Simulation of the Present and Future Projection of Permafrost on the Qinghai-Tibet Plateau with Statistical and Machine Learning Models | |
Ni, Jie1,2; Wu, Tonghua1,3; Zhu, Xiaofan1; Hu, Guojie1; Zou, Defu1; Wu, Xiaodong1; Li, Ren1; Xie, Changwei1; Qiao, Yongping1; Pang, Qiangqiang1 | |
2021-01-27 | |
发表期刊 | JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES |
ISSN | 2169-897X |
卷号 | 126期号:2页码:- |
摘要 | The comprehensive understanding of the occurred changes of permafrost, including the changes of mean annual ground temperature (MAGT) and active layer thickness (ALT), on the Qinghai-Tibet Plateau (QTP) is critical to project permafrost changes due to climate change. Here, we use statistical and machine learning (ML) modeling approaches to simulate the present and future changes of MAGT and ALT in the permafrost regions of the QTP. The results show that the combination of statistical and ML method is reliable to simulate the MAGT and ALT, with the root-mean-square error of 0.53 degrees C and 0.69 m for the MAGT and ALT, respectively. The results show that the present (2000-2015) permafrost area on the QTP is 14 x 10(6) km(2) (0.80-1.28 x 10(6) km(2)), and the average MAGT and ALT are -1.35 +/- 0.42 degrees C and 2.3 +/- 0.60 m, respectively. According to the classification system of permafrost stability, 37.3% of the QTP permafrost is suffering from the risk of disappearance. In the future (2061-2080), the near-surface permafrost area will shrink significantly under different Representative Concentration Pathway scenarios (RCPs). It is predicted that the permafrost area will be reduced to 42% of the present area under RCP8.5. Overall, the future changes of MAGT and ALT are pronounced and region-specific. As a result, the combined statistical method with ML requires less parameters and input variables for simulation permafrost thermal regimes and could present an efficient way to figure out the response of permafrost to climatic changes on the QTP. |
关键词 | active layer climate change mean annual ground temperature permafrost Qinghai-Tibet Plateau |
DOI | 10.1029/2020JD033402 |
收录类别 | SCIE |
语种 | 英语 |
WOS研究方向 | Meteorology & Atmospheric Sciences |
WOS类目 | Meteorology & Atmospheric Sciences |
WOS记录号 | WOS:000613703000007 |
出版者 | AMER GEOPHYSICAL UNION |
来源库 | WOS |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | https://ir.lut.edu.cn/handle/2XXMBERH/147319 |
专题 | 土木工程学院 |
通讯作者 | Wu, Tonghua |
作者单位 | 1.Chinese Acad Sci, Cryosphere Res Stn Qinghai Tibet Plateau, State Key Lab Cryospher Sci, Northwest Inst Ecoenvironm & Resources, Lanzhou, Peoples R China; 2.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing, Peoples R China; 3.Southern Marine Sci & Engn Guangdong Lab, Guangzhou, Peoples R China; 4.Lanzhou Univ Technol, Sch Civil Engn, Lanzhou, Peoples R China |
推荐引用方式 GB/T 7714 | Ni, Jie,Wu, Tonghua,Zhu, Xiaofan,et al. Simulation of the Present and Future Projection of Permafrost on the Qinghai-Tibet Plateau with Statistical and Machine Learning Models[J]. JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES,2021,126(2):-. |
APA | Ni, Jie.,Wu, Tonghua.,Zhu, Xiaofan.,Hu, Guojie.,Zou, Defu.,...&Yang, Cheng.(2021).Simulation of the Present and Future Projection of Permafrost on the Qinghai-Tibet Plateau with Statistical and Machine Learning Models.JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES,126(2),-. |
MLA | Ni, Jie,et al."Simulation of the Present and Future Projection of Permafrost on the Qinghai-Tibet Plateau with Statistical and Machine Learning Models".JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES 126.2(2021):-. |
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