Massimo Melillo

Definition and performance of a threshold-based regional early warning model for rainfall-induced landslides

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.

Rainfall thresholds for the possible landslide occurrence in Sicily (Southern Italy) based on the automatic reconstruction of rainfall events

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.

Catalogue of Rainfall Events with Shallow Landslides and New Rainfall Thresholds in Italy

In Italy, rainfall-induced shallow landslides are frequent and harmful phenomena. The prediction of their occurrence is of social significance for civil protection purposes. For the operational prediction of rainfall-induced shallow landslides empirical rainfall thresholds based on the statistical analysis of past rainfall conditions that triggered slope failures are commonly used. The paper describes a catalogue of 1981 rainfall events, which caused 2408 shallow landslides in Italy in the period 1996–2012. Information on rainfall-induced landslides was collected searching chiefly online newspaper archives, blogs, and fire brigade reports. For each documented failure, we reconstructed the triggering rainfall conditions (rainfall duration D and cumulated rainfall E) using national and regional rain gauge networks. We analysed the rainfall conditions to determine new ED rainfall thresholds for Italy. The calculated thresholds can be implemented in a landslide forecasting system to mitigate landslide hazard and risk.

Implications of climate change on landslide hazard in Central Italy

The relation between climate change and its potential effects on the stability of slopes remains an open issue. For rainfall induced landslides, the point consists in determining the effects of the projected changes in the duration and amounts of rainfall that can initiate slope failures. We investigated the relationship between fine-scale climate projections obtained by downscaling and the expected modifications in landslide occurrence in Central Italy. We used rainfall measurements taken by 56 rain gauges in the 9-year period 2003-2011, and the RainFARM technique to generate downscaled synthetic rainfall fields from regional climate model projections for the 14-year calibration period 2002-2015, and for the 40-year projection period 2010-2049. Using a specific algorithm, we extracted a number of rainfall events, i.e. rainfall periods separated by dry periods of no or negligible amount of rain, from the measured and the synthetic rainfall series. Then, we used the selected rainfall events to forcethe Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability Model TRIGRS v. 2.1. We analyzed the results in terms of variations (or lack of variations) in the rainfall thresholds for the possible initiation of landslides, in the probability distribution of landslide size (area), and in landslide hazard. Results showed that the downscaled rainfall fields obtained by RainFARM can be used to single out rainfall events, and to force the slope stability model. Results further showed that while the rainfall thresholds for landslide occurrence are expected to change in future scenarios, the probability distribution of landslide areas are not. We infer that landslide hazard in the study area is expected to change in response to the projected variations in the rainfall conditions. We expect our results to contribute to regional investigations of the expected impact of projected climate variations on slope stability conditions and on landslide hazards.

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.

TXT-tool 2.039-1.5: An Algorithm for the Objective Reconstruction of Rainfall Events Responsible for Landslides

The primary trigger of damaging landslides in Italy is intense or prolonged rainfall. Definition of the rainfall conditions responsible for landslides is a crucial issue and may contribute to reducing landslide risk. Criteria for identifying the rainfall conditions that could initiate slope failures are still lacking or uncertain. Expert investigators usually reconstruct rainfall events manually. In this paper, we propose an algorithm for the objective and reproducible definition of rainfall conditions responsible for landslides, from a series of hourly rainfall data. The algorithm, which is implemented in R (http://www.r-project.org), performs a series of actions: (i) removes isolated events with negligible amount of rainfall and random noise generated by the rain gauge; (ii) aggregates rainfall measurements in order to obtain a sequence of distinct rainfall events; (iii) identifies single or multiple rainfall conditions responsible for the slope failures. The result is the objective reconstruction of the duration, D, and the cumulated rainfall, E, for rainfall events, and for rainfall conditions that have resulted in landslides. We tested the algorithm using rainfall and landslide information for the period between January 2002 and December 2012 in Sicily, Southern Italy. The algorithm reconstructed 13,537 rainfall events and 343 rainfall conditions as possible triggers using the information on 163 documented landslides. The comparison between automatic and manually method highlights that most (87.7%) of the rainfall conditions obtained manually were reconstructed accurately. Use of the algorithm should contribute to reducing the current subjectivity inherent in the manual treatment of the rainfall and landslide data.