Hydrological Contributions of Snow and Glacier Melt from the Gangotri Glacier System and Their Climatic Controls Since 1980

Abstract

A limited number of glaciohydrological studies discuss the climatic drivers controlling the long-term discharge in the Hindu Kush Himalaya (HKH). The present study reconstructs the long-term discharge series of the Gangotri Glacier System (GGS) by applying the high-resolution glaciohydrological model Spatial Processes in Hydrology (SPHY), forced with the Indian Monsoon Data Assimilation and Analysis (IMDAA) reanalysis data for the period 1980–2020. The SPHY model was calibrated using available in-situ discharge measurements (2000–2003) and geodetic mass balance estimates (2000–2019). The calibrated model parameters were validated against improved MODIS snow cover area data (2002–2020). Maximum GGS discharge occurs during the summer months, with a peak in July (129 m3/s). The mean annual GGS discharge was estimated as 28 ± 1.9 m3/s, with the major contribution from snowmelt (64%) followed by glacier melt (21%), rainfall-runoff (11%) and baseflow (4%) over 1980–2020. Decadal discharge analysis showed a shift in the discharge peak from August to July post-1990, attributed to decreased winter precipitation and enhanced melting in early summer. The mean decadal GGS discharge showed the highest volumetric increase of 7.8% from 1991–2000 to 2001–2010. While the mean annual temperature increased, no significant trend was observed in mean annual precipitation or glacier melt. Despite the warming, snowmelt declined, mainly due to a decreasing trend in mean snow cover area, whereas rainfall-runoff and baseflow increased over GGS during 1980–2020. Statistical analysis revealed that the mean annual discharge of GGS is mainly controlled by summer precipitation (r = 0.62), followed by winter temperature (r = 0.52). This study emphasizes the necessity of more climate-focused, long-term glaciohydrological studies and stresses the need for more in-situ observations across the HKH region, while highlighting the essential role of remote sensing datasets in model calibration and validation. © 2025 Elsevier B.V., All rights reserved.