Dual forcing and state correction via soil moisture assimilation for improved rainfall-runoff modeling

Abstract

Uncertainties 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..