Remote Detection of Surge-Related Glacier Terminus Change across High Mountain Asia
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Vale, A. B., Arnold, N., Rees, G., & Lea, J. M. (2021). Remote Detection of Surge-Related Glacier Terminus Change across High Mountain Asia. REMOTE SENSING, 13 (7. ARTN 1309)https://doi.org/10.3390/rs13071309
High Mountain Asia (HMA) hosts the largest glacier concentration outside of polar regions. It is also distinct glaciologically as it forms one of two major surge clusters globally, and many glaciers there contradict the globally-observed glacier recession trend. Surging glaciers are critical to our understanding of HMA glacier dynamics, threshold behaviour and flow instability, and hence have been the subject of extensive research, yet many dynamical uncertainties remain. Using the cloud-based geospatial data platform, Google Earth Engine (GEE) and GEE-developed tool, GEEDiT, to identify and quantify trends in the distribution and phenomenological characteristics of surging glaciers synoptically across HMA, we identified 137 glaciers as surging between 1987-2019. Of these, 55 were newly identified, 15 glaciers underwent repeat surges, and 18 were identified with enhanced glaciological hazard potential, most notably from Glacier Lake Outburst Floods (GLOFs). Terminus position time series analysis from 1987-2019 facilitated the development of a six-part phenomenological classification of glacier behaviour, as well as quantification of surge variables including active phase duration, terminus advance distance and rate, and surge periodicity. This research demonstrates the application of remote sensing techniques and the GEE platform to develop our understanding of surging glacier distribution and terminus phenomenology across large areas, as well as their ability to highlight potential geohazard locations, which can subsequently be used to focus monitoring efforts.
glacier surging, High Mountain Asia, glaciology, cryosphere, remote sensing, Google Earth Engine, hazard
A.V’s work was supported by a University of Bristol Scholarship (Grant no. U104481-102). J.M.L’s work on this research was supported by UKRI Future Leaders Fellowship (Grant No. MR/S017232/1).
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External DOI: https://doi.org/10.3390/rs13071309
This record's URL: https://www.repository.cam.ac.uk/handle/1810/319265
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