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 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.

Empirical α–β runout modelling of snow avalanches in the Catalan Pyrenees

A variation in the α−β model which is a regression model that allows a deterministic prediction of the extreme runout to be expected in a given path, was applied for calculating avalanche runout in the Catalan Pyrenees. Present knowledge of major avalanche activity in this region and current mapping tools were used. The model was derived using a dataset of 97 ‘extreme’ avalanches that occurred from the end of 19th century to the beginning of 21st century. A multiple linear regression model was obtained using three independent variables: inclination of the avalanche path, horizontal length and area of the starting zone, with a good fit of the function (R2 = 0.81). A larger starting zone increases the runout and a larger length of the path reduces the runout. The new updated equation predicts avalanche runout for a return period of ~100 years. To study which terrain variables explain the extreme values of the avalanche dataset, a comparative analysis of variables that influence a longer or shorter runout was performed. The most extreme avalanches were treated. The size of the avalanche path and the aspect of the starting zone showed certain association between avalanches with longer or shorter runouts.

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 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.

Dynamic analysis of the 2012 Johnsons Landing landslide at Kootenay Lake, British Columbia: the importance of undrained flow potential

The Johnsons Landing landslide occurred on 12 July 2012 on the shores of Kootenay Lake, British Columbia. The landslide consisted of poorly sorted, low-plasticity debris that initially descended a steep channel, before avulsing onto a glaciofluvial terrace. This event destroyed three homes and killed four people who lived on this terrace. This paper presents a back-analysis of this event using numerical runout modelling. It is shown that undrained flow can explain the observed channel avulsion with fewer model parameters than needed by previous analyses. This mechanism should be considered in similar settings, as ignoring it can lead to underestimation of runup and overtopping of natural and anthropogenic flow obstacles, such as landslide protection structures.