Use of remotely sensed soil moisture data in hydrological modelling

Abstract

Accurate hydrological data is crucial for understanding streamflow changes and predicting extreme events in river basins. By utilising remotely sensed soil moisture products, the estimation of soil moisture distribution at the basin scale in hydrological modelling becomes feasible, addressing practical challenges. This study investigates the integration of remotely sensed soil moisture estimates to enhance the accuracy of hydrological model simulations in the upper Peradeniya catchment, Sri Lanka. A hydrological model (ABCD model) was developed for the catchment area. Soil moisture data from NASA's soil moisture active passive (SMAP L4) were integrated with the model's estimations to improve river flow simulations. The study reveals that the integration of SMAP L4 did not significantly enhance accuracy. However, notable differences in calibrated parameters emerged, highlighting the importance of incorporating multiple inputs for calibration. These findings demonstrate the potential of remotely sensed soil moisture in enhancing hydrological model simulations. Incorporating remotely sensed soil moisture can contribute to more reliable predictions and management of water resources in river basins, aiding in sustainable development and climate change adaptation.