The Assimilation of Remote Sensing-Derived Soil Moisture Data into a Hydrological Model for the Mahanadi Basin, India

Accurate knowledge of the spatio-temporal variation in soil moisture provides insight into larger-scale hydrological processes and can, therefore, help in improving hydrological predictions. The strength of remote sensing for mapping surface soil moisture is well proven. In addition, data assimilation offers the opportunity to combine the advantages of modelling with those of remote sensing data to achieve higher accuracy and continuous improvement in hydrological forecasts. In this study, Advanced Microwave Scanning Radiometer for Earth observation science soil moisture product was assimilated into Variable Infiltration Capacity (VIC) hydrological model using Kalman filter data assimilation technique. Further, the updated multilayer spatio-temporal soil moisture distributions across the Mahanadi Basin, India, were simulated using the hydrological model. The VIC model was set up and parameterized using field-observed and remote sensing-derived data. Based on the sensitivity analysis of the model, the ‘four-parameter’ (Tmax, Tmin, Prec, and WS) meteorological forcing scenario was selected as the operational scenario. The output fluxes obtained from VIC were routed to simulate discharge at five stations for the calibration and validation of the model. With R2 and model efficiency values close to 0.95 and 0.99, respectively, the model was proven to be suitable for simulating the hydrological responses of the basin. Soil moisture was assimilated in the top soil layer of the model using the Kalman filter approach, and the multilayer soil moisture regime was generated using the modelling approach. The validation of soil moisture (assimilated) products proves that these products are better than remote sensing and traditionally modelled soil moisture products, in both spatial and temporal domains in terms of availability and accuracy.