Landslide size matters: A new data-driven, spatial prototype
Citation
Lombardo, L., Tanyas, H., Huser, R., Guzzetti, F., Castro-Camilo, D. (2021). Landslide size matters: A new data-driven, spatial prototype. Engineering Geology, 293: 106288. Link to paper
Abstract
This paper introduces for the first time a statistically-based model able to estimate the planimetric area of landslides aggregated per slope units. The authors implemented a Bayesian version of a Generalized Additive Model where the maximum landslide size per slope unit and the sum of all landslide sizes per slope unit are predicted via a Log-Gaussian model. The models were tested on a global dataset expressing the distribution of co-seismic landslides due to 24 earthquakes across the globe. This is the first statistically-based model in the literature able to provide information about the extent of the failed surface across a given landscape. The model offers a new predictive paradigm that goes beyond traditional susceptibility mapping by predicting not only where landslides may occur but also their expected size. This advancement could become part of official landslide risk assessment protocols, providing critical information for loss estimation and risk management. The results demonstrate that slope morphology, ground motion characteristics, and geological conditions significantly influence landslide size distributions.