Calibrating on Downscaled Satellite Soil Moisture Data Can Improve Watershed Model Performance in Predicting Soil Moisture Variability

Abstract. Watershed streamflow is often the focus of hydrological model calibration and evaluation, despite other potential objectives, including water quality management, flood protection, and 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 the performance of downscaled and bias corrected soil moisture calibrated models against streamflow calibrated models both under single and multi-objective scenarios on field scale soil moisture estimation performance. Downscaled satellite soil moisture data and streamflow data are used to calibrate a Soil and Water Assessment Tool – Variable Source Area model initialized using topographic index classes to create hydrologic response units. In-situ soil moisture measurements at 25 locations across a 4-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 performance in estimating streamflow. The multi-objective calibration using streamflow and satellite soil moisture improved overall model performance both in estimating streamflow and soil moisture. A three-class topographic index hydrologic response unit definition allowed for adequate representation of saturation excess runoff process. Downscaling enabled calibration in a small 14 km² watershed using coarse satellite soil moisture data.