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Background: 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. Description: The paper presents, after a brief review on global and national landslide databases, a new georeferenced 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. Conclusions: 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 timedependent 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.

Landslide early warning systems (LEWS) can be categorized into two groups: territorial and local systems. Territorial landslide early warning systems (Te-LEWS) deal with the occurrence of several landslides in wide areas: at municipal/regional/national scale. The aim of such systems is to forecast the increased probability of landslide occurrence in a given warning zone. The performance evaluation of such systems is often overlooked, and a standardized procedure is still missing. This paper describes a new Excel user-friendly tool for the application of the EDuMaP method, originally proposed by (Calvello and Piciullo 2016). A description of indicators used for the performance evaluation of different Te-LEWS is provided, and the most useful ones have been selected and implemented into the tool. The EDuMaP tool has been used for the performance evaluation of the “SMART” warning model operating in Piemonte region, Italy. The analysis highlights the warning zones with the highest performance and the ones that need threshold refinement. A comparison of the performance of the SMART model with other models operating in different Te-LEWS has also been carried out, highlighting critical issues and positive aspects. Lastly, the SMART performance has been evaluated with both the EDuMaP and a standard 2 × 2 contingency table for comparison purposes. The result highlights that the latter approach can lead to an imprecise and not detailed assessment of the warning model, because it cannot differentiate among the levels of warning and the variable number of landslides that may occur in a time interval.

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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.