Thermokarst landslides (TL) caused by the thaw of ground ice in permafrost slopes are increasing on the Qinghai-Tibet Plateau (QTP), but the understanding of the spatially suitable environmental conditions including terrains and climate for them has not been fully established. Here, we applied multiple machine learning models and their ensemble to explore factors controlling the TL and map its susceptibility at a fine resolution. The models were calibrated and validated using a split-sample approach based on an inventory of TLs from the remote sensing data. The models indicated that summer air temperature and rainfall were the most two important factors controlling the occurrence and distribution of TLs, provided that other geomorphic conditions (i.e., slope, solar radiation, and fine soil) were suitable. The final ensemble susceptibility map based on downscaled climate data and terrain data suggested that ca. 1.4% of the QTP land was classified in high- to very high-susceptibility zone, which is likely to increase in response to future climate change. This study integrated local topography and climate in susceptibility modeling and provided new insights into the geomorphic sensitivity to climate change but also the engineering support over the QTP.

在青藏高原（QTP），多年冻土坡面的地面冰解冻引起的热喀斯特滑坡（TL）越来越多，但是对它们的空间适宜环境条件（包括地形和气候）的理解尚未完全建立。在这里，我们应用了多种机器学习模型及其集成，以探索控制TL的因素，并以精细的分辨率绘制其易感性。使用基于来自遥感数据的TL清单的拆分样本方法对模型进行校准和验证。这些模型表明，夏季气温和降雨是控制TL发生和分布的最重要的两个重要因素，但前提是其他地貌条件（如坡度，太阳辐射和细土）也适用。基于缩小的气候数据和地形数据的最终合奏敏感性图表明，QTP土地的1.4％被划为高敏感度到极高敏感度区域，随着未来的气候变化，该区域可能会增加。这项研究在敏感性模型中整合了当地的地形和气候，并提供了对地貌对气候变化敏感性的新见解，以及对QTP的工程支持。