Probabilistic modeling of shallow landslide initiation using regional scale random fields

Regional mapping of landslide susceptibility aims to identify zones of potential instability across geological settings. Given their predictive capabilities, physically based, deterministic models are useful tools for landslide triggering studies at regional scale. However, they rely on detailed input parameters that are rarely available for large areas. To address these limitations, this work proposes a computational framework to incorporate the spatial uncertainty of input data into physically based, landslide hazard zonation models through the use of regional scale random fields (RSRF). For this purpose, input parameters are treated as spatially correlated random variables with assigned statistical attributes, while a vectorization strategy is used to reduce the computational cost of large-scale stochastic analyses. Deterministic simulations based on a hydro-mechanical model are then performed for multiple Monte Carlo realizations to compute maps of failure probability (p(f)). The methodology was applied to a well-documented series of rainfall-induced shallow landslides in a volcanic site for which field measurements were available to constrain the statistical variability of the hydraulic conductivity and treat this parameter as an RSRF. To analyze the results, four classes of landslide susceptibility characterized by different p(f) thresholds were used. Such classes were mapped over the study zone and throughout the storm event, allowing a direct comparison with the spatio-temporal evidence of landslide triggering. The results indicate that (i) uncertainty analyses neglecting the role of spatial correlation may lead to non-conservative estimates of landslide susceptibility and (ii) there is an interval of spatial correlation distance that optimizes the performance of the model, thus providing an indirect estimate of the heterogeneity of the site. Such results highlight the benefits of accounting for the uncertainty of the soil properties in regional-scale models and offer a new predictive stochastic framework to assess the implications of future rainfall scenarios over large areas.