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We present the size statistics of landslides on Earth, Mars, the Moon and Mercury. We used two existing landslide inventories for New Mexico (USA) and for Valles Marineris, Mars, and two new inventories of lunar and Mercurian landslides. Failures on the Moon and Mercury were detected and mapped along the internal walls of impact craters. The statistical distributions of the extraterrestrial landslide area were exploited to compare the results with similar distributions obtained for terrestrial landslides.

The contribution describes the recognition and mapping of large rock slides in impact craters on the Moon and Mercury.

Objective criteria for the identification of rainfall events are lacking or ambiguous, particularly when dealing with the forecast of rainfall-induced landslides. The definition of rainfall conditions that have resulted in past landslides remains somewhat subjective. To overcome this problem, we propose an algorithm for the objective and reproducible reconstruction of rainfall events and of rainfall conditions responsible for failures. The algorithm calculates the duration (D in h) and the cumulated event rainfall (E in mm) of rainfall events and of rainfall conditions that have resulted in past landslides. The algorithm is implemented in a code written in R language (http://www.r-project.org). The algorithm analyses continue rainfall series performing three actions: (i) it removes isolated events with negligible amount of rainfall from the input data series; (ii) it aggregates rainfall measurements in order to obtain a sequence of distinct rainfall events; (iii) it identifies one or more rainfall conditions responsible for the observed slope failures. The algorithm is independent from the geographical context since it uses a set of parameters to account for different physical settings and climatic conditions. We tested the algorithm in Sicily, southern Italy, using geo-localized rainfall and landslide information between January 2002 and December 2012. Using data from 59 rain gauges, the algorithm reconstructed 13,537 rainfall events, corresponding to a mean of 23 rainfall events per year. Next, using information on 163 documented landslides we reconstructed 343 rainfall conditions as possible triggers of the observed landslides. The algorithm may contribute to reducing the current subjectivity both in the manual reconstruction of rainfall events and in the definition of the rainfall triggering landslides. The objective definition of rainfall conditions responsible for failures is necessary to define reliable rainfall thresholds for the forecasting of landslides. We consider that the objective identification of rainfall events performed by the algorithm could be useful for other hydrological, hydraulic or geomorphological purposes.

In Italy, rainfall induced shallow landslides cause casualties every year, and represent a serious hazard to the population. As a result, the prediction of shallow slope failures triggered by intense or prolonged rainfall is of primary importance for decision makers and civil protection authorities. At the national and the regional scales, the prediction of the possible occurrence of rainfall induced shallow landslides is achieved by comparing rainfall measurements and estimates with empirical rainfall thresholds, established through the analysis of past rainfall events that have resulted in slope instabilities. Using multiple sources of information (including newspapers and fire brigade reports), we have updated an historical catalogue listing > 500 rainfall events that have resulted in documented landslides in the Abruzzo, Marche and Umbria regions, central Italy, between 2002 and 2011. We have exploited this catalogue to determine new cumulated event rainfall - rainfall duration (ED) thresholds for the possible occurrence of slope failures in central Italy. It is known that the local topographical (morphological), lithological, soil and climatic settings condition the occurrence and spatial distribution of shallow landslides in an area. For this work, we have exploited the catalogue of rainfall events with landslides to investigate the role of morphology and soil types in the cumulated amount (E) and the duration (D) of the rainfall that has resulted in shallow landslides in our study area, in the 10-year considered period. For the purpose, we have subdivided the study area in 3 topographic sub-divisions and 7 regional soil domains, and we have analysed the (D,E) rainfall conditions in the two different terrain classifications. Adopting a Frequentist statistical method already used to define lithological and climatic thresholds in the study area, and their associated uncertainties, we have defined new ED rainfall thresholds for possible landslide occurrence for the individual topographic sub-divisions and regional soil domains. The new thresholds were compared with the existing geographical, lithological, and climatological thresholds in the same study area. We expect the results of this study to contribute to the understanding of the influence of topography and soil characteristics on the occurrence and spatial pattern of rainfall induced shallow landslides in central Italy. We further expect that the new topographic and soil thresholds can improve the ability to forecast shallow landslides in central Italy, and in neighbouring and distant areas characterized by similar morphological and soil settings.

We compiled a catalogue of 442 rainfall events with landslides in the Abruzzo, Marche, and Umbria regions, central Italy, in the period 2002-2010. The landslide information was obtained through the analysis of national, regional, and local newspapers, and reports of landslide events compiled by fire brigades. For each rainfall event that has resulted in one or more landslides, we calculated the cumulated rainfall E (mm) and the duration D (h) of the rainfall event, using a dense network of 150 rain gauges. Landslides were mapped as single points using Google Earth®. We exploited the catalogue to determine new cumulated event rainfall - rainfall duration (ED) thresholds. To determine the ED thresholds, we modified a Frequentist probabilistic method first proposed to determine ID thresholds. The method assumes that the threshold curve is a power law E = ? × D^?, where E is the cumulated rainfall (mm), D is the duration of the rainfall event (h), ? is a scaling constant (the intercept), and ? is the slope of the power law curve. We improved the method adopting a bootstrapping statistical technique to evaluate the uncertainties in the parameters that define the threshold curve model, and the sensitivity (robustness) of the thresholds to the number of (D,E) data points in the empirical data set. We determined ED thresholds for 1% and 5% exceedance probability levels for the entire study area, and for the three individual regions that comprise the study area (Abruzzo, Marche, and Umbria). To investigate the role of lithology and season on the occurrence of rainfall-induced landslides, we segmented the rainfall ED data by the main lithological domains in the study area (i.e., Post-Orogenic sediments, Flysch deposits, and Carbonate rocks), and season of the events (May - September, October - April). Comparison of the ED thresholds indicates that the cumulated amount of rainfall necessary to trigger landslides in Flysch deposits is larger than in Post-Orogenic sediments and Carbonate rocks, and that the thresholds for Post-Orogenic sediments and Carbonate rocks were statistically indistinguishable in the study area. The seasonal thresholds are statistically different for D < 12 h, and for D > 100 h. In particular, for short rainfall periods (D < 12 h) the cumulated rainfall required to initiate landslides is higher in the summer period May - September than in the period October - April. The results obtained are applicable for landslide forecasting based on empirical rainfall thresholds, and have implications for landslide hazard and risk assessment.