High-resolution analysis and prediction of heavy precipitation-induced GLOF events in North Sikkim Himalayas using the WRF model

Abstract

The northern Sikkim Himalaya hosts numerous high-altitude glacial lakes, many of which exhibit rapid expansion, rendering them highly susceptible to Glacial Lake Outburst Floods (GLOFs). These events release substantial volumes of water downstream, posing significant hydrometeorological hazards. The downstream regions of Sikkim, particularly the lower Teesta basin, experience flood hazards due to both direct precipitation and enhanced discharge from upstream glacial lakes. Among these, South Lhonak Lake (SLL), one of the largest lakes in the Teesta basin, has undergone rapid volumetric expansion, making it a critical hotspot for GLOF hazards. The study explores the application of the WRF model for GLOF-related research, offering a novel approach to understanding and predicting such events. This study used Weather Research and Forecasting (WRF) model to simulate and analyze heavy precipitation events across Sikkim, with a specific focus on the GLOF-triggering precipitation event of October 3–4, 2023, over the SLL region. The results suggest that intense precipitation, coupled with ice mass instability and lateral moraine failure, acted as the primary triggers for the catastrophic GLOF. Model simulations were conducted using high-resolution precipitation data acquired from the newly deployed X-band Doppler Weather Radar (DWR) installed by DGRE-DRDO at NIT Sikkim. The resulting outburst flood caused extensive damage to settlements, infrastructure, and hydropower projects along its course, extending several tens of kilometers downstream. The findings underscore the urgent need for an integrated monitoring framework combining high-resolution numerical weather prediction, satellite-based glacial lake surveillance, and real-time radar observations. This study represents the first application of the WRF model for GLOF-related precipitation forecasting in Sikkim, demonstrating its potential for enhancing early warning capabilities and risk mitigation strategies in the region.