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Rainfall triggered landslides occur in all mountain ranges posing threats to people and the environment. Given the projected climate changes, the risk posed by landslides is expected to increase, and the ability to anticipate their occurrence is key for effective risk reduction. Empirical thresholds and physically-based models are used to anticipate the short-term occurrence of rainfall-induced shallow landslides. But, evidence suggests that they may not be effective for operational forecasting over large areas. We propose a deep-learning based strategy to link rainfall to landslide occurrence. We inform and test the system with rainfall and landslide data available for the last 20 years in Italy. Our results indicate that it is possible to anticipate effectively the occurrence of rainfall-induced landslides over large areas, and that their location and timing are controlled primarily by the precipitation, opening to the possibility of operational landslide forecasting based on rainfall measurements and quantitative meteorological forecasts. How much rain does it take to trigger a landslide? This work shows that deep learning can identify the driving forces that can cause rainfall induced landslides, opening up the possibility of forecasting landslide events over large areas

In many areas of the world, the prediction of rainfall-induced landslides is usually carried out by means of empirical rainfall thresholds. Their definition is complicated by several issues, among which are the evaluation and quantification of diverse uncertainties resulting from data and methods. Threshold effectiveness and reliability strongly depend on the quality and quantity of rainfall measurements and landslide information used as input. In this work, the influence of the temporal resolution of rainfall measurements on the calculation of landslide-triggering rainfall thresholds is evaluated and discussed. For the purpose, hourly rainfall measurements collected by 172 rain gauges and geographical and temporal information on the occurrence of 561 rainfall-induced landslides in Liguria region (northern Italy) in the period 2004–2014 are used. To assess the impact of different temporal resolutions on the thresholds, rainfall measurements are clustered in increasing bins of 1, 3, 6, 12 and 24 h. A comprehensive tool is applied to each dataset to automatically reconstruct the rainfall conditions responsible for the failures and to calculate frequentist cumulated event rainfall–rainfall duration (ED) thresholds. Then, using a quantitative procedure, the calculated ED thresholds are validated. The main finding of the work is that the use of rainfall measurements with different temporal resolutions results in considerable variations of the shape and the validity range of the thresholds. Decreasing the rainfall temporal resolution, thresholds with smaller intercepts, higher slopes, shorter ranges of validity and higher uncertainties are obtained. On the other hand, it seems that the rainfall temporal resolution does not influence the validation procedure and the threshold performance indicators. Overall, the use of rainfall data with coarse temporal resolution causes a systematic underestimation of thresholds at short durations, resulting in relevant drawbacks (e.g. false alarms) if the thresholds are implemented in operational systems for landslide prediction.

A process chain for the definition and the performance assessment of an operational regional warning model for rainfall-induced landslides, based on rainfall thresholds, is proposed and tested in a landslide-prone area in the Campania region, southern Italy. A database of 96 shallow landslides triggered by rainfall in the period 2003–2010 and rainfall data gathered from 58 rain gauges are used. First, a set of rainfall threshold equations are defined applying a well-known frequentist method to all the reconstructed rainfall conditions responsible for the documented landslides in the area of analysis. Several thresholds at different exceedance probabilities (percentiles) are evaluated, and nine different percentile combinations are selected for the activation of three warning levels. Subsequently, for each combination, the issuing of warning levels is computed by comparing, over time, the measured rainfall with the pre-defined warning level thresholds. Finally, the optimal percentile combination to be employed in the regional early warning system, i.e. the one providing the best model performance in terms of success and error indicators, is selected employing the “event, duration matrix, performance” (EDuMaP) method.

In Italy, rainfall-induced landslides are recurrent phenomena that cause societal and economic damage. Thus, assessing the rainfall conditions responsible for landslides is important and may contribute to reducing risk. The prediction of rainfall-induced landslides relies primarily on empirical rainfall thresholds. However, the thresholds are affected by uncertainties that limit their use in operational warning systems. A source of uncertainty lies in the characterization of the rainfall events responsible for landslides. Objective criteria for the definition of rainfall events are lacking. To overcome the problem, we propose an algorithm that reconstructs the rainfall events, identifies the rainfall conditions that have resulted in landslides, and measures the duration and the cumulated rainfall for the events. The algorithm is independent from the local settings and uses a reduced set of parameters to account for different physical settings and operational conditions. We tested the algorithm in Sicily, Italy, with rainfall and landslide information between January 2002 and December 2012. The rainfall conditions responsible for landslides identified by the algorithm were compared against results obtained manually. The algorithm was proven capable of accurately reconstructing most (87.7 %) of the rainfall events. For each landslide, the algorithm identified a variable number of rainfall conditions responsible for the failures, which are equally likely triggers of the landslide. This opens the possibility of evaluating the uncertainty introduced by different criteria to determine the rainfall events responsible for landslides. Use of the algorithm shall contribute to reducing the uncertainty in the definition of landslide-triggering rainfall events, to compiling large catalogues of rainfall events with landslides and to determining reliable rainfall thresholds for possible landslide occurrence.

With the increasing use of data-driven landslide prediction models also based on artificial intelligence, the availability of accurate information on the occurrence of landslides and the rigorous reconstruction of their triggering rainfall conditions are crucial. To this end, an enhanced rainfall-induced landslide catalogue, e-ITALICA, is presented here. e-ITALICA contains spatial and temporal information on 6312 rainfall-induced landslides that occurred in Italy between 1996 and 2021 (already listed in the previous ITALICA catalogue published in 2023), with the addition of their rainfall triggering conditions in terms of rainfall duration D (h) and cumulative event rainfall E (mm). The triggering conditions are calculated using hourly rainfall measurements from 4033 rain gauges and applying a rigorous and reproducible method. In addition, topographic and land cover information is also provided. e-ITALICA can be used to analyse rainfall conditions capable of triggering landslides, to calibrate and validate physically based landslide prediction models, and to define empirical rainfall thresholds from local to national scales in Italy, thus contributing to landslide risk reduction.

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.

Lake Trasimeno is a shallow, endorheic lake located in central Italy. It is the fourth Italian largest lake and is one of the largest endorheic basins in western Europe. Because of its shallow depth and the absence of natural outflows, the lake, in historical times, alternated from periods of floods to strong decreases of the water level during periods of prolonged drought. Lake water is characterised by a NaCl composition and relatively high salinity. The geochemical and isotopic monitoring of lake water from 2006 to 2018 shows the presence of well-defined seasonal trends, strictly correlated to precipitation regime and evaporation. These trends are clearly highlighted by the isotopic composition of lake water (δ18O and δD) and by the variations of dissolved mobile species. In the long term, a progressive warming of lake water and a strong increase of total dissolved inorganic solids have been observed, indicating Lake Trasimeno as a paradigmatic example of how climate change can cause large variations of water quality and quantity. Furthermore, the rate of variation of lake water temperature is very close to the rate of variation of land-surface air temperature, LSAT, suggesting that shallow endorheic lakes can be used as a proxy for global warming measurements.

Based on a minimum amount of rainfall that when reached or exceeded can trigger landslides, rainfall thresholds are used to predict potential landslide occurrence and are essential parts of many landslide early warning systems. Despite the extensive literature on the definition and use of rainfall thresholds, little attention has been given to examining and comparing the mathematical methods that can be used to define thresholds as lower bounds of clouds of empirical rainfall conditions known to have triggered landslides. When multiple thresholds are available, it is unclear how to combine them. Here, we address both issues. We test and compare four mathematical methods to define event cumulated rainfall—rainfall duration, ED thresholds using 2259 measurements of rainfall duration ( D , in hours) and cumulated rainfall ( E , in mm) that resulted in mostly shallow landslides in Italy between January 2002 and December 2012. The methods cover a broad spectrum of data driven approaches, including a frequentist least square method, a frequentist quantile regression method, a Bayesian quantile regression method, and a machine-learning symbolic regression method. We apply and compare the methods for three non-exceedance probability levels, p = 0.01, 0.05, 0.10, and we propose a voting strategy to combine the predictions into a single, dichotomous—i.e. ‘sharp’—non-probabilistic landslide prediction that we apply to the available dataset of rainfall measurements.

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 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 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 one-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 has 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 can be accessed at https://doi.org/10.5281/zenodo.8009366 (Brunetti et al., 2023). ITALICA's information on rainfall-induced landslides in Italy places a special emphasis on their spatial and temporal locations, making the catalogue especially suitable for defining the rainfall conditions capable of triggering future landslides in the Italian territory. This information is fundamental for decision-making in landslide risk management.

Bhutan is highly prone to landslides, particularly during the monsoon season. Several landslides often occur along the Phuentsholing–Thimphu highway, a very important infrastructure for the country. Worldwide, empirical rainfall thresholds represent a widely used tool to predict the occurrence of rainfall-induced landslides. Nevertheless, no thresholds are currently designed and proposed for any region in Bhutan. In this work, we define empirical cumulated event rainfall–rainfall duration thresholds for the possible initiation of landslides using information on 269 landslides that occurred between 1998 and 2015 along the 90-km highway stretch between the towns of Phuentsholing and Chukha, in southwestern Bhutan, and daily rainfall measurements obtained from three rain gauges. For this purpose, we apply a consolidated frequentist method and use an automatic tool that identifies the rainfall conditions responsible for the failures and calculates thresholds at different exceedance probabilities and the uncertainties associated with them. Analyzing rainfall and landslide data, we exclude from the analysis all the landslides for which the rainfall trigger is not certain, so we reduce the number of landslides from 269 to 43. The calculated thresholds are useful to identify the triggering conditions of rainfall-induced landslides and to predict the occurrence of the failures in the study area, which is, to date, poorly studied. These rainfall thresholds might be implemented in an early warning system, in order to reduce the risk posed by these phenomena to the population living and traveling along the investigated road stretch.