A probabilistic model for slope stability analysis including the root reinforcement effects

The presence of vegetation plays an important role in slope stability, especially in triggering of shallow landslides. It influences the mechanical and hydrological behaviour of soils, generating both stabilizing and destabilizing actions 1, 2. Variation in vegetation related to land use change can affect slope stability and can be evidenced in terms of variation of probability of failure. In this study we implement a module for the calculation of root reinforcement in the slope stability physically-based probabilistic model PGTRIGRS (Probabilistic, Geostatistic-based, TranSient Rainfall Infiltration and Grid-based Slope stability, 3). Such model allows the wide-area assessment of the probability of rainfall-induced failure, considering the spatial variability of the soil properties treated as random variables. In this work, we apply the model to an area prone to landslides in Central Italy assuming the spatial variability of vegetation. To investigate the influence of the spatial layout of plant roots on slope stability, the root reinforcement is implemented in the PGTRIGRS probabilistic model. The considered root cohesion values were derived from literature and were determined according to vegetation maps available for the study area. In addition, root cohesion variation is also considered along the vertical profile as a function of rooting depth. Finally, the resulting probability of failure distribution is compared to the results obtained for the bare soil with the absence of roots. 1 Pollen-Bankhead, N. , Simon, A. (2010). Hydrologic and hydraulic effects of riparian root networks on streambank stability: Is mechanical root-reinforcement the whole story? . Geomorphology, 116 (3-4), 353-362. 2 Masi, E. B. , Segoni, S. , Tofani, V. (2021). Root reinforcement in slope stability models: a review. Geosciences, 11 (5), 212. 3 Salciarini, D. , Fanelli, G. , Tamagnini, C. (2017). A probabilistic model for rainfallinduced shallow landslide prediction at the regional scale. Landslides, 14, 1731-1746.