Modelling Snow-Glacier Melt Runoff Dynamics In Bhilangana Basin

The vast network of glaciers in the Himalayas serves as a vital source of freshwater for the main river systems. These are essential in determining a region's climate and hydrology. Ecological balance, agricultural output, and hydrological systems all depend on these glaciers. However, the stability of hydrological systems and long-term water availability have become major concerns in recent decades due to the acceleration of glacier melting brought on by climate change. In this study, globally available gridded satellite and reanalysis datasets, including ERA5, IMDAA, IMD, APHRODITE, and others, were evaluated to identify the most accurate dataset for the Bhilangana Basin. A thorough performance evaluation was conducted to assess the suitability of these datasets for the region. Furthermore, a hybrid rainfall dataset was developed using a bias correction approach to improve accuracy and reliability, ensuring a more robust representation of precipitation dynamics. The Spatial Processes in Hydrology (SPHY) model was utilized to examine the dynamics of snow-glacier melt during the years 2020–2023. The performance matrix revealed that the ERA5 dataset performed better than other datasets except the hybrid precipitation dataset. The average variation during 2000-2023 in snow q was found in the range of 15 to 26 percent, rain q from 12 to 58 percent, glacier q from 56 to 18 percent and base q from 8 to 18 percent. The analysis further revealed that 11 parameters were found to be critical in influencing the model's output e.g. Degree day factor for snow(DDFS), Glacier debris degree day factor(DDFG), Tcritical, Glacier melt frac runoff. The SPHY model's applicability for studying snow-glacier melt runoff dynamics and the significance of combining various climate datasets to precisely forecast the water resource scenarios in glaciated basins are further highlighted by this study.