Comment on nhess-2021-22

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. IntroductionKnowledge 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). Commonly, debris-flow runout modelling for large areas is performed by first delineating source areas and then 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). There is a wide selection of heuristic, statistical and machine learning methods suitable for predicting sources areas in large regions (Chung and Fabbri 1999;Carrara