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Natural Hazards and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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https://doi.org/10.5194/nhess-2017-193
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/nhess-2017-193
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

  16 Jun 2017

16 Jun 2017

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This preprint was under review for the journal NHESS. A revision for further review has not been submitted.

Spatial prediction of earthquake-induced landslide probability

Robert N. Parker1, Nicholas J. Rosser2, and Tristram C. Hales1 Robert N. Parker et al.
  • 1School of Earth and Ocean Sciences, Sustainable Places Research Institute, Cardiff University
  • 2Institute of Hazard, Risk and Resilience, Durham University

Abstract. 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.

Robert N. Parker et al.

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Status: closed (peer review stopped)
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Robert N. Parker et al.

Robert N. Parker et al.

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Short summary
In mountainous regions, large earthquakes often trigger widespread and destructive landslides. Understanding and predicting where these landslides occur is important for assessing hazards, as well as investigating their impact on the physical landscape. Based on correlations between landslides and different landscape and earthquake characteristics in nine past earthquakes, we developed a generalised algorithm for predicting and mapping the probability of earthquake-triggered landslides.
In mountainous regions, large earthquakes often trigger widespread and destructive landslides....
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