Two methodologies to calibrate landslide runout models

Aaron, Jordan; McDougall, Scott; Nolde, Natalia

doi:10.1007/s10346-018-1116-8

Cited by 44 publications

(44 citation statements)

References 30 publications

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“…These uncertainties in height estimates likely also apply to the results at San Rafael (Megamaxi). For such cases, the calibration approach developed by Aaron et al (2019) would allow including a specific uncertainty for each criterion, but these have not been quantified in Mothes et al (2004) and later estimation seems inappropriate. If we exclude the height constraint at Hacienda Valencia, the total performance value would lower to 13% compared to currently 23% for μ = 0.005/ ξ = 1400 m/s 2 .…”

Section: Discussionmentioning

confidence: 99%

Modelling future lahars controlled by different volcanic eruption scenarios at Cotopaxi (Ecuador) calibrated with the massively destructive 1877 lahar

Frimberger

Andrade

Weber

et al. 2021

Earth Surf Processes Landf

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Lahars are among the most hazardous mass flow processes on earth and have caused up to 23 000 casualties in single events in the recent past. The Cotopaxi volcano, 60 km southeast of Quito, has a well-documented history of massively destructive lahars and is a hotspot for future lahars due to (i) its $10 km 2 glacier cap, (ii) its 117-147-year return period of (Sub)-Plinian eruptions, and (iii) the densely populated potential inundation zones (300 000 inhabitants). Previous mechanical lahar models often do not (i) capture the steep initial lahar trajectory, (ii) reproduce multiple flow paths including bifurcation and confluence, and (iii) generate appropriate key parameters like flow speed and pressure at the base as a measure of erosion capacity. Here, we back-calculate the well-documented 1877 lahar using the RAMMS debris flow model with an implemented entrainment algorithm, covering the entire lahar path from the volcano edifice to an extent of $70 km from the source. To evaluate the sensitivity and to constrain the model input range, we systematically explore input parameter values, especially the Voellmy-Salm friction coefficients μ and ξ. Objective selection of the most likely parameter combinations enables a realistic and robust lahar hazard representation. Detailed historic records for flow height, flow velocity, peak discharge, travel time and inundation limits match best with a very low Coulomb-type friction μ (0.0025-0.005) and a high turbulent friction ξ (1000-1400 m/s 2). Finally, we apply the calibrated model to future eruption scenarios (Volcanic Explosivity Index = 2-3, 3-4, >4) at Cotopaxi and accordingly scaled lahars. For the first time, we anticipate a potential volume growth of 50-400% due to lahar erosivity on steep volcano flanks. Here we develop a generic Voellmy-Salm approach across different scales of high-magnitude lahars and show how it can be used to anticipate future syneruptive lahars.

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Section: Discussionmentioning

confidence: 99%

Modelling future lahars controlled by different volcanic eruption scenarios at Cotopaxi (Ecuador) calibrated with the massively destructive 1877 lahar

Frimberger

Andrade

Weber

et al. 2021

Earth Surf Processes Landf

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“…Generally, the choice of the rheology to use and the consequent calibration of the soil parameters becomes a tricky and essential phase of the analysis. Various authors have applied different approaches to calibrate the model, thus obtaining the most reliable rheological parameters to be assigned [6,[17][18][19][20]. In almost all these cases, Geosciences 2021, 11, 364 2 of 22 the user has to assign the basal topography of the site, which remains unchanged during the simulation; then, the detachment volume has to be defined to clearly distinguish the deforming part of the slope.…”

Section: Introductionmentioning

confidence: 99%

“…Clearly, the unstable volume is known only if the failure has already occurred, but, when the model is implemented for a prediction, the assumption about the starting geometry is affected by a great uncertainty degree. Based on the authors' knowledge, when a propagation model is used for prediction, the authors focus more on the soil model calibration [17][18][19][20][21][22] taking for granted the geometry of the unstable volume. However, an error at this stage can seriously affect the estimation of the area finally involved in the run-out.…”

Section: Introductionmentioning

confidence: 99%

Post-Collapse Evolution of a Rapid Landslide from Sequential Analysis with FE and SPH-Based Models

et al. 2021

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Propagation models can study the runout and deposit of potential flow-like landslides only if a reliable estimate of the shape and size of the volumes involved in the phenomenon is available. This aspect becomes critical when a collapse has not yet occurred and the estimation of the unstable volume is not uniquely predictable. This work proposes a strategy to overcome this problem, using two established analysis methods in sequence; first, a Strength Reduction Method (SRM)-based 3D FEM allows the estimate of the instable volume; then, this data becomes an input for a Smoothed Particle Hydrodynamics (SPH)-based model. This strategy is applied to predict the possible evolution of Sant’Andrea landslide (North-Eastern Italian Alps). Such a complex landslide, which affects anhydrite–gypsum rocks and is strongly subject to rainfall triggering, can be considered as a prototype for the use of this procedure. In this case, the FEM–SRM model is adopted, which calibrates using mapping, monitoring, geophysical and geotechnical data to estimate the volume involved in the potential detachment. This volume is subsequently used as the input of the SPH model. In this second phase, a sensitivity analysis is also performed to complete the evaluation of the most reliable final soil deposits. The performed analyses allow a satisfactory prediction of the post-collapse landslide evolution, delivering a reliable estimate of the volumes involved in the collapse and a reliable forecast of the landslide runout.

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“…Approaches for model calibration include adjusting parameters based on visual inspection (i.e. trial and error; Hungr 1995;Mergili et al 2012); expert knowledge (Horton et al 2013); posterior analysis (Mergili et al 2019;Aaron et al 2019); and optimization algorithms that aim to minimize a cost function, i.e. a quantitative measure of runout model performance.…”

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confidence: 99%

“…a quantitative measure of runout model performance. Some measures of performance include estimates of the intersection-over-union (Galas et al 2007), area under the receiver operating characteristic curve (AUROC; Cepeda et al 2010;Mergili et al 2015) and depth error (Aaron et al 2019) of simulated and observed debris flows. Since most of these calibration approaches are for single observed events, they rarely consider how transferable tuned parameter sets are from local to regional applications.…”

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confidence: 99%

Comment on nhess-2021-22

2021

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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

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Two methodologies to calibrate landslide runout models

Cited by 44 publications

References 30 publications

Modelling future lahars controlled by different volcanic eruption scenarios at Cotopaxi (Ecuador) calibrated with the massively destructive 1877 lahar

Modelling future lahars controlled by different volcanic eruption scenarios at Cotopaxi (Ecuador) calibrated with the massively destructive 1877 lahar

Post-Collapse Evolution of a Rapid Landslide from Sequential Analysis with FE and SPH-Based Models

Comment on nhess-2021-22

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