Exploiting the land use to predict shallow landslide susceptibility : a probabilistic implementation of LAPSUS-LS

Kurzfassung/Abstract

Due to the significant role of land use on the occurrence of rainfall-induced shallow landslides, this factor is commonly employed as a landslide susceptibility predictor. However, the land use classification is oftentimes very broad, neglecting the proven mechanical and hydrogeological role of the land management on slope stability. Given the necessity of including spatially distributed and management-specific inputs, the process-based landscape evolution model LAPSUS-LS was chosen and adapted to achieve a probabilistic approach which takes into account land management as an input by adopting management-specific values of root cohesion. The model was applied to four test sites in the Oltrepò Pavese (Italy), where different vineyard management techniques play a significant role in triggering landslides. The results for the four test areas had, cumulatively, an Area Under the Roc curve greater than 0.73, with false negative cells being < 1 % of the total for all simulations. In the model’s application, land use practices characterised by higher root cohesion proved to benefit slope stability, whereas tilled vineyards, shrublands and abandoned vineyards were more prone to the formation of shallow landslides. In addition, we found that the inclusion of management-specific input parameters produced more accurate outputs and that in catchments characterised by average slope angles lower than 15°, varying the vineyard management, did not appear to affect the landslide susceptibility. Due to the model’s high dependency on the land use and its ability to include land management, it can take into account the spatial variability of input values such as the root cohesion. Additionally, it can be applied i) to manage current conditions, ii) to explore future land use change, iii) to study less invasive yet beneficial land use management change scenarios and iv) provide farmers of at-risk areas insight on how to improve slope stability.