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Spatial distribution and variation trends of soil freezing front on the Qingzang Plateau revealed by machine learning models

文献类型：期刊论文

作者Wen, Bo1; Zhang, Tingbin1,2,3; Zhou, Xiaobing4; Yi, Guihua5; Yang, Bin2,6; Chen, Dongmei7; Li, Jingji3,8; Wang, Xiang9; Ma, Xianglong1
刊名CLIMATE DYNAMICS
出版日期2025-12-11
卷号64期号:1页码:9
关键词Frozen soil Freezing front Qingzang Plateau Machine learning
ISSN号0930-7575
DOI10.1007/s00382-025-07989-x
产权排序9
文献子类Article
英文摘要The depth of the soil freezing front serves as an integrated indicator of land-atmosphere interactions during the freezing period and plays a critical role in regulating the hydrological cycle, ecological processes, and regional climate on the Qingzang Plateau (QP). While previous studies have primarily focused on interannual variations in the annual maximum freezing depth, limited attention has been paid to the spatiotemporal dynamics of the soil freezing front throughout the freezing season. In this study, we simulated the spatiotemporal variations of the soil freezing front on the QP during the freezing period using the optimal model selected from three machine learning algorithms: Random Forest (RF), Support Vector Machine (SVM), and Multilayer Perceptron (MLP). The results demonstrated that RF outperforms MLP and SVM in accurately simulating the depth of the soil freezing front (R2 = 0.81, RMSE = 28.09 cm, MAE = 18.02 cm). Spatially, the soil freezing front during the freezing period was deeper in the west and north and shallower in the east and south. From 1983 to 2019, both permafrost and seasonally frozen ground regions across the QP exhibited statistically significant declines in soil freezing front depth. From October to November, freezing depth decreases faster in permafrost than in seasonally frozen ground, whereas from December to January it decreases faster in seasonally frozen ground than in permafrost. A comparison between the sub-periods 1983-2000 and 2001-2019 reveals a marked acceleration in the reduction of freezing depth. Additionally, the influence of air temperature on the freezing front is modulated by its depth. The elevation effect is weak in October, strengthens to a predominantly negative influence in November-December, and becomes nonlinear in January, with the strongest negative impact at mid-high elevations and a weaker effect at the highest elevations.
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WOS关键词QINGHAI-TIBET PLATEAU ; RIVER SOURCE REGION ; CLIMATE-CHANGE ; ACTIVE-LAYER ; 3RD POLE ; PERMAFROST ; STATE ; IMPACTS ; MOUNTAINS ; DEPTH
WOS研究方向Meteorology & Atmospheric Sciences
语种英语
WOS记录号WOS:001637739200001
出版者SPRINGER
源URL[http://ir.igsnrr.ac.cn/handle/311030/219374]  
专题生态系统网络观测与模拟院重点实验室_外文论文
通讯作者Zhang, Tingbin
作者单位1.Chengdu Univ Technol, Coll Earth & Planetary Sci, Chengdu 610059, Peoples R China;
2.Middle Yarlung Zangbo River Nat Resources Observat, Lhasa 850013, Peoples R China;
3.Chengdu Univ Technol, State Environm Protect Key Lab Synerget Control &, Chengdu 610059, Peoples R China;
4.Montana Tech Univ Montana, Geophys Engn Dept, Butter, MT 59701 USA;
5.Chengdu Univ Technol, Coll Geog & Planning, Chengdu 610059, Peoples R China;
6.China Geol Survey, Res Ctr Appl Geol, Chengdu 610036, Peoples R China;
7.Queens Univ, Dept Geog & Planning, Kingston, ON K7L 3N6, Canada;
8.Chengdu Univ Technol, Coll Ecol & Environm, Chengdu 610059, Peoples R China;
9.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Ecosyst Network Observat & Modeling, Beijing 100101, Peoples R China
推荐引用方式
GB/T 7714		 Wen, Bo,Zhang, Tingbin,Zhou, Xiaobing,et al. Spatial distribution and variation trends of soil freezing front on the Qingzang Plateau revealed by machine learning models[J]. CLIMATE DYNAMICS,2025,64(1):9.
APA Wen, Bo.,Zhang, Tingbin.,Zhou, Xiaobing.,Yi, Guihua.,Yang, Bin.,...&Ma, Xianglong.(2025).Spatial distribution and variation trends of soil freezing front on the Qingzang Plateau revealed by machine learning models.CLIMATE DYNAMICS,64(1),9.
MLA Wen, Bo,et al."Spatial distribution and variation trends of soil freezing front on the Qingzang Plateau revealed by machine learning models".CLIMATE DYNAMICS 64.1(2025):9.

入库方式： OAI收割

来源：地理科学与资源研究所

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