Zhang Wenjie, Zhao Yang, Zhang Fan, Shi Xiaonan, Zeng Chen, Maerker Michael
ECMI Team, State Key Laboratory of Tibetan Plateau Earth System Science, Resources and Environment (TPESRE), Institute of Tibetan Plateau Research, Chinese Academy of Sciences (CAS), Beijing, China; University of Chinese Academy of Sciences, Beijing, China.
ECMI Team, State Key Laboratory of Tibetan Plateau Earth System Science, Resources and Environment (TPESRE), Institute of Tibetan Plateau Research, Chinese Academy of Sciences (CAS), Beijing, China.
Sci Total Environ. 2024 Nov 1;949:174949. doi: 10.1016/j.scitotenv.2024.174949. Epub 2024 Jul 25.
In the alpine region, climate warming has led to the retreat of glaciers, snow cover, and permafrost. This has intensified water cycling, soil erosion, and increased the occurrence of natural disasters in the alpine region. This study investigated the Lhasa River Basin in the southern Tibetan Plateau, serving as a representative case study of a typical alpine basin, with a specific focus on gully erosion. Based on field investigations and interpretation using high-resolution satellite remote sensing images, the Random Forest (RF) algorithm was applied to evaluate gully erosion susceptibility on watershed level. The Shapley Additive Interpretation method was then used to interpret the RF model and gain deeper insights into the influencing variables of gully erosion. The results showed that the RF model achieved an area under the receiver operating characteristic (AUC) accuracy of 0.99 and 0.98 for the training and testing datasets, respectively, indicating an outstanding performance of the model. The resulting susceptibility map based on the RF model shows that areas with moderate and higher levels of gully erosion susceptibility are covering 50 % of the basin. The model interpretation results indicated that elevation, slope, permafrost, rainstorm, silt loam topsoil, human activity, stream power, and vegetation were the explaining variables with the highest importance for gully erosion occurrence. Different variables are characterized by specific thresholds promoting gully erosion such as: i) elevations higher than 4950 m, ii) slopes steeper than 13.5°, iii) extreme rainstorms longer than 11 days per year, iv) silt loam topsoil, v) presence of permafrost, vi) stream power index higher than 1.2, and vii) normalized difference vegetation index (NDVI) lower than 0.25. Our findings provide the scientific basis to improve soil erosion control in such highly vulnerable alpine area.
在高寒地区，气候变暖导致冰川、积雪和多年冻土消退。这加剧了水循环、土壤侵蚀，并增加了高寒地区自然灾害的发生频率。本研究以青藏高原南部的拉萨河流域为典型高寒流域的代表性案例进行研究，特别关注沟壑侵蚀。基于实地调查和高分辨率卫星遥感影像解译，应用随机森林（RF）算法在流域尺度上评估沟壑侵蚀敏感性。然后使用Shapley加性解释方法对RF模型进行解释，以更深入地了解沟壑侵蚀的影响变量。结果表明，RF模型在训练数据集和测试数据集上的受试者工作特征曲线下面积（AUC）准确率分别达到0.99和0.98，表明该模型性能优异。基于RF模型生成的敏感性地图显示，沟壑侵蚀敏感性中等和较高的区域占流域面积的50%。模型解释结果表明，海拔、坡度、多年冻土、暴雨、粉质壤土表土、人类活动、水流功率和植被是对沟壑侵蚀发生影响最重要的解释变量。不同变量具有促进沟壑侵蚀的特定阈值，例如：i）海拔高于4950米，ii）坡度大于13.5°，iii）每年极端暴雨天数超过11天，iv）粉质壤土表土，v）存在多年冻土，vi）水流功率指数高于1.2，以及vii）归一化植被指数（NDVI）低于0.25。我们的研究结果为改善这种高度脆弱的高寒地区的土壤侵蚀控制提供了科学依据。
