Rainfall thresholds for possible landslide occurrence in Italy

Abstract The large physiographic variability and the abundance of landslide and rainfall data make Italy an ideal site to investigate variations in the rainfall conditions that can result in rainfall-induced landslides. We used landslide information obtained from multiple sources and rainfall data captured by 2228 rain gauges to build a catalogue of 2309 rainfall events with – mostly shallow – landslides in Italy between January 1996 and February 2014. For each rainfall event with landslides, we reconstructed the rainfall history that presumably caused the slope failure, and we determined the corresponding rainfall duration D (in hours) and cumulated event rainfall E (in mm). Adopting a power law threshold model, we determined cumulated event rainfall–rainfall duration (ED) thresholds, at 5% exceedance probability, and their uncertainty. We defined a new national threshold for Italy, and 26 regional thresholds for environmental subdivisions based on topography, lithology, land-use, land cover, climate, and meteorology, and we used the thresholds to study the variations of the rainfall conditions that can result in landslides in different environments, in Italy. We found that the national and the environmental thresholds cover a small part of the possible DE domain. The finding supports the use of empirical rainfall thresholds for landslide forecasting in Italy, but poses an empirical limitation to the possibility of defining thresholds for small geographical areas. We observed differences between some of the thresholds. With increasing mean annual precipitation (MAP), the thresholds become higher and steeper, indicating that more rainfall is needed to trigger landslides where the MAP is high than where it is low. This suggests that the landscape adjusts to the regional meteorological conditions. We also observed that the thresholds are higher for stronger rocks, and that forested areas require more rainfall than agricultural areas to initiate landslides. Finally, we observed that a 20% exceedance probability national threshold was capable of predicting all the rainfall-induced landslides with casualties between 1996 and 2014, and we suggest that this threshold can be used to forecast fatal rainfall-induced landslides in Italy. We expect the method proposed in this work to define and compare the thresholds to have an impact on the definition of new rainfall thresholds for possible landslide occurrence in Italy, and elsewhere

A tool for the automatic calculation of rainfall thresholds for landslide occurrence

Abstract Empirical rainfall thresholds are commonly used to forecast landslide occurrence in wide areas. Thresholds are affected by several uncertainties related to the rainfall and the landslide information accuracy, the reconstruction of the rainfall responsible for the failure, and the method to calculate the thresholds. This limits the use of the thresholds in landslide early warning systems. To face the problem, we developed a comprehensive tool, CTRL–T ( C alculation of T hresholds for R ainfall-induced L andslides− T ool) that automatically and objectively reconstructs rainfall events and the triggering conditions responsible for the failure, and calculates rainfall thresholds at different exceedance probabilities. CTRL−T uses a set of adjustable parameters to account for different morphological and climatic settings. We tested CTRL−T in Liguria region (Italy), which is highly prone to landslides. We expect CTRL−T has an impact on the definition of rainfall thresholds in Italy, and elsewhere, and on the reduction of the risk posed by rainfall-induced landslides

A new procedure for an effective management of geo-hydrological risks across the "Sentiero Verde-Azzurro" trail, Cinque Terre National Park, Liguria (North-Western Italy)

In recent years, Cinque Terre National Park, one of the most famous UNESCO sites in Italy, experienced a significant increase in tourist visits. This unique landscape is the result of the rough morphology of a small coastal basin with a very steep slope and a long-term human impact, mainly represented by anthropic terraces. This setting promotes the activation of numerous geo-hydrological instabilities, primarily related to heavy rainfall events that often affect this area. Currently, the main challenge for the administrators of Cinque Terre National Park is the correct maintenance of this environment along with the functional management of the hiking trail to ensure the safety of tourists. The definition of a methodology for effective management is mandatory for the sustainable administration of this unique site. We implement a new codified procedure based on the combined use of the Operative Monography and the Survey Form, focusing on the "Sentiero Verde-Azzurro" trail, for a proper description of the known landslides affecting the trail and the identification of damage and/or landslides activated by critical meteorological events. This guarantees effective geo-hydrological risk management, which is also applicable to other similar sites in a unique environmental and cultural heritage site such as Cinque Terre Park

Deciphering seasonal effects of triggering and preparatory precipitation for improved shallow landslide prediction using generalized additive mixed models

The increasing availability of long-term observational data can lead to the development of innovative modelling approaches to determine landslide triggering conditions at regional scale, opening new avenues for landslide prediction and early warning. This research blends the strengths of existing approaches with the capabilities of generalized additive mixed models (GAMMs) to develop an interpretable approach that identifies seasonally dynamic precipitation conditions for shallow landslides. The model builds upon a 21-year record of landslides in South Tyrol (Italy) and separates precipitation that induced landslides from precipitation that did not. The model accounts for effects acting at four temporal scales: short-term &ldquo;triggering&rdquo; precipitation, medium-term &ldquo;preparatory&rdquo; precipitation, seasonal effects and across-year data variability. It provides relative landslide probability scores that were used to establish seasonally dynamic thresholds with optimal performance in terms of hit and false alarm rates, as well as additional thresholds related to user-defined performance scores. The GAMM shows a high predictive performance and indicates that more precipitation is required to induce a landslide in summer than in winter/spring, which can presumably be attributed mainly to vegetation and temperature effects. The discussion illustrates why the quality of input data, study design and model transparency are crucial for landslide prediction using advanced data-driven techniques.</p

Landslides, floods and sinkholes in a karst environment: the 1–6 September 2014 Gargano event, southern Italy

Abstract. In karst environments, heavy rainfall is known to cause multiple geohydrological hazards, including inundations, flash floods, landslides and sinkholes. We studied a period of intense rainfall from 1 to 6 September 2014 in the Gargano Promontory, a karst area in Puglia, southern Italy. In the period, a sequence of torrential rainfall events caused severe damage and claimed two fatalities. The amount and accuracy of the geographical and temporal information varied for the different hazards. The temporal information was most accurate for the inundation caused by a major river, less accurate for flash floods caused by minor torrents and even less accurate for landslides. For sinkholes, only generic information on the period of occurrence of the failures was available. Our analysis revealed that in the promontory, rainfall-driven hazards occurred in response to extreme meteorological conditions and that the karst landscape responded to the torrential rainfall with a threshold behaviour. We exploited the rainfall and the landslide information to design the new ensemble–non-exceedance probability (E-NEP) algorithm for the quantitative evaluation of the possible occurrence of rainfall-induced landslides and of related geohydrological hazards. The ensemble of the metrics produced by the E-NEP algorithm provided better diagnostics than the single metrics often used for landslide forecasting, including rainfall duration, cumulated rainfall and rainfall intensity. We expect that the E-NEP algorithm will be useful for landslide early warning in karst areas and in other similar environments. We acknowledge that further tests are needed to evaluate the algorithm in different meteorological, geological and physiographical settings

Temporal correlations and clustering of landslides

This paper examines temporal correlations and temporal clustering of a proxy historical landslide time series, 2255 reported landslides 1951–2002, for an area in the Emilia-Romagna Region, Italy. Landslide intensity is measured by the number of reported landslides in a day (DL) and in an ‘event’ (Sevent) of consecutive days with landsliding. The non-zero values in both time series DL and Sevent are unequally spaced in time, and have heavy-tailed frequency-size distributions. To examine temporal correlations, we use power-spectral analysis (Lomb periodogram) and surrogate data analysis, confronting our original DL and Sevent time series with 1000 shuffled (uncorrelated) versions. We conclude that the landslide intensity series DL has strong temporal correlations and Sevent has likely temporal correlations. To examine temporal clustering in DL and Sevent, we consider extremes over different landslide intensity thresholds. We first examine the statistical distribution of interextreme occurrence times, τ, and find Weibull distributions with parameter γ << 1·0 [DL] and γ < 1·0 [Sevent]; thus DL and Sevent each have temporal correlations, but Sevent to a lesser degree. We next examine correlations between successive interextreme occurrence times, τ. Using autocorrelation analysis applied to τ, combined with surrogate data analysis, we find for DL linear correlations in τ, but for Sevent inconclusive results. However, using Kendall's rank correlation analysis we find for both DL and Sevent the series of τ are strongly correlated. Finally, we apply Fano Factor analysis, finding for both DL and Sevent the timings of extremes over a given threshold exhibit a fractal structure and are clustered in time. In this paper, we provide a framework for examining time series where the non-zero values are strongly unequally spaced and heavy-tailed, particularly important in the Earth Sciences due to their common occurrence, and find that landslide intensity time series exhibit temporal correlations and clustering. Many landslide models currently are designed under the assumption that landslides are uncorrelated in time, which we show is false. Copyright © 2010 John Wiley & Sons, Ltd

Analysis of historical landslide time series in the Emilia-Romagna region, northern Italy

A catalogue of historical landslides, 1951–2002, for three provinces in the Emilia-Romagna region of northern Italy is presented and its statistical properties studied. The catalogue consists of 2255 reported landslides and is based on historical archives and chronicles. We use two measures for the intensity of landsliding over time: (i) the number of reported landslides in a day (DL) and (ii) the number of reported landslides in an event (Sevent), where an event is one or more consecutive days with landsliding. From 1951–2002 in our study area there were 1057 days with 1 ≤ DL ≤?45 landslides per day, and 596 events with 1 ≤ Sevent ≤ 129 landslides per event. In the first set of analyses, we find that the probability density of landslide intensities in the time series are power-law distributed over at least two-orders of magnitude, with exponent of about −2·0. Although our data is a proxy for landsliding built from newspaper reports, it is the first tentative evidence that the frequency-size of triggered landslide events over time (not just the landslides in a given triggered event), like earthquakes, scale as a power-law or other heavy-tailed distributions. If confirmed, this could have important implications for risk assessment and erosion modelling in a given area. In our second set of analyses, we find that for short antecedent rainfall periods, the minimum amount of rainfall necessary to trigger landslides varies considerably with the intensity of the landsliding (DL and Sevent); whereas for long antecedent periods the magnitude is largely independent of the cumulative amount of rainfall, and the largest values of landslide intensity are always preceded by abundant rainfall. Further, the analysis of the rainfall trend suggests that the trigger of landslides in the study area is related to seasonal rainfall. Copyright © 2010 John Wiley & Sons, Ltd

Rainfall thresholds for the possible initiation of shallow landslides in the Italian Alps

Abstract Rainfall-induced shallow landslides are frequent in the Italian Alps, where they cause severe economic damages and loss of life. The prediction of rainfall-induced slope failures is of utmost importance for civil protection purposes and relies upon the definition of physically based or empirical rainfall thresholds. Reliable empirical rainfall thresholds require a large amount of information on the geographical and temporal location of past rainfall events that caused the observed mass movements. We have compiled a catalogue listing 453 rainfall events that have triggered landslides in the Italian Alps in the 13-year period 2000-2012. For the purpose, we searched national and local newspapers, blogs, technical reports, historical databases, and scientific journals. In the catalogue, for each rainfall event that triggered one or more failures, the information includes: (i) landslide geographical position, (ii) date of the landslide occurrence, (iii) landslide type (if available from the source of information), and (iv) rainfall information. Using the available information, we calculated the cumulated amount (E) and the duration (D) of the rainfall that likely caused the documented slope failures. We exploited the catalogue to calculate new ED threshold curves and their associated uncertainties for the Italian Alps adopting a frequentist approach. To define seasonal rainfall thresholds, we also investigated the monthly distribution of the landslides. The new thresholds are compared with similar curves in the same general area. We expect the results of our study to improve the ability to forecast shallow landslides in the Italian Alps and, more generally, in the wider Alpine region