Abstract ID: 3.9077 | Accepted as Talk | Talk | TBA | TBA

Field-based studies are limited in Himalaya-Karakoram (HK); therefore, remote sensing and glaciohydrological modeling provide alternative solutions to investigate runoff evolution under changing climate conditions. Due to limited in-situ runoff data in HK, glaciohydrological models are often calibrated using high-resolution remote sensing data. In this line, present study introduces satellite-based Sentinel-1 Synthetic Aperture Radar (SAR) wet snow maps, along with available geodetic mass balance estimates, for calibration of glaciohydrological model SPHY (Spatial Processes in Hydrology) at glacier catchment-scale over 2000-2023 in HK. The selected calibrated model parameters are validated against in-situ runoff data to test the robustness of satellite-based calibration for Chhota Shigri Glacier (CSG), Dokriani Bamak Glacier (DBG), and Gangotri Glacier System (GGS) catchments in HK. The SPHY modeled and in-situ catchment-wide runoff estimates show good agreement with each other. The Sentinel-1 SAR-derived wet snow % area shows strong spatial and temporal variability from 2015 to2023. The mean annual runoff is 1.79 ± 0.15 m3s-1, 1.63 ± 0.09 m3s-1 and 39.40 ± 3.15 m3s-1 over 2000-2023 for CSG, DBG and GGS catchments, respectively with maximum annual runoff in 2021/2022, mainly due to heatwaves in early spring/summer 2022. Snow runoff is highest in CSG (61%) and GGS (49%), while rainfall-runoff is highest in DBG (42%). Satellite-based glaciohydrological model calibration offers a unique opportunity to improve runoff estimates for glacierized catchments in data-sparse regions. Applying present study to glacierized catchments lacking in-situ runoff data will strengthen our past, present, and future glaciohydrological understanding of glacierized regions such as HK and Andes.