Assessing snow cover patterns in the Indus-Ganga-Brahmaputra River Basins of the Hindu Kush Himalayas using snow persistence and snow line as metrics

Abstract

This study investigates the utilization of snow persistence (SP) as a metric to assess snow cover patterns in the Upper Indus-Ganga-Brahmaputra (UIGB) river basins, an area notably susceptible to climate change. The objectives of the study are to establish seasonal snow persistence, calculate seasonal snow lines, and quantify the temporal trend of snow lines. It also underscores the importance of accounting for climate zones, elevation, and seasonal variations when investigating snow persistence. Furthermore, the study estimates the snow line elevation using the Regional Snow Line Elevation (RSLE) approach with remote sensing data. Over a span of 19 years (2003–2021), the analysis of SP% in UIGB basins indicates a notable reduction in the mean annual permanent snow zone area, decreasing by -2.61 %, -2.37 %, and -4.18 % from winter (DJF) to summer (JJA). The results uncover considerable fluctuations in snow cover across various climate zones and seasons. The impact of warming is clearly visible on snow cover, instigating a transition from permanent and seasonal snow zones towards intermittent and minimal or no snow zones. The seasonal analysis of the snow line from 2003 to 2021 illustrates fluctuation in the snow line for UIGB basins, from 2349 ± 117 m to 4802 ± 51.7 m, 3003 ± 286 m to 4871 ± 64.4 m, and 3238 ± 127 m to 5047 ± 73 m between winter and summer seasons, with the most significant fluctuation occurring in the Brahmaputra basin. A substantial increase in Snow Line Elevation (SLE) is observed for Ganga (6.67 m yr−1), and Brahmaputra (7.26 m yr−1) at the conclusion of the melting season. These findings offer invaluable insights for formulating targeted mitigation strategies in the snow accumulation regions of each river basin. They equip us with the ability to respond effectively to the spatiotemporal variability of snow cover caused by climate change.