Wang Rui, Guo Lanlan, Yang Yuting, Zheng Hao, Jia Hong, Diao Baijian, Li Hang, Liu Jifu
Key Laboratory of Environmental Change and Natural Disasters, Ministry of Education, Beijing Normal University, Beijing 100875, China; State Key Laboratory of Earth Surface Processes and Resource Ecology (ESPRE), Beijing Normal University, Beijing, 100875, China; Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China.
Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China.
Sci Total Environ. 2023 Nov 20;900:165709. doi: 10.1016/j.scitotenv.2023.165709. Epub 2023 Jul 28.
Ice-rich permafrost thaws in response to rapid Arctic warming, and ground subsidence facilitates the formation of thermokarst lakes. Thermokarst lakes transform the surface energy balance of permafrost, affecting geomorphology, hydrology, ecology, and infrastructure stability, which can further contribute to greenhouse gas emissions. Currently, the spatial distribution of thermokarst lakes at large scales remains a challenging task. Based on multiple high-resolution environmental factors and thermokarst lake inventories, we used machine learning methods to estimate the spatial distributions of present and future thermokarst lake susceptibility (TLS) maps. We also identified key environmental factors of the TLS map. At 1.8 × 10 km, high and very high susceptible regions were estimated to cover about 10.4 % of the region poleward of 60°N, which were mainly distributed in permafrost-dominated lowland regions. At least 23.9 % of the area of TLS maps was projected to disappear under representative concentration pathway scenarios (RCPs), with increased susceptibility levels in northern Canada. The slope was the key conditioning factor for the occurrence of thermokarst lakes in Arctic permafrost regions. Compared with similar studies, the reliability of the TLS map was further evaluated using probability calibration curve and coefficient of variation (CV). Our results provide a means for assessing the spatial distribution of thermokarst lakes at the circum-Arctic scale but also improve the understanding of their dynamics in response to the climate system.
富含冰的永久冻土因北极迅速变暖而融化，地面沉降促进了热喀斯特湖的形成。热喀斯特湖改变了永久冻土的地表能量平衡，影响地貌、水文、生态和基础设施稳定性，进而可能导致温室气体排放增加。目前，大尺度上热喀斯特湖的空间分布仍是一项具有挑战性的任务。基于多种高分辨率环境因素和热喀斯特湖清单，我们使用机器学习方法来估计当前和未来热喀斯特湖易发性（TLS）地图的空间分布。我们还确定了TLS地图的关键环境因素。在1.8×10千米的尺度上，高易发性和极高易发性区域估计覆盖60°N以北地区的约10.4%，主要分布在以永久冻土为主的低地地区。在代表性浓度路径情景（RCPs）下，预计TLS地图至少23.9%的区域将消失，加拿大北部的易发性水平会增加。坡度是北极永久冻土区热喀斯特湖形成的关键制约因素。与类似研究相比，使用概率校准曲线和变异系数（CV）进一步评估了TLS地图的可靠性。我们的结果为评估环北极尺度热喀斯特湖的空间分布提供了一种方法，同时也增进了对其响应气候系统动态变化的理解。
