Abstract
Abstract. The main assumption on which landslide susceptibility assessment by means of stochastic modelling lies is that the past is the key to the future. As a consequence, a stochastic model able to classify past known landslide events should be able to predict a future unknown scenario as well. However, storm-triggered multiple debris flow events in the Mediterranean region could pose some limits on the operative validity of such an expectation, as they are typically resultant of a randomness in time recurrence and magnitude and a great spatial variability, even at the scale of small catchments. This is the case for the 2007 and 2009 storm events, which recently hit north-eastern Sicily with different intensities, resulting in largely different disaster scenarios. The study area is the small catchment of the Itala torrent (10 km2), which drains from the southern Peloritani Mountains eastward to the Ionian Sea, in the territory of the Messina province (Sicily, Italy). Landslides have been mapped by integrating remote and field surveys, producing two event inventories which include 73 debris flows, activated in 2007, and 616 debris flows, triggered by the 2009 storm. Logistic regression was applied in order to obtain susceptibility models which utilize a set of predictors derived from a 2 m cell digital elevation model and a 1 : 50 000 scale geologic map. The research topic was explored by performing two types of validation procedures: self-validation, based on the random partition of each event inventory, and chrono-validation, based on the time partition of the landslide inventory. It was therefore possible to analyse and compare the performances both of the 2007 calibrated model in predicting the 2009 debris flows (forward chrono-validation), and vice versa of the 2009 calibrated model in predicting the 2007 debris flows (backward chrono-validation). Both of the two predictions resulted in largely acceptable performances in terms of fitting, skill and reliability. However, a loss of performance and differences in the selected predictors arose between the self-validated and the chrono-validated models. These are interpreted as effects of the non-linearity in the domain of the trigger intensity of the relationships between predictors and slope response, as well as in terms of the different spatial paths of the two triggering storms at the catchment scale.
Highlights
Debris flows are among the most hazardous geological phenomena, which directly threat human lives in the light of their high energy and rapid propagation over slopes and drainage systems
The application of binary logistic regression (BLR) for landslide susceptibility assessment typically requires the following steps: the partition of the study area into mapping units, which are characterized with respect to a set of potential predictors; the assignment of stability conditions to each mapping unit, based on its spatial relation with a set of known landslides; the extraction of a balanced data set from the whole set of mapping units; the regression of the modelling function; and the verification of the performance of the model in correctly predicting stability conditions for each pixel, the latter defined on the basis of a set of unknown landslides
A larger set of variables (17) was included by BLR in the 2009 model suite (Fig. 10), 15 of which were selected more than five times
Summary
Debris flows are among the most hazardous geological phenomena, which directly threat human lives in the light of their high energy and rapid propagation over slopes and drainage systems. The stochastic approach is very implementable on geographic informative systems (GIS), making use of the very diffused nature of present databases of physical–environmental attribute layers These methods are based on some generally accepted assumptions, the basic one being the past is the key to the future (Carrara et al, 1995). A susceptibility model constructed to reproduce a past known landslide spatial distribution, will be able to predict the future locations of new failures. For a given study area, statistical techniques allow the derivation and testing of the multivariate relationships between the spatial distributions of an inventory of landslides (the known target pattern) for significance as well as testing a set of physical–environmental variables (the predictors), which, acting as controlling factors, are supposed to drive the slope failures, on the basis of a geomorphological model. Von Ruette et al (2014) adopted a spatial partition scheme, with a partial insight into temporal validation which was limited for predicting the landslides triggered by two rainfall events in two close, but different, catchments. Chang et al (2014) concentrated their focus on exploring the role of rainfall in controlling the chrono-validation performance for a much largerscale case, demonstrated in a larger area (2868 km2), where a network of 24 rain gauges recorded nine great typhoon events
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