Multi-criteria hydrological modeling using snow and soil moisture satellite images

In recent years, advances in snow and soil moisture satellite imagery have facilitated the development of cloud-free snow cover and satellite soil moisture data sets with higher spatial and temporal resolutions. In this study, we first analyzed SMAP soil moisture, MODIS snow cover, and cloud cover data for Turkey and its regions. Subsequently, we tested SMAP soil moisture and cloud-free MODIS snow cover data using a weighting method for multiple calibrations of an HBV-type hydrological model in two snow-dominated basins. Particularly in mountainous catchments, incorporating both soil moisture and snow remote sensing products alongside flow modeling significantly enhances model performance by simultaneously considering multiple observations. Including soil moisture data improves soil moisture simulations, while including snow data improves snow simulations. Furthermore, the inclusion of both soil moisture and snow cover data improves model performance to the same extent as when they are included individually, with minimal impact on flow performance. The calibrated snow-related parameters are significantly influenced by snow data, while soil moisture-related parameters are affected by soil moisture data. These findings demonstrate the critical role of satellite data in hydrological modeling and their substantial contribution to improving model accuracy.