Preprints
https://doi.org/10.5194/nhess-2021-22
https://doi.org/10.5194/nhess-2021-22

  03 Feb 2021

03 Feb 2021

Review status: this preprint is currently under review for the journal NHESS.

Optimizing and validating the Gravitational Process Path model for regional debris-flow runout modelling

Jason Goetz1, Robin Kohrs1, Eric Parra Hormazábal2, Manuel Bustos Morales3, María Belén Araneda Riquelme3, Cristián Henríquez3, and Alexander Brenning1 Jason Goetz et al.
  • 1Department of Geography, Friedrich Schiller University Jena, German
  • 2Institute of Geosciences, University of Potsdam, Germany
  • 3Instituto de Geografía, Pontificia Universidad Católica de Chile, Chile. Centre for Sustainable Urban Development, CEDEUS. Centro de Cambio Global UC

Abstract. Knowing the source and runout of debris-flows can help in planning strategies aimed at mitigating these hazards. Our research in this paper focuses on developing a novel approach for optimizing runout models for regional susceptibility modelling, with a case study in the upper Maipo river basin in the Andes of Santiago, Chile. We propose a two-stage optimization approach for automatically selecting parameters for estimating runout path and distance. This approach optimizes the random walk and Perla's two-parameter modelling components of the open-source Gravitational Process Path (GPP) modelling framework. To validate model performance, we assess the spatial transferability of the optimized runout model using spatial cross-validation, including exploring the model's sensitivity to sample size. We also present diagnostic tools for visualizing uncertainties in parameter selection and model performance. Although there was considerable variation in optimal parameters for individual events, we found our runout modelling approach performed well at regional prediction of potential runout areas. We also found that although a relatively small sample size was sufficient to achieve generally good runout modelling performance; larger samples sizes (i.e. ≥ 80) had higher model performances and lower uncertainties for estimating runout distances at unknown locations. We anticipate that this automated approach using open-source software R and SAGA-GIS will make process-based debris-flow models more readily accessible and thus enable researchers and spatial planners to improve regional-scale hazard assessments.

Jason Goetz et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on nhess-2021-22', Anonymous Referee #1, 14 Feb 2021
    • AC1: 'Reply on RC1', Jason Goetz, 27 Apr 2021
  • RC2: 'Comment on nhess-2021-22', Anonymous Referee #2, 02 Mar 2021
    • AC2: 'Reply on RC2', Jason Goetz, 27 Apr 2021

Jason Goetz et al.

Data sets

Debris flow inventory and data for regionally modelling runout in the upper Maipo river basin, Chile Eric Parra Hormazábal, Jazmine Calabrese Fernández, Manuel Bustos Morales, María Belén Araneda Riquelme, Jason Goetz, Robin Kohrs, Alex Brenning, and Cristián Henríquez https://doi.org/10.5281/zenodo.4428080

Model code and software

runoptGPP: An R package for optimizing mass movement runout models Jason Goetz https://doi.org/10.5281/zenodo.4428050

Jason Goetz et al.

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Short summary
Debris flows are fast moving landslides that can cause incredible destruction to lives and property. Using the Andes of Santiago as an example, we developed tools to fine tune and validate models predicting likely runout paths over large regions. We anticipate that our automated approach using open-source software R and SAGA-GIS will make debris-flow runout simulation more readily accessible and thus enable researchers and spatial planners to improve regional-scale hazard assessments.
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