocessor of ensemble forecasts to generate probabilistic flood forecasts is presented. The main purpose is to quantify dominant uncertainties and enhance flood forecast reliability. HUP is based on Bayes’s theorem and designed to capture hydrologic uncertainty. Ensemble forecasts are forced by ensemble weather forecasts from the Global Ensemble Prediction System (GEPS) that are inherently uncertain, and the input uncertainty propagates through the model chain and integrates with hydrologic uncertainty in HUP. The bias of GEPS was removed using multivariate bias correction, and several scenarios were developed by different combinations of GEPS with HUP. The performance of different forecast horizons for these scenarios was compared using multifaceted evaluation metrics. Results show that HUP is able to improve the performance for both short- and medium-range forecasts; the improvement is significant for short lead times and becomes less obvious with increasing lead time. Overall, the performances for short-range forecasts when using HUP are promising, and the most satisfactory result for the short range is obtained by applying bias correction to each ensemble member plus applying the HUP postprocessor.
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Liu, Xiaoli, and Paulin Coulibaly. "Downscaling Ensemble Weather Predictions for Improved Week-2 Hydrologic Forecasting." Journal of Hydrometeorology 12, no. 6 (December 1, 2011): 1564–80. http://dx.doi.org/10.1175/2011jhm1366.1.
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Abstract This study investigates the use of large-scale ensemble weather predictions provided by the National Centers for Environmental Prediction (NCEP) Global Forecast System [GFS; formerly known as Medium-Range Forecast (MRF)] for improving week-2 hydrologic forecasting. The ensemble weather predictor variables are used to downscale daily precipitation and temperature series at two meteorological stations in the Saguenay watershed in northeastern Canada. Three data-driven methods—namely, the statistical downscaling model (SDSM), the time-lagged feed-forward neural network (TLFN), and evolutionary polynomial regression (EPR)—are used as comparative downscaling models. The downscaled results of the best models are used as additional inputs in two hydrological models, namely Hydrologiska Byråns Vattenbalansavdelning (HBV2005) and a Bayesian neural network (BNN)-based hydrologic model, for up to 14-day-ahead reservoir inflow and river flow forecasting. The performance of the two hydrologic models is compared, the ultimate objective being to improve week-2 (7–14-day ahead) forecasts. To identify a suitable approach for using the ensemble weather data in the downscaling experiments, six scenarios are evaluated. It is found that the best approach to downscaling the ensemble weather predictions is to use the means of the predictor members derived from the two grid points closest to the local meteorological station of interest. The downscaling results show that all three models have a relatively good performance in downscaling daily temperature series, but the results are in general less accurate for daily precipitation. The TLFN and EPR models have quite close performance in most cases, and they both perform better than SDSM. The hydrologic forecasting results show that for both reservoir inflow and river flow, the HBV model has better performance when downscaled meteorological predictions are included, while there is no significant improvement for the BNN model. For the week-2 forecast, an improvement of about 18% on average is obtained for both streamflow and reservoir inflow forecasts. However, for the spring season where accurate peak flow forecast is of main concern, an improvement of about 26% on average is achieved. It is also shown that using only downscaled temperature in spring reservoir inflow forecasting, the improvements for week 2 range from 16% to 24%. Overall, the forecast results show that large-scale ensemble weather predictions can be effectively exploited through statistical downscaling tools for improved week-2 hydrologic forecasts. The forecast results also indicate that even imperfect medium-range (week 2) weather predictions can be very useful for producing significantly improved week-2 hydrologic forecasts.
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Shah, Reepal, Atul Kumar Sahai, and Vimal Mishra. "Short to sub-seasonal hydrologic forecast to manage water and agricultural resources in India." Hydrology and Earth System Sciences 21, no. 2 (February 2, 2017): 707–20. http://dx.doi.org/10.5194/hess-21-707-2017.
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Abstract. Water resources and agriculture are often affected by the weather anomalies in India resulting in disproportionate damage. While short to sub-seasonal prediction systems and forecast products are available, a skilful hydrologic forecast of runoff and root-zone soil moisture that can provide timely information has been lacking in India. Using precipitation and air temperature forecasts from the Climate Forecast System v2 (CFSv2), the Global Ensemble Forecast System (GEFSv2) and four products from the Indian Institute of Tropical Meteorology (IITM), here we show that the IITM ensemble mean (mean of all four products from the IITM) can be used operationally to provide a hydrologic forecast in India at a 7–45-day accumulation period. The IITM ensemble mean forecast was further improved using bias correction for precipitation and air temperature. Bias corrected precipitation forecast showed an improvement of 2.1 mm (on the all-India median mean absolute error – MAE), while all-India median bias corrected temperature forecast was improved by 2.1 °C for a 45-day accumulation period. Moreover, the Variable Infiltration Capacity (VIC) model simulated forecast of runoff and soil moisture successfully captured the observed anomalies during the severe drought years. The findings reported herein have strong implications for providing timely information that can help farmers and water managers in decision making in India.
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Saleh, Firas, Venkatsundar Ramaswamy, Nickitas Georgas, Alan F. Blumberg, and Julie Pullen. "A retrospective streamflow ensemble forecast for an extreme hydrologic event: a case study of Hurricane Irene and on the Hudson River basin." Hydrology and Earth System Sciences 20, no. 7 (July 8, 2016): 2649–67. http://dx.doi.org/10.5194/hess-20-2649-2016.
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Abstract. 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.
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Mo, Kingtse C., and Dennis P. Lettenmaier. "Hydrologic Prediction over the Conterminous United States Using the National Multi-Model Ensemble." Journal of Hydrometeorology 15, no. 4 (July 30, 2014): 1457–72. http://dx.doi.org/10.1175/jhm-d-13-0197.1.
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Abstract The authors analyzed the skill of monthly and seasonal soil moisture (SM) and runoff (RO) forecasts over the United States performed by driving the Variable Infiltration Capacity (VIC) hydrologic model with forcings derived from the National Multi-Model Ensemble hindcasts (NMME_VIC). The grand ensemble mean NMME_VIC forecasts were compared to ensemble streamflow prediction (ESP) forecasts derived from the VIC model forced by resampling of historical observations during the forecast period (ESP_VIC), using the same initial conditions as NMME_VIC. The forecast period is from 1982 to 2010, with the forecast initialized on 1 January, 1 April, 5 July, and 3 October. Overall, forecast skill is seasonally and regionally dependent. The authors found that 1) the skill of the grand ensemble mean NMME_VIC forecasts is comparable with that of the individual model that has the highest skill; 2) for all forecast initiation dates, the initial conditions play a dominant role in forecast skill at 1-month lead, and at longer lead times, forcings derived from NMME forecasts start to contribute to forecast skill; and 3) the initial conditions dominate contributions to skill for a dry climate regime that covers the western interior states for all seasons and the north-central part of the country for January. In this regime, the forecast skill for both methods is high even at 3-month lead. This regime has low mean precipitation and precipitation variations, and the influence of precipitation on SM and RO is weak. In contrast, a wet regime covers the region from the Gulf states to the Tennessee and Ohio Valleys for forecasts initialized in January and April, the Southwest monsoon region, the Southeast, and the East Coast in summer. In these dynamically active regions, where rainfall depends on the path of the moisture transport and atmospheric forcing, forecast skill is low. For this regime, the climate forecasts contribute to skill. Skillful precipitation forecasts after lead 1 have the potential to improve SM and RO forecast skill, but it was found that this mostly was not the case for the NMME models.
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Carr, Rachel Hogan, Burrell Montz, Kathryn Semmens, Keri Maxfield, Samantha Connolly, Peter Ahnert, Rob Shedd, and Jason Elliott. "Major Risks, Uncertain Outcomes: Making Ensemble Forecasts Work for Multiple Audiences." Weather and Forecasting 33, no. 5 (October 1, 2018): 1359–73. http://dx.doi.org/10.1175/waf-d-18-0018.1.
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Abstract When extreme river levels are possible in a community, effective communication of weather and hydrologic forecasts is critical to protecting 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. A new suite of products from the National Weather Service’s Hydrologic Ensemble Forecast System (HEFS) provides short- and long-range forecasts, ranging from 6 h to 1 yr, and shows uncertainty in hydrologic forecasts. To understand how various audiences use and interpret ensemble forecasts showing a range of hydrologic forecast possibilities, a research project was conducted using scenario-based focus groups and surveys with community residents, emergency managers, and water resource managers in West Virginia and Maryland. The research assessed the utility of the HEFS products, identified barriers to proper understanding of the products, and suggested modifications to product design that could improve the understandability and accessibility for a range of users. There was a difference between the residential users’ reactions to the HEFS compared to the emergency managers and water resource managers, with the public reacting less favorably to all versions. The emergency managers preferred the revised HEFS products but had suggestions for additional changes, which were incorporated. Features such as interactive text boxes and forecaster’s notes further enhanced the utility and understandability of the products.
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Verbunt, M., A. Walser, J. Gurtz, A. Montani, and C. Schär. "Probabilistic Flood Forecasting with a Limited-Area Ensemble Prediction System: Selected Case Studies." Journal of Hydrometeorology 8, no. 4 (August 1, 2007): 897–909. http://dx.doi.org/10.1175/jhm594.1.
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Abstract A high-resolution atmospheric ensemble forecasting system is coupled to a hydrologic model to investigate probabilistic runoff forecasts for the alpine tributaries of the Rhine River basin (34 550 km2). Five-day ensemble forecasts consisting of 51 members, generated with the global ensemble prediction system (EPS) of the European Centre for Medium-Range Weather Forecasts (ECMWF), are downscaled with the limited-area model Lokal Modell (LM). The resulting limited-area ensemble prediction system (LEPS) uses a horizontal grid spacing of 10 km and provides one-hourly output for driving the distributed hydrologic model Precipitation–Runoff–Evapotranspiration–Hydrotope (PREVAH) hydrologic response unit (HRU) with a resolution of 500 × 500 m2 and a time step of 1 h. The hydrologic model component is calibrated for the river catchments considered, which are characterized by highly complex topography, for the period 1997–98 using surface observations, and validated for 1999–2002. This study explores the feasibility of atmospheric ensemble predictions for runoff forecasting, in comparison with deterministic atmospheric forcing. Detailed analysis is presented for two case studies: the spring 1999 flood event affecting central Europe due to a combination of snowmelt and heavy precipitation, and the November 2002 flood in the Alpine Rhine catchment. For both cases, the deterministic simulations yield forecast failures, while the coupled atmospheric–hydrologic EPS provides appropriate probabilistic forecast guidance with early indications for extreme floods. It is further shown that probabilistic runoff forecasts using a subsample of EPS members, selected by a cluster analysis, properly represent the forecasts using all 51 EPS members, while forecasts from randomly chosen subsamples reveal a reduced spread compared to the representative members. Additional analyses show that the representation of horizontal advection of precipitation in the atmospheric model may be crucial for flood forecasts in alpine catchments.
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Thielen, J., J. Bartholmes, M. H. Ramos, and A. de Roo. "The European Flood Alert System – Part 1: Concept and development." Hydrology and Earth System Sciences 13, no. 2 (February 5, 2009): 125–40. http://dx.doi.org/10.5194/hess-13-125-2009.
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Abstract. This paper presents the development of the European Flood Alert System (EFAS), which aims at increasing preparedness for floods in trans-national European river basins by providing local water authorities with medium-range and probabilistic flood forecasting information 3 to 10 days in advance. The EFAS research project started in 2003 with the development of a prototype at the European Commission Joint Research Centre (JRC), in close collaboration with the national hydrological and meteorological services. The prototype covers the whole of Europe on a 5 km grid. In parallel, different high-resolution data sets have been collected for the Elbe and Danube river basins, allowing the potential of the system under optimum conditions and on a higher resolution to be assessed. Flood warning lead-times of 3–10 days are achieved through the incorporation of medium-range weather forecasts from the German Weather Service (DWD) and the European Centre for Medium-Range Weather Forecasts (ECMWF), comprising a full set of 51 probabilistic forecasts from the Ensemble Prediction System (EPS) provided by ECMWF. The ensemble of different hydrographs is analysed and combined to produce early flood warning information, which is disseminated to the hydrological services that have agreed to participate in the development of the system. In Part 1 of this paper, the scientific approach adopted in the development of the system is presented. The rational of the project, the system�s set-up, its underlying components, basic principles and products are described. In Part 2, results of a detailed statistical analysis of the performance of the system are shown, with regard to both probabilistic and deterministic forecasts.
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Thirel, G., E. Martin, J. F. Mahfouf, S. Massart, S. Ricci, F. Regimbeau, and F. Habets. "A past discharge assimilation system for ensemble streamflow forecasts over France – Part 2: Impact on the ensemble streamflow forecasts." Hydrology and Earth System Sciences Discussions 7, no. 2 (April 22, 2010): 2455–97. http://dx.doi.org/10.5194/hessd-7-2455-2010.
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Abstract. The use of ensemble streamflow forecasts is developing in the international flood forecasting services. Such systems can provide more accurate forecasts and useful information about the uncertainty of the forecasts, thus improving the assessment of risks. Nevertheless, these systems, like all hydrological forecasts, suffer from errors on initialization or on meteorological data, which lead to hydrological prediction errors. This article, which is the second part of a 2-part article, concerns the impacts of initial states, improved by a streamflow assimilation system, on an ensemble streamflow prediction system over France. An assimilation system was implemented to improve the streamflow analysis of the SAFRAN-ISBA-MODCOU (SIM) hydro-meteorological suite, which initializes the ensemble streamflow forecasts at Météo-France. This assimilation system, using the Best Linear Unbiased Estimator (BLUE) and modifying the initial soil moisture states, showed an improvement of the streamflow analysis with low soil moisture increments. The final states of this suite were used to initialize the ensemble streamflow forecasts of Météo-France, which are based on the SIM model and use the European Centre for Medium-range Weather Forecasts (ECMWF) 10-day Ensemble Prediction System (EPS). Two different configurations of the assimilation system were used in this study: the first with the classical SIM model and the second using improved soil physics in ISBA. The effects of the assimilation system on the ensemble streamflow forecasts were assessed for these two configurations, and a comparison was made with the original (i.e. without data assimilation and without the improved physics) ensemble streamflow forecasts. It is shown that the assimilation system improved most of the statistical scores usually computed for the validation of ensemble predictions (RMSE, Brier Skill Score and its decomposition, Ranked Probability Skill Score, False Alarm Rate, etc.), especially for the first few days of the time range. The assimilation was slightly more efficient for small basins than for large ones.