Towards an early-warning system for rainfall-induced landslides in forested catchments: a case study in Valsassina (Northern Italy)

Rainfall-induced soil slips are one of the most common and critical natural phenomena affecting the steep slopes in mountainous regions. These soil processes cause - directly and indirectly - huge damages to human-life, infrastructures and properties, especially when evolve into rapid soil movements such as debris avalanches, debris flows, flow slides, and rockslides. In this context, a landslide risk management that includes an accurate and robust real-time landslide early warning system at large scale (catchment or regional) for assessing the triggering soil slips both in space and in time, is necessary. This purpose appears more complicated where the forest covers most of the territory of a region and landslide-triggering thresholds cannot catch the exact process. In addition, most of physically-based models for real-time landslide warning neglect the role of vegetation, which is well-recognised to be fundamental in preventing shallow soil movements. In fact, forests influence hydrological and mechanical properties of the shallower soil layers through the beneficial effects of root systems and the canopy cover (reducing soil moisture, intercepting precipitation, reinforcing the soil resistance, etc.).

The present study proposes a modified version of the physically-based stability model, SLIP (Shallow Landslides Instability Prediction), based on the limit equilibrium method applied to an infinite slope and on a simplified modelling of the water down-flow. SLIP was integrated with a quantification of the rainfall interception by the forest canopy, and of the soil reinforcement provided by root systems as a function of tree species and tree density (which are data available from the forest management plans). The adapted model was applied to two mountainous catchments located in Valsassina (Northern Italy) and almost completely covered by forests (conifers, broadleaves and mixed). The study area was affected by shallow landslides and debris flows occurred after extreme meteorological events during autumn 2018. The model accuracy was tested through a back-analysis on the recent soil slips, mapped into a landslide inventory that was produced comparing high-resolution multi-temporal satellite images. The results provide an accurate risk map, identifying the areas of sediments source that can evolve into more threating soil movements.

The specific development of more accurate physically-based model can reasonably be an important tool for landslide risk management. Combined with a radar rainfall forecasting method, SLIP can be useful for addressing the real-time civil protection response to the emergencies. Moreover, the proposed method can play a key role in identifying the priorities inside the catchment management strategy, e.g. removing accumulated sediments in reservoirs, designing additional geotechnical or soil-bioengineering countermeasures, or evaluating the protection function of the forests.