Hydrology and Earth System Sciences www.hydrol-earth-syst-sci.net 1027-5606 1607-7938 11 2 2007 10.5194/hess-11-939-2007 http://www.hydrol-earth-syst-sci.net/11/939/2007/ http://www.hydrol-earth-syst-sci.net/11/939/2007/hess-11-939-2007.html http://www.hydrol-earth-syst-sci.net/11/939/2007/hess-11-939-2007.pdf 939 950 2007-02-27 Evaluation of bias-correction methods for ensemble streamflow volume forecasts T. Hashino A. A. Bradley allen-bradley@uiowa.edu S. S. Schwartz University of Wisconsin, Department of Atmospheric and Ocean Sciences, Madison, WI, USA The University of Iowa, IIHR – Hydroscience & Engineering, Iowa City, IA, USA Center for Urban Environmental Research and Education, UMBC, Baltimore, MD, USA Ensemble prediction systems are used operationally to make probabilistic streamflow forecasts for seasonal time scales. However, hydrological models used for ensemble streamflow prediction often have simulation biases that degrade forecast quality and limit the operational usefulness of the forecasts. This study evaluates three bias-correction methods for ensemble streamflow volume forecasts. All three adjust the ensemble traces using a transformation derived with simulated and observed flows from a historical simulation. The quality of probabilistic forecasts issued when using the three bias-correction methods is evaluated using a distributions-oriented verification approach. Comparisons are made of retrospective forecasts of monthly flow volumes for a north-central United States basin (Des Moines River, Iowa), issued sequentially for each month over a 48-year record. The results show that all three bias-correction methods significantly improve forecast quality by eliminating unconditional biases and enhancing the potential skill. 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