pp 1–14 | Cite as

Two methodologies to calibrate landslide runout models

  • Jordan AaronEmail author
  • Scott McDougall
  • Natalia Nolde
Original Paper


Extremely rapid, flow-like landslides pose a significant hazard worldwide; however, the analysis of the impact area and velocity of these flows is not routine. Semi-empirical numerical models are one tool that is available for performing this sort of analysis. These models are physically based; however, certain input parameters are determined through model calibration, using back-analysis of real landslide cases. Objective, repeatable calibration methods are needed for this approach to be useful for landslide runout prediction. The present analysis describes the application of optimization theory and Bayesian statistics to calibrate these types of models. Two complementary methods are presented. The first uses the Gauss-Marquardt-Levenberg optimization algorithm to efficiently determine a set of best-fit calibrated model parameters. The second uses a posterior analysis to quantify errors associated with parameter calibration, which can then be used for probabilistic forward analysis. Three case histories are presented to demonstrate how the new methods are able to rapidly calibrate a runout model, reduce subjectivity inherent in the calibration process, and provide information on parameter uncertainty.


Runout modeling Flow-like landslides Hazard mapping Rock avalanche Flowslide 



This paper was greatly improved by many insightful conversations with Oldrich Hungr. We are grateful to two anonymous reviewers for providing thorough and constructive comments that improved the manuscript.

Funding information

Financial support for this work was provided by a graduate scholarship given by the Natural Science and Engineering Research Council of Canada (NSERC), as well as scholarships given by the Department of Earth, Ocean and Atmospheric Sciences at the University of British Columbia.


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Earth Sciences, Geological InstituteETH ZürichZürichSwitzerland
  2. 2.Department of Earth, Ocean and Atmospheric SciencesThe University of British ColumbiaVancouverCanada
  3. 3.Department of StatisticsThe University of British ColumbiaVancouverCanada

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