Remote Detection of Surge-Related Glacier Terminus Change across High Mountain Asia

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...

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Main Authors: Amelia B. Vale, Neil S. Arnold, W. Gareth Rees, James M. Lea
Format: Article
Language:English
Published: MDPI AG 2021-03-01
Series:Remote Sensing
Subjects:
Online Access:https://www.mdpi.com/2072-4292/13/7/1309
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spelling doaj-1812005540ba48c3ac3eb3a434c5fb8a2021-03-30T23:01:28ZengMDPI AGRemote Sensing2072-42922021-03-01131309130910.3390/rs13071309Remote Detection of Surge-Related Glacier Terminus Change across High Mountain AsiaAmelia B. Vale0Neil S. Arnold1W. Gareth Rees2James M. Lea3Scott Polar Research Institute, University of Cambridge, Cambridge CB2 1ER, UKScott Polar Research Institute, University of Cambridge, Cambridge CB2 1ER, UKScott Polar Research Institute, University of Cambridge, Cambridge CB2 1ER, UKDepartment of Geography and Planning, School of Environmental Sciences, University of Liverpool, Liverpool L69 7ZT, UKHigh 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.https://www.mdpi.com/2072-4292/13/7/1309glacier surgingHigh Mountain Asiaglaciologycryosphereremote sensingGoogle Earth Engine
collection DOAJ
language English
format Article
sources DOAJ
author Amelia B. Vale
Neil S. Arnold
W. Gareth Rees
James M. Lea
spellingShingle Amelia B. Vale
Neil S. Arnold
W. Gareth Rees
James M. Lea
Remote Detection of Surge-Related Glacier Terminus Change across High Mountain Asia
Remote Sensing
glacier surging
High Mountain Asia
glaciology
cryosphere
remote sensing
Google Earth Engine
author_facet Amelia B. Vale
Neil S. Arnold
W. Gareth Rees
James M. Lea
author_sort Amelia B. Vale
title Remote Detection of Surge-Related Glacier Terminus Change across High Mountain Asia
title_short Remote Detection of Surge-Related Glacier Terminus Change across High Mountain Asia
title_full Remote Detection of Surge-Related Glacier Terminus Change across High Mountain Asia
title_fullStr Remote Detection of Surge-Related Glacier Terminus Change across High Mountain Asia
title_full_unstemmed Remote Detection of Surge-Related Glacier Terminus Change across High Mountain Asia
title_sort remote detection of surge-related glacier terminus change across high mountain asia
publisher MDPI AG
series Remote Sensing
issn 2072-4292
publishDate 2021-03-01
description 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.
topic glacier surging
High Mountain Asia
glaciology
cryosphere
remote sensing
Google Earth Engine
url https://www.mdpi.com/2072-4292/13/7/1309
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