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 geo-morphology, 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 x 106 km2, high and very high susceptible regions were estimated to cover about 10.4 % of the region poleward of 60 degrees 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.

s The rapidly warming climate on the Qinghai-Tibet Plateau (QTP) leads to permafrost degradation, and the thawing of ice-rich permafrost induces land subsidence to facilitate the development of thermokarst lakes. Thermokarst lakes exacerbate the instability of permafrost, which significantly alters regional geomorphology and hydrology, affecting biogeochemical cycles. However, the spatial distribution and future changes in thermokarst lakes have rarely been assessed at large scales. In this study, we combined various conditioning factors and an inventory of thermokarst lakes to assess the spatial distribution of susceptibility maps using machine-learning algorithms. The results showed that the extremely randomized trees (EXT) performed the best in the susceptibility modeling process, followed by random forest (RF) and logistic regression (LR). According to the assessment based on EXT, the high- and very high-susceptibility area of the present (2000-2016) susceptibility map was 196,222 km(2), covering 19.67% of the permafrost region of the QTP. In the future (the 2070s), the area of the susceptibility map was predicted to shrink significantly under various representative concentration pathway scenarios (RCPs). The susceptibility map area would be reduced to 37.06% of the present area in RCP 8.5. This paper also performed correlation and importance analysis on the conditioning factors and thermokarst lakes, which indicated that thermokarst lakes tended to form in areas with flat topography and high soil moisture. The uncertainty of the susceptibility map was further assessed by the coefficient of variation (CV). Our results demonstrate a way to study the spatial distribution of thermokarst lakes at the QTP scale and provide a scientific basis for understanding thermokarst processes in response to climate change.

Thermokarst lakes (TLs) caused by the thaw of massive ground ice in ice-rich permafrost landscapes are increasing and have strong impacts on the hydro-ecological environment and human infrastructure on the Qinghai-Tibet Plateau (QTP), however, its spatial distribution characteristics and environmental controls have not been underrepresented at the local scale. Here, we analyzed the spatial distribution of small TLs along the Qinghai-Tibet Engineering Corridor (QTEC) based on high-resolution (up to 2.0 m) satellite images. The TLs gathered in the plains and upland plateau and covered 8.3% of the QTEC land. We deployed a random-frost method to investigate the suitable environmental conditions for TLs. Climate including summer rainfall and the air temperature was the most important factor controlling the TL distribution, followed by topography and soil characteristics that affected the ground ice content. TL susceptibility was mapped based on the combinations of climate, soil, and topography grid data. On average, around 20% of the QTEC area was in a high to very-high-susceptibility zone that is likely to develop TLs in response to climate change. This study improved the understanding of controlling factors for TL development but also provided insights into the conditions of massive ground ice and was helpful to assess the impacts of climate change on ecosystem processes and engineering design.