David J. Peres

SPI-Based Probabilistic Analysis of Drought Areal Extent in Sicily

Drought is a natural phenomenon that presents spatial and temporal features whose knowledge is fundamental for an appropriate water resources management. In particular, the assessment of probabilities and return periods of areal extent of droughts of different severities over a region can provide useful information for planning drought management. In this study, an analytical methodology to characterize probabilistically the relationship between meteorological drought severity (computed in terms of Standardized Precipitation Index, SPI) and areal extent, expressed as Drought severity-Area-Frequency (SAF) curves, is proposed. In particular, analytical expressions of SAF curves describing the proportion of the total area of the region of interest where the SPI values are below a fixed threshold are derived. The developed curves enable to characterize a given drought event in a region, by computing the probability of occurrence of SAF curves exceeding the one observed. The proposed methodology is validated through the investigation of the spatio-temporal features of drought occurrences over Sicily, Italy, for the period 1921–2005.

Defining Rainfall Thresholds for Early Warning of Rainfall-Triggered Landslides: The Case of North-East Sicily

Extreme rainfall is the main cause of landslides and, depending on the magnitude of the rainfall event and the geomorphological characteristics of the landslide-prone area, its occurrence can lead to debris-flows, causing higher damage than floods. Empirical rainfall thresholds of landslide triggering have been proposed by researchers and used as a basis of early warning systems activated throughout the world. The present paper shows the results of an analysis aimed to formulate, in an empirical fashion, the landslide triggering conditions for the north-eastern region of Sicily, in which a catastrophic debris-flow has caused 37 deaths, on October 1, 2009. More specifically, we have investigated the possibility to exploit annual maxima of rainfall for fixed durations, available in the annual reports of the Water Observatory of the Department of Water and Waste of Sicily Region. Calibration of rainfall thresholds has been carried out using the National Research Council’s AVI database of historical information on landslides developed by Guzzetti et al. (Environ Manage 18(4):623–633, 1994). Also the FLaIR model (Forecasting of Landslides Induced by Rainfall), proposed by Sirangelo and Versace (Atti del XXIII Convegno di Idraulica e Costruzioni Idrauliche, Firenze, pp D361–D373, 1992), and used as a basis in the realization of several early warning systems in Italy, has been implemented and tested on the case-study area. The results of the work can find application in view of the development of a landslide early warning system in the area.

Accounting for Variability in Rain-Event Intensity and Initial Conditions in Landslide Triggering Return Period Mapping via a Monte Carlo Approach

In this study, a Monte Carlo simulation approach is proposed for mapping landslide hazard in terms of return period, in order to account for both rainfall high frequency variability and antecedent precipitation that determine initial conditions. The Monte Carlo approach combines a stochastic rainfall generator with a physically-based landslide triggering model. More in detail, the Monte Carlo simulation methodology comprises the following elements: (a) a seasonal Neyman-Scott Rectangular Pulses (NSRP) model to generate 1,000-years of synthetic hourly point rainfall data; (b) a module for rainfall event identification and separation from dry intervals; (c) the Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability (TRIGRS) model, version 2 (Baum et al. 2008, 2010) to simulate landslide triggering by rainfall infiltration, integrated with (d) a water table recession (WTR) model aimed at computing the initial water table height to be used in simulating rainfall events with event-based model TRIGRS.

Assessing Potential Effects of Climate Change on Rainfall-Induced Shallow Landslides in the Peloritani Mountains Area, Sicily

Climate change due to atmospheric greenhouse gas emissions may cause significant modifications to precipitation and other meteorological processes, with potential consequences on the frequency of occurrence of shallow landslides. This study aims at investigating the potential effects of climate-change induced rainfall modifications on the future occurrence of rainfall-induced shallow landslides, based on the analysis of RCM projections and hydrologic and slope stability simulations. Changes in precipitation are assessed by comparing future RCM-simulated rainfall series with hindcasts valid for the historical baseline (assumed as 1961–1990), and by considering the two emission scenarios RCP4.5 (intermediate) and RCP8.5 (high-emissions). The estimated changes enable to adjust the parameters of a stochastic rainfall model, used as input to carry out Monte Carlo simulations allowing to estimate the probability of landslide triggering for future periods. The method is applied to the Peloritani mountains area in Sicily (Italy), which has been hit several times by diffused shallow landslides in the past decade. The obtained results prevalently indicate a future decrease of the probability of landslide triggering, directly connected with climate-change induced increase of the inter-arrival times of rainfall events. Outcomes of the study also evidence the importance of assessing RCM data uncertainty, given that different climate-projection data may provide opposite indications.