Estimating the Likelihood and Impact of Seismically Induced Landslides in Near Real-Time

Date: 4/24/2019

Time: 06:00 PM

Room: Grand Ballroom

We present a new, high-resolution, globally applicable statistical model for estimating the distribution and impact of earthquake-triggered landslides in near real-time. A previous version of the model has been adapted for use in the USGS Ground Failure product, which runs in near real-time for significant earthquakes around the globe. An important addition to previous models is testing the incorporation of high-resolution (1 arc-second, roughly 30m) data to improve detailed estimates of slope vulnerability. We use standardized estimates of ground shaking from the USGS ShakeMap tool, together with broadly available landslide susceptibility proxies, including topographic slope, surface geology, and high-resolution land cover data, to develop an empirical landslide probability model. We include observations from landslide-triggering earthquakes from a variety of tectonic and geomorphic settings for which we have obtained landslide inventories. We plan to test additional landslide susceptibility proxies, including high-resolution geologic, hydrologic, and climatic parameters. Using logistic regression, this database is used to build a predictive model that can estimate the probability of landsliding within minutes of an earthquake’s occurrence. As ground shaking data become available, we can automatically generate maps of landslide probabilities. These landslide probability estimates can be combined with population density values and past landslide fatality counts to provide order-of-magnitude fatality loss estimates. We also apply this approach to scenario earthquakes to assess probabilities for future landslides in areas of known seismic risk, and calculate the effect of changing seismological parameters on the expected distribution of landsliding. These models can be used to rapidly provide regional estimates of the probability and distribution of seismically induced landslides, as well as their potential impact on the surrounding population. Future efforts can extend this work to estimate impact on infrastructure and economic losses as well.

Presenting Author: Anna Nowicki Jessee

Authors