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Journal articles on the topic 'Hydrologic Ensemble Forecast Service'

Author: Grafiati

Published: 6 September 2023

Last updated: 31 July 2025

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1

Hapuarachchi, Hapu Arachchige Prasantha, Mohammed Abdul Bari, Aynul Kabir, et al. "Development of a national 7-day ensemble streamflow forecasting service for Australia." Hydrology and Earth System Sciences 26, no. 18 (2022): 4801–21. http://dx.doi.org/10.5194/hess-26-4801-2022.

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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 p

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Bari, Mohammed Abdul, Mohammad Mahadi Hasan, Gnanathikkam Emmanual Amirthanathan, et al. "Performance Evaluation of a National Seven-Day Ensemble Streamflow Forecast Service for Australia." Water 16, no. 10 (2024): 1438. http://dx.doi.org/10.3390/w16101438.

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The Australian Bureau of Meteorology offers a national operational 7-day ensemble streamflow forecast service covering regions of high environmental, economic, and social significance. This semi-automated service generates streamflow forecasts every morning and is seamlessly integrated into the Bureau’s Hydrologic Forecasting System (HyFS). Ensemble rainfall forecasts, European Centre for Medium-Range Weather Forecasts (ECMWF), and Poor Man’s Ensemble (PME), available in the Numerical Weather Prediction (NWP) suite, are used to generate these streamflow forecasts. The NWP rainfall undergoes pr

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3

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 (2015): 1895–912. http://dx.doi.org/10.1175/bams-d-14-00003.1.

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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 devel

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4

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 (2010): 642–65. http://dx.doi.org/10.1175/2009jhm1188.1.

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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

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Schaake, J., J. Demargne, R. Hartman, et al. "Precipitation and temperature ensemble forecasts from single-value forecasts." Hydrology and Earth System Sciences Discussions 4, no. 2 (2007): 655–717. http://dx.doi.org/10.5194/hessd-4-655-2007.

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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 ob

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6

Kim, Sunghee, Hossein Sadeghi, Reza Ahmad Limon, et al. "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 (2018): 1467–83. http://dx.doi.org/10.1175/jhm-d-18-0027.1.

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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. Th

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Demargne, Julie, Limin Wu, Satish K. Regonda, et al. "The Science of NOAA's Operational Hydrologic Ensemble Forecast Service." Bulletin of the American Meteorological Society 95, no. 1 (2014): 79–98. http://dx.doi.org/10.1175/bams-d-12-00081.1.

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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 (2015): 1550008. http://dx.doi.org/10.1142/s2345737615500086.

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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 a

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9

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 (2020): 3505. http://dx.doi.org/10.3390/w12123505.

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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 Centr

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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 (2011): 3367–82. http://dx.doi.org/10.5194/hess-15-3367-2011.

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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 metho

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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 (2011): 3085–131. http://dx.doi.org/10.5194/hessd-8-3085-2011.

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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 metho

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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 (2004): 1076–90. http://dx.doi.org/10.1175/jhm-381.1.

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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 clima

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Wanders, Niko, Stephan Thober, Rohini Kumar, et al. "Development and Evaluation of a Pan-European Multimodel Seasonal Hydrological Forecasting System." Journal of Hydrometeorology 20, no. 1 (2019): 99–115. http://dx.doi.org/10.1175/jhm-d-18-0040.1.

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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 Demo

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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 (2020): 265–85. http://dx.doi.org/10.1175/jhm-d-19-0164.1.

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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. Streamf

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Velázquez, J. A., T. Petit, A. Lavoie, et al. "An evaluation of the canadian global meteorological ensemble prediction system for short-term hydrological forecasting." Hydrology and Earth System Sciences Discussions 6, no. 4 (2009): 4891–917. http://dx.doi.org/10.5194/hessd-6-4891-2009.

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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 waters

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Velázquez, J. A., T. Petit, A. Lavoie, et al. "An evaluation of the Canadian global meteorological ensemble prediction system for short-term hydrological forecasting." Hydrology and Earth System Sciences 13, no. 11 (2009): 2221–31. http://dx.doi.org/10.5194/hess-13-2221-2009.

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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 waters

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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 (2013): 1587–604. http://dx.doi.org/10.1175/jhm-d-12-0136.1.

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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

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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 (2021): 1189–209. http://dx.doi.org/10.5194/hess-25-1189-2021.

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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

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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 (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 Chang

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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 (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 f

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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, et al. "Challenges of Operational River Forecasting." Journal of Hydrometeorology 15, no. 4 (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) a

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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 (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

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Khan, Zaved, Julien Lerat, Katayoon Bahramian, Elisabeth Vogel, Andrew J. Frost, and Justin Robinson. "Assessment of Flood Risk Predictions Based on Continental-Scale Hydrological Forecast." Water 17, no. 5 (2025): 625. https://doi.org/10.3390/w17050625.

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The Australian Bureau of Meteorology provides flood forecasting and warning services across Australia for most major rivers in Australia, in cooperation with other government, local agencies and emergency services. As part of this service, the Bureau issues a flood watch product to provide early advice on a developing situation that may lead to flooding up to 4 days prior to an event. This service is based on (a) an ensemble of available Numerical Weather Prediction (NWP) rainfall forecasts, (b) antecedent soil moisture, stream and dam conditions, (c) hydrological forecasts using event-based m

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Donegan, Seán, Conor Murphy, Shaun Harrigan, 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 (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

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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 (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 con

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Voces-Aboy, Jose, Inmaculada Abia-Llera, Eroteida Sánchez-García, et al. "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

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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 (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 co

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Moulds, Simon, Louise Slater, Louise Arnal, and Andrew W. Wood. "Skilful probabilistic predictions of UK flood risk months ahead using a large-sample machine learning model trained on multimodel ensemble climate forecasts." Hydrology and Earth System Sciences 29, no. 11 (2025): 2393–406. https://doi.org/10.5194/hess-29-2393-2025.

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Abstract. Seasonal streamflow forecasts are an important component of flood risk management. Hybrid forecasting methods that predict seasonal streamflow using machine learning (ML) models driven by climate model outputs are currently underexplored, yet they have some important advantages over traditional approaches using hydrological models. Here we develop a hybrid subseasonal to seasonal (S2S) streamflow forecasting system to predict the monthly maximum daily streamflow up to 4 months ahead. We train a quantile regression forest model on dynamical precipitation and temperature forecasts from

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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 (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 statisti

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Sparrow, Sarah, Andrew Bowery, Glenn D. Carver, et al. "OpenIFS@home version 1: a citizen science project for ensemble weather and climate forecasting." Geoscientific Model Development 14, no. 6 (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 res

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Liang, Xin-Zhong, Min Xu, Xing Yuan, et al. "Regional Climate–Weather Research and Forecasting Model." Bulletin of the American Meteorological Society 93, no. 9 (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, C

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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 (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 catc

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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 (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 contri

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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 inf

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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 (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 h

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Thirel, G., E. Martin, J. F. Mahfouf, et al. "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 (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

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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 objective

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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

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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 (2008): 866–84. http://dx.doi.org/10.1175/2008jhm980.1.

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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

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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 (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

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Samaniego, Luis, Stephan Thober, Niko Wanders, 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 (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

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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 (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 forecas

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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 (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 evolut

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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 (2011): 281–98. http://dx.doi.org/10.1016/j.jhydrol.2011.01.013.

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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 (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

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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 (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)

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Carr, Rachel Hogan, Burrell Montz, Kathryn Semmens, et al. "Major Risks, Uncertain Outcomes: Making Ensemble Forecasts Work for Multiple Audiences." Weather and Forecasting 33, no. 5 (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

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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 (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 201

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