Quantifying pre-collapse dynamics of hanging rock-ice masses using remote sensing datasets

Changing climate is enhancing the occurrence and intensity of natural disasters, profoundly impacting human lives, livelihoods, infrastructure, and economic growth. Modelling and prediction of deadly high-mountain slope failure hazards such as snow, ice, and rock avalanches have always been challenging. Current in-situ sensor-based approaches for slope failure predictions of hanging glaciers and rock faces are quite limited in their spatial continuity and extent and there is also a research gap on linking the pre-collapse slope movements with subsequent avalanche runouts. Earth observation datasets can offer a viable alternative for quantifying and monitoring pre-collapse dynamics at larger spatial scales. For the catastrophic 2021 rock-ice collapse in Chamoli, India, several studies had reported some anomalous movements weeks-to-months prior to the collapse. However, we need more analyses to understand how common such pre-collapse anomalous movements are before we can even start considering investigating them as potential precursors for effective avalanche predictions. To fill this research gap, using satellite remote sensing datasets and digital elevation models, we investigated several high-mountain slope failure events (e.g., Piz Scerscen in 2024, Piz Cengalo Bondo in 2017) of varying magnitudes and nature (i.e., rockfall, rock-ice avalanche, and ice avalanche) in different topographical and climate settings. While we were able to quantify pre-collapse dynamics for these events, we also observed variations in the occurrence and magnitude of anomalous movements prior to the events. These preliminary findings are encouraging and the future research and results from such analyses can bridge the knowledge gap on the detection and modelling capabilities, ultimately enhancing resilience to mountain hazards.