Introducing Glaciohydrological Model Calibration Using Sentinel‐1 SAR Wet Snow Maps in the Himalaya‐Karakoram

Abstract 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. This study introduces the calibration of the glaciohydrological model Spatial Processes in Hydrology (SPHY), at glacier catchment‐scale over 2000–2023 using satellite‐based Sentinel‐1 Synthetic Aperture Radar (SAR) wet snow maps, along with available geodetic mass balance estimates in the HK region. 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. The Sentinel‐1 SAR‐derived wet snow percentage area shows strong spatial and temporal variability from 2015 to 2023. The mean annual runoff is 1.79 ± 0.15 m 3 s −1 , 1.63 ± 0.09 m 3 s −1 and 39.40 ± 3.15 m 3 s −1 over 2000–2023 for CSG, DBG and GGS catchments, respectively. Maximum annual runoff occurred in 2021/2022, mainly due to heatwaves in early spring/summer 2022. Snowmelt runoff is highest in CSG (61%) and GGS (49%), while rainfall‐runoff dominates 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 regions such as HK and Andes.