气候变化领域集成服务门户(CCPortal): Machine learning-based predictions of current and future susceptibility to retrogressive thaw slumps across the Northern Hemisphere

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DOI	10.1016/j.accre.2024.03.001
	Machine learning-based predictions of current and future susceptibility to retrogressive thaw slumps across the Northern Hemisphere
发表日期	2024
ISSN	1674-9278
起始页码	15
结束页码	2
卷号	15 期号:2
英文摘要	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.
英文关键词	Retrogressive thaw slump; Machine learning; Susceptibility map; Permafrost; Northern Hemisphere
语种	英语
WOS研究方向	Environmental Sciences & Ecology ; Meteorology & Atmospheric Sciences
WOS类目	Environmental Sciences ; Meteorology & Atmospheric Sciences
WOS记录号	WOS:001235195200001
来源期刊	ADVANCES IN CLIMATE CHANGE RESEARCH
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/307248
作者单位	Chinese Academy of Sciences; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS
推荐引用方式 GB/T 7714	. Machine learning-based predictions of current and future susceptibility to retrogressive thaw slumps across the Northern Hemisphere[J],2024,15(2).
APA	(2024).Machine learning-based predictions of current and future susceptibility to retrogressive thaw slumps across the Northern Hemisphere.ADVANCES IN CLIMATE CHANGE RESEARCH,15(2).
MLA	"Machine learning-based predictions of current and future susceptibility to retrogressive thaw slumps across the Northern Hemisphere".ADVANCES IN CLIMATE CHANGE RESEARCH 15.2(2024).

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