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Satellite-based precipitation and soil moisture products are often associated with significant uncertainties, rendering them less reliable for hydrological applications. The present study proposes a dual correction scheme employing satellite-based soil moisture estimates to update satellite-based rainfall and modelled soil moisture states. First, the artificial neural network (ANN) was utilised to correct TRMM 3B42RT rain rate estimates using ASCAT soil moisture observations. Subsequently, the ASCAT surface soil moisture observations were scaled to root-zone level using the Soil Moisture Analytical Relationship and assimilated into the Soil and Water Assessment Tool model through the ensemble Kalman filter (EnKF) technique. The correction to the 3B42RT rainfall was evaluated using observed rainfall data, whereas the modelled streamflow was assessed under three correction schemes: sole rainfall correction (forcing correction), sole soil moisture assimilation (state correction), and combined forcing and state correction (dual correction). The results demonstrated that the artificial neural network-based rainfall correction technique improved the 3B42RT rainfall, with an average reduction in RMSE of 7.5 mm and a 10% improvement in NSE. The streamflow evaluation revealed that the forcing correction primarily enhanced the quick-flow component of simulated streamflow, with an assimilation efficiency of 17.3%, whereas the state correction scheme improved the base-flow component (assimilation efficiency of 21.9%). The dual correction combined the benefits of both schemes to achieve an assimilation efficiency of 28.9%. The forecasting performance indicated that the dual correction strategy provided maximum improvement of up to two lead days in the selected catchment. Overall, the dual correction strategy based on ANN and the EnKF promotes the use of satellite-based rainfall and soil moisture data for hydrological applications.
Ramesh et al. (Wed,) studied this question.