Spatiotemporal Patterns and Regional Differences in Soil Thermal Conductivity on the Qinghai–Tibet Plateau

The Qinghai–Tibet Plateau is an area known to be sensitive to global climate change, and the problems caused by permafrost degradation in the context of climate warming potentially have far-reaching effects on regional hydrogeological processes, ecosystem functions, and engineering safety. Soil ther...

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Published in:Remote Sensing
Main Authors: Wenhao Liu, Ren Li, Tonghua Wu, Xiaoqian Shi, Lin Zhao, Xiaodong Wu, Guojie Hu, Jimin Yao, Dong Wang, Yao Xiao, Junjie Ma, Yongliang Jiao, Shenning Wang, Defu Zou, Xiaofan Zhu, Jie Chen, Jianzong Shi, Yongping Qiao
Format: Text
Language:English
Published: Multidisciplinary Digital Publishing Institute 2023
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Online Access:https://doi.org/10.3390/rs15041168
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author Wenhao Liu
Ren Li
Tonghua Wu
Xiaoqian Shi
Lin Zhao
Xiaodong Wu
Guojie Hu
Jimin Yao
Dong Wang
Yao Xiao
Junjie Ma
Yongliang Jiao
Shenning Wang
Defu Zou
Xiaofan Zhu
Jie Chen
Jianzong Shi
Yongping Qiao
author_facet Wenhao Liu
Ren Li
Tonghua Wu
Xiaoqian Shi
Lin Zhao
Xiaodong Wu
Guojie Hu
Jimin Yao
Dong Wang
Yao Xiao
Junjie Ma
Yongliang Jiao
Shenning Wang
Defu Zou
Xiaofan Zhu
Jie Chen
Jianzong Shi
Yongping Qiao
author_sort Wenhao Liu
collection MDPI Open Access Publishing
container_issue 4
container_start_page 1168
container_title Remote Sensing
container_volume 15
description The Qinghai–Tibet Plateau is an area known to be sensitive to global climate change, and the problems caused by permafrost degradation in the context of climate warming potentially have far-reaching effects on regional hydrogeological processes, ecosystem functions, and engineering safety. Soil thermal conductivity (STC) is a key input parameter for temperature and surface energy simulations of the permafrost active layer. Therefore, understanding the spatial distribution patterns and variation characteristics of STC is important for accurate simulation and future predictions of permafrost on the Qinghai–Tibet Plateau. However, no systematic research has been conducted on this topic. In this study, based on a dataset of 2972 STC measurements, we simulated the spatial distribution patterns and spatiotemporal variation of STC in the shallow layer (5 cm) of the Qinghai–Tibet Plateau and the permafrost area using a machine learning model. The monthly analysis results showed that the STC was high from May to August and low from January to April and from September to December. In addition, the mean STC in the permafrost region of the Qinghai–Tibet Plateau was higher during the thawing period than during the freezing period, while the STC in the eastern and southeastern regions is generally higher than that in the western and northwestern regions. From 2005 to 2018, the difference between the STC in the permafrost region during the thawing and freezing periods gradually decreased, with a slight difference in the western hinterland region and a large difference in the eastern region. In areas with specific landforms such as basins and mountainous areas, the changes in the STC during the thawing and freezing periods were different or even opposite. The STC of alpine meadow was found to be most sensitive to the changes during the thawing and freezing periods within the permafrost zone, while the STC for bare land, alpine desert, and alpine swamp meadow decreased overall between 2005 and 2018. The results of this study ...
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op_doi https://doi.org/10.3390/rs15041168
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https://dx.doi.org/10.3390/rs15041168
op_rights https://creativecommons.org/licenses/by/4.0/
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spelling ftmdpi:oai:mdpi.com:/2072-4292/15/4/1168/ 2025-01-17T00:13:46+00:00 Spatiotemporal Patterns and Regional Differences in Soil Thermal Conductivity on the Qinghai–Tibet Plateau Wenhao Liu Ren Li Tonghua Wu Xiaoqian Shi Lin Zhao Xiaodong Wu Guojie Hu Jimin Yao Dong Wang Yao Xiao Junjie Ma Yongliang Jiao Shenning Wang Defu Zou Xiaofan Zhu Jie Chen Jianzong Shi Yongping Qiao agris 2023-02-20 application/pdf https://doi.org/10.3390/rs15041168 EN eng Multidisciplinary Digital Publishing Institute Ecological Remote Sensing https://dx.doi.org/10.3390/rs15041168 https://creativecommons.org/licenses/by/4.0/ Remote Sensing; Volume 15; Issue 4; Pages: 1168 soil thermal conductivity permafrost climate change freeze–thaw period Qinghai–Tibet Plateau machine learning Text 2023 ftmdpi https://doi.org/10.3390/rs15041168 2023-08-01T08:54:36Z The Qinghai–Tibet Plateau is an area known to be sensitive to global climate change, and the problems caused by permafrost degradation in the context of climate warming potentially have far-reaching effects on regional hydrogeological processes, ecosystem functions, and engineering safety. Soil thermal conductivity (STC) is a key input parameter for temperature and surface energy simulations of the permafrost active layer. Therefore, understanding the spatial distribution patterns and variation characteristics of STC is important for accurate simulation and future predictions of permafrost on the Qinghai–Tibet Plateau. However, no systematic research has been conducted on this topic. In this study, based on a dataset of 2972 STC measurements, we simulated the spatial distribution patterns and spatiotemporal variation of STC in the shallow layer (5 cm) of the Qinghai–Tibet Plateau and the permafrost area using a machine learning model. The monthly analysis results showed that the STC was high from May to August and low from January to April and from September to December. In addition, the mean STC in the permafrost region of the Qinghai–Tibet Plateau was higher during the thawing period than during the freezing period, while the STC in the eastern and southeastern regions is generally higher than that in the western and northwestern regions. From 2005 to 2018, the difference between the STC in the permafrost region during the thawing and freezing periods gradually decreased, with a slight difference in the western hinterland region and a large difference in the eastern region. In areas with specific landforms such as basins and mountainous areas, the changes in the STC during the thawing and freezing periods were different or even opposite. The STC of alpine meadow was found to be most sensitive to the changes during the thawing and freezing periods within the permafrost zone, while the STC for bare land, alpine desert, and alpine swamp meadow decreased overall between 2005 and 2018. The results of this study ... Text permafrost MDPI Open Access Publishing Remote Sensing 15 4 1168
spellingShingle soil thermal conductivity
permafrost
climate change
freeze–thaw period
Qinghai–Tibet Plateau
machine learning
Wenhao Liu
Ren Li
Tonghua Wu
Xiaoqian Shi
Lin Zhao
Xiaodong Wu
Guojie Hu
Jimin Yao
Dong Wang
Yao Xiao
Junjie Ma
Yongliang Jiao
Shenning Wang
Defu Zou
Xiaofan Zhu
Jie Chen
Jianzong Shi
Yongping Qiao
Spatiotemporal Patterns and Regional Differences in Soil Thermal Conductivity on the Qinghai–Tibet Plateau
title Spatiotemporal Patterns and Regional Differences in Soil Thermal Conductivity on the Qinghai–Tibet Plateau
title_full Spatiotemporal Patterns and Regional Differences in Soil Thermal Conductivity on the Qinghai–Tibet Plateau
title_fullStr Spatiotemporal Patterns and Regional Differences in Soil Thermal Conductivity on the Qinghai–Tibet Plateau
title_full_unstemmed Spatiotemporal Patterns and Regional Differences in Soil Thermal Conductivity on the Qinghai–Tibet Plateau
title_short Spatiotemporal Patterns and Regional Differences in Soil Thermal Conductivity on the Qinghai–Tibet Plateau
title_sort spatiotemporal patterns and regional differences in soil thermal conductivity on the qinghai–tibet plateau
topic soil thermal conductivity
permafrost
climate change
freeze–thaw period
Qinghai–Tibet Plateau
machine learning
topic_facet soil thermal conductivity
permafrost
climate change
freeze–thaw period
Qinghai–Tibet Plateau
machine learning
url https://doi.org/10.3390/rs15041168