Abstract. Worldwide, shallow landslides repeatedly pose a risk to infrastructure and residential areas. To analyse and predict the risk posed by shallow landslides, a wide range of scientific methods and tools to model shallow landslide probability exist for both local and regional scale However, most of these tools do not take the protective effect of vegetation into account. Therefore, we developed SlideforMap (SfM), which is a probabilistic model that allows for a regional assessment of shallow landslide probability while considering the effect of different scenarios of forest cover, forest management and rainfall intensity. SfM uses a probabilistic approach by distributing hypothetical landslides to uniformly randomized coordinates in a 2D space. The surface areas for these hypothetical landslides are derived from a distribution function calibrated from observed events. For each randomly generated landslide, SfM calculates a factor of safety using the limit equilibrium approach. Relevant soil parameters, i.e. angle of internal friction, soil cohesion and soil depth, are assigned to the generated landslides from normal distributions based on mean and standard deviation values representative for the study area. The computation of the degree of soil saturation is implemented using a stationary flow approach and the topographic wetness index. The root reinforcement is computed based on root proximity and root strength derived from single tree detection data. Ultimately, the fraction of unstable landslides to the number of generated landslides, per raster cell, is calculated and used as an index for landslide probability. Inputs for the model are a digital elevation model, a topographic wetness index and a file containing positions and dimensions of trees. We performed a calibration of SfM for three test areas in Switzerland with a reliable landslide inventory, by randomly generating 1000 combinations of model parameters and then maximising the Area Under the Curve (AUC) of the receiver operation curve (ROC). These test areas are located in mountainous areas ranging from 0.5–7.5 km², with varying mean slope gradients (18–28°). The density of inventoried historical landslides varied from 5–59 slides/km². AUC values between 0.67 and 0.92 indicated a good model performance. A qualitative sensitivity analysis indicated that the most relevant parameters for accurate modeling of shallow landslide probability are the soil depth, soil cohesion and the root reinforcement. Further, the use of single tree detection in the computation of root reinforcement significantly improved model accuracy compared to the assumption of a single constant value of root reinforcement within a forest stand. In conclusion, our study showed that the approach used in SfM can reproduce observed shallow landslide occurrence at a catchment scale.

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介绍SlideforMap；森林条件下浅层滑坡概率建模的概率有限斜率方法

摘要。在全球范围内，浅层滑坡屡次对基础设施和居民区构成风险。为了分析和预测浅层滑坡带来的风险，在地方和区域范围内都存在多种模拟浅层滑坡概率的科学方法和工具。但是，这些工具大多数都没有考虑到植被的保护作用。因此，我们开发了SlideforMap（SfM），这是一种概率模型，可以在考虑森林覆盖率，森林管理和降雨强度等不同情景的影响的情况下，对浅层滑坡概率进行区域评估。SfM使用概率方法，将假设的滑坡分布到2D空间中的统一随机坐标。这些假设的滑坡的表面积是根据观测到的事件校准后的分布函数得出的。对于每个随机产生的滑坡，SfM使用极限平衡法计算安全系数。根据代表研究区域的平均值和标准偏差值，将相关的土壤参数（即内摩擦角，土壤内聚力和土壤深度）分配给从正态分布生成的滑坡。土壤饱和度的计算是使用固定流方法和地形湿度指数来实现的。根据从单棵树检测数据得出的根邻近度和根强度来计算根加固。最终，每个栅格像元中不稳定滑坡占生成滑坡数量的比例，被计算出来并用作滑坡发生率的指标。该模型的输入是数字高程模型，地形湿度指数以及包含树木位置和尺寸的文件。通过随机生成1000个模型参数组合，然后最大化接收器工作曲线（ROC）的曲线下面积（AUC），我们对瑞士三个测试区域的SfM进行了可靠的滑坡清单校准。这些测试区域位于0.5-7.5公里的山区 通过随机生成1000个模型参数组合，然后最大化接收器操作曲线（ROC）的曲线下面积（AUC）。这些测试区域位于0.5-7.5公里的山区 通过随机生成1000个模型参数组合，然后最大化接收器操作曲线（ROC）的曲线下面积（AUC）。这些测试区域位于0.5-7.5公里的山区²，具有不同的平均斜率梯度（18–28°）。盘存的历史滑坡的密度在5–59个滑坡/ km ^{2之间变化}。0.67至0.92之间的AUC值表明模型性能良好。定性敏感性分析表明，对浅层滑坡概率进行精确建模的最相关参数是土壤深度，土壤黏附力和根部加固。此外，与在林分中假设单个单一的根系加固值相比，在根系加固计算中使用单个树检测显着提高了模型准确性。总之，我们的研究表明，SfM中使用的方法可以在流域尺度上再现观察到的浅层滑坡的发生。