From scenario-based seismic hazard to scenario-based landslide hazard: rewinding to the past via statistical simulations

TL;DR

This study develops a Bayesian statistical simulation framework to analyze how historical ground motion scenarios influence landslide susceptibility, enabling both retrospective and prospective hazard assessments over the past century.

Contribution

It introduces a novel Bayesian modeling approach that incorporates temporal ground motion variability into landslide susceptibility mapping, bridging the gap between hazard assessment and land use planning.

Findings

Simulated susceptibility patterns for ground motions from 1933 to 2017.

Quantified the impact of earthquake ground motion on slope instability.

Provided a method for both retrospective and future landslide hazard prediction.

Abstract

The vast majority of landslide susceptibility studies assumes the slope instability process to be time-invariant under the definition that "the past and present are keys to the future". This assumption may generally be valid. However, the trigger, be it a rainfall or an earthquake event, clearly varies over time. And yet, the temporal component of the trigger is rarely included in landslide susceptibility studies and only confined to hazard assessment. In this work, we investigate a population of landslides triggered in response to the 2017 Jiuzhaigou earthquake ($M_w = 6.5$) including the associated ground motion in the analyses, these being carried out at the Slope Unit (SU) level. We do this by implementing a Bayesian version of a Generalized Additive Model and assuming that the slope instability across the SUs in the study area behaves according to a Bernoulli probability distribution. This procedure would generally produce a susceptibility map reflecting the spatial pattern of the specific trigger and therefore of limited use for land use planning. However, we implement this first analytical step to reliably estimate the ground motion effect, and its distribution, on unstable SUs. We then assume the effect of the ground motion to be time-invariant, enabling statistical simulations for any ground motion scenario that occurred in the area from 1933 to 2017. As a result, we obtain the full spectrum of potential susceptibility patterns over the last century and compress this information into a susceptibility model/map representative of all the possible ground motion patterns since 1933. This backward statistical simulations can also be further exploited in the opposite direction where, by accounting for scenario-based ground motion, one can also use it in a forward direction to estimate future unstable slopes.