Dual Forcing and State Correction via Soil Moisture Assimilation for Improved Rainfall–Runoff Modeling

Abstract

ncertainties in precipitation forcing and prestorm soil moisture states represent important sources of error in streamflow predictions obtained from a hydrologic model. An earlier synthetic twin experiment has demonstrated that error in both antecedent soil moisture states and rainfall forcing can be filtered by assimilating remotely sensed surface soil moisture retrievals. This opens up the possibility of applying satellite soil moisture estimates to address both key sources of error in hydrologic model predictions. Here, in an attempt to extend the synthetic analysis into a real-data environment, two satellite-based surface soil moisture products?based on both passive and active microwave remote sensing?are assimilated using the same dual forcing/state correction approach. A bias correction scheme is implemented to remove bias in background forecasts caused by synthetic perturbations in the ensemble filtering routines, and a triple collocation?based technique is adopted to derive rescaled observations and observation error variances. Results are largely in agreement with the earlier synthetic analysis. That is, the correction of satellite-derived rainfall forcing is able to improve streamflow prediction, especially during relatively high-flow periods. In contrast, prestorm soil moisture state correction is more efficient in improving the base flow component of streamflow. When rainfall and soil moisture state corrections are combined, the RMSE of both the high- and low-flow components of streamflow can be reduced by ~40% and ~30%, respectively. However, an unresolved issue is that soil moisture data assimilation also leads to underprediction of very intense precipitation/high-flow events.