Assimilation of blended in situ-satellite snow water equivalent into the National Water Model for improving hydrologic simulation in two US river basins.

This study investigates the potential of assimilating a 1/8° blended in situ-satellite snow water equivalent (SWE) product for improving snow and streamflow predictions of the National Water Model (NWM). The blended product is assimilated into the NWM via a three-dimensional variational (3DVAR) scheme and a direct insertion (DI) scheme, with a daily (1d) and a every 5 days (5d) assimilation frequencies. The experiments are for the Upper Colorado River Basin (UCRB) and Susquehanna River Basin (SRB), which feature seasonal and ephemeral snow covers, respectively. Results indicate that 3DVAR with a 5d assimilation frequency generally outperforms the other scenarios. The assimilation of the blended SWE product mitigates the underestimation of SWE evident in the open-loop simulations for both basins and its impacts are more pronounced for UCRB than for SRB since snowfall is the main source of precipitation in the former. Assimilation leads to improved streamflow over a majority of SRB subbasins, but over a minority of UCRB subbasins. The degradations in streamflow for UCRB subbasins are mainly caused by the overestimated SWE. In addition, the open-loop simulation often produces an earlier streamflow peak in UCRB, and this error is mitigated to a limited extent by assimilation. These findings in aggregate suggest that the efficacy of snow assimilation is strongly dependent upon the types of snowpack and differential assimilation methods and frequencies.