Shallow landslides induced by rainfall are very common movements that occur in hilly and mountainous areas causing losses of human life, ecological and environmental impacts and considerable economical costs. The predisposing factors for shallow landslides are represented by morphology, lithology, soil type, land cover, and land use, and their changes. Land use is constantly evolving because it is linked to human activities; the increase of population pressure and economic development forced more people to use all areas available. A lot of scientific contributions analyzed the positive effects of vegetation cover on slope stability, focusing on the mechanical effects of vegetation (both vegetation cover and roots) in terms of providing additional mechanical root reinforcement. Conversely, the effects of agricultural practices on slope stability conditions are poorly investigated. Indeed, while agriculture contributes positively to the landscape, biodiversity, climate and fires, on the other hand, improper agricultural practices and soil uses can modify the mechanical properties of the involved soils leading to a possible increase of instability phenomena. To evaluate the effect of agricultural practices on the slope stability conditions, we present an application of a probabilistic, physically-based model for the triggering of rainfall-induced landslides (PG_TRIGRS - a probabilistic, geostatistic-based extension of the TRIGRS model) to the Collazzone area, a cultivated area located in Umbria, central Italy, characterized by a high susceptibility to landslides, which is studied and periodically monitored through systematic image analysis and on-site surveys. The method applied in this research included the comparison between landslides observed in situ and the spatial distribution of the probability of failure derived from the application of PG_TRIGRS model, which models the study area in a GIS grid and treats each cell as an infinite slope. More in detail, a heuristic approach was adopted_ after a first run of the model with unbiased parameters, the slope stability analysis has been carried out assuming several percentages of reduction of the effective soil cohesion (c') to mimic an increasing impact of agricultural practices on the strength conditions. A back-analysis methodology, with the support of sensitivity indices, was adopted to provide a preliminary quantitative evaluation of the effect induced by agricultural practices on the mechanical properties of the soil. To test the reliability of the method, standard contingency matrix and skill scores were adopted and the best compromise between correct and incorrect model outcomes was obtained considering a reduction of c' between 20% and 30%. In conclusion, we could estimate that in the analyzed area the agricultural practices can cause a 20 to 30% reduction in soil.

Several territorial landslide early warning systems in different parts of the world are based on empirical rainfall thresholds for landslide triggering. The calculation of such thresholds, using rainfall measurements gathered from rain gauges, has been examined frequently, especially considering uncertainties, modeling complexity, spatial assumptions, and analytical tools. Installed rain gauge networks that are spatially clustered in crowded areas have different spatial and attribute settings based on landslide occurrence conditions, such as rainfall record accessibility, processing, and usability, as well as specific locational, morphological, and hydrological settings. In this research work, we introduce an automatic tool called DEWS (Distance, Elevation, Watershed, and Slope unit) for rainfall-induced landslide spatial reference rain gauge selection. DEWS can be considered supplementary and complementary to the CTRL-T tool (Calculation of Thresholds for Rainfall-induced Landslides Tool) developed earlier, and works on a macro-to-micro scale of the spatial components of CTRL-T rain gauge selections. The output information, i.e. the list of selected reference rain gauges, can be used as input for CTRL-T to calculate frequentist rainfall thresholds at different non-exceedance probabilities. The DEWS tool fills the gap of the current literature, where the selection of reference rain gauges is mostly based on the nearest distance location and on statistical or manual procedures, without considering the morphological and hydrological settings of the area in which landslides occurred. The tool allows extracting rain gauges referring to landslide locations by employing four spatial filters_ F1 (Distance), F2 (Elevation), F3 (Watershed), F4 (Slope unit), needing only a DEM, the coordinates of landslide and rain gauge locations and the parameters of the filter's algorithms as inputs. More in detail, F1 selects rain gauges within a specified buffer distance from the landslide locations using the setting parameters and the coordinates of the landslides and rain gauges. Then, F2 uses the DEM to extract the elevation of the rain gauges and the landslides and then calculates the differences within each buffer circle; therefore, the filter keeps only the rain gauge with closest elevation values to each landslide (within F1 results) using the recommended/preferred/ or allowable elevation difference defined by the parameter's settings. In F3, the rain gauges falling in the watershed that contains the landslide locations are extracted (within F1 and F2 results). F4, which is the smallest and most focused filter, uses a previously developed tool pack (within F1, F2, and F3 results) to extract the slope units associated with each landslide. Consequently, only the rain gauges falling within these slope units are selected. DEWS was implemented in a free tool pack in QGIS software, with default parameter values for non-expert users. The tool pack is divided into three main blocks following the filter structure (F1 and F2 are kept together). The reliability of DEWS was tested at a territorial scale in South Korea, using 223 landslides and 328 rain gauges. As a second step, frequentist rainfall thresholds were calculated in the study area.

The occurrence of rainfall-induced shallow landslides is frequently caused by an interplay of predisposing environmental factors and dynamic preparatory and triggering conditions. For large-area assessments and for regional early warning, event-based landslide inventories are often employed to establish critical rainfall thresholds using statistical procedures (e.g., non-exceedance probability curves). These approaches typically put the spotlight on rainfall conditions associated with known landslide occurrences. Not accounting for rainfall conditions that did not induce slope instability comes along with a variety of criticalities, such as the impossibility to discriminate landslide from non-landslide rainfall conditions or the difficulty to validate the results. This contribution proposes a data-driven approach based on Generalized Additive Mixed Models (GAMM) to identify season-dependent shallow landslide rainfall conditions for the province of South Tyrol, Italy. The work builds upon high resolution gridded daily rainfall data and landslide observations for the period from 2000 to 2020. The workflow comprised an initial filtering of rainfall-induced landslides (presence data) and a rule-based stratified random sampling procedure to select non-landslide rainy days at the same locations (absence data). The time periods (time windows in days) to describe preparatory and triggering cumulative rainfall conditions were determined using an optimization procedure based on cross validation. In addition to modelling a yearly effect, a circular day-of-the-year variable was included in the model to consider additional seasonal influences. The underlying nested data structure (i.e., repeated measurements at each landslide location) was accounted for via a location-dependent random intercept. The resulting probability scores for the analysed variables were validated using space-time cross validation, visualized in the form of probability surface plots and complemented with quantitative thresholds (e.g., curves that optimally separate landslide presences and absences). Validation of the model showed a high capability to distinguish the two groups (presence vs. absence observations). The results further indicate that the temporal prediction of shallow landslides in South Tyrol can be improved by accounting for systematic seasonal effects other than triggering and preparatory rainfall variables. This novel approach is flexible and will further be extended to derive space-time predictions. Strengths and limitations for regional landslide early warning will be discussed. The research leading to these results are related to the Proslide project that received funding from the research program Research Südtirol/Alto Adige 2019 of the Autonomous Province of Bozen/Bolzano - Südtirol/Alto Adige.

Landslides are among the most dangerous natural hazards, especially in developing countries. In these areas, where rain gauge networks are scarce, satellite rainfall products can be a viable alternative for landslide prediction. To date, only a few studies have investigated the capability and effectiveness of these products in regional-scale landslide prediction. We performed a comparative study on the reliability of ground-based rainfall products and satellite rainfall products for landslide prediction in India. We used a catalog of 197 rainfall-induced landslides over the 13-year period between April 2007 and October 2019. We calculated frequentist rainfall thresholds using GPM, SM2RAIN-ASCAT satellite products, and their merging, at daily and hourly temporal resolution, and ground-based data from the rainfall network of the Indian Meteorological Department (IMD) at daily resolution. The results indicate that satellite rainfall products outperform ground-based observations in the prediction of landslides due to their improved spatial (0.1° vs. 0.25°/pixel) and temporal (hourly vs. daily) resolutions. The best performance is achieved through the merging of GPM and SM2RAIN-ASCAT. These results open up the possibility for using satellite rainfall products in landslide early warning systems, particularly in poorly gauged areas.

Earthquake ground motion is the primary driver of earthquake-triggered landslides; however, their predictability is poor. We use Newmark's critical acceleration method to map landslides triggered by the 2015 Gorkha earthquake in central Nepal. In this method, we compare the critical acceleration of a block (landslide pixel or slope unit) with the peak ground acceleration (PGA) to assess landslide susceptibility. We analyse earthquake-triggered landslide occurrence on each grid cell and each slope unit using two different sources of peak ground acceleration (from USGS ShakeMap and KTP seismic station). A total of four susceptibility maps were developed, which were compared with the existing landslide inventory of the 2015 Gorkha earthquake and validated using the Matthews correlation coefficient. Results from this study showed that the slope unit-based mapping using ShakeMap's PGA yields the best results. The landslide susceptibility map generated using this technique resembles well with the landslide inventory map and can be used for prioritizing rapid earthquake response, detailed investigations and planning.