Evaluating earthquake-triggered landslide hazard at the basin scale through GIS in the Upper Sele river Valley

To evaluate techniques for assessing earthquake-triggered landslide hazard in the Southern Apennines (Italy), a GIS-based analysis was used to model seismically induced slope deformations. Geological, geotechnical, geomorphological and seismological data were integrated into a standard earthquake slope stability model. The model assessed the landslide potential that existed during the 1980 Irpinian earthquake in the Upper Sele river Valley. The standard Newmark displacement analysis, widely used for predicting the location of shallow unstable slopes, does not take into account errors and/or uncertainties in the input parameters. Therefore, a probabilistic Newmark displacement analysis technique has been used. Probabilistic techniques allow, e. g., an estimation of the probability that a slope will exceed a certain critical value of Newmark displacement. In our probabilistic method, a Monte-Carlo based simulation model is used in conjunction with a GIS. The random variability of geotechnical data is modelled by probability density functions (pdfs), while for the seismic input three different regression laws were considered. Input probability distributions are sampled and the resulting values input into empirical relations for estimating Newmark displacement. The outcome is a map in which to each site is related a spatial probability distribution for the expected displacement in response to seismic loading. Results of the experiments show a high grade of uncertainty in the application of the Newmark analysis both for the deterministic and probabilistic approach in a complex geological setting such as the high Sele valley, quite common in the Southern Apennines. They show a strong dependence on the reliability of the spatial data used in input, so that, when the model is used at basin scale, results are strongly influenced by local environmental condition (e. g., topography, lithology, groundwater condition) and decrease the model performance.