Abstract
AbstractImproving river flow forecasts for longer lead times by incorporating numerical weather predictions (NWP) into streamflow forecasting systems has attracted hydrologists in recent years. The process turns considerably complex and resource hungry when ensembles of NWP forecasts instead of any single NWP output are used to feed the flow forecasting models in order to capture the uncertainties in hydrological forecasting. This paper presents, for the first time, a comparison of three statistical stratification techniques for simplifying the input precipitation ensemble forecasts driving a river flow forecasting system. A data-driven flow forecasting model developed for the Waikato River in New Zealand using genetic expression programming (GEP) was forced by the 10-days-ahead ensemble precipitation forecasts issued by three meteorological .centers in different parts of the world including the United Kingdom, Canada, and China. The three precipitation ensembles, comprising 51, 21, and 15 members respect...
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