Streamflow forecasting is critical to water resources management in the western United States. Yet, despite the passage of almost 50 years since the development of the first computerized hydrologic simulation models and over 30 years since the development of hydrologic ensemble forecast methods, the prevalent method used for forecasting seasonal streamflow in the western United States remains the regression of spring and summer streamflow volume on spring snowpack and/or the previous winter's accumulated precipitation. A recent retrospective analysis have shown that the skill of the regression-based forecasts have not improved in the last 40 years, despite large investments in science and technology related to the monitoring and assessment of the land surface and in climate forecasting. We describe an experimental streamflow forecast system for the western United States that applies a modern macroscale land surface model (akin to those now used in numerical weather prediction and climate models) to capture hydrologic states (soil moisture and snow) at the time of forecast, incorporates data assimilation methods to improve estimates of initial state, and uses a range of climate prediction ensembles to produce ensemble forecasts of streamflow and associated hydrologic states for lead times of up to one year. The forecast system is intended to be a real-time test bed for evaluating new seasonal streamflow forecast methods. Experience with the forecast system is illustrated using results from the 2004/05 forecast season, in which an evolving drought in the Pacific Northwest diverged strikingly from extreme snow accumulations to the south. We also discuss how the forecast system relates to ongoing changes in seasonal streamflow forecast methods in the two U.S. operational agencies that have major responsibility for seasonal streamflow forecasts in the western United States.
Department of Civil and Environmental Engineering, University of Washington, Seattle, Washington