Bedfast and Floating Ice Dynamics of Thermokarst Lakes Using a Temporal Deep Learning Mapping Approach: Case Study of the Old Crow Flats, Yukon, Canada
In light of the recent climate warming, monitoring of lake ice in Arctic and subarctic regions is becoming increasingly important. Many shallow Arctic lakes and ponds of thermokarst origin freeze to the bed in the winter months, maintaining the underlying permafrost in its frozen state. However, as...
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ftcopernicus:oai:publications.copernicus.org:egusphere103602 2023-06-11T04:09:26+02:00 Bedfast and Floating Ice Dynamics of Thermokarst Lakes Using a Temporal Deep Learning Mapping Approach: Case Study of the Old Crow Flats, Yukon, Canada Shaposhnikova, Maria Duguay, Claude R. Roy-Léveillée, Pascale 2023-04-19 application/pdf https://doi.org/10.5194/egusphere-2022-388 https://egusphere.copernicus.org/preprints/2022/egusphere-2022-388/ eng eng doi:10.5194/egusphere-2022-388 https://egusphere.copernicus.org/preprints/2022/egusphere-2022-388/ eISSN: Text 2023 ftcopernicus https://doi.org/10.5194/egusphere-2022-388 2023-04-24T16:23:14Z In light of the recent climate warming, monitoring of lake ice in Arctic and subarctic regions is becoming increasingly important. Many shallow Arctic lakes and ponds of thermokarst origin freeze to the bed in the winter months, maintaining the underlying permafrost in its frozen state. However, as air temperatures rise and precipitation increases, fewer lakes are expected to develop bedfast ice. In this work, we propose a novel temporal deep-learning approach to lake ice regime mapping from synthetic aperture radar (SAR) and employ it to study lake ice dynamics in the Old Crow Flats (OCF), Yukon, Canada, over the 1992/1993 to 2020/2021 period. We utilized a combination of Sentinel-1, ERS-1 and ERS-2, and RADARSAT-1 to create an extensive annotated dataset of SAR time series labeled as either bedfast ice, floating ice, or land, which was used to train a temporal convolutional neural network (TempCNN). The trained TempCNN, in turn, allowed us to automatically map lake ice regimes. The classified maps aligned well with the available field measurements and ice thickness simulations obtained with a thermodynamic lake ice model. Reaching a mean overall classification accuracy of 95 %, the TempCNN was determined to be suitable for automated lake ice regime classification. The fraction of bedfast ice in the OCF increased by 11 % over the 29-year period of analysis. Findings suggest that the OCF lake ice dynamics are dominated by lake drainage events, brought on by thermokarst processes accelerated by climate warming, and fluctuations in water level and winter snowfall. Catastrophic drainage and lowered water levels cause surface water area and lake depth to decrease and lake ice to often transition from floating to bedfast ice, while a reduction in snowfall allows for the growth of thicker ice. The proposed lake ice regime mapping approach allowed us to assess the combined impacts of warming, drainage, and changing precipitation patterns on transitions between bedfast and floating-ice regimes, which is crucial to ... Text Arctic Ice Old Crow permafrost Subarctic Thermokarst Yukon Copernicus Publications: E-Journals Arctic Canada Old Crow Flats ENVELOPE(-139.755,-139.755,68.083,68.083) Yukon |
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Open Polar |
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Copernicus Publications: E-Journals |
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ftcopernicus |
language |
English |
description |
In light of the recent climate warming, monitoring of lake ice in Arctic and subarctic regions is becoming increasingly important. Many shallow Arctic lakes and ponds of thermokarst origin freeze to the bed in the winter months, maintaining the underlying permafrost in its frozen state. However, as air temperatures rise and precipitation increases, fewer lakes are expected to develop bedfast ice. In this work, we propose a novel temporal deep-learning approach to lake ice regime mapping from synthetic aperture radar (SAR) and employ it to study lake ice dynamics in the Old Crow Flats (OCF), Yukon, Canada, over the 1992/1993 to 2020/2021 period. We utilized a combination of Sentinel-1, ERS-1 and ERS-2, and RADARSAT-1 to create an extensive annotated dataset of SAR time series labeled as either bedfast ice, floating ice, or land, which was used to train a temporal convolutional neural network (TempCNN). The trained TempCNN, in turn, allowed us to automatically map lake ice regimes. The classified maps aligned well with the available field measurements and ice thickness simulations obtained with a thermodynamic lake ice model. Reaching a mean overall classification accuracy of 95 %, the TempCNN was determined to be suitable for automated lake ice regime classification. The fraction of bedfast ice in the OCF increased by 11 % over the 29-year period of analysis. Findings suggest that the OCF lake ice dynamics are dominated by lake drainage events, brought on by thermokarst processes accelerated by climate warming, and fluctuations in water level and winter snowfall. Catastrophic drainage and lowered water levels cause surface water area and lake depth to decrease and lake ice to often transition from floating to bedfast ice, while a reduction in snowfall allows for the growth of thicker ice. The proposed lake ice regime mapping approach allowed us to assess the combined impacts of warming, drainage, and changing precipitation patterns on transitions between bedfast and floating-ice regimes, which is crucial to ... |
format |
Text |
author |
Shaposhnikova, Maria Duguay, Claude R. Roy-Léveillée, Pascale |
spellingShingle |
Shaposhnikova, Maria Duguay, Claude R. Roy-Léveillée, Pascale Bedfast and Floating Ice Dynamics of Thermokarst Lakes Using a Temporal Deep Learning Mapping Approach: Case Study of the Old Crow Flats, Yukon, Canada |
author_facet |
Shaposhnikova, Maria Duguay, Claude R. Roy-Léveillée, Pascale |
author_sort |
Shaposhnikova, Maria |
title |
Bedfast and Floating Ice Dynamics of Thermokarst Lakes Using a Temporal Deep Learning Mapping Approach: Case Study of the Old Crow Flats, Yukon, Canada |
title_short |
Bedfast and Floating Ice Dynamics of Thermokarst Lakes Using a Temporal Deep Learning Mapping Approach: Case Study of the Old Crow Flats, Yukon, Canada |
title_full |
Bedfast and Floating Ice Dynamics of Thermokarst Lakes Using a Temporal Deep Learning Mapping Approach: Case Study of the Old Crow Flats, Yukon, Canada |
title_fullStr |
Bedfast and Floating Ice Dynamics of Thermokarst Lakes Using a Temporal Deep Learning Mapping Approach: Case Study of the Old Crow Flats, Yukon, Canada |
title_full_unstemmed |
Bedfast and Floating Ice Dynamics of Thermokarst Lakes Using a Temporal Deep Learning Mapping Approach: Case Study of the Old Crow Flats, Yukon, Canada |
title_sort |
bedfast and floating ice dynamics of thermokarst lakes using a temporal deep learning mapping approach: case study of the old crow flats, yukon, canada |
publishDate |
2023 |
url |
https://doi.org/10.5194/egusphere-2022-388 https://egusphere.copernicus.org/preprints/2022/egusphere-2022-388/ |
long_lat |
ENVELOPE(-139.755,-139.755,68.083,68.083) |
geographic |
Arctic Canada Old Crow Flats Yukon |
geographic_facet |
Arctic Canada Old Crow Flats Yukon |
genre |
Arctic Ice Old Crow permafrost Subarctic Thermokarst Yukon |
genre_facet |
Arctic Ice Old Crow permafrost Subarctic Thermokarst Yukon |
op_source |
eISSN: |
op_relation |
doi:10.5194/egusphere-2022-388 https://egusphere.copernicus.org/preprints/2022/egusphere-2022-388/ |
op_doi |
https://doi.org/10.5194/egusphere-2022-388 |
_version_ |
1768383299404693504 |