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Abstract Systematic and timely documentation of triggered (i.e. event) landslides is fundamental to build extensive datasets worldwide that may help define and/or validate trends in response to climate change. More in general, preparation of landslide inventories is a crucial activity since it provides the basic data for any subsequent analysis. In this work we present an event landslide inventory map (E-LIM) that was prepared through a systematic reconnaissance field survey in about 1 month after an extreme rainfall event hit an area of about 5000 km2 in the Marche-Umbria regions (central Italy). The inventory reports evidence of 1687 triggered landslides in an area of ~550 km2. All slope failures were classified according to type of movement and involved material, and documented with field pictures, wherever possible. The database of the inventory described in this paper as well as the collection of selected field pictures associated with each feature is publicly available at figshare.

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.

The increasing availability of long-term observational data can lead to the development of innovative modelling approaches to determine landslide triggering conditions at a 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.

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 detection and monitoring process to predict 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 prediction in reducing the impact of this hazard on the population. We have performed a comparative study of ground- and satellite-based rainfall products for landslide prediction 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 that 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 (0.1∘ vs. 0.25∘) and temporal (hourly vs. daily) resolutions. 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.

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.

In karst environments, heavy rainfall is known to cause multiple geohydrological hazards, including inundations, flash floods, landslides and sinkholes. We studied a period of intense rainfall from 1 to 6 September 2014 in the Gargano Promontory, a karst area in Puglia, southern Italy. In the period, a sequence of torrential rainfall events caused severe damage and claimed two fatalities. The amount and accuracy of the geographical and temporal information varied for the different hazards. The temporal information was most accurate for the inundation caused by a major river, less accurate for flash floods caused by minor torrents and even less accurate for landslides. For sinkholes, only generic information on the period of occurrence of the failures was available. Our analysis revealed that in the promontory, rainfall-driven hazards occurred in response to extreme meteorological conditions and that the karst landscape responded to the torrential rainfall with a threshold behaviour. We exploited the rainfall and the landslide information to design the new ensemble–non-exceedance probability (E-NEP) algorithm for the quantitative evaluation of the possible occurrence of rainfall-induced landslides and of related geohydrological hazards. The ensemble of the metrics produced by the E-NEP algorithm provided better diagnostics than the single metrics often used for landslide forecasting, including rainfall duration, cumulated rainfall and rainfall intensity. We expect that the E-NEP algorithm will be useful for landslide early warning in karst areas and in other similar environments. We acknowledge that further tests are needed to evaluate the algorithm in different meteorological, geological and physiographical settings.

The research project ‘Moon Mapping’ has been established in 2014 between the Italian and Chinese Governments to promote cooperation and exchange between undergraduate students from both countries. The operational phase of the project started in early 2015, and will end in 2017, for a total length of three years. The main aim is to train new scholars to be able to work on different kinds of remotely-sensed data collected over the Moon surface by the Chinese space missions Chang’E-1/2. The project coordination has been assigned to the Italian Space Agency for the Italian side and to the Center of Space Exploration, China Ministry of Education, for the Chinese side. Several Chinese universities and Italian national research institutes and universities have been officially involved in this project. Six main research topics have been identified: (1) map of the solar wind ion; (2) geomorphological map of the Moon; (3) data preprocessing of Chang’E-1 mission; (4) map of element distribution; (5) establishment of 3D digital visualization system; and (6) compilation and publication of a tutorial on joint lunar mapping.

Geological slope failure processes have been observed on the Moon surface for decades, nevertheless a detailed and exhaustive lunar landslide inventory has not been produced yet. For a preliminary survey, WAC images and DEM maps from LROC at 100 m/pixels have been exploited in combination with the criteria applied by Brunetti et al. (2015) to detect the landslides. These criteria are based on the visual analysis of optical images to recognize mass wasting features. In the literature, Chebyshev polynomials have been applied to interpolate crater cross-sections in order to obtain a parametric characterization useful for classification into different morphological shapes. Here a new implementation of Chebyshev polynomial approximation is proposed, taking into account some statistical testing of the results obtained during Least-squares estimation. The presence of landslides in lunar craters is then investigated by analyzing the absolute values off odd coefficients of estimated Chebyshev polynomials. A case study on the Cassini A crater has demonstrated the key-points of the proposed methodology and outlined the required future development to carry out.

In many areas, rainfall is the primary trigger of landslides. Determining the rainfall conditions responsible for landslide occurrence is important, and may contribute to saving lives and properties. In a long-term national project for the definition of rainfall thresholds for possible landslide occurrence in Italy, we compiled a catalogue of 186 rainfall events that resulted in 251 shallow landslides in Calabria, southern Italy, from January 1996 to September 2011. Landslides were located geographically using Google Earth®, and were given a mapping and a temporal accuracy. We used the landslide information, and sub-hourly rainfall measurements obtained from two complementary networks of rain gauges, to determine cumulated event vs. rainfall duration (ED) thresholds for Calabria. For this purpose, we adopted an existing method used to prepare rainfall thresholds and to estimate their associated uncertainties in central Italy. The regional thresholds for Calabria were found to be nearly identical to previous ED thresholds for Calabria obtained using a reduced set of landslide information, and slightly higher than the ED thresholds obtained for central Italy. We segmented the regional catalogue of rainfall events with landslides in Calabria into lithology, soil regions, rainfall zones, and seasonal periods. The number of events in each subdivision was insufficient to determine reliable thresholds, but allowed for preliminary conclusions about the role of the environmental factors in the rainfall conditions responsible for shallow landslides in Calabria. We further segmented the regional catalogue based on administrative subdivisions used for hydro-meteorological monitoring and operational flood forecasting, and we determined separate ED thresholds for the Tyrrhenian and the Ionian coasts of Calabria. We expect the ED rainfall thresholds for Calabria to be used in regional and national landslide warning systems. The thresholds can also be used for landslide hazard and risk assessments, and for erosion and landscape evolution studies, in the study area and in similar physiographic regions in the Mediterranean area.

In Italy, rainfall is the primary trigger of landslides that frequently cause fatalities and large economic damage. Using a variety of information sources, we have compiled a catalogue listing 753 rainfall events that have resulted in landslides in Italy. For each event in the catalogue, the exact or approximate location of the landslide and the time or period of initiation of the slope failure is known, together with information on the rainfall duration D, and the rainfall mean intensity I, that have resulted in the slope failure. The catalogue represents the single largest collection of information on rainfall-induced landslides in Italy, and was exploited to determine the minimum rainfall conditions necessary for landslide occurrence in Italy, and in the Abruzzo Region, central Italy. For the purpose, new national rainfall thresholds for Italy and new regional rainfall thresholds for the Abruzzo Region were established, using two independent statistical methods, including a Bayesian inference method and a new Frequentist approach. The two methods proved complementary, with the Bayesian method more suited to analyze small data sets, and the Frequentist method performing better when applied to large data sets. The new regional thresholds for the Abruzzo Region are lower than the new national thresholds for Italy, and lower than the regional thresholds proposed in the literature for the Piedmont and Lombardy Regions in northern Italy, and for the Campania Region in southern Italy. This is important, because it shows that landslides in Italy can be triggered by less severe rainfall conditions than previously recognized. The Frequentist method experimented in this work allows for the definition of multiple minimum rainfall thresholds, each based on a different exceedance probability level. This makes the thresholds suited for the design of probabilistic schemes for the prediction of rainfall-induced landslides. A scheme based on four probabilistic thresholds is proposed. The four thresholds separate five fields, each characterized by different rainfall intensity-duration conditions, and corresponding different probability of possible landslide occurrence. The scheme can be implemented in landslide warning systems that operate on rainfall thresholds, and on precipitation measurements or forecasts.

We examine 19 datasets with measurements of landslide volume, VL, for sub-aerial, submarine, and extraterrestrial mass movements. Individual datasets include from 17 to 1019 landslides of different types, including rock fall, rock slide, rock avalanche, soil slide, slide, and debris flow, with individual landslide volumes ranging over 10−4 m3≤VL≤1013 m3. We determine the probability density of landslide volumes, p(VL), using kernel density estimation. Each landslide dataset exhibits heavy tailed (self-similar) behaviour for their frequency-size distributions, p(VL) as a function of VL, for failures exceeding different threshold volumes, VL*, for each dataset. These non-cumulative heavy-tailed distributions for each dataset are negative power-laws, with exponents 1.0≤β≤1.9, and averaging β≈1.3. The scaling behaviour of VL for the ensemble of the 19 datasets is over 17 orders of magnitude, and is independent of lithological characteristics, morphological settings, triggering mechanisms, length of period and extent of the area covered by the datasets, presence or lack of water in the failed materials, and magnitude of gravitational fields. We argue that the statistics of landslide volume is conditioned primarily on the geometrical properties of the slope or rock mass where failures occur. Differences in the values of the scaling exponents reflect the primary landslide types, with rock falls exhibiting a smaller scaling exponent (1.1≤β≤1.4) than slides and soil slides (1.5≤β≤1.9). We argue that the difference is a consequence of the disparity in the mechanics of rock falls and slides.