Constraints on Entrainment and Deposition Models in Avalanche Simulations from High-Resolution Radar Data

Rauter, Matthias; Köhler, Anselm

doi:10.3390/geosciences10010009

Cited by 14 publications

(8 citation statements)

References 47 publications

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“…The small estimated values of the mean deposition speed for the bedload can be explained by the combination of deposition and erosion processes (cf. Parker et al 1986;Cao et al 2004;Trinh et al 2017;Rauter and Köhler 2020). The experimental observations showed that saltating/rolling of particles occurred in the bedload (Brosch and Lube 2020); the saltating/rolling of particles accompany a complicated combination of deposition and erosion processes.…”

Section: Discussionmentioning

confidence: 99%

Validation of a two-layer depth-averaged model by comparison with an experimental dilute stratified pyroclastic density current

Shimizu

Koyaguchi

Suzuki

et al. 2021

Preprint

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Numerical results of a two-layer depth-averaged model of pyroclastic density currents (PDCs) were compared with an experimental PDC generated at the international eruption simulator facility (the Pyroclastic flow Eruption Large-scale Experiment; PELE) to establish a minimal dynamical model of PDCs with stratification of particle concentrations. In the present two-layer model, the stratification in PDCs is modeled as a voluminous dilute turbulent suspension layer with low particle volume fractions (<10-2) and a thin basal bedload layer with high particle volume fractions (~10-2) on the basis of the source condition in the experiment. Numerical results for the dilute layer quantitatively reproduce the time evolutions of the front position and body thickness of the dilute part in the experimental PDC. The numerical results of the bedload thickness and deposit mass depend on an assumed value of mean deposition speed at the bottom of the bedload (D). We show that the thicknesses of bedload and deposit in the simulations agree well with the experimental data, when D is set to about 3.5 x 10-4 m/s. This value of the deposition speed is two orders of magnitude smaller than that predicted by a hindered-settling model. The small value of D suggests that the erosion process accompanied by saltating/rolling of particles plays a role in the sedimentation in the bedload.

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Section: Discussionmentioning

confidence: 99%

Validation of a two-layer depth-averaged model by comparison with an experimental dilute stratified pyroclastic density current

Shimizu

Koyaguchi

Suzuki

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…It is also important to note that depth averaged model approaches do not provide any vertical velocity information and are therefore unable of reproducing turbulent structures resulting in large local velocity variations, that are particularly interesting in the frontal region of the avalanche [52]. The challenge of properly considering velocities in avalanche simulation evaluations has been previously debated yielding similar results [29,30] and new, promising measurement [53] and evaluation approaches [54] are currently discussed.…”

Section: Discussionmentioning

confidence: 99%

Bayesian Inference in Snow Avalanche Simulation with r.avaflow

et al. 2020

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Simulation tools for gravitational mass flows (e.g., avalanches, debris flows) are commonly used for research and applications in hazard assessment or mitigation planning. As a basis for a transparent and reproducible decision making process, associated uncertainties need to be identified in order to quantify and eventually communicate the associated variabilities of the results. Main sources of variabilities in the simulation results are associated with parameter variations arising from observation and model uncertainties. These are connected to the measurement inaccuracies or poor process understanding and the numerical model implementation. Probabilistic approaches provide various theoretical concepts to treat these uncertainties, but their direct application is not straightforward. To provide a comprehensive tool, introducing conditional runout probabilities for the decision making process we (i) introduce a mathematical framework based on well-established Bayesian concepts, (ii) develop a work flow that couples this framework to the existing simulation tool r.avaflow, and (iii) apply the work flow to two case studies, highlighting its application potential and limitations. The presented approach allows for back, forward and predictive calculations. Back calculations are used to determine parameter distributions, identifying and mapping the model, implementation and data uncertainties. These parameter distributions serve as a base for forward and predictive calculations, embedded in the probabilistic framework. The result variability is quantified in terms of conditional probabilities with respect to the observed data and the associated simulation and data uncertainties. To communicate the result variability the conditional probabilities are visualized, allowing to identify areas with large or small result variability. The conditional probabilities are particularly interesting for predictive avalanche simulations at locations with no prior information where visualization explicitly shows the result variabilities based on parameter distributions derived through back calculations from locations with well-documented observations.

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“…Recently, Rauter and Köhler (2019) proposed an empirical deposition model, which is here rewritten for a mass block (i.e. neglecting the pressure gradient term):…”

Section: Dense-core Deposition Modelmentioning

confidence: 99%

MoT-PSA: a two-layer depth-averaged model for simulation of powder snow avalanches on 3-D terrain

Vicari,

Issler

2024

Ann. Glaciol.

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For snow-avalanche hazard mapping, one needs efficient tools that nevertheless capture the essential physical processes. The code MoT-PSA (Method of Transport – Powder Snow Avalanche) described here is based on the two-layer depth-averaged formulation for mixed snow avalanches developed by Eglit and co-workers in the 1980s but is extended to 3-D terrain and uses a fast numerical scheme based on the method of transport. Compared to previous works, we introduce novel formulations for the suspension and deposition of snow from the dense core. Snow cover and air entrainment are quantified with physics-based models. A sensitivity study of the model parameters on an idealized topography shows that both the dense core and the parameters of the powder snow cloud (PSC) governing particle suspension and settling significantly affect the dynamics. As expected, we observe that snow cover entrainment favours the formation of large PSCs with long runout. The powder-snow avalanche that occurred in Lom (Norway) on 27 February 2020 is back-calculated using MoT-PSA. With plausible parameter values, the model reproduces the dense core stopping at the gully's base and the dilute PSC travelling across the frozen lake for almost 1 km.

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Constraints on Entrainment and Deposition Models in Avalanche Simulations from High-Resolution Radar Data

Cited by 14 publications

References 47 publications

Validation of a two-layer depth-averaged model by comparison with an experimental dilute stratified pyroclastic density current

Validation of a two-layer depth-averaged model by comparison with an experimental dilute stratified pyroclastic density current

Bayesian Inference in Snow Avalanche Simulation with r.avaflow

MoT-PSA: a two-layer depth-averaged model for simulation of powder snow avalanches on 3-D terrain

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