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

Author:

Liu Wenhao12ORCID,Li Ren12ORCID,Wu Tonghua12ORCID,Shi Xiaoqian3,Zhao Lin4ORCID,Wu Xiaodong12ORCID,Hu Guojie12,Yao Jimin12ORCID,Wang Dong12,Xiao Yao1ORCID,Ma Junjie12,Jiao Yongliang12,Wang Shenning12,Zou Defu1ORCID,Zhu Xiaofan1,Chen Jie1ORCID,Shi Jianzong1,Qiao Yongping1

Affiliation:

1. Cryosphere Research Station on the Qinghai-Tibet Plateau, State Key Laboratory of Cryospheric Science, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China

2. University of Chinese Academy of Sciences, Beijing 100049, China

3. PetroChina Research Institute of Petroleum Exploration and Development-Northwest, Lanzhou 730020, China

4. School of Geographical Sciences, Nanjing University of Information Science & Technology, Nanjing 210044, China

Abstract

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 provide important baseline data for the subsequent analysis and simulation of the permafrost on the Qinghai–Tibet Plateau.

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

State Key Laboratory of Cryospheric Science

Youth Science and Technology Fund Plan of Gansu Province

Gansu Province Science and Technology Plan Project

Publisher

MDPI AG

Subject

General Earth and Planetary Sciences

Reference118 articles.

1. High Carbon Emissions from Thermokarst Lakes of Western Siberia;Serikova;Nat. Commun.,2019

2. Linkage between Permafrost Distribution and River Runoff Changes across the Arctic and the Tibetan Plateau;Song;Sci. China Earth Sci.,2020

3. Warming Amplification over the Arctic Pole and Third Pole: Trends, Mechanisms and Consequences;You;Earth-Sci. Rev.,2021

4. Pörtner, H.-O., Roberts, D.C., Masson-Delmotte, V., Zhai, P., Tignor, M., Poloczanska, E., Mintenbeck, K., Nicolai, M., Okem, A., and Petzold, J. (2019). IPCC Special Report on the Ocean and Cryosphere in a Changing Climate, IPCC Intergovernmental Panel on Climate Change.

5. Arctic Amplification of Global Warming Strengthened by Sunlight Oxidation of Permafrost Carbon to CO2;Bowen;Geophys. Res. Lett.,2020

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3