Assessing short to medium range ensemble streamflow forecast approaches in small to medium scale watersheds across CONUS

As part of the Hydrologic Ensemble Forecast Service, the US National Weather Service River Forecasting Centers have implemented short to medium range ensemble streamflow forecasts. Hydrologic models are forced with meteorological forecast ensembles derived using a downscaling and calibration technique, MEFP, that leverages correlations at multiple temporal scales between large scale GEFS forecast ensemble mean and local scale observed precipitation and temperature. Strengths of MEFP include its use of multi-decade hindcast for calibration of local scale forecasts and production of verification information, but possible weaknesses include the use of precipitation and temperature ensemble mean information only, which requires the statistical synthesis of ensemble members. We explore whether using a larger set of atmospheric predictors and full ensemble members from the GEFS can lead to greater meteorological and hydrological predictability. Using 30+ year streamflow hindcasts, we evaluate 1-15 day streamflow predictions using the Snow-17/Sacramento hydrologic modeling approach in small to medium-sized watersheds across CONUS. We compare the MEFP approach and performance with regressive and analog-based statistical downscaling and calibration methods that rely on a range of atmospheric predictors to produce watershed-scale ensemble forecasts. This presentation describes the strengths and weaknesses of the two approaches.