Water supply forecasting in the Upper Colorado River Basin using assimilation of ground and satellite snow observations in a distributed hydrologic modeling framework

Abstract:

Runoff from the Upper Colorado River Basin (UCRB) is an important water resource in the western US. The primary objective of this study was to evaluate the impact of snow data assimilation in a distributed hydrologic model on water supply volume (WSV) forecasting error and skill in headwater catchments of the UCRB by building a framework with the Localized Ensemble transform Kalman filter (LETKF) to update modeled snow water equivalent (SWE) states in the Hydrology Laboratory-Research Distributed Hydrologic Model (HL-RDHM) with SNOTEL snow water equivalent observations and gap-filled MODIS Snow Covered-Area and Grain size data (STC-SSP MODSCAG). The data assimilation approach was evaluated by assessing the forecast skill in April-July ensemble streamflow prediction (ESP) reforecasts over a 20-year period (1990-2010) for 23 catchments in the UCRB. Overall, the water supply forecast skill was improved in the majority of pilot basins. This work demonstrates the capacity for spatially-distributed and point snow products to be used to objectively update model states in an operational forecasting setting.