Remote sensing as a tool to detect sudden glacier detachment events

Glacier detachment events, where a large section of ice suddenly detaches from a glacier, have the potential to cause catastrophic damage in mountainous environments. Most notably in recent years, a large ice-rock avalanche in Uttarakhand, India in February 2021 killed more than 200 people (Shugar et al., 2020) and a detachment from the Marmolada glacier in Italy in July 2022 killed 11 people. Further such detachments have been observed in mountain glaciers worldwide and may be a more frequent hazard than originally thought (Kääb et al., 2021). However, given the rarity of their occurrence, little is known about the processes that lead up to sudden detachment, which if detected, could be used to warn of an imminent event.

As climate change accelerates the decline of the mountain cryosphere, a challenge exists in being able to robustly track these changes using observation and modelling methods. Research which analyses the events leading up to glacier collapses have shown that there are a number of observable precursors, such as expanding crevasses (Leinss et al., 2021), changes in the seismic signals from glacier movement (Roux et al., 2010), and surge-like acceleration in velocity (Kääb et al., 2018). Many of these precursors are exhibited days, or even weeks, prior to collapse. Disentangling these signals from normal glaciological processes is a key part of incorporating them within a warning system that imminent collapse is likely.

A new generation of remote sensing techniques for the mountain cryosphere (Taylor et al., 2021) may be able to detect, and predict, large glacier detachments even in the world's remotest region. With an expanded dataset of detachment events, machine learning algorithms can be trained to separate the unique surface signals which indicate collapse and those that indicate normal glacier behaviour (as is being applied over ice sheets; Zhao et al., 2022). In addition, low-cost field sensors can be deployed liberally...