Alvioli M.(1), Guzzetti F.(1), Rossi M.(1,2), 2013, Scaling Properties of Rainfall-Induced Landslides Predicted by a Physically Based Model, Geomorphology (Amst.) 213 (2013): 38–47. doi_10.1016/j.geomorph.2013.12.039,
URL: http://www.cnr.it/prodotto/i/273451

Natural landslides exhibit scaling properties, including the frequency of the size of the landslides, and the rainfall conditions responsible for landslides. Reasons for the scaling behavior of landslides are poorly known, and only a few attempts were made to describe the empirical evidences of the self-similar scaling behavior of landslides with physically based models. We investigate the possibility of using the TRIGRS code, a consolidated, physically motivated, numerical model to describe the stability conditions of natural slopes forced by rainfall, to determine the frequency of the area of the unstable slopes and the rainfall intensity-duration (I-D) conditions that result in landslides in a region.We apply TRIGRS in a portion of the Upper Tiber River Basin, Central Italy. The spatially distributed model predicts the stability conditions of individual grid cells, given the terrain and rainfall conditions. We run TRIGRS using multiple rainfall histories, and we compare the results to empirical evidences of the size of landslides and of the rainfall conditions that have caused landslides in the area. TRIGRS reproduces two scaling properties of landslides i.e., i) the size of the patches of terrain predicted as unstable by the model, that match the frequency size statistics of landslides in the study area, and ii) the mean rainfall D,I conditions that result in unstable slopes, that match rainfall I-D thresholds for possible landslide occurrence. Our results prove that even a relatively simple physically based model that describes the complex interactions controlling the stability conditions of natural slopes forced by rainfall is capable of reproducing two well known scaling properties of landslides events e.g., I-D thresholds for possible landslide occurrence, and the frequency density of landslide areas, also corroborating the robustness of the model.

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