A technique for incorporating large‐scale climate information in basin‐scale ensemble streamflow forecasts

Abstract

Water managers throughout the western United States depend on seasonal forecasts to assist with operations and planning. In this study, we develop a seasonal forecasting model to aid water resources decision making in the Truckee‐Carson River System. We analyze large‐scale climate information that has a direct impact on our basin of interest to develop predictors to spring runoff. The predictors are snow water equivalent (SWE) and 500 mbar geopotential height and sea surface temperature (SST) “indices” developed in this study. We use local regression methods to provide ensemble (probabilistic) forecasts. Results show that the incorporation of climate information, particularly the 500 mbar geopotential height index, improves the skills of forecasts at longer lead times when compared with forecasts based on snowpack information alone. The technique is general and could be used to incorporate large‐scale climate information into ensemble streamflow forecasts for other river basins.