Calibration and validation of rainfall thresholds for shallow landslide forecasting in Sicily, southern Italy

Empirical rainfall thresholds are tools to forecast the possible occurrence of rainfall-induced shallow landslides. Accurate prediction of landslide occurrence requires reliable thresholds, which need to be properly validated before their use in operational warning systems. We exploited a catalogue of 200 rainfall conditions that have resulted in at least 223 shallow landslides in Sicily, southern Italy, in the 11-year period 2002-2011, to determine regional event duration-cumulated event rainfall (ED) thresholds for shallow landslide occurrence. We computed ED thresholds for different exceedance probability levels and determined the uncertainty associated to the thresholds using a consolidated bootstrap nonparametric technique. We further determined subregional thresholds, and we studied the role of lithology and seasonal periods in the initiation of shallow landslides in Sicily. Next, we validated the regional rainfall thresholds using 29 rainfall conditions that have resulted in 42 shallow landslides in Sicily in 2012. We based the validation on contingency tables, skill scores, and a receiver operating characteristic (ROC) analysis for thresholds at different exceedance probability levels, from 1% to 50%. Validation of rainfall thresholds is hampered by lack of information on landslide occurrence. Therefore, we considered the effects of variations in the contingencies and the skill scores caused by lack of information. Based on the results obtained, we propose a general methodology for the objective identification of a threshold that provides an optimal balance between maximization of correct predictions and minimization of incorrect predictions, including missed and false alarms. We expect that the methodology will increase the reliability of rainfall thresholds, fostering the operational use of validated rainfall thresholds in operational early warning system for regional shallow landslide forecasting. (C) 2014 Elsevier B.V. All rights reserved.