Project P10 by Joaquin Vicente Ferrer, (University of Potsdam & PIK Potsdam): Predicting large landslides from complex networks

Timescale: Oct 2021-Sept 2024

Supervisors:

Prof. Oliver Korup, University of Potsdam

Prof. Jürgen Kurths, PIK Potsdam & Hu Berlin

PD Dr. Norbert Marwan, PIK Potsdam & University of Potsdam

Summary of the project

Numerous studies devoted to landslide prediction rely on intersecting data of past slope failures and the associated geological, topographic, and meteorological conditions. One common notion is that contemporary atmospheric warming is increasing the magnitude and frequency of landslides. We test this notion by making use of detailed time series of meteorological and seismological data that have become available at high spatial and temporal resolution and encourage means to predict landslides from patterns of weather and seismic activity. In this project we aim to learn whether and how well it is feasible to predict, large—and often destructive—landslides from precursory weather conditions. We also aim to test how to disentangle such predictions from the influence of historic seismic activity. This approach is novel in that it focuses on climatic and seismic precursors for each landslide, and in that it focuses on the larger to largest slope failures that have eluded most comparable studies.

Objectives and Methods

  • To predict the largest landslide in a given regional using meteorological and seismic data
  • Methods: Probabilistic prediction, extreme-value analyses, Bayesian methods

Dedicated Regional Cluster(s): Japan, United States, (and worldwide given some datasets)

Related PhD-projects: P1 (first cohort, Ugur Öztürk) and I8 (second cohort, Katharina Müller, Lisa Luna): this project builds on P1 and I8. With our Japanese collaborators we also have access to landslide damage data, so that we hope to gain first insights into the damage portfolio (and possibly future exposure) of rainfall- and earthquake-triggered landslides at the scale of an entire nation.

Planned external collaborations:

  • University of Tokyo, Gankyo University, University of Kyoto
  • University of Eugene, Oregon, United States