Basal Melt Patterns around the Deep Ice Core Drilling Site in the Dome A Region from Ice-Penetrating Radar Measurements
Basal melt in the Dome A region will influence the deep-ice-core drilling at Kunlun Station. The melting point (wet bedrock) has a higher reflectivity than the surrounding area, which can be assessed using radar echoes from the bedrock. This paper uses a linear absorption model to determine wet and...
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ftmdpi:oai:mdpi.com:/2072-4292/15/7/1726/ 2023-08-20T04:07:11+02:00 Basal Melt Patterns around the Deep Ice Core Drilling Site in the Dome A Region from Ice-Penetrating Radar Measurements Hao Wang Xueyuan Tang Enzhao Xiao Kun Luo Sheng Dong Bo Sun agris 2023-03-23 application/pdf https://doi.org/10.3390/rs15071726 EN eng Multidisciplinary Digital Publishing Institute Environmental Remote Sensing https://dx.doi.org/10.3390/rs15071726 https://creativecommons.org/licenses/by/4.0/ Remote Sensing; Volume 15; Issue 7; Pages: 1726 basal melt deep ice core drilling ice-penetrating radar (IPR) artificial intelligence Dome A Text 2023 ftmdpi https://doi.org/10.3390/rs15071726 2023-08-01T09:23:57Z Basal melt in the Dome A region will influence the deep-ice-core drilling at Kunlun Station. The melting point (wet bedrock) has a higher reflectivity than the surrounding area, which can be assessed using radar echoes from the bedrock. This paper uses a linear absorption model to determine wet and dry ice–bedrock interfaces around the Kunlun drilling site. In the determination process, an artificial intelligence model was applied to extract the ice–bedrock interface for inferring the ice thickness. Additionally, the various depth-averaged attenuation rates were used to identify the maximal range of basal melting. We mapped the patterns of the wet points on the bottom of the ice sheet and the modeled basal temperature to verify the results of the wet bed conditions. According to these maps of wet bed conditions, the areas with basal melting around the drilling site primarily appear in valley walls with low basal temperatures and are linked with hydraulic potential and bedrock elevation. Identifying the ice–bedrock interface is challenging in the valley bottom area, where the melting points are less than at the valley walls. Additionally, the melting proportions of 11.8% and 3.62% were calculated from two ice-penetrating radar data in this research. The mapped melting points around the site of Kunlun ice core drilling suggest complex ice flow effects and can be used to better interpret archives of old ice for paleoclimate research. Text ice core Ice Sheet MDPI Open Access Publishing Remote Sensing 15 7 1726 |
institution |
Open Polar |
collection |
MDPI Open Access Publishing |
op_collection_id |
ftmdpi |
language |
English |
topic |
basal melt deep ice core drilling ice-penetrating radar (IPR) artificial intelligence Dome A |
spellingShingle |
basal melt deep ice core drilling ice-penetrating radar (IPR) artificial intelligence Dome A Hao Wang Xueyuan Tang Enzhao Xiao Kun Luo Sheng Dong Bo Sun Basal Melt Patterns around the Deep Ice Core Drilling Site in the Dome A Region from Ice-Penetrating Radar Measurements |
topic_facet |
basal melt deep ice core drilling ice-penetrating radar (IPR) artificial intelligence Dome A |
description |
Basal melt in the Dome A region will influence the deep-ice-core drilling at Kunlun Station. The melting point (wet bedrock) has a higher reflectivity than the surrounding area, which can be assessed using radar echoes from the bedrock. This paper uses a linear absorption model to determine wet and dry ice–bedrock interfaces around the Kunlun drilling site. In the determination process, an artificial intelligence model was applied to extract the ice–bedrock interface for inferring the ice thickness. Additionally, the various depth-averaged attenuation rates were used to identify the maximal range of basal melting. We mapped the patterns of the wet points on the bottom of the ice sheet and the modeled basal temperature to verify the results of the wet bed conditions. According to these maps of wet bed conditions, the areas with basal melting around the drilling site primarily appear in valley walls with low basal temperatures and are linked with hydraulic potential and bedrock elevation. Identifying the ice–bedrock interface is challenging in the valley bottom area, where the melting points are less than at the valley walls. Additionally, the melting proportions of 11.8% and 3.62% were calculated from two ice-penetrating radar data in this research. The mapped melting points around the site of Kunlun ice core drilling suggest complex ice flow effects and can be used to better interpret archives of old ice for paleoclimate research. |
format |
Text |
author |
Hao Wang Xueyuan Tang Enzhao Xiao Kun Luo Sheng Dong Bo Sun |
author_facet |
Hao Wang Xueyuan Tang Enzhao Xiao Kun Luo Sheng Dong Bo Sun |
author_sort |
Hao Wang |
title |
Basal Melt Patterns around the Deep Ice Core Drilling Site in the Dome A Region from Ice-Penetrating Radar Measurements |
title_short |
Basal Melt Patterns around the Deep Ice Core Drilling Site in the Dome A Region from Ice-Penetrating Radar Measurements |
title_full |
Basal Melt Patterns around the Deep Ice Core Drilling Site in the Dome A Region from Ice-Penetrating Radar Measurements |
title_fullStr |
Basal Melt Patterns around the Deep Ice Core Drilling Site in the Dome A Region from Ice-Penetrating Radar Measurements |
title_full_unstemmed |
Basal Melt Patterns around the Deep Ice Core Drilling Site in the Dome A Region from Ice-Penetrating Radar Measurements |
title_sort |
basal melt patterns around the deep ice core drilling site in the dome a region from ice-penetrating radar measurements |
publisher |
Multidisciplinary Digital Publishing Institute |
publishDate |
2023 |
url |
https://doi.org/10.3390/rs15071726 |
op_coverage |
agris |
genre |
ice core Ice Sheet |
genre_facet |
ice core Ice Sheet |
op_source |
Remote Sensing; Volume 15; Issue 7; Pages: 1726 |
op_relation |
Environmental Remote Sensing https://dx.doi.org/10.3390/rs15071726 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3390/rs15071726 |
container_title |
Remote Sensing |
container_volume |
15 |
container_issue |
7 |
container_start_page |
1726 |
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1774718651097677824 |