ALBERT

Description: We developed a generalized model to describe and predict the spatial distribution of earthquake-induced landslides, based on a regression analysis of 9 co-seismic landslide inventories from different earthquakes and regions. Our model expresses the absolute spatial probability of landslides as a function of peak ground acceleration and hillslope gradient, based on data from global topographic and seismic ground motion datasets. The output from our model predicts probabilities for landslides triggered in sedimentary, meta-sedimentary, igneous and volcanic lithology, and is applicable to shallow continental earthquakes of moment magnitude range 6.2 to 7.9, and depths between 10 and 21 km. To obtain absolute probability predictions, we use only landslide source areas as input data, and explicitly estimate and correct for known incompleteness in input datasets, through a novel Monte Carlo approach. We estimate the uncertainty of these predictions, through extensive testing of the performance of the model, when making out-of-sample predictions for all 9 earthquakes. Our model is notably simpler than others developed to predict spatial probability of landsliding, as we have only included variables that could be constrained consistently at the global-scale, and eliminated those that did not influence landslide probability in a consistent manner across all earthquakes in our dataset. The model outputs also provide a baseline to further investigate spatial and temporal sources of unexplained variability in co-seismic landslide distributions. Using freely available topographic and ground motion data, we suggest that our model can be applied more widely, to provide landslide predictions for earthquakes with no landslide data.