The geo-hydrologic event in the Peloritan – Ionian area of 2009: debris-flow susceptibility assessment by means of forward logistic regression

On the 1st of October 2009, the area centred on the village of Giampilieri (Messina), on the Ionian side of the Peloritan belt, suffered thousands of landslides activated in the time lapse of about five hours, which caused 36 victims, more than 100 injured and more than 0.5M€ of damage to structures. This unprecedented phenomenon was triggered by an exceptional meteorological event, recorded at the foothills with 250mm of rain in just 8 hours; this amount of rainfall was cumulated to two previous events (16/IX: 75mm; 23/IX: 190mm) for a total amount of more than 500mm in less than two weeks. Due to the peculiar triggering conditions a huge number of debris flows involved the shallow weathered layer of the outcropping lithologies, consisting of phyllites of the Mandanici Units, mica schists of the Mela Units and medium to high grade Varisic metamorphic rocks. The purpose of this study is to evaluate the exportability of a landslide susceptibility model, obtained by using logistic regression method, within a training hydrographic unit (the “Torrente Briga” catchment) to predict the landslide spatial distribution in a test hydrographic units (the “Torrente Giampilieri” catchment). Both the basins extend for about 10km2. Exporting procedures for susceptibility model are in fact of great importance to optimize the survey costs or when facing phenomena which are locally triggered, such as the ones activated under extreme rainfall events; in fact, in this cases the landslide scenario used to train the statistical model is local and spatially more limited than the extension of while investigated area. In this research we prepared a susceptibility model by means of forward logistic regression in the “Torrente Briga” catchment (871 landslides, regressing an optimized set of computed physicalenvironmental predictors and obtaining the log-function which was then applied to the “Torrente Giampilieri” catchment (1121 landslides). By using a 2m cell DEM (from which we calculated and tested a large set of primary and secondary topographic attributes) and some thematic maps (geology and land use), the following predictors have been selected: height, slope, stream power index, topographic wetness index, profile and plan curvatures, landform classification, outcropping geology, landuse. Unstable slope conditions were assigned to the cells within a 2.9m neighbourhood of the landslide identification points (located on the highest point of the landslide areas). Models were built by merging the unstable cells with an equal number of randomly selected stable cells. To assess the sensitivity of the models with respect to the selection, 8 extractions were performed for each of the two basins, obtaining 8 models for the “Torrente Briga” area and 8x8 exporting combination, for the “Torrente Giampilieri”. The results attest for a high performance of the models, as we obtained excellent AUC both for the the 8 Briga models (>0.84) and the 64 exported Giampilieri models (>0.8). High steepness, low height, plan concave and profile convex curvatures, together with south and south-west verging slopes, are the main controlling factors of debris flow initiations in the two areas.