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 across high risk regions to gather vital data into on-the-ground changes in a glacier prior to collapse.

The aim of this project is to constrain the likely precursors of glacier collapse events with remote sensing. A broad range of remote sensing techniques will be used, including harnessing global satellite archives in cloud computing environments such as Google Earth Engine to conduct global-scale analyses. Very high resolution optical and SAR images will also be used to automatically detect key surface-based features which indicate imminent collapse. Machine learning may also be used to automate the detection of precursors on a wider spatial scale.

There will also be an opportunity to deploy field-based tools, such as low-cost time-lapse cameras and seismometers, in high risk areas, in partnership with regional stakeholders and policymakers. The final field site(s) will be chosen based on the assessment of risk of collapse, prior connections within the supervisory team to on-the-ground partners, and in discussion with the successful candidate. Potential sites include the Alps, Himalaya, and Peruvian Andes.

Objectives:
1. Produce a global-scale risk map of mountain glaciers based on their vulnerability towards future collapse;
2. Use very high resolution remote sensing and machine learning techniques to detect the precursors to sudden collapse based on historical events;
3. Deploy low-cost field-based sensors in vulnerable areas to detect on-the-ground changes in glaciers which may indicate sudden collapse."