No abstract

<p>Shallow landslides are one of the most frequent gravitative natural hazards in the Alpine region that could affect human infrastructures. Forests can play a direct protective function, preventing the triggering of such events thanks to the role they play in water regulation and mechanical effects, in particular with root reinforcement. Few studies, however, report empirical assessments, based on after-events shallow landslides inventory, of the protective effects given by the presence of the forest on this natural hazard. With this study, an attempt was made to assess the possible influence of the presence of the forest on the topographic triggering conditions and the magnitude of the landslides respect to those triggered in open lands. A comparison was then developed between the structural characteristics of forest stands, in which landslides were recorded, and the reference parameters of the protection forests guidelines. In addition, it has been evaluated how root reinforcement can have an influence at a local scale on the location of shallow landslide triggering. Finally, a subsample of the forest landslides was selected for field surveys, in order to analyze the influence of stand structure on the magnitude of landslides. The study area corresponds to the territory of upper Agordino valley (405 km<sup>2</sup>), in the Veneto Region (Italy), which was severely affected by storm Vaia in October 2018 that caused widely the trigger of numerous shallow landslides and large windthrows. Through the analysis of the orthophotos pre and post-event and the Dem of Difference of DTMs, overall 469 (116 triggered in the forest) shallow landslides were identified with median values of area of 177 m<sup>2</sup> and volume of 163 m<sup>3</sup>. In terms of density, forest landslides are less frequent than those in open lands and are triggered on slopes with higher inclination. Forest stands where landslides were recorded show median values of coverage of 60%, gap area of 551 m<sup>2</sup>, gap length of 18 m, and gap width of 16 m. It turned out that comparing to the silvicultural guidelines on the management of protection forests, the most important parameter appears to be the gap length. Such gaps represent the weakest zone in terms of root reinforcement where the landslides can be triggered more easily. This has been confirmed by the application, at the plot scale, of the SOSlope model (Cohen and Schwarz, 2017) which results that most of the landslide scarps (42 out of 53) were located in the zones with the lowest lateral root reinforcement. A multivariate analysis carried out on data collected in the field on a subsample of 20 forest landslides highlights that landslides with higher volume and area were recorded mostly in young forests with high density. A stand with a good amount of large trees and an uneven-aged structure seems to be the most effective in these terms. These results emphasize the protective effects of forests against shallow landslides and suggest the need for their optimal silviculture management, taking also into account the increasing susceptibility to other natural disturbances which could compromise the protective function.</p>

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<p>Landslide susceptibility maps are often not validated after significant landslide events. In this work, we analyse the impact of the Vaia windstorm on landslide activity in Belluno province (Veneto Region, NE, Italy). The storm hit the area on October 27-30, 2018, causing 8,679 ha of damaged forests and widespread landslides. As shown in the case of windstorm Vivian (1990) and Lothar (1999) (Switzerland), extreme meteorological events can influence slope stability after three to ten years (Bebi et al 2019). Through multi-temporal landslide inventory mapping post Vaia event, we want to access and validate the landslide susceptibility maps produced by using pre-event data from the Italian Landslide Inventory IFFI and assess if the susceptibility has increased in the areas affected by the storm. We used artificial intelligence techniques to prepare multi-temporal inventory and susceptibility maps pre and post-event. In the pre-event event inventory, 5934 landslides and 14 landslide conditioning factors were used to prepare the susceptibility models. We then validate the pre-event landslide susceptibility maps using post-event inventory from the 2018 Vaia windstorm and a following intense rainfall event that occurred in the same area in December 2020. A total of 542 landslides were mapped after the 2018 Vaia storm event, and an update to the landcover map as forest damage layer was used for post-event susceptibility analysis. This study is one of the first attempts to validate pre-event susceptibility maps by utilising multi-temporal artificial intelligence-based landslide inventories in Belluno province (Veneto Region, NE, Italy).</p><p> </p><p><em>Bebi, P., Bast, A., Ginzler, C., Rickli, C., Schöngrundner, K., and Graf, F., 2019, Forest dynamics and shallow landslides: A large-scale GIS-analysis: Schweizerische Zeitschrift fur Forstwesen, v. 170, p. 318–325, doi:10.3188/szf.2019.0318.</em></p>