Abstract:
The accuracy of terrestrial water storage (TWS) estimates is limited by a lack of observations and by inherent uncertainties in the model simulation. Although the Gravity and Recovery Climate Experiment (GRACE) has revolutionized large-scale remote sensing of the Earth’s terrestrial hydrologic cycle, its coarse-scale (in space and time), vertically-integrated measure of TWS limits the applicability to smaller scale hydrologic applications. In order to enhance modelbased estimates of TWS and its constituent components while effectively adding resolution (in space and time) to the coarse-scale TWS retrievals, a multi-variate, multi-sensor data assimilation framework is presented here that simultaneously assimilates gravimetric retrievals of TWS in conjunction with passive microwave (PMW) brightness temperature (Tb) observations over snow-covered terrain. The framework uses the NASA Catchment Land Surface Model (Catchment) and an ensemble Kalman filter (EnKF). A synthetic case study is presented for the Volga River Basin in Russia that compares model results with and without assimilation against synthetic observations of hydrologic states and fluxes. The AMSRE/AMSR-2-only assimilation improved snow water equivalent (SWE) estimates. The GRACEonly assimilation improved TWS estimation but not always produced accurate estimates of SWE. The dual assimilation typically led to more accurate TWS and SWE estimates. The results demonstrate that GRACE TWS and AMSE-E can be jointly assimilated to produce improved TWS component estimate.