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

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

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 t...

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Bibliographic Details
Published in:Natural hazards and earth system sciences Vol. 21; no. 8; pp. 2543 - 2562
Main Authors: Goetz, Jason, Kohrs, Robin, Parra Hormazábal, Eric, Bustos Morales, Manuel, Araneda Riquelme, María Belén, Henríquez, Cristián, Brenning, Alexander
Format: Journal Article
Language:English
Published: Katlenburg-Lindau Copernicus GmbH 25-08-2021
Copernicus Publications
Subjects:
Algorithms
Analysis
Calibration
Debris flow
Detritus
Estimation
Fault lines
Geographic information systems
Geographical information systems
Gravity
Hazard assessment
Hazard mitigation
Information systems
Mathematical models
Modelling
Numerical analysis
Optimization
Parameter estimation
Parameter uncertainty
Parameters
Polygons
Public software
Regional analysis
Regional development
Remote sensing
River basins
Software
Source code
Chile
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Summary: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 et al.'s (PCM) two-parameter friction model 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 performance and lower uncertainties for estimating runout distances at unknown locations. We anticipate that this automated approach using the open-source R software and the System for Automated Geoscientific Analyses geographic information system (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.
ISSN:1684-9981
1561-8633
1684-9981
DOI:10.5194/nhess-21-2543-2021