Calibration using downscaled and bias-corrected satellite soil-moisture data can improve watershed model representation of soil-moisture variability

Watershed streamflow is often the focus of hydrological model calibration and evaluation, despite other potential objectives, including water quality management, flood protection, or agricultural management. When hydrological models are calibrated on streamflow, intermediate processes such as those affecting soil-moisture are not necessarily well represented. This research evaluated whether calibration using downscaled and bias-corrected satellite soil-moisture improves prediction of field-scale soil-moisture relative to conventional streamflow-based calibration. In this work, downscaled satellite soil-moisture and streamflow data are used to calibrate a soil and water assessment tool – variable source area model initialized using a terrain informed process to create hydrologic response units. In-situ soil-moisture measurements at 25 locations across a 4.2-ha mixed-grass pasture located in southwestern Virginia were used to estimate field-scale average soil-moisture variability for model evaluation. Leveraging downscaled satellite soil-moisture data substantially improved estimation of temporal soil-moisture variability without affecting the model streamflow performance. The multi-objective calibration using streamflow and satellite soil-moisture improved soil-moisture performance while maintaining streamflow performance comparable to streamflow-only calibration. These results demonstrate the potential for satellite soil-moisture–informed calibration to improve internal hydrologic state estimation in small, saturation excess watersheds. Furthermore, these results highlight the importance of coupling statistical performance gains with evaluation of hydrologic realism when extending such approaches to broader modeling applications.