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

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Advances in Climate Change Research 2024年第2期15卷 253-264页

作者： Jing LUO Guo-An YIN Fu-Jun NIU Tian-Chun DONG Ze-Yong GAO Ming-Hao LIU Fan YU State Key Laboratory of Frozen Soil Engineering Northwest Institute of Eco-Environment and ResourcesChinese Academy of SciencesLanzhou 730000China China Railway Qinghai-Tibet Group Co. LtdXining 810000China University of Chinese Academy of Sciences Beijing 100049China

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 ***,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 *** machine learning methodologies,we mapped the current and future RTSs susceptibility distributions by incorporating a range of environmental factors and RTSs *** identified freezing-degree days and maximum summer rainfall as the primary environmental factors affecting RTSs *** 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 *** 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 *** 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

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