In Italy landslides are widespread natural phenomena causing a significant number of fatalities and huge economic losses throughout the country every year. Information on the spatial and temporal distribution of landslides at national scale is critical for developing landslide susceptibility, hazard and risk maps, as well as, more generally, for decision making in landslide risk management. The paper presents, after a brief review on global and national landslide databases, a new geo-referenced catalog of recent landslides affecting the Italian territory. The catalog, called Franeitalia, includes both fatal landslide events and events that did not produce physical harm to people. It has been developed consulting online news sources from 2010 onwards. The following seven steps have been performed to define and populate the catalog: i) selection of news sources; ii) identification of effective search keywords; iii) collection of relevant news articles; iv) identification of landslide categories; v) definition of catalog fields; vi) information mining from news articles; vii) geo-referencing of the events. Landslide events are classified considering two numerosity categories and three consequence categories. The numerosity categories are: single landslide events (SLE), for records only reporting one landslide; and areal landslide events (ALE), for records referring to multiple landslides triggered by the same cause in the same geographic area. Both SLEs and ALEs are divided in three consequence classes according to whether the event produced victims and/or missing people (C1, very severe), injured persons and/or evacuations (C2, severe), or did not cause any physical harm to people (C3, minor). Information on the landslide events collected in the catalog always includes: data on the location of the event, day of occurrence of the landslide (s), source (s) of information, and number of landslides in case of areal events. Additional information may include: onset and duration of the landslide event, landslide characteristics, phase of activity, details on the consequences. Reports and statistics on the landslides included in the catalog are presented highlighting: the main figures of the landslide inventory, currently spanning from the 2010 to 2017 and including 8931 landslides; and time-dependent national and regional trends, with a focus on the consequences induced by the events. The paper also compares and discusses the figures in relation to other catalogs reporting recent landslides that occurred in the Italian territory.

The present study addresses the pattern distribution of recent landslides in Italy. The main objective is the detection and mapping of spatio-temporal clusters of landslides that occurred in the period 2010–2017 in the country. To this aim, a subdivision of the national area into 158 warning zones, as identified by the 21 civil protection regional centres to deal with weather-induced hydro-geological hazards, is adopted. Information on landslides comes from FraneItalia, a geo-referenced catalogue developed consulting online news sources. Analyses are performed both at national scale and at a regional scale, focusing on the Campania region. The space–time permutation scan statistics model is applied to detect statistically significant clustering, accounting for the geographical spatial dimension and for the temporal dimension. Two types of analyses are performed: annual, considering each single year; and multi-annual, encompassing the entire 8-year study period. In both cases, spatio-temporal cluster analyses are able to detect areas and frame-periods characterised by relevant and recurrent landslide activity. Finally, the obtained results are compared with a standard landslide density map, highlighting the complementarity of the two approaches.

A methodology designed to integrate widespread meteorological monitoring and pore water pressure measurements is proposed. The procedure is tested in 30 hydrological basins highly susceptible to weather-induced landslides in Norway. The following data are used: a catalog of 125 weather-induced landslides in soils registered between January 2013 and June 2017, widespread meteorological monitoring data employed in a territorial warning model, and pore water pressure measurements retrieved from boreholes installed for a variety of geotechnical projects. The territorial warning model is initially applied to identify the warning events and the correspondent warning level in the test areas over the analysis period. Afterwards, a method for assessing the territorial warning events by analyzing the trends of the monitored pore water pressures is proposed. Finally, an augmented territorial warning model is calibrated and validated using statistical indicators widely adopted in literature. The analysis of the results reveals a satisfactory correspondence between days with landslides and the warning levels provided by the augmented territorial warning model. A final comparison between the results of the model calibration and the model validation highlighted the consistency of the model performance, once the three model parameters are adequately set.

Slow-moving landslides are widespread natural hazards that can affect social and economic activities, causing damage to structures and infrastructures. This paper aims at proposing a procedure to analyze road damage induced by slow-moving landslides based on the joint use of landslide susceptibility maps, a road-damage database developed using Google Street View images and ground-displacement measurements derived from the interferometric processing of satellite SAR images. The procedure is applied to the municipalities of Vaglio Basilicata and Trivigno in the Basilicata region (southern Italy) following a matrix-based approach. First, a susceptibility analysis is carried out at the municipal scale, using data from landslide inventories and thematic information available over the entire municipalities. Then, the susceptibility index, the class of movement and the level of damage are calculated for the territorial units corresponding to the road corridors under investigation. Finally, the road networks are divided into stretches, each one characterized by a specific level of risk (or attention required) following the aggregation of the information provided by the performed analyses. The results highlight the importance of integrating all of these different approaches and data for obtaining quantitative information on the spatial and temporal behavior of slow-moving landslides affecting road networks.

Landslide early warning systems at regional scale are typically based on correlations between landslides that occurred in the past and rainfall monitoring data in order to identify trigger rainfall thresholds. Recently, the availability of large datasets of atmospheric measurements allows including additional variables, increasing the reliability of the models. However, conducting the analyses with traditional techniques can be quite complex and time-consuming. The purpose of this preliminary study is to demonstrate that machine learning techniques can be used to analyze monitoring data in order to select the most relevant variables for the triggering of shallow rainfall-induced landslides at regional scale. The models developed herein were tested in one of the alert zones defined by civil protection for the management of geo-hydrological risk in Campania region, Italy. Two data sources were used in the analysis. The atmospheric variables are derived from the ERA5-Land atmospheric reanalysis. The data on landslide events are retrieved from “FraneItalia”, a georeferenced catalog of landslides occurred in Italy developed by consulting online sources from 2010 onwards. The models developed were calibrated and validated in order to define combinations of rainfall variables and soil water content for the prediction of the occurrence of landslides. Finally, the performance of the models was assessed using statistical indicators derived from contingency matrices.