Predictive power of a shallow landslide model in a high resolution landscape: dissecting the effects of forest roads

Abstract

This work evaluates the predictive power of the quasi-dynamic shallow landslide model QD-SLaM to simulate shallow landslide locations in a small-scale Mediterranean landscape: the Giampilieri catchment located in Sicily (Italy). The catchment was impacted by a sequence of high-intensity storms over the years 2007-2009. The effect of high 5 resolution Digital Terrain Models (DTMs) on the quality of model predictions is tested by considering four DTM resolutions: 2 m, 4 m, 10 m and 20 m. Moreover, the impact of the dense forest road network on the model performance is evaluated by considering separately road-related landslides and natural landslides. The landslide model does not incorporate the description of road-related failures. The model predictive power is 10 shown to be DTM-resolution dependent. When assessed over the sample of mapped natural landslides, better model performances are reported for 4 m and 10 m DTM resolution , thus highlighting the fact that higher DTM resolution does not necessarily mean better model performances. Model performances over road-related failures are, as expected , lower than for the other cases. These findings show that shallow landslide 15 predictive power can benefit from increasing DTM resolution only when the model is able to describe the physical processes emerging at the smaller spatial scales resolved by the digital topography. Model results show also that the combined use of high DTM resolution and a model capable to deal with road-related processes may lead to substantially better performances in landscapes where forest roads are a significant factor 20 of slope stability.