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Real-time operational hydrological forecasts are needed for several reasons, including hydroelectric power generation, water resource planning and warning of possible waterrelated disasters. There are several modeling approaches to real-time hydrological forecasts, each having strengths and weaknesses. In an attempt to assess the costs and benefits of one model approach as compared to another, we compared the performance of real-time forecasts produced by a statistically based model with forecasts produced by an explicit distributed hydrological model for both long and short-term forecasting.

The approach employed for the statistical forecasts used a type of constructed analogs approach with nonparametric resampling. The explicit model being used in the evaluation was the Distributed Hydrology Soil Vegetation Model (DHSVM). DHSVM is a spatially distributed hydrological model that explicitly represents the effects of diverse topography and heterogeneous subsurface conditions on the downslope redistribution of subsurface moisture that provides a dynamic representation of the spatial distribution of soil moisture, snow cover, evapotranspiration, and runoff.