Abstract
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 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.
Highlights
Knowledge of where debris flows are initiated and how far they travel is crucial for assessing their impact over large regions (Aleotti and Chowdhury, 1999; van Westen et al, 2006)
Debris-flow runout modelling for large areas is performed by first delineating source areas and applying empirical–statistical or process-based numerical methods for simulating the runout characteristics (Blahut et al, 2010a; Horton et al, 2013; Mergili et al, 2019)
The overall performance of the source-area prediction based on the generalized additive model (GAM) was good with a spatially cross-validated median area under the receiver-operating characteristic curve (AUROC) of 0.80 and an interquartile range (IQR) of 0.001
Summary
Knowledge of where debris flows are initiated and how far they travel is crucial for assessing their impact over large regions (Aleotti and Chowdhury, 1999; van Westen et al, 2006). Debris-flow runout modelling for large areas is performed by first delineating source areas and applying empirical–statistical or process-based numerical methods for simulating the runout characteristics (Blahut et al, 2010a; Horton et al, 2013; Mergili et al, 2019). Many of the physically based methods require eventspecific geotechnical and rheological parameters, such as material composition (e.g. bulk density and source depths) and flow characteristics (e.g. flow discharge rates). These parameters, such as debris-flow volume, can be extremely difficult to obtain for large areas, let alone single unobserved events (Marchi and D’Agostino, 2004; Dong et al, 2009)
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