Simulation of the Present and Future Projection of Permafrost on the Qinghai-Tibet Plateau with Statistical and Machine Learning Models

doi:10.1029/2020JD033402

CORC > 兰州理工大学 > 兰州理工大学 > 土木工程学院

	Simulation of the Present and Future Projection of Permafrost on the Qinghai-Tibet Plateau with Statistical and Machine Learning Models
	Ni, Jie 1,2; Wu, Tonghua 1,3; Zhu, Xiaofan 1; Hu, Guojie 1; Zou, Defu 1; Wu, Xiaodong 1; Li, Ren 1; Xie, Changwei 1; Qiao, Yongping 1; Pang, Qiangqiang 1
刊名	JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES
	2021-01-27
卷号	126 期号:2 页码:-
关键词	active layer climate change mean annual ground temperature permafrost Qinghai-Tibet Plateau
ISSN号	2169-897X
DOI	10.1029/2020JD033402
英文摘要	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.
WOS研究方向	Meteorology & Atmospheric Sciences
语种	英语
出版者	AMER GEOPHYSICAL UNION
WOS记录号	WOS:000613703000007
内容类型	期刊论文
源URL	[http://ir.lut.edu.cn/handle/2XXMBERH/147319]
专题	土木工程学院
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):-.