Assessing Eolian Snow Redistribution in Din-Gad Catchment, Central Himalaya, using Remote Sensing and Modelling

Abstract

The Himalaya are amongst the world’s most important water towers, supplying water to hundreds of millions of people. They are also vulnerable to climate change, stimulating research on their hydrological behaviour. One important aspect of the hydrological system that is often overlooked in these studies is the eolian redistribution of snow. This study aims to identify eolian snow redistribution patterns using an unprecedented combination of optical remote sensing, downscaled wind fields, a Temperature Index (TI)-based hydrological model (SPHY) and a novel Snow Redistribution mechanism Classification Model (SRCM). This method was applied to Din-Gad Catchment in the Central Himalaya for the period 2017-2020. The results show that eolian snow redistribution patterns are strongly related to the prevailing wind directions and topographic exposure. The most frequent wind-induced snow removal typically occurs in concentrated areas that are exposed in the wind direction, whereas areas of frequent wind-supplied snow deposition are more widespread and located in sheltered areas like valleys and the lee sides of ridges. The wind speed threshold for snow transport is low during the winter months and increases in the monsoon season, due to the increasing age and wetness of the snowpack in that period. SRCM classified 21.6% of snow cover changes as wind-driven and 14.1% as gravity-driven. The classified snow redistribution mechanisms were evaluated using satellite images, finding that SPHY had difficulty classifying observed snow cover changes with the chosen model parameters. This indicates that modelling snow cover change using only temperature and precipitation data is an oversimplification and that wind-induced snow dynamics play a significant role in the spatiotemporal distribution of snow cover in Din-Gad Catchment. This also suggests that TI-based hydrological models like SPHY could benefit from the incorporation of snow redistribution processes. Recommendations for improving these methods include using radar imagery, incorporating field observations and performing sensitivity analyses and error propagation tests. This study contributes to understanding the role of snow redistribution in high-mountain areas, which is critical for designing accurate models that can make reliable predictions of hydrological behaviour.