Machine learning-based predictions of current and future susceptibility to retrogressive thaw slumps across the Northern Hemisphere  

作　　者：Jing LUO Guo-An YIN Fu-Jun NIU Tian-Chun DONG Ze-Yong GAO Ming-Hao LIU Fan YU 

机构地区：[1]State Key Laboratory of Frozen Soil Engineering,Northwest Institute of Eco-Environment and Resources,Chinese Academy of Sciences,Lanzhou 730000,China [2]China Railway Qinghai-Tibet Group Co.,Ltd,Xining 810000,China [3]University of Chinese Academy of Sciences,Beijing 100049,China

出　　处：《Advances in Climate Change Research》2024年第2期253-264,共12页气候变化研究进展（英文版）

基　　金：This study was jointly supported by the National Science Foundation of China(42071097 and 42372334);the Second Tibetan Plateau Scientific Expedition and Research(STEP)program(2019QZKK0905);the Youth Innovation Promotion Association of the Chinese Academy of Sciences(2020421);the Program of China State Railway Group Co.Ltd.(K2022G017).

摘　　要：Retrogressive thaw slumps(RTSs)caused by the thawing of ground ice on permafrost slopes have dramatically increased and become a common permafrost hazard across the Northern Hemisphere during previous decades.However,a gap remains in our comprehensive understanding of the spatial controlling factors,including the climate and terrain,that are conducive to these RTSs at a global scale.Using machine learning methodologies,we mapped the current and future RTSs susceptibility distributions by incorporating a range of environmental factors and RTSs inventories.We identified freezing-degree days and maximum summer rainfall as the primary environmental factors affecting RTSs susceptibility.The final ensemble susceptibility map suggests that regions with high to very high susceptibility could constitute(11.6±0.78)%of the Northern Hemisphere's permafrost region.When juxtaposed with the current(2000-2020)RTSs susceptibility map,the total area with high to very high susceptibility could witness an increase ranging from(31.7±0.65)%(SSP585)to(51.9±0.73)%(SSP126)by the 2041-2060.The insights gleaned from this study not only offer valuable implications for engineering applications across the Northern Hemisphere,but also provide a long-term insight into the potential change of RTSs in permafrost regions in response to climate change.

关 键 词：Retrogressive thaw slump Machine learning Susceptibility map PERMAFROST Northern Hemisphere 

分 类 号：P64[天文地球—地质矿产勘探]