Prediction of subglacial lake melt source regions from site characteristics
Subglacial melt has important implications for ice-sheet dynamics. Locating and identifying subglacial lakes are expensive and time-consuming, requiring radar surveys or satellite methods. We explore three methods to identify source regions for lakes using seven continent-wide environmental characte...
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Cambridge University Press (CUP)
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ftunivadelaidedl:oai:digital.library.adelaide.edu.au:2440/138522 2023-12-17T10:22:31+01:00 Prediction of subglacial lake melt source regions from site characteristics Willcocks, S. Hasterok, D. 2023 https://hdl.handle.net/2440/138522 https://doi.org/10.1017/s0954102023000032 en eng Cambridge University Press (CUP) http://purl.org/au-research/grants/arc/DP180104074 Antarctic Science, 2023; 35(2):127-140 0954-1020 1365-2079 https://hdl.handle.net/2440/138522 doi:10.1017/s0954102023000032 Hasterok, D. [0000-0002-8257-7975] © The Author(s), 2023. Published by Cambridge University Press on behalf of Antarctic Science Ltd http://dx.doi.org/10.1017/s0954102023000032 active lakes machine learning principal component analysis subglacial lakes Journal article 2023 ftunivadelaidedl https://doi.org/10.1017/s0954102023000032 2023-11-20T23:15:49Z Subglacial melt has important implications for ice-sheet dynamics. Locating and identifying subglacial lakes are expensive and time-consuming, requiring radar surveys or satellite methods. We explore three methods to identify source regions for lakes using seven continent-wide environmental characteristics that are sensitive to or influenced by ice-sheet temperature. A simple comparison of environmental properties at lake locations with their continent-wide distributions suggests a statistical relationship (high Kolmogorov-Smirnov statistic) between stable lake locations and ice thickness and surface temperatures, indicating melting under passive conditions. Active lakes, in contrast, show little correlation with direct thermally influenced parameters, instead exhibiting large statistical differences with horizontal velocity and bedrock elevation. More sophisticated techniques, including principal component analysis (PCA) and machine learning (ML) classification, provide better spatial identification of lake types. Positive PCA scores derived from the environmental characteristics correlate with stable lakes, whereas negative values correspond to active lakes. ML methods can also identify regions where subglacial lake melt sources are probable. While ML provides the most accurate classification maps, the combination of approaches adds deeper knowledge of the primary controls on lake formation and the environmental settings in which they are likely to be found. Simon Willcocks and Derrick Hasterok Article in Journal/Newspaper Antarctic Science Ice Sheet The University of Adelaide: Digital Library Antarctic Science 35 2 127 140 |
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Open Polar |
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The University of Adelaide: Digital Library |
op_collection_id |
ftunivadelaidedl |
language |
English |
topic |
active lakes machine learning principal component analysis subglacial lakes |
spellingShingle |
active lakes machine learning principal component analysis subglacial lakes Willcocks, S. Hasterok, D. Prediction of subglacial lake melt source regions from site characteristics |
topic_facet |
active lakes machine learning principal component analysis subglacial lakes |
description |
Subglacial melt has important implications for ice-sheet dynamics. Locating and identifying subglacial lakes are expensive and time-consuming, requiring radar surveys or satellite methods. We explore three methods to identify source regions for lakes using seven continent-wide environmental characteristics that are sensitive to or influenced by ice-sheet temperature. A simple comparison of environmental properties at lake locations with their continent-wide distributions suggests a statistical relationship (high Kolmogorov-Smirnov statistic) between stable lake locations and ice thickness and surface temperatures, indicating melting under passive conditions. Active lakes, in contrast, show little correlation with direct thermally influenced parameters, instead exhibiting large statistical differences with horizontal velocity and bedrock elevation. More sophisticated techniques, including principal component analysis (PCA) and machine learning (ML) classification, provide better spatial identification of lake types. Positive PCA scores derived from the environmental characteristics correlate with stable lakes, whereas negative values correspond to active lakes. ML methods can also identify regions where subglacial lake melt sources are probable. While ML provides the most accurate classification maps, the combination of approaches adds deeper knowledge of the primary controls on lake formation and the environmental settings in which they are likely to be found. Simon Willcocks and Derrick Hasterok |
format |
Article in Journal/Newspaper |
author |
Willcocks, S. Hasterok, D. |
author_facet |
Willcocks, S. Hasterok, D. |
author_sort |
Willcocks, S. |
title |
Prediction of subglacial lake melt source regions from site characteristics |
title_short |
Prediction of subglacial lake melt source regions from site characteristics |
title_full |
Prediction of subglacial lake melt source regions from site characteristics |
title_fullStr |
Prediction of subglacial lake melt source regions from site characteristics |
title_full_unstemmed |
Prediction of subglacial lake melt source regions from site characteristics |
title_sort |
prediction of subglacial lake melt source regions from site characteristics |
publisher |
Cambridge University Press (CUP) |
publishDate |
2023 |
url |
https://hdl.handle.net/2440/138522 https://doi.org/10.1017/s0954102023000032 |
genre |
Antarctic Science Ice Sheet |
genre_facet |
Antarctic Science Ice Sheet |
op_source |
http://dx.doi.org/10.1017/s0954102023000032 |
op_relation |
http://purl.org/au-research/grants/arc/DP180104074 Antarctic Science, 2023; 35(2):127-140 0954-1020 1365-2079 https://hdl.handle.net/2440/138522 doi:10.1017/s0954102023000032 Hasterok, D. [0000-0002-8257-7975] |
op_rights |
© The Author(s), 2023. Published by Cambridge University Press on behalf of Antarctic Science Ltd |
op_doi |
https://doi.org/10.1017/s0954102023000032 |
container_title |
Antarctic Science |
container_volume |
35 |
container_issue |
2 |
container_start_page |
127 |
op_container_end_page |
140 |
_version_ |
1785549560698896384 |