Samuele Segoni

Rainfall thresholds for the forecasting of landslide occurrence at regional scale

This paper concerns a regional scale warning system for landslides that relies on a decisional algorithm based on the comparison between rainfall recordings and statistically defined thresholds. The latter were based on the total amount of rainfall, which was cumulated considering different time intervals: 1-, 2- and 3-day cumulates took into account the critical rainfall influencing shallow movements, whilst a variable time interval cumulate (up to 240 days) was used to consider the triggering of deep-seated landslides in low permeability terrains. A prototypal version of the model was initially set up to define statistical thresholds. Then, thresholds were calibrated using a database of past georegistered and dated landslides. A validation procedure showed that the calibration highly improves the results and therefore the model was integrated in the regional warning system of Emilia Romagna (Italy) for civil protection purposes. The proposed methodology could be easily implemented in other similar regions and countries where a sufficiently organised meteorological network is present.

Statistical and environmental analyses for the definition of a regional rainfall threshold system for landslide triggering in Tuscany (Italy)

The aim of this work is the determination of regional-scale rainfall thresholds for the triggering of landslides in the Tuscany Region (Italy). The critical rainfall events related to the occurrence of 593 past landslides were characterized in terms of duration (D) and intensity (I). I and D values were plotted in a log-log diagram and a lower boundary was clearly noticeable: it was interpreted as a threshold representing the rainfall conditions associated to landsliding. That was also confirmed by a comparison with many literature thresholds, but at the same time it was clear that a similar threshold would be affected by a too large approximation to be effectively used for a regional warning system. Therefore, further analyses were performed differentiating the events on the basis of seasonality, magnitude, location, land use and lithology. None of these criteria led to discriminate among all the events different groups to be characterized by a specific and more effective threshold. This outcome could be interpreted as the demonstration that at regional scale the best results are obtained by the simplest approach, in our case an empirical black box model which accounts only for two rainfall parameters (I and D). So a set of thresholds could be conveniently defined using a statistical approach: four thresholds corresponding to four severity levels were defined by means of the prediction interval technique and we developed a prototype warning system based on rainfall recordings or weather forecasts.

Landslides triggered by rainfall: A semi-automated procedure to define consistent intensity-duration thresholds

In this paper, a methodology to automate and standardize the identification of rainfall intensity-duration thresholds for landslides triggering is presented. A newly developed software called MaCumBA (MAssive CUMulative Brisk Analyzer) can be used to analyze rain-gauge records, extract the intensities (I) and durations (D) of the rainstorms associated with the initiation of landslides, plot these values on a diagram and identify thresholds that define the lower bounds of the aforementioned I-D values. Because the methodology is automated, it is possible to process a relevant amount of data in short times, while allowing for user decision input. A back analysis using data from past events that did not trigger landslides can be used to identify the threshold conditions associated with the least amount of false alarms. We applied the methodology in two test sites. A validation procedure returned satisfactory results, demonstrating the potential utility of the proposed methodology in the development of landslide warning systems.

Updating and tuning a regional-scale landslide early warning system

This work presents the last improvements of an operative regional-scale warning system developed for the management of the risk related to rainfall-induced landslides (both shallow and deep seated). The warning system is named Sistema Integrato Gestione Monitoraggio Allerta, and it is based on a set of spatially variable statistical rainfall thresholds (Martelloni et al. Landslides 9(4): 485–495, 2012b). The performance of the warning system was enhanced using a larger landslide dataset for the calibration of thresholds and readjusting the boundaries of the territorial units (TUs, the basic spatial unit of application of the warning system). Our tuning leads to define a larger number of TUs and to change some of the previous reference rain gauges. In particular, a statistical analysis highlighted that the spatial organization of missed and correctly predicted landslides does not depend on lithology, land use, and morphometric attributes; therefore, the redefinition of TUs was based on the administrative borders between municipalities. This allowed combining the TU outputs into a decisional procedure which, in a completely automated way, is able to forecast the warning levels based on objective and quantitative criteria (the number of expected landslides), in full accordance with the regional civil protection guidelines. The implementation of these updates was straightforward and could be conveniently applied to similar warning systems based on rainfall thresholds.

Improving basin scale shallow landslide modelling using reliable soil thickness maps

Soil thickness is a well-known factor controlling shallow landsliding. Notwithstanding, its spatial organisation over large areas is poorly understood, and in basin scale slope analyses it is often established using simple methods. In this paper, we apply five different soil thickness models in two test sites, and we use the obtained soil thickness maps to feed a slope stability model. Validation quantifies how errors in soil thickness influence the resulting factor of safety and points out which method grants the best results. In particular, in our cases, slope-derived soil thickness patterns produced the worst slope stability assessment, while the use of reliable soil thickness maps obtained by means of a more complex geomorphologically indexed model improved shallow landslides modelling.

GIS techniques for regional-scale landslide susceptibility assessment: the Sicily (Italy) case study

This study describes the assessment of landslide susceptibility in Sicily (Italy) at a 1:100,000 scale using a multivariate logistic regression model. The model was implemented in a GIS environment by using the ArcSDM (Arc Spatial Data Modeller) module, modified to develop spatial prediction through regional data sets. A newly developed algorithm was used to automatically extract the detachment area from mapped landslide polygons. The following factors were selected as independent variables of the logistic regression model: slope gradient, lithology, land cover, a curve number derived index and a pluviometric anomaly index. The above-described configuration has been verified to be the best one among others employing from three to eight factors. All the regression coefficients and parameters were calculated using selected landslide training data sets. The results of the analysis were validated using an independent landslide data set. On an average, 82% of the area affected by instability and 79% of the not affected area were correctly classified by the model, which proved to be a useful tool for planners and decision-makers.

Updating EWS rainfall thresholds for the triggering of landslides

In this paper, the updating of rainfall thresholds for landslide early warning systems (EWSs) is presented. Rainfall thresholds are widely used in regional-scale landslide EWSs, but the efficiency of those systems can decrease during the time, so a periodically updating should be required to keep their functionality. The updating of 12 of the 25 thresholds used in the EWS of Tuscany region (central Italy) is presented, and a comparison between performances of new and previous thresholds has been made to highlight the need of their periodical update. The updating has been carried out by collecting ca. 1200 new landslide reports (from 2010 to March 2013) and their respective rainfall data, collected by 332 rain gauges. The comparison has been made by the use of several statistical indexes and showed a marked increasing in the performances of the new thresholds with respect to previous ones.

Landslide Susceptibility Mapping at National Scale: The Italian Case Study

Landslide susceptibility maps are key tools for land use planning, management and risk mitigation. The Landslide susceptibility map of Italy, scale 1:1,000,000 is being realized by using the Italian Landslide Inventory – Progetto IFFI and a set of contributing factors, such as surface parameters derived from 20 to20 m DEM, lithological map obtained from the geological map of Italy 1:500,000, and land use map (Corine Land Cover 2000). These databases have been subjected to a quality analysis with the aim of assessing the completeness, homogeneity and reliability of data, and identifying representative areas which may be used as training and test areas for the implementation of landslide susceptibility models. In order to implement the models, physiographic domains of homogeneous geology and geomorphology have been identified, and landslides have been divided into three main classes in order to take into account specific sets of conditioning factors: (a) rockfalls and rock-avalanches; (b) slow mass movements, (c) debris flows. The modelling tests performed with different techniques (Discriminant Anaysis, Logistic Regression, Bayesian Tree Random Forest) provided good results, once applied with the appropriate selection of training and validations sets and with a significant number of statistical units.

Rainfall thresholds for rainfall-induced landslides in Slovenia

In Slovenia, rainfall-induced landslides lead to considerable damages, even causing human losses. In order to reduce the impact of this kind of landslide, several researchers analyzed rainfall-induced landslides in this country, but to date, no rainfall thresholds have been developed for a landslide warning system at national scale. In this paper, the definition of rainfall thresholds for rainfall-induced landslides in Slovenia is presented. The thresholds have been calculated by collecting approximately 900 landslide data and the relative rainfall amounts, which have been collected from 41 rain gauges. To define the thresholds, an existing procedure characterized by a high degree of objectiveness has been used. This approach is based on a software that was developed for a test site with very different characteristics (Tuscany, central Italy). At first, a single national threshold has been defined; subsequently, the country was divided into four zones, on the basis of the major river basins. The effectiveness of the thresholds has been verified by the use of several statistical parameters and it resulted in quite good performances, even if with some uncertainties, probably due to the quality of the available data. Besides the setting of a threshold system, usable for civil protection purposes at national scale, an additional outcome of this work was the possibility of applying, with good results, a methodology defined for another region, therefore testing its degree of exportability in different settings.

Soil characterization for shallow landslides modeling: a case study in the Northern Apennines (Central Italy)

In this paper, we present preliminary results of the IPL project No. 198 “Multi-scale rainfall triggering models for Early Warning of Landslides (MUSE).” In particular, we perform an assessment of the geotechnical and hydrological parameters affecting the occurrence of landslides. The aim of this study is to improve the reliability of a physically based model high resolution slope stability simulator (HIRESSS) for the forecasting of shallow landslides. The model and the soil characterization have been tested in Northern Tuscany (Italy), along the Apennine chain, an area that is historically affected by shallow landslides. In this area, the main geotechnical and hydrological parameters controlling the shear strength and permeability of soils have been determined by in situ measurements integrated by laboratory analyses. Soil properties have been statistically characterized to provide more refined input data for the slope stability model. Finally, we have tested the ability of the model to predict the occurrence of shallow landslides in response to an intense meteoric precipitation.