Rapid prediction of the magnitude scale of landslide events triggered by an earthquake

A landslide event is characterized by the distribution of landslides caused by a single triggering event. The severity of earthquake-induced landslide events can be quantified by the landslide-event magnitude, a metric derived from the frequency-size distribution of landslide inventories. However, reliable landslide inventories are not available for all earthquakes, because the preparation of a suitable inventory requires data, time, and expertise. Prediction of landslide-event magnitude immediately following an earthquake provides an estimate of the total landslide area and volume based on empirical relations. It allows to make an assessment of the severity of a landslide event in near-real time and to estimate the frequency-size distribution curve of the landslides. In this study, we used 23 earthquake-induced landslide inventories and propose a method to predict landslide-event magnitude. We selected five predictors, both morphometric and seismogenic, which are globally and readily available. We used the predictors within a stepwise linear regression and validated using the leave-one-out technique. We show that our approach successfully predicts landslide-event magnitude values and provides results along with their statistical significance and confidence levels. However, to test the validity of the approach globally, it should be calibrated using a larger and more representative dataset. A global, near real-time assessments regarding landslide-event magnitude scale can then be achieved by retrieving the readily available ShakeMaps, along with topographic and thematic information, and applying the calibrated model. The results may provide valuable information regarding landscape evolution processes, landslide hazard assessments, and contribute to the rapid emergency response after earthquakes in mountainous terrain.