Pre-collapse motion of the February 2021 Chamoli rock–ice avalanche, Indian Himalaya

• Published in: Natural Hazards and Earth System Sciences
• Main Authors: M. Van Wyk de Vries, S. Bhushan, M. Jacquemart, C. Deschamps-Berger, E. Berthier, S. Gascoin, D. E. Shean, D. H. Shugar, A. Kääb
• Format: Article in Journal/Newspaper
• Language: English
• Published: Copernicus Publications 2022
• Subjects: Environmental technology. Sanitary engineering; TD1-1066; Geography. Anthropology. Recreation; G; Environmental sciences; GE1-350; Geology; QE1-996.5; Indian; Ice; permafrost
• Online Access: https://doi.org/10.5194/nhess-22-3309-2022; https://doaj.org/article/bee2d4854190428784d19488bd546189

Landslides are a major geohazard that cause thousands of fatalities every year. Despite their importance, identifying unstable slopes and forecasting collapses remains a major challenge. In this study, we use the 7 February 2021 Chamoli rock–ice avalanche as a data-rich example to investigate the potential of remotely sensed datasets for the assessment of slope stability. We investigate imagery over the 3 decades preceding collapse and assess the precursory signs exhibited by this slope prior to the catastrophic collapse. We evaluate monthly slope motion from 2015 to 2021 through feature tracking of high-resolution optical satellite imagery. We then combine these data with a time series of pre- and post-event digital elevation models (DEMs), which we use to evaluate elevation change over the same area. Both datasets show that the 26.9 ×10 6 m 3 collapse block moved over 10 m horizontally and vertically in the 5 years preceding collapse, with particularly rapid motion occurring in the summers of 2017 and 2018. We propose that the collapse results from a combination of snow loading in a deep headwall crack and permafrost degradation in the heavily jointed bedrock. Despite observing a clear precursory signal, we find that the timing of the Chamoli rock–ice avalanche could likely not have been forecast from satellite data alone. Our results highlight the potential of remotely sensed imagery for assessing landslide hazard in remote areas, but that challenges remain for operational hazard monitoring.