Abstract. We present a Bayesian probabilistic framework for landslide forecasting, explicitly accounting for the sources of epistemic uncertainty that affect landslide occurrence. The method describes the probability of landslide occurrence as a distribution, rather than a single value, allowing a more realistic treatment of uncertainty arising from incomplete landslide inventories, variable measurements, and the inherent complexity of landslide processes. We apply the probabilistic framework to a 22-year dataset of shallow landslides and daily rainfall records from the Campania region (southern Italy). Each landslide is associated with the nearest rain gauge, and forecasts are computed within Thiessen polygons representing the area of influence of each rain gauge. Posterior landslide probabilities are calculated for different daily rainfall thresholds using Bayes' theorem, with prior and likelihood terms modelled as uniform and Beta distributions, respectively. Results show that posterior probabilities increase progressively with rainfall, and no sharp physical threshold emerges. The retrospective forecast skill improves with rainfall information, as demonstrated by consistent gains in posterior over prior probabilities. This gradual trend supports the view of landslide triggering as a probabilistic process, challenging the use of deterministic rainfall thresholds in operational contexts. The proposed Bayesian probabilistic framework is designed to be generalizable to other triggering mechanism (e.g., earthquakes) and potentially adaptable to other regions, provided that sufficient data are available. Although the method is data-intensive, it enables transparent, uncertainty-informed forecasts, with potential applications in early warning systems and risk management strategies. Future developments may include the incorporation of antecedent rainfall and geological conditioning factors across broader spatial and temporal scales.

Abstract. Geo-hydrological hazards, including floods and landslides, are among the most widespread hazards and a major cause of human loss worldwide. Their impacts are shaped by meteorological forcing and by long-term changes in exposure, vulnerability, and settlement patterns. Yet robust assessments of long-term fatality trends are rare because sufficiently long, accurate, and homogeneous records of damaging events are lacking. A key unresolved question is whether the human toll of geo-hydrological events has changed structurally through time, and which climatic, socio-economic, or policy factors best explain that change. Here we show, using a unique 1950–2024 catalogue of fatal events in Italy, that geo-hydrological risk underwent a marked transition, with fatalities and fatal days declining sharply until about 1970 and then stabilising at much lower levels. This decline was driven mainly by landslides, whereas flood-related losses showed weaker long-term change. The decline was not explained by specific risk policies or management programmes, as most of it preceded their implementation. Instead, precipitation was the main climatic driver of the remaining interannual variability, particularly for floods and days with fatal events. By contrast, population and gross domestic product did not account for the long-term trends once shared temporal changes were considered. Fatal days were less overdispersed and more predictable than fatalities, suggesting that meteorological factors control more directly the occurrence of dangerous conditions than the final death toll. Fatalities, instead, depend strongly on exposure, vulnerability, timing, location, and other event-specific circumstances. Together, these findings indicate that broad socio-economic dynamics can reduce geo-hydrological mortality even as hazardous processes persist, whereas meteo-climatic conditions continue to shape year-to-year risk.

A common and largely unresolved problem of national-scale landslide early warning systems is their independent evaluation. In this work, we evaluated the performance of a recently proposed deep-learning-based system for short-term forecasting of rainfall-induced shallow landslides in Italy. For our evaluation, we used hourly rainfall measurements from the same rain gauge network used to construct the forecasting system, and different and independent information on the timing and location of 163 rainfall-induced landslides that occurred in Italy in a period non considered in the construction of the forecasting system, obtained from the FraneItalia catalogue (https://zenodo.org/records/7923683). The independent evaluation confirmed the good predictive performance of the forecasting system and revealed no geographical or temporal bias in the forecasts. The analysis also revealed that the forecasting system was more effective at predicting multiple landslides in the same general area than single landslides. This was a good result, as multiple landslides are potentially more dangerous than single failures. Analysis of the few misclassified landslide cases showed that approximately one-third of the landslides were rockfalls, and for approximately another third there was uncertainty about when or where the landslides occurred. We conclude that, despite the inevitable misclassifications inherent in any probabilistically based national-scale landslide forecasting system, the deep-learning-based system analysed is well suited for short-term operational forecasting of rainfall-induced shallow landslides in Italy.

As prioritized by the Sendai Framework, enhancing disaster preparedness is fundamental for the effective response, for taking actions in anticipation of events, and to ensure that the appropriate capacities are in place for effective response and recovery at all levels. Under this view early warning systems can be seen as irreplaceable tools to supporting the Civil Protection authorities in the preparedness and response phases. This is particularly relevant for the case of rainfall-induced slope failures that occur worldwide every year, claiming lives and causing severe economic disruption. Implementing early warning systems to forecast the occurrence of such geo-hydrological phenomena is difficult and challenging both from the scientific and technological side. Here we present a framework developed in Italy for the operational forecasting of rainfall induced landslides over large areas, which includes (i) models tools and technological supports for landslide prediction; (ii) algorithms for nowcasts and forecasts production using diversified inputs; (iii) operational early warning system procedures and technological supports for landslide forecasting; (iv) interfaces for the query and analysis of the early warning system outputs; (v) criteria, tools and technological supports for the validation of the early warning system outputs. The main lessons learned in the last two decades during the implementation of such framework are presented and discussed, highlighting the possible future challenges.

Landslides are globally pervasive natural hazards posing growing risks under intensifying climate and land-use pressures. Italy, the most landslide-prone country in Europe, has achieved substantial progress in landslide hazard identification, mapping, modelling, forecasting, and risk reduction. Yet, critical gaps remain in inventory completeness and representativeness, medium- to long-term forecasting, monitoring coverage, vulnerability assessment, governance, and public awareness. Drawing on insights from a 2025 workshop organized by the Accademia Nazionale dei Lincei, this paper synthesizes current knowledge, practice, and technological capacity for landslide risk management in Italy, identifies persistent challenges, and outlines key priorities for scientific, technical, institutional, and organizational progress toward more effective risk management. We call for a coordinated strategy that integrates improved inventories, real-time monitoring, dynamic modelling, and data accessibility, supported by institutional reform and cultural change. While grounded in the Italian context, the findings and recommendations are broadly applicable to other landslide-prone countries and regions worldwide.

The wide physiographic variability and the abundance of rainfall and landslide data make Italy an appropriate site to study variations in the rainfall conditions responsible for triggering landslides.For more than two decades, the Research Institute for Geo-Hydrological Protection of the Italian National Research Council (CNR-IRPI) has been carrying out a specific research activity aimed at collecting information on rainfall-induced landslides in Italy. The information comes mainly from chronicle sources (newspapers in print or electronic format, websites, etc.) and institutional sources (reports on interventions carried out by the Fire Brigade and other institutional entities following reports of weather-induced landslides). The information collected has been used to compile the ITAlian rainfall-induced LandslIdes CAtalogue (ITALICA), freely accessible at https://zenodo.org/records/8009366. A description of the main features of the catalogue and the procedures adopted to fill it out can be found at https://essd.copernicus.org/articles/15/2863/2023/.ITALICA, which is being continuously updated, to date contains data on more than 6300 rainfall-induced landslides that occurred in Italy during the period 1996-2021. The peculiarity and specificity of the catalogue lies in the mastery and control of the landslide records, which have very high levels of spatial and temporal accuracy. In particular, for more than one third of the catalogue, landslides are spatially and temporally localized with an uncertainty of less than one km2 and one hour, respectively. The availability of accurate and up-to-date information on the geographic location and time of onset of landslides is essential for improving the predictive ability of landslides. Different subsets of the catalogue have been already used to calculate national and regional rainfall thresholds implemented in early warning systems in Italy.The first published version of ITALICA did not contain information on the rainfall conditions associated with the landslides. In the new release, presented here, we add the cumulate rainfall, rainfall duration and mean rainfall intensity values of the rainfall conditions responsible for the failures listed in the catalogue. The rainfall conditions are reconstructed by means of the CTRL-T automatic tool (https://zenodo.org/records/4533719) and using hourly rainfall measurements from more than 3000 rain gauges distributed over the Italian territory. Rainfall records are provided by the Italian National Department for Civil Protection. The spatial and temporal features of the reconstructed landslide-triggering rainfall conditions are analysed in depth.Given the rising demand for high-quality data to be used in comprehensive analyses and data-driven models, this dataset might be very useful for assessing the rainfall triggering conditions of landslides in Italy, either by empirical or physically based models. In particular, we expect our results to have an impact on the definition of new rainfall thresholds to be implemented in landslide early warning systems at regional and national scales. Work financially supported by the Italian National Department for Civil Protection (Accordo di Collaborazione 2022-2024) and the PRIN-ITALERT project (PRIN2022 call, grant number: 202248MN7N, funded by NextGenerationEU).

Rainfall is the primary natural trigger for landslides, and their threat is expected to rise as the climate warms. To mitigate the consequences of rain-induced landslides, predicting where and when landslides may occur is crucial. We propose a probabilistic modelling framework for synoptic-scale, short-term (hours to days) to long-term (years to decades) space-time prediction of rain-induced landslides. The framework employs a Poisson binomial distribution for the number of successes in a set of Bernoulli trials, each representing a landslide prediction with its own success probability. Our predictors are 35 deep networks that predict landslides occurrence based on rainfall data and information on past landslides. We tested the framework in Italy using hourly rainfall data from 4031 rain gauges and historical landslide records between 2002 and 2022. Results show that hourly rainfall history provides sufficient information to predict the location and timing of landslides without the need for rainfall thresholds or to define event-based rainfall metrics. Applying the forecasting system to 184,080 h between 1 January 2002 and 31 December 2022 we generated a unique, multi-decadal representation of the expected long-term occurrence probability of rain-induced landslides in Italy, which was not otherwise available from landslide catalogues, inventory maps or susceptibility zoning. We expect the modelling framework to enhance landslide early warning systems and to support long-term landslide adaptation and risk reduction strategies. The approach opens the possibility to consider landslide hazard as a combination of independent prediction models of landslide occurrence with associated uncertainty, thus changing the existing paradigm for landslide hazard assessment.

In compliance with the national legal framework, the regional offices (CFDs) of the Italian Civil Protection Department have the daily duty to issue warnings to the local population on the account of the weather and hydrology-related impacts, predicted by forecast models and refined through their expertise and experience: this composite of objective (model) and subjective (analyst) assessments are both contributing to the actual colour-coded warning system. Given its hybrid nature, it is of paramount importance to evaluate the predictive ability of the warning decision-making process as a whole. To this end, this study compares the return period T of the occurred flood (estimated through an hydrological model fed with observations) to the warning level that was issued. The novelty of this approach is that, by applying this methodology extensively in space and time, the probability curves of the variable T for each warning level are computed, allowing to evaluate the consistency between the warnings and the actual (estimated) severity of the event. As results suggest, the national early warning system is proven to be overall reliable for most cases, though very fine scale events (e.g., severe, localised, short-lived thunderstorms) are still an open challenge.

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.

This study explores the practical utilization of open data to analyze and improve urban transportation systems, focusing on leveraging real-time Google Traffic maps (GTM) data for automatic extraction of road traffic delay patterns. The research began by defining the study area geographically for data extraction from open street maps. A specialized extraction procedure, executed via the "r.tikrit" R code deployed on a server, tailored extraction intervals considering server constraints and area dimensions. The resulting traffic delay values were transformed into a spatial object table, enabling Geographic Information System (GIS) data analysis and visualization. The dataset, stored as daily tabular records, with hourly data input, accompanied by contextual raster images, facilitated comprehensive analysis. The methodological framework comprises three primary phases: firstly, defining the study area and segmenting roads; secondly, setting up the r.tikrit code; and thirdly, conducting data analysis and extracting traffic flow patterns. Throughout the developmental phases, Generative AI tools played a pivotal role as assistants, aiding in the development of analysis codes, streamlining the extraction process, and facilitating the literature survey. Subsequent research phases involved testing multiple applications: 1)Traffic Flow and Commuting Delay: Calculating traffic delay, peak hours and roads congestion situation 2) Spatial-Temporal Semi-Stationary Traffic Flow: Identifying unusual delays in selected roads segments that might be linked to external factors like accidents or natural hazards. 3) Traffic Flow Delay - Rainfall Relationship: Evaluating the impact of rainfall events on traffic networks and traffic delay. The approach's strength lies in its ability to generate high-resolution spatial and temporal road traffic delay data continuously. However, acknowledging inherent limitations of Google Traffic maps—such as inactive roads and discrepancies with open street maps—is vital. Despite these limitations, This methodology serves as an indispensable tool for researchers aiming to gain comprehensive insights into the complex status of urban traffic congestion patterns. Furthermore, it facilitates extended research into understanding the correlations between urban traffic and the ambient environment, enabling a deeper exploration of their impacts.

A common and largely unresolved problem of national-scale landslide early warning systems is their independent evaluation. In a recent paper, Mondini et al. (Nat Commun 14:2466, 2023) proposed a deep-learning system for short-term forecasting of rain-induced shallow landslides in Italy. Here, we independently evaluate the performance of this national-scale system by demonstrating its application between 1 January and 31 May 2021. For the purpose, we use hourly rainfall measurements from the same rain gauge network and different and independent information on the timing and location of 163 rain-induced landslides obtained from the FraneItalia catalogue that occurred in Italy in a period non considered in the construction of the system ( https://zenodo.org/records/7923683 ). Independent demonstration confirmed the good predictive performance of the forecasting system and revealed no geographical or temporal bias in the forecasts. The analysis also showed that the system was more effective at predicting multiple landslides in the same general area than single landslides. This was a good result as multiple landslides are inherently more dangerous than single failures. Analysis of the few misclassified landslides showed that approximately one-third of the landslides were rockfalls, and for approximately another third there was uncertainty about when or where the landslides occurred. We conclude that, despite the inevitable misclassifications inherent in any probabilistically based national-scale landslide forecasting system, the deep-learning system analysed is well suited for short-term operational forecasting of rain-induced shallow landslides in Italy.

Flood events are among the most damaging natural disasters, with billions of people being directly exposed to the risk of intense flooding worldwide. The economic and societal consequences of these events are expected to increase in the coming years. Flood societal risk can be determined by analyzing the relationship between the frequency of fatal flood events and the magnitude of the resulting consequences to the population (evaluated by the number of fatalities due to the event). Here, we test an approach previously proposed for landslides to estimate the flood societal risk in Italy, using historical sparse data on flood fatalities, available through national catalogues. Such an approach is based on the use of the Zipf distribution, which has previously been widely adopted for the modeling of societal risk for different natural hazards. The model allowed the evaluation of the spatial and temporal distribution of societal flood risk over the Italian territory over a regularly spaced grid. Different risk scenarios are presented and discussed.

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.

Significant effort has been devoted during the last few decades to the development of methodologies for landslide hazard and risk assessment. All of this work requires harmonization of the methodologies and terminology to facilitate communication within the landslide community, as well as with stakeholders and researchers from other disciplines. Currently, glossaries, and methodological recommendations exist for preparing landslide hazard and risk studies. Nevertheless, there is still debate on the usage of some terms and their implementation in practice. In 2016, the IAEG commission C-37 established a working group with the objective of preparing a standard multilingual glossary of landslide hazard and risk terms. The glossary aims for the international harmonization of the terms and definitions with those used in associated disciplines (e.g., seismology, hydrology) while considering landslides specifically. The glossary is based on previously published glossaries, including those prepared by ISSMGE TC32, FedIGS, JTC1, and UNISDR. This article presents comments on the meaning of some of the terms that have required further discussion. The English version of the glossary is also included.

Recent estimates suggest that landslides occur in about 17.1% of the landmasses, that about 8.2% of the global population live in landslide prone areas, and that population exposure to landslides is expected to increase. It is threfore not surprising that landslide early warning is gaining attention in the scientific and the technical literature, and among decision makers. Thanks to important scientific and technological advancements, landslide prediction and early warning are now possible, and landslide early warning systems (LEWSs) are becoming valuable resources for risk mitigation. A review of geographical LEWSs examined 26 regional, national and global systems in the 44.5-year period from January 1977 to June 2019. The study relevaled that only five nations, 13 regions, and four metropolitan areas benefited from operational LEWSs, and that large areas where landslide risk to the population is high lack LEWS coverage. The review also revealed that the rate of LEWSs deployment has increased in the recent years, but remains low, and that reniewed efforts are needed to accelerate the deployment of LEWSs. Building on the review, recommendations for the further development and improvement of geographical LEWSs are proposed. The recommendations cover six areas, including design, deployment, and operation of LEWS; collection and analysis of landslide and rainfall data used to design, operate, and validate LEWSs; landslide forecast models and advisories used in LEWSs; LEWSs evaluation and performance assessment; operation and management; and communication and dissemination. LEWSs are complex and multi-faceted systems that require care in their design, implementation and operation. To avoid failures that can lead to loss of credibility and liability consequences, it is critical that the community of scientists and professionals who design, implement and operate LEWSs takes all necessary precautions, guided by rigorous scientific practices.

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.

Recent estimates suggest that landslides occur in about 17.1% of the landmasses, that about 8.2% of the global population live in landslide prone areas, and that population exposure to landslides is expected to increase. It is threfore not surprising that landslide early warning is gaining attention in the scientific and the technical literature, and among decision makers. Thanks to important scientific and technological advancements, landslide prediction and early warning are now possible, and landslide early warning systems (LEWSs) are becoming valuable resources for risk mitigation. A review of geographical LEWSs examined 26 regional, national and global systems in the 44.5-year period from January 1977 to June 2019. The study relevaled that only five nations, 13 regions, and four metropolitan areas benefited from operational LEWSs, and that large areas where landslide risk to the population is high lack LEWS coverage. The review also revealed that the rate of LEWSs deployment has increased in the recent years, but remains low, and that reniewed efforts are needed to accelerate the deployment of LEWSs. Building on the review, recommendations for the further development and improvement of geographical LEWSs are proposed. The recommendations cover six areas, including design, deployment, and operation of LEWS; collection and analysis of landslide and rainfall data used to design, operate, and validate LEWSs; landslide forecast models and advisories used in LEWSs; LEWSs evaluation and performance assessment; operation and management; and communication and dissemination. LEWSs are complex and multi-faceted systems that require care in their design, implementation and operation. To avoid failures that can lead to loss of credibility and liability consequences, it is critical that the community of scientists and professionals who design, implement and operate LEWSs takes all necessary precautions, guided by rigorous scientific practices.

Different approaches exist to describe the seismic triggering of rockfalls. Statistical approaches rely on the analysis of local terrain properties and their empirical correlation with observed rockfalls. Conversely, deterministic, or physically based approaches, rely on the modeling of individual trajectories of boulders set in motion by seismic shaking. They require different data and allow various interpretations and applications of their results. Here, we present a new method for earthquake-triggered rockfall scenario assessment adopting ground shaking estimates, produced in near real-time by a seismological monitoring network. Its key inputs are the locations of likely initiation points of rockfall trajectories, namely, rockfall sources, obtained by statistical analysis of digital topography. In the model, ground shaking maps corresponding to a specific earthquake suppress the probability of activation of sources at locations with low ground shaking while enhancing that in areas close to the epicenter. Rockfall trajectories are calculated from the probabilistic source map by three-dimensional kinematic modeling using the software STONE. We apply the method to the 1976 MI = 6.5 Friuli earthquake, for which an inventory of seismically-triggered rockfalls exists. We suggest that using peak ground acceleration as a modulating parameter to suppress/enhance rockfall source probability, the model reasonably reproduces observations. Results allow a preliminary impact evaluation before field observations become available. We suggest that the framework may be suitable for rapid rockfall impact assessment as soon as ground-shaking estimates (empirical or numerical models) are available after a seismic event.

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.