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The Campania region, in Southern Italy, is affected by hundreds of wildfires every year, mainly during the summer season. Starting from the month of September, mountain watersheds including those hit by wildfires are impacted by even more frequent intense rainstorms. In such conditions, the high sediment availability, lack of recovered vegetation and a likely stronger soil water repellency increase the likelihood of surface runoff and soil erosion, leading to potential post-fire debris flows downstream.This work provides information on more than 100 post-fire debris flows (PFDFs) that occurred in Campania between 2001 and 2021, with a particular focus on the triggering rainfall conditions. Rainfall measurements at a high temporal resolution (10 min) were gathered from a dense rain gauge network, with an average distance between sensors and PFDFs initiation areas of 2.6 km. Information on the occurrence of PFDFs was obtained from web news, social networks, and reports produced by the Fire Brigades. The collection of accurate information related to the debris flow timing and location allowed retrieving and analyzing properties of the triggering rainfall inputs, by identifying the minimum triggering conditions with rainfall thresholds. Moreover, to evaluate the temporal structure and type of the storms associated with the PFDFs (e.g., convective or frontal systems), the standardized rainfall profiles of the triggering events were defined. The return times of the peak cumulative rainfall of the bursts in 10, 20, and 30 minutes were also calculated.Results show that the triggering rainfall events are very short (37 minutes on average), with high average intensity (73.2 mm/h and 49 mm/h in 10 and 30 minutes, respectively), and mostly associated with severe convective systems (i.e., thunderstorms). The estimated return times are quite low, with 75° percentiles of the related distribution ranging from 2.7 to 3.2 years, indicating that these rainfall events are neither rare nor extreme, as also observed by other authors worldwide. Differences are observed in return times and the spatial distribution of the events that occurred in July-September (higher rainfall magnitudes and longer return times) rather than in October-December. The time window in which PFDFs are more likely to occur in the study area has an extension of four months, from September to December. According to the defined triggering rainfall threshold, a rainfall of 11.4 mm in 30 minutes (corresponding to an average intensity of 22.8 mm/h) is likely sufficient to trigger a PFDF in the study area.These research outcomes provide reliable and effective support to inform decision-makers engaged in hazard assessment and risk management, in order to implement suitable countermeasures in terms of monitoring and early warning systems. It is worth noting that PFDFs often occur in small-scale watersheds characterized by very short concentration times, in response to intense bursts of less than 60 minutes. This means insufficient lead time to fully develop an effective emergency response. This and other criticalities represent serious challenges requiring additional work.

Young generation will have to manage, in the not-too-distant future, the increase of climate-change-related hazards. Infact, extreme rainfall events, increases in mean temperatures, decreases in mean rainfall, tropical cyclones, thunderstorms, droughts are already having a significant impacts on people’s life and to the environment. In light of this, the more young people will be aware of the climate change (CC) and will know how to deal with the risks induced by it, the more they will be able to take measures and adopt behaviors to reduce negative impacts as well as to influence the choices of policy makers. For this reason, knowing the perception, the degree of knowledge and the preparedness of young people to these issues is becoming increasingly important.By means of a quantitative research method (structured questionnaire), a survey on a group of Italian high public school students, aged between 13 and 20 and located in central (Umbria region) and southern (Calabria region) Italy, was carried out. This survey focused on young people awareness, perception and preparation concerning natural risks, including landslide and flood risk, and climate change. The survey's results show the following informations: a) the questioned students are aware of the CC issue and they generally believe that human actions and lifestyle affect CC; moreover, young people are more worried than adults about how CC may affect nature and the environment; b) the findings confirm the close relationship between youth and the media in terms of perception, information, and understanding of CC and its impacts; c) besides believing that better risk communication between authorities and citizens is necessary, the students have low level of trust in local administrations and on the policy regarding geo-hydrological risk; d) young interviewed who participated in a specific educational activity on landslide and flood hazard revealed that they had a good understanding of the triggering processes of these phenomena. Even though the samples is not highly representative of the whole Italian young population, the survey shows that Italian political institutions need to plan responsible, morally sound, and convincing activities in order to win over citizens' credibility and trust, especially younger ones. Additionally, it is crucial that scientists collaborate with politicians, policy makers, and schools to i) promote shared and sustainable actions involving public, particularly the younger generation, and thus increasing people resilience; and ii) develop an integrated and multidisciplinary approach to comprehend a complex phenomenon like climate change and its effects on people and environment.

India is heavily affected by rainfall-induced landslides that cause fatalities and damage. Therefore, the development of effective and reliable models for the landslide forecasting and their possible integration in early warning systems (LEWSs) is necessary to mitigate the risk posed by such phenomena. Within the LANDSLIP (LANDSLIde Multi-Hazard Risk Assessment, Preparedness and Early Warning in South Asia: Integrating Meteorology, Landscape and Society; www.landslip.org) project, we developed threshold-based forecasting models to predict the occurrence of rainfall-induced landslides. The models were calibrated in two Indian pilot areas: the Darjeeling and Nilgiris districts, in the states of West Bengal and Tamil Nadu, respectively. For the purpose, we built two catalogs of 84 and 116 rainfall conditions likely responsible for landslide triggering in Darjeeling and Nilgiris, respectively, and daily rainfall measurements, which were used to define frequentist rainfall thresholds at different non-exceedance probabilities by means of an automatic tool (CTRL-T). A revision of the methodology to identify the rainfall conditions that triggered the failures was necessary due to possible inaccuracies in the landslide occurrence date and the daily temporal resolution of rainfall measurements in India. Triggering rainfall conditions were also related to the different monsoon regimes in the study areas. For a few uncertain events, the rainfall conditions automatically reconstructed by CTRL-T were revised after a consensus among several investigators. In agreement with the rainfall regimes of the two pilot areas, the thresholds for Darjeeling are higher than those for Nilgiris; regardless of the rainfall duration, a larger amount of rainfall is necessary to trigger landslides in the Darjeeling area. Despite some limitations, mostly due to the daily temporal resolution of rainfall data and the spatial and temporal distribution of the reported landslides, the uncertainties of the calculated thresholds were acceptable (also thanks to the double checking) to allow their implementation in the LANDSLIP prototype LEWS. The thresholds require ongoing evaluation and refinement. For the purpose, additional landslide and rainfall data were used to validate thresholds and improve forecasts.

The increasing availability of long-term observational data can lead to the development of innovative modelling approaches to determine landslide triggering conditions at regional scale, opening new avenues for landslide prediction and early warning. This research blends the strengths of existing approaches with the capabilities of generalized additive mixed models (GAMMs) to develop an interpretable approach that identifies seasonally dynamic precipitation conditions for shallow landslides. The model builds upon a 21-year record of landslides in South Tyrol (Italy) and separates precipitation that induced landslides from precipitation that did not. The model accounts for effects acting at four temporal scales: short-term “triggering” precipitation, medium-term “preparatory” precipitation, seasonal effects and across-year data variability. It provides relative landslide probability scores that were used to establish seasonally dynamic thresholds with optimal performance in terms of hit and false alarm rates, as well as additional thresholds related to user-defined performance scores. The GAMM shows a high predictive performance and indicates that more precipitation is required to induce a landslide in summer than in winter/spring, which can presumably be attributed mainly to vegetation and temperature effects. The discussion illustrates why the quality of input data, study design and model transparency are crucial for landslide prediction using advanced data-driven techniques.

Italy is frequently hit and damaged by landslides, resulting in substantial and widespread disruptions. In particular, slope failures have a high impact on the population, communication infrastructure and the economic and productive sectors. The hazard posed by landslides requires adequate responses for landslide risk mitigation, with special attention to the risk to the population. In 2006 the Italian Department of Civil Protection, an Office of the Prime Minister, commissioned the Research Institute for Geo-Hydrological Protection (Istituto di Ricerca per la Protezione Idrogeologica), a research institute of the Italian National Research Council, to carry out operational forecasting of rainfall induced-landslides. Collecting landslide information in a catalogue is a preliminary action toward landslide forecasting. The use of spatially and temporally inaccurate landslide catalogues results in an uncertain and unreliable operational landslide forecasting. Consequently, accurate catalogues are needed to reduce the uncertainties, which are to some extent unavoidable. To this end, over the last 15 years many researchers have been involved in compiling a catalogue called ITALICA (ITAlian rainfall-induced LandslIdes CAtalogue), which currently lists 6312 records with information on rainfall-induced landslides that occurred over the Italian territory between January 1996 and December 2021. Overall, more than a third of the catalogue has very high geographic accuracy (less than 1 km2) and hourly temporal resolution. In contrast, less than 2 % of the catalogue have low and very low geographical accuracy and daily temporal resolution. This makes ITALICA the largest catalogue of rainfall-induced landslides accurately located in space and time available in Italy. Without this high level of accuracy, the precipitation responsible for the initiation of landslides cannot be reliably reconstructed, thus making the prediction of landslide occurrence ineffective. ITALICA's information on rainfall-induced landslides in Italy places a special emphasis on their spatial and temporal location, making the catalogue especially suitable for defining the rainfall conditions capable of triggering future landslides on the Italian territory. This information is fundamental for decision-making in landslide risk management.

In India, rainfall-induced landslides cause a high toll in terms of fatalities and damages. Therefore, the adoption of tools to predict the occurrence of such phenomena is urgent. For the purpose, the LANDSLIP project aimed at developing a landslide early warning system (LEWS) to forecast the occurrence of rainfall-induced landslides in two Indian pilot areas: Darjeeling and Nilgiris. Rainfall thresholds are a widely used tool to define critical probability levels for the possible occurrence of landslides in large areas, and are particularly suitable to be implemented in LEWSs.In this work, we exploited two catalogues of 84 and 116 rainfall conditions likely responsible for landslide triggering in Darjeeling and Nilgiris, respectively. Adopting a frequentist statistical method and using an automatic tool, we determined rainfall thresholds at different non-exceedance probabilities for the two pilot areas. Despite the daily temporal resolution of rainfall data and the spatial and temporal distribution of the documented landslides, the thresholds calculated for the two areas have acceptable uncertainties and were implemented in the LANDSLIP LEWS prototype. We expect that the new thresholds and the whole system will contribute to mitigate the landslide risk in the study areas.

Shallow landslides are frequently caused by an interplay of static predisposing factors (e.g., topography), mid-term preparatory factors (e.g., prolonged rainfall, seasonal changes of vegetation, snow melt), and short-term triggers (e.g., heavy rainfall). For large-area assessments, statistical analyses and data-driven approaches are often used to model landslide susceptibility based on spatial environmental variables or to derive critical landslide-triggering rainfall conditions through the definition of empirical rainfall thresholds. Attempts to integrate the spatial and temporal domains in the context of quantitative regional-scale landslide prediction are still rare.This contribution focuses on the landslide-prone area of South Tyrol, northern Italy (7,400 km²) and presents a novel data-driven modelling procedure that integrates spatial predisposing factors and dynamic preparatory and triggering factors to predict the probability of landslide occurrence in space and time. The approach is based on time-stamped landslide inventory data from 2000 to 2021, high-resolution gridded daily precipitation observations for the same period, and a set of relevant static environmental variables (e.g., including topographic indices, lithology). Data preparation included an initial filtering of rainfall-induced landslide presence observations and a rule-based stratified sampling of landslide absence observations at landslide locations and at non-landslide locations. Cross-validation was implemented in the model developing stage to select optimal time windows to represent pre-landslide preparatory and triggering cumulative rainfall conditions. Modelling was based on a binomial Hierarchical Generalized Additive Model (HGAM) that considered the non-linear influence and interactions (i.e., via tensor products) of static and dynamic environmental variables on landslide occurrence (presence vs. absence) while simultaneously accounting for the nested data structure (i.e., multiple considerations of each location) and seasonal effects. The study also considered different biases inherent in the input data, such as the known underrepresentation of landslide data in locations far from infrastructure or potential reporting biases across years, by averaging-out associated random effect variables. The results were validated quantitatively (spatial and temporal cross-validation) and qualitatively (geomorphic plausibility) and visualized as maps and probability surface plots.The assessment and validation confirmed the high generalizability and predictive performance of the model. A closer look at the derived relationships allowed to uncover the effects of predisposing, preparatory and triggering factors on landslide occurrence as well as the associated season-dependent variations. From our perspective, this new approach represents a good compromise between the high model flexibility for landslide prediction purposes and the high interpretability for understanding the underlying relationships.The research leading to these results is 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.

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.

In Alpine regions changes in seasonal climatic parameters, such as temperature, rainfall, and snow amount have already been observed. Specifically, in the South Tyrol area, meteorological observations indicate that temperatures are increasing and the number of snow days has generally diminished over time with perennial snow line now observed at higher elevations. Changes in rainfall have also been observed with more events associated with higher temperatures in the summer season. Natural hazards - mainly debris and mud flows, landslides, avalanches, rock falls, and (flash) floods - that affect this area every year, damaging population and infrastructures, are either weather or cryosphere-related. While these events have been recorded sporadically since the beginning of the 20th century, a systematic approach of their inventory has been done by local authorities since the 1990s. So far, Earth observation data has not been exploited to complete or complement existing inventories nor have they been used to investigate the influence of climate perturbation on potentially dangerous natural phenomena. The research presented here thus has three objectives: (i) analyse long time series of climate data and hazard occurrence in South Tyrol to examine if these records exhibit a coherent response of hazards to changes in climate; (ii) measure the spatio-temporal evolution of climatic and natural hazard events recorded, and (iii) explore potential relations between meteorological conditions and the hazard occurrence. In this context, in-situ and satellite-based climate data are exploited to study natural hazard triggers while the potential of Earth observation data is evaluated as a complement to the existing historical records of natural hazards. Specifically, Copernicus Sentinel-1 images are used to detect the spatio-temporal distribution of slow earth surface deformations and the results used for checking the completeness of the actual slow-moving landslide inventories. Hazard-related changes in the South Tyrolian landscape have also been analysed in relation to particular meteorological events at a regional scale, assessing trends and anomalies. Results show that: (i) satellite data are very useful to complement the existing natural hazard inventories; (ii) in-situ and satellite-based climate records show similar patterns but differ due to regional versus local variability; (iii) even in a data-rich region such as the analysed area, the overall response of natural hazard occurrence, magnitude, and frequency to change in climate variables is difficult to decipher due to the presence of multiple triggers and locally driven ground responses. However, an increase in the average annual duration of rainfall events and debris flow occurrence can be observed.

A better detection of landslide occurrence is critical for disaster prevention and mitigation. Over the past four decades, great achievements have been made, ranging from inventories to mapping, susceptibility analysis to triggering threshold identification. Here, we proposed a model to establish global distributed rainfall thresholds, by linking triggering rainfall with geo-environmental causes related to landslide events. The model was based on multiple linear regression method, to define rainfall thresholds as a function of diverse geo-environmental variables, fitted and validated by a combined and relatively accurate landslide dataset. Results show primarily feasible performances for training and testing datasets, with low mean absolute error (0.22 log(mm)) and a high coefficient of determination (0.67) totally. We further prepared global distributed threshold maps for sub- and multi-daily rainfall durations. They share similar spatial distributions in line with previous research. The normalized rainfall index, defined as the ratio of precipitation amount over distributed rainfall thresholds, can be an index of possible landslide occurrence, that is, regions with a normalized index over 1.0 correspond to high probability. We argue that distributed rainfall threshold models are an improvement of empirical threshold models and susceptibility assessments by considering the interaction between triggering rainfall and geo-environmental causes, and promising for better hazard assessment.

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.

Capitolo sulle interazioni tra piogge e frane con specifici riferimenti ai modelli di previsione dell'innesco delle frane, sia fisicamente basati che statistici. Particolari paragrafi dedicati alle soglie pluviometriche

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.

Earth observation data are useful to analyze the impact of climate-related variables on geomorphological processes. This work aims at evaluating the impact of rainfall on slow-moving landslides, by means of a quantitative procedure for identifying satellite-based displacement clusters, comparing them with rainfall series, and applying statistical tests to evaluate their relationships at the regional scale. The chosen study area is the Basento catchment in the Basilicata region (southern Italy). Rainfall series are gathered from rain gauges and are analyzed to evaluate the presence of temporal trends. Ground displacements are obtained by applying the P-SBAS (Parallel Small BAseline Subset) to three datasets of Sentinel-1 images: T146 ascending orbit, and T51 and T124 descending orbits, for the period 2015–2020. The displacement series of the pixels located in areas mapped as landslides by the Italian Landslide Inventory and sited within rain gauge influence regions (defined as 10 km circular buffers) are studied. Those displacement series are analyzed and compared to the rainfall series to search for correlations, by employing statistical and non-parametric tests. In particular, two landslides are selected and investigated in detail. Significant results were obtained for the T124 descending orbit for both landslides, for a 3-day cumulative rainfall and a 7-day delay of the slope response. Challenges in the whole procedure are highlighted and possible solutions to overcome the raised problems are proposed. Given the replicability of the proposed quantitative procedure it might be applied to any study area.

Rainfall-triggered shallow landslides represent a major threat to people and infrastructure worldwide. Predicting the possibility of a landslide occurrence accurately means understanding the trigger mechanisms adequately. Rainfall is the main cause of slope failures in Slovenia, and rainfall thresholds are among the most-used tools to predict the possible occurrence of rainfall-triggered landslides. The recent validation of the prototype landslide early system in Slovenia highlighted the need to define new reliable rainfall thresholds. In this study, several empirical thresholds are determined using an automatic tool. The thresholds are represented by a power law curve that links the cumulated event rainfall (E, in mm) with the duration of the rainfall event (D, in h). By eliminating all subjective criteria thanks to the automated calculation, thresholds at diverse non-exceedance probabilities are defined and validated, and the uncertainties associated with their parameters are estimated. Additional thresholds are also calculated for two different environmental classifications. The first classification is based on mean annual rainfall (MAR) with the national territory divided into three classes. The area with the highest MAR has the highest thresholds, which indicates a likely adaptation of the landscape to higher amounts of rainfall. The second classification is based on four lithological units. Two-thirds of the considered landslides occur in the unit of any type of clastic sedimentary rocks, which proves an influence of the lithology on the occurrence of shallow landslides. Sedimentary rocks that are prone to weathering have the lowest thresholds, while magmatic and metamorphic rocks have the highest thresholds. Thresholds obtained for both classifications are far less reliable due to the low number of empirical points and can only be used as indicators of rainfall conditions for each of the classes. Finally, the new national thresholds for Slovenia are also compared with other regional, national, and global thresholds. The thresholds can be used to define probabilistic schemes aiming at the operative prediction of rainfall-induced shallow landslides in Slovenia, in the framework of the Slovenian prototype early warning system.

In this report, we summarise the activities of the Research Institute for Geo-Hydrological Protection (IRPI, http://www.irpi.cnr.it/en/ ), of the Italian National Research Council (CNR, http://www.cnr.it/en/ ), as a World Centre of Excellence (WCoE) on Landslide Risk, of the International Programme on Landslides (IPL, http://iplhq.org ). The report is organised as follows. After a brief description of CNR IRPI (“The Research Institute for Geo-Hydrological Protection”), we present recent developments related to landslide early warning systems (“Landslide early warning systems”), climate change and landslides interactions (“Landslides in a changing climate”), and landslide risk assessment, mitigation and dissemination efforts (“Landslide risk assessment, mitigation and dissemination efforts”). We conclude listing relevant papers published between 2017 and 2018 by CNR IRPI scientists (“References”).

A better detection of landslide occurrence is critical for disaster prevention and mitigation, and a standing pursuit owing to increasing and widespread impact of slope failures on human activities and natural environment in a changing world. However, the detection of rainfall-induced landslide is limited in some areas by data scarcity and method applicability. In this study, we proposed distributed rainfall thresholds within homogeneous slope units, by considering the interaction of landslide-influencing geo-environmental conditions and landslide-triggering rainfall variables. Homogeneous slope units are extracted based on detailed terrain analysis. Various landforms are identified and used to obtain slope units with homogeneous slope traits. The concept behind the distributed rainfall threshold models is that rainfall threshold for landslide occurrence varies with geo-environmental conditions such as slope gradient. Thus, a link can be established between landslide-influencing geo-environmental conditions and landslide-triggering rainfall variables. We used elevation, slope, plan and profile curvature, mean annual precipitation and temperature, soil texture and land cover as independent variables. Rainfall duration and cumulated rainfall of landslide-triggering rainfall events are automatically calculated and used, the former as one of independent variables, and the latter as the dependent variable. A support vector regression (SVR) and a multiple linear regression (MLR) method are used. The error and correlation coefficient measurement indicate a better performance of SVR method. Compared with grid units, the model scores high accuracy for slope units. The models are implemented at a regional scale (Guangdong, China). The SVR model in slope units ran with error of 0.16 mm and correlation coefficient of 0.93.

Landslides are among the most dangerous natural hazards, particularly in developing countries where ground observations for operative early warning systems are lacking. In these areas, remote sensing can represent an important tool to forecast landslide occurrence in space and time, particularly satellite rainfall products that have improved in terms of accuracy and resolution in recent times. Surprisingly, only a few studies have investigated the capability and effectiveness of these products in landslide forecasting, to reduce the impact of this hazard on the population. We have performed a comparative study of ground- and satellite-based rainfall products for landslide forecasting in India by using empirical rainfall thresholds derived from the analysis of historical landslide events. Specifically, we have tested Global Precipitation Measurement (GPM) and SM2RAIN-ASCAT satellite rainfall products, and their merging, at daily and hourly temporal resolution, and Indian Meteorological Department (IMD) daily rain gauge observations. A catalogue of 197 rainfall-induced landslides occurred throughout India in the 13-year period between April 2007 and October 2019 has been used. Results indicate that satellite rainfall products outperform ground observations thanks to their better spatial (10 km vs 25 km) and temporal (hourly vs daily) resolution. The better performance is obtained through the merged GPM and SM2RAIN-ASCAT products, even though improvements in reproducing the daily rainfall (e.g., overestimation of the number of rainy days) are likely needed. These findings open a new avenue for using such satellite products in landslide early warning systems, particularly in poorly gauged areas.

Earth observation (EO) data are useful tools to analyse geomorphological processes, among which slow-moving landslides triggered by rainfall. EO data are also used to evaluate climate change and to assess its impact on geomorphological processes and geo-hydrological phenomena. The latter is the topic of the Project OT4Clima (Innovative Earth Observation technologies to study Climate Change and its impact on the environment) joined by CNR-IRPI within a consortium that includes other CNR institutes, universities and private companies. The OT4CLIMA project moves from the awareness that the impacts of climate change on the environment need to be better observed, understood, and modelled, especially at a regional scale, in order to put in place appropriate and effective risk mitigation strategies. Within the project, the CNR-IRPI group works on the development of rigorous methods and procedures for evaluating the impact of climate and its change on landslides, in particular on those characterized by a slow cinematic, at a regional scale. The test site is represented by four catchments located in the Basilicata region, southern Italy, namely the basins of the Bradano, Basento, Agri, and Sinni rivers. Long-term rainfall series gathered from 22 rain gauges located in the four catchments are analysed to evaluate the presence of temporal trends. To this aim, non-parametric and statistical tests are applied to the series. Historical landslide information is gathered from the analysis of the IFFI (Inventario dei Fenomeni Franosi in Italia) database, the Idrogeo platform (https://idrogeo.isprambiente.it/app/) and the AVI (Aree Vulnerate in Italia) catalogue. Only some types of landslide movements are considered, namely rotational-translational slides, slow slides/flows, complex movements. Moreover, Copernicus Sentinel-1 images are employed to detect the spatial and temporal distribution of slow earth surface deformations. The obtained results are used for checking the completeness of the landslide inventories. More in detail, the deformation maps of the test site are obtained by means of the application of the SBAS (Small BAseline Subset) technique to three datasets of Sentinel-1 images: t146 ascending orbit and t51 and t124 descending orbits, for the period 2015-2020. Then, a comparative analysis of rainfall data with displacement series is carried out with the aim of identifying clusters of satellite measurements with homogeneous behaviour likely correlated to variations in the rainfall regime. In particular, only the points with a mean velocity in the observation higher than 0.1 cm/year are considered to be moving. Moreover, only the displacement series of points located in areas mapped as landslides - as for the historical inventories - and sited within the influence regions of each rain gauge in the study area are analysed. A 10-km circular buffer centred in the stations are used to define the influence region of each station. The displacement series are analysed and compared to the rainfall series to search for correlations and to evaluate the effects of climate drivers on slow moving landslides.

Although mass-movements can be caused by a variety of natural phenomena and human actions, in most areas of the world rainfall is their primary trigger. An often-neglected complication of the operational forecasting of rainfall-induced landslides is global warming, in particular the related ongoing and expected changes in rainfall and temperature. The evaluation of the effects of the current and the projected climate change on the stability/instability conditions of natural and engineered slopes remains complicated. In this chapter, we address the main questions related to the assessment of the impact of climate change on mass-movements. Starting from the results of a review of studies on the topic, we analyze and discuss how climate factors (and their changes) influence slope stability conditions and landslide hazard. This is not trivial, due to multiple reasons, including the fact that (i) the temporal and the geographical scales of climate and landslides are very different; (ii) different climate variables affect landslides; (iii) landslides are complex and diversified phenomena that respond differently to changes in climate. Moreover, we outline the main research needs and we provide general and specific recommendations for the operational forecasting of rainfall-induced landslides considering the current and the expected changes in climate. In particular, a holistic approach is needed to understand and measure how climate variables and their variability affect mass-movements, and to provide useful projections that will allow effective short- and long-term actions to reduce landslide risk.

Analyses of historical records of landslides and climate variables are useful tools to search for correlations between damaging landslide events and their triggers. In this work, we investigate the temporal and geographical relationships between two long-term historical series of catalogued landslide occurrences and daily rainfall data in Umbria, a central Italian region, from 1928 to 2001. Moreover, we search for changes in the frequency and density of landslides, and in the characteristics of the associated rainfall events. Using a consolidated approach, partially modified, we find that the rainfall events that have produced rainfall-induced landslides in Umbria changed in space and time during observation period and between two considered sub-periods (1928-1975 and 1976-2001). In particular, we find that: (i) the monthly distribution of landslides associated with rainfall events is quite different than that of all landslides in the regional catalogue; (ii) the spatial and temporal distribution of REL changed from the older (most events occurred in winter) to the recent period (most events occurred in autumn); (iii) the recent most rainfall events associated with landslides are characterized by a lower cumulated rainfall and a shorter duration, sign of an increased propensity of the regional territory to produce landslides over time.

At variance with conventional landslide susceptibility assessment, non-susceptibility analysis aims at selecting locations in which the likelihood of landslide occurrence is null or negligible. The advantage of this approach is that it does not require estimating different degrees of likelihood outside of the locations of negligible susceptibility. Thus, it entails the use of simplified classification methods. In this work, we tested and validated the existing non-susceptibility model with 18 global and regional landslide datasets, as a prior for the global application. The existing model was applied previously in Italy and the Mediterranean region, and defined by a non-linear relief vs. slope threshold curve, below which landslide susceptibility is negligible. Then, we applied a similar analysis, and proposed a global map, using relief and slope obtained from global elevation data at about 90-m resolution. The global map classifies 82.9% of the landmasses with negligible landslide susceptibility. The non-susceptible areas are broadly consistent with the “very-low” susceptibility class in existing global and continental landslide susceptibility maps and a national non-susceptibility map in the conterminous United States. Quantitative analyses revealed that population and settlements are denser within non-susceptible area than elsewhere, which makes the map of potential interest for non-exposure analysis, land planning and disaster responses at a global scale.

Il presente documento contiene la definizione delle procedure per la validazione del sistema SARF Sardegna. Le attività qui riportate sono state realizzate nell'ambito delle Modifiche all'Estensione dell'Accordo di Collaborazione tra la Direzione Generale della Protezione Civile della Regione Autonoma della Sardegna servizio previsione rischi e dei sistemi informativi, infrastrutture e reti e il Consiglio Nazionale delle Ricerche, Istituto di Ricerca per la Protezione Idrogeologica (CNR IRPI) per la "Sperimentazione, validazione e sviluppo del Sistema di Allertamento Regionale per la previsione di Frane pluvio-indotte in Sardegna (SARF Sardegna)". Il rapporto rappresenta la prima valutazione delle prestazioni del SARF Sardegna per la previsione di frane pluvio-indotte sul territorio regionale da aprile 2020 a febbraio 2021. Tale attività ha riguardato la raccolta e l'uso di informazioni sull'occorrenza di frane pluvio-indotte sul territorio regionale e l'implementazione di procedure per il confronto delle previsioni del SARF Sardegna con l'occorrenza di frane nei giorni successivi, nel periodo di validità delle previsioni stesse. Il presente documento è suddiviso in quattro capitoli. Il Capitolo 1 contiene un'introduzione ai contenuti del documento. Nel Capitolo 2 sono descritti i dati utilizzati nella procedura di validazione del SARF Sardegna, mentre nel Capitolo 3 è descritta la procedura di validazione stessa. Il Capitolo 4 riporta le schede di validazione e un'analisi complessiva della capacità predittiva del sistema sulla base dei risultati della validazione.

Landslides are frequent and widespread destructive processes causing casualties and damage worldwide [...]

Hydrogeological hazards now exacerbated by the ongoing climate change pose serious challenges for the safety of the population worldwide. Among the others, the landslide risk can be mitigated by setting up efficient and reliable early warning systems. To date, rainfall thresholds are one of the most used tools to forecast the possible occurrence of rainfall-induced failures in large regions. In Italy a dense rain gauge network with hourly or sub-hourly temporal resolution is available. However, in some developing countries, where ground measurements are still absent or are available at coarser (daily) temporal resolution, satellite-based rainfall estimates could be a vital alternative. For this purpose, the reliability of rainfall thresholds defined using both satellite (SB) and ground-based (GB) data and with hourly or daily temporal resolution is assessed in a study area comprising the Abruzzo, Marche and Umbria regions (AMU), central Italy. The comparison between the performance of the different products allows to test their capability in eventually can GB rainfall measurements are gathered at hourly time steps (OBS-H) from a national network and aggregated on a daily scale (OBS-D); SB rainfall estimates are retrieved from the Climate Prediction Center Morphing Technique (CMORPH, hourly resolution), and from the SM2RASC product, based on the application of SM2RAIN algorithm to ASCAT (Advanced SCATterometer) soil moisture product (daily resolution). Results show that thresholds defined with GB rainfall data perform better than those obtained using SB estimates regardless of the temporal resolution. CMORPH and SM2RASC thresholds are still able to predict landslide occurrence although with a high number of false predictions.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Areas non susceptible to landslides are regions where landslides are not expected, or where susceptibility is negligible. Thus they can provide new insights into landslide hazard assessment and land use management and can be targeted as areas for urban planning and dwelling. Non-susceptible landslide areas can be determined with substantially less information as compared with landslide susceptibility. Previous works in Italy and the Mediterranean region and in the USA showed that only morphometric information is needed to distinguish non-susceptible landslide areas. We used 90-m digital terrain data (SRTM DEM V4.1) to calculate global slope and relief maps, and applied globally the quantile non-linear (QNL) model previously obtained in Italy. We define the output map a global landslide non-susceptibility map (GLNSM). The QNL model is a relationship between terrain relief and slope based on an Italian landslide inventory dataset with high completeness and accuracy. Results indicate that 82.89% of the landmasses are non-susceptible areas across the globe, which is more than the percentage of non-mountainous areas (73.6% based on GEO-GNOME). We further considered GLNSM in relation to global climate, elevation, geology, land use, precipitation and seismicity classifications. High percentage (more than 85.0%) of non-susceptible areas are detected in the tropical and arid, flat (low than 500 m), sedimentary, artificial and high vegetated, less rainy (less than 400 mm per year) and seismicity inactive (less than 0.4) regions. Our results of GLNSM was also validated with some well-represented regional landslide inventory datasets, for which we used four national (Austria, China, Ireland and USA) datasets and nine regional (Arizona, Missouri, Oregon, Utah and Washington in USA, Guangdong and Yunnan in China, and Koshi river region in Nepal) datasets. Applicability of GLNSM reveals that 0.7% of non-susceptible areas are covered by artificial structures, about three times of that in susceptible areas (i.e., not non-susceptible areas), while population density of non-susceptible areas are about twice of that in susceptible areas. About 90.5% of population resides in the non-susceptible areas.

In January 2020, the Istituto di Ricerca per la Protezione Idrogeologica of the Italian National Research Council, the British Geological Survey, the Norwegian Water Resources and Energy Directorate, the Swiss Federal Institute for Forest, Snow and Landscape Research, and the University of Salerno - Italy have organised a 3-day workshop on regional Landslide Early Warning Systems (LEWS). The workshop, held in Perugia, Italy, follows a previous meeting held in Oslo, Norway, in October 2016. The main aims of the initiative are: to collect experiences from worldwide invited experts involved in the design, the development, the operation or the analysis of LEWS, and to exchange knowledge, experiences, challenges and best practices.The first day of the workshop is dedicated to presentations from identified participants on specific topics relevant for the optimal design, implementation, and operation of global, national and regional LEWS. This is followed by a long discussion session, aimed at addressing many of the issues that are relevant for regional LEWS, including system performance, warning communication and involvement of the stakeholders. The second day is organized around four round tables on the following four topics: (i) data; (ii) landslide forecast models; (iii) warning models; (iv) scope, management structure, stakeholder involvement, and communication. The third day is focused on summarizing and formalizing the main issues discussed in an open document to be later shared with colleagues interested in LEWS.The final purpose of the workshop is to establish and consolidate a community of experts in LEWS and to build relationships with other communities (e.g., meteorologists, climate scientists, communications scientists). This will help to level up the quality of both theory and practice, and to define standards in early warnings in order to provide timely advisories and to initiate emergency responses to landslides (particularly rainfall-induced) avoiding or reducing life and economic losses. The main outcomes of the workshop, the most debated issues, and the key recommendations included in the open document will be presented and shared.

In January 2020, the Istituto di Ricerca per la Protezione Idrogeologica of the Italian National Research Council, the British Geological Survey, the Norwegian Water Resources and Energy Directorate, the Swiss Federal Institute for Forest, Snow and Landscape Research, and the University of Salerno - Italy have organised a 3-day workshop on regional Landslide Early Warning Systems (LEWS). The workshop, held in Perugia, Italy, follows a previous meeting held in Oslo, Norway, in October 2016. The main aims of the initiative are: to collect experiences from worldwide invited experts involved in the design, the development, the operation or the analysis of LEWS, and to exchange knowledge, experiences, challenges and best practices. The first day of the workshop is dedicated to presentations from identified participants on specific topics relevant for the optimal design, implementation, and operation of global, national and regional LEWS. This is followed by a long discussion session, aimed at addressing many of the issues that are relevant for regional LEWS, including system performance, warning communication and involvement of the stakeholders. The second day is organized around four round tables on the following four topics: (i) data; (ii) landslide forecast models; (iii) warning models; (iv) scope, management structure, stakeholder involvement, and communication. The third day is focused on summarizing and formalizing the main issues discussed in an open document to be later shared with colleagues interested in LEWS. The final purpose of the workshop is to establish and consolidate a community of experts in LEWS and to build relationships with other communities (e.g., meteorologists, climate scientists, communications scientists). This will help to level up the quality of both theory and practice, and to define standards in early warnings in order to provide timely advisories and to initiate emergency responses to landslides (particularly rainfall-induced) avoiding or reducing life and economic losses. The main outcomes of the workshop, the most debated issues, and the key recommendations included in the open document will be presented and shared.

We reviewed the Italian scientific literature published in the period 2008–2018 on the topic of rainfall thresholds for the landslide triggering, with the aim of analyzing the most significant advances and the main open issues. In the international literature, Italy occupies a relevant position from both a quantitative and a qualitative viewpoint: 65 out of the 163 thresholds published worldwide in the considered period are defined in Italy. The main improvements can be ascribed to rigorous cataloguing of landslides; definition of standard and objective methods for thresholds analysis; quantitative validation of the results and evaluation of the performance of related warning systems; attempts to improve the spatial resolution of the forecasts. However, some shortcomings still limit the research on landslide rainfall thresholds and some open issues recently emerged as priorities to be further investigated: the effects of climatic and environmental changes on the thresholds; their implementation into hazard management procedures and early warning systems; the adoption of combined approaches to account for the hydrological conditions of the slopes; the quantification of diverse uncertainties. This review disseminates the best practices among scientists and stakeholders involved in landslide hazard management, and it draws a national framework of procedures for defining reliable rainfall thresholds, in particular for early warning purposes.

Rockfalls are frequent and harmful phenomena occurring in mountain ranges, coastal cliffs, and slope cuts. Although several natural processes occur in their formation and triggering, rainfall is one of the most common causes. The prediction of rock failures is of social significance for civil protection purposes and can rely on the statistical analysis of past rainfall conditions that caused the failures. The paper describes the analysis of information on rainfall-induced rockfalls in Gran Canaria and Tenerife, Canary Islands (Spain). An analysis of the monthly rainfall versus the monthly distribution of rockfalls reveals that they are correlated for most of the year, except in summer, when other triggers act to induce collapses. National and regional catalogs with hourly and daily rainfall measurements are used to reconstruct the cumulated amount (E) and the duration (D) of the rainfall responsible for the rock failures. Adopting a consolidated statistical approach, new ED rainfall thresholds for possible rockfall occurrence and the associated uncertainties are calculated for the two test sites. As far as is known, this is the first attempt to predict this type of failure using the threshold approach. Using the rainfall information, a map of the mean annual rainfall is obtained for Gran Canaria and Tenerife, and it is used to assess the differences between the thresholds. The results of this study are expected to improve the ability to forecast rockfalls in the Canary Islands in view of implementing an early-warning system to mitigate the rockfall hazard and reduce the associated risk.

Il documento contiene l'aggiornamento delle soglie di pioggia per il possibile innesco di frane nella regione Puglia. In particolare, il documento è suddiviso in 6 capitoli. Il Capitolo 1 contiene un'introduzione ai contenuti del documento. Nel Capitolo 2 sono riportate le soglie pluviometriche definite a novembre 2018 [DR-002]. A seguito di un'analisi approfondita, le soglie sono state ricalcolate eliminando 12 condizioni pluviometriche responsabili dell'innesco di frane in quanto duplicate nel catalogo o relative a frane non verificatesi come primo innesco. Nel Capitolo 3 è descritto e analizzato il nuovo catalogo contenente le informazioni spaziali e temporali sui fenomeni franosi occorsi in Puglia. Il catalogo è stato integrato con le informazioni su frane pluvio-indotte occorse nelle regioni limitrofe (Molise, Campania e Basilicata) entro una distanza di 15 km dal confine del territorio pugliese. Il Capitolo 4 riporta e analizza le condizioni pluviometriche utilizzate per l'aggiornamento delle soglie frequentiste in Puglia. Il Capitolo 5 mostra le nuove soglie regionali frequentiste di tipo Cumulata-Durata (CD) corrispondenti alle percentuali di non superamento del 5% con le relative incertezze e il confronto con la soglia regionale preliminare. Nello stesso capitolo viene mostrato il confronto tra la nuova soglia frequentista al 5% per la regione Puglia e le soglie precedentemente definite dal CC CNR-IRPI per altre regioni e per l'intero territorio nazionale. Il Capitolo 6 contiene l'elenco della bibliografia di riferimento.

Il rapporto rappresenta la seconda valutazione delle prestazioni di SANF-RFI per la previsione di frane pluvio-indotte lungo le linee ferroviarie di RFI da giugno a novembre 2019. Tale attività ha incluso l'uso di informazioni sull'occorrenza di frane pluvio-indotte in prossimità o in corrispondenza dell'infrastruttura ferroviaria RFI, e l'implementazione di procedure per il confronto delle previsioni di SANF-RFI con l'occorrenza di frane nei giorni successivi, nel periodo di validità delle previsioni stesse.