首页|Machine learning-based predictions of current and future susceptibility to retrogressive thaw slumps across the Northern Hemisphere
Machine learning-based predictions of current and future susceptibility to retrogressive thaw slumps across the Northern Hemisphere
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国家科技期刊平台
NETL
NSTL
万方数据
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 under-standing 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.
国家科技期刊平台
NSTL
万方数据
