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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. [ABSTRACT FROM AUTHOR]

The term debris flow is associated with a wide variety of phenomena as debris slides, debris torrents, debris floods, mudflows, mudslides, hyper-concentrated flows and lahars (in volcanic slopes). In historical times, they frequently occurred in Campania Region (Southern Italy) producing damage and casualties. In this paper, more than 200 events described as "debris flow" between 1924 and 2020 were collected. They are mainly located in the central sector of the region, which is characterized by pyroclastic deposits resting on steep carbonate and volcanic slopes and highly prone to hydrogeological hazards. Debris flow events registered in the region were classified as "gravity processes" (flow-like landslides, FL) or "fluvial processes" (flash floods, FF). This classification is fundamental for designing effective early warning systems and risk mitigation plans, since the two phenomena are triggered by different types of rainfall events and are responsible for damages of different magnitude. In this study, a large rainfall database was explored to identify the time scales and seasonality of the two classes of rainfall events, respectively triggering the two phenomena. [ABSTRACT FROM AUTHOR]

Active landslides are generally characterized by variations in displacement rate in response to cumulated precipitation. Velocities that are only exceeded in a limited number of days during the year might be considered as critical events, since they might determine, or prelude to, a significant evolution of the landslide. The purpose of this paper is to present a novel approach based on the use of receiver operating characteristic (ROC) curves for assessing cumulated precipitation thresholds that can provide early warning for the occurrence of critical events such as the exceedance of rare displacement rates. The approach has been developed and tested in the Piagneto landslide, an active complex rock slide-debris slide in the Northern Apennines of Italy, for which a 5-year continuous surveying monitoring dataset is available. On the basis of the first 4 years of monitoring data (training dataset), threshold curves relating cumulative precipitation (mm) to precipitation moving windows (days) have been generated by using different benchmarks that, in literature, are used to estimate the maximum predictive performance of ROC curves. These threshold curves have been successfully validated using the last 1 year of monitoring data (validation dataset). They have then been used to simulate how they might help defining different early warning levels in due advance. The proposed methodology can be replicated in any landslide for which a monitoring dataset that includes recurrent acceleration events in response to precipitation is available. [ABSTRACT FROM AUTHOR]

Landslide risk assessment is fundamental in identifying risk areas, where mitigation measures must be introduced. Most of the existing methods are based on susceptibility assessment strongly site-specific and require information often unavailable for damage quantification. This study proposes a simplified methodology, specific for rainfall-induced shallow landslides, that tries to overcome both these limitations. Susceptibility assessed from a physically-based model SLIP (shallow landslides instability prediction) is combined with distance derived indices representing the interference probability with elements at risk in the anthropized environment. The methodology is applied to Gioiosa Marea municipality (Sicily, south Italy), where shallow landslides are often triggered by rainfall causing relevant social and economic damage because of their interference with roads. SLIP parameters are first calibrated to predict the spatial and temporal occurrence of past surveyed phenomena. Susceptibility is then assessed in the whole municipality and validated by comparison with areas affected by slide movements according to the regional databases of historical landslides. It is shown that all the detected areas are covered by points where the SLIP safety factor ranges between 0 and 2. Risk is finally assessed after computation of distances from elements at risk, selected from the land use map. In this case, results are not well validated because of lack of details in the available regional hydrogeological plan, both in terms of extension and information. Further validation of the proposed interference indices is required, e.g., with studies of landslide propagation, which can also allow considerations on the provoked damage. [ABSTRACT FROM AUTHOR]

A process chain for the definition and the performance assessment of an operational regional warning model for rainfall-induced landslides, based on rainfall thresholds, is proposed and tested in a landslide-prone area in the Campania region, southern Italy. A database of 96 shallow landslides triggered by rainfall in the period 2003-2010 and rainfall data gathered from 58 rain gauges are used. First, a set of rainfall threshold equations are defined applying a well-known frequentist method to all the reconstructed rainfall conditions responsible for the documented landslides in the area of analysis. Several thresholds at different exceedance probabilities (percentiles) are evaluated, and nine different percentile combinations are selected for the activation of three warning levels. Subsequently, for each combination, the issuing of warning levels is computed by comparing, over time, the measured rainfall with the pre-defined warning level thresholds. Finally, the optimal percentile combination to be employed in the regional early warning system, i.e. the one providing the best model performance in terms of success and error indicators, is selected employing the 'event, duration matrix, performance' (EDuMaP) method. [ABSTRACT FROM AUTHOR]

Review of the literature on the reconstruction of the rainfall responsible for slope failures reveals that criteria for the identification of rainfall events are lacking or somewhat subjective. To overcome this problem, we developed an algorithm for the objective and reproducible reconstruction of rainfall events and of rainfall conditions responsible for landslides. The algorithm consists of three distinct modules for (i) the reconstruction of distinct rainfall events, in terms of duration ( D, in h) and cumulated event rainfall ( E, in mm), (ii) the identification of multiple ED rainfall conditions responsible for the documented landslides, and (iii) the definition of critical rainfall thresholds for possible landslide occurrences. The algorithm uses pre-defined parameters to account for different seasonal and climatic settings. We applied the algorithm in Sicily, southern Italy, using rainfall measurements obtained from a network of 169 rain gauges, and information on 229 rainfall-induced landslides occurred between July 2002 and December 2012. The algorithm identified 29,270 rainfall events and reconstructed 472 ED rainfall conditions as possible triggers of the observed landslides. The algorithm exploited the multiple rainfall conditions to define objective and reproducible empirical rainfall thresholds for the possible initiation of landslide in Sicily. The calculated thresholds may be implemented in an operational early warning system for shallow landslide forecasting. [ABSTRACT FROM AUTHOR]