Modelling societal landslide risk in Italy

Landslides cause every year worldwide severe damages to the population. A quantitative knowledge of the impact of landsliding phenomena on the society is fundamental for a proper and accurate assessment of the risk posed by such natural hazards. In this work, a novel approach is proposed to evaluate the spatial and temporal distribution of societal landslide risk from historical, sparse, point information on fatal landslides and their direct human consequence.s (Rossi et al., Accepted). The approach was tested in Italy, using a detailed catalogue listing 5571 fatalities caused by 1017 landslides at 958 sites across Italy, in the 155-year period 1861 – 2015. The model adopting a Zipf distribution to evaluate societal landslide risk for the whole of Italy, and for seven physiographic and 20 administrative subdivisions of Italy. The model is able to provide estimates of the frequency (and the probability) of fatal landslides, based on the parameters, namely (i) the largest magnitude landslide F, (ii) the number of fatal events E, and (iii) the scaling exponent of the Zipf distribution s, which controls the relative proportion of low vs. large magnitude landslides. Different grid spacings, g and circular kernel sizes, r were tested finally adopting g = 10 km and r = 55 km. Using such geometrical model configuration, the values of the F, E and s parameters were derived for each grid cells revealing the complexity of landslide risk in Italy, which cannot be described properly with a single set of such parameters. Based on such modeling configuration. This model configuration allowed to estimate different risk scenarios for landslides of increasing magnitudes, which were validated checking the anticipated return period of the fatal events against information on 130 fatal landslides between 1000 and 1860, and eleven fatal landslides between January 2016 and August 2018. Despite incompleteness in the old part of the record for the low magnitude landslides, and the short length and limited number of events in the recent period 2016 – 2018, the anticipated return periods are in good agreement with the occurrence of fatal landslides in both validation periods. Despite the known difficulty in modelling sparse datasets, the proposed approach was able to provide a coherent and realistic representation and new insight on the spatial and temporal variations of societal landslide risk in Italy.