Rainfall-induced landslides represent an important threat in mountainous areas. Therefore, a physically-based model called “Fast Shallow Landslide Assessment Model” (FSLAM) was developed to calculate large areas (>100 km²) with a high-resolution topography in a very short computational time. FSLAM applies a simplified hydrological model and the infinite slope theory, while the two most sensitive soil properties regarding slope stability (cohesion and friction angle) can be stochastically included. The model has five necessary input raster files including information of soil properties, vegetation, elevation and rainfall. The principal output is the probability of failure (PoF) map. The Principality of Andorra was selected as case study, where FSLAM was successfully applied and validated using the existing landslide inventory. The PoF raster file of Andorra (including 19 million cells) was calculated in only 2 min. Therefore, an accurate calibration of the input parameters was easy, which strongly improved the final outcomes.

降雨引起的滑坡是山区的重要威胁。因此，开发了基于物理的模型“快速浅层滑坡评估模型”（FSLAM），可以在很短的计算时间内计算出高分辨率的大面积区域（> 100 km ²）。FSLAM应用简化的水文模型和无限边坡理论，而关于边坡稳定性（内聚力和摩擦角）的两个最敏感的土壤特性可以随机包含在内。该模型有五个必要的输入栅格文件，包括土壤特性，植被，海拔和降雨量的信息。主要输出是故障概率（PoF） 地图。选择安道尔公国作为案例研究，在该案例中，FSLAM已成功应用并使用现有滑坡清单进行了验证。仅2分钟即可计算出安道尔（包括1900万个像元）的PoF栅格文件。因此，输入参数的准确校准很容易，这大大改善了最终结果。