Snow avalanche mass-balance calculation and simulation-model verification

Sailer, Rudolf; Fellin, Wolfgang; Fromm, Reinhard; Jörg, P.; Rammer, Lambert; Sampl, Peter; Schaffhauser, Andreas

doi:10.3189/172756408784700707

Cited by 31 publications

(15 citation statements)

References 16 publications

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“…This is not valid for modeling small-scale avalanches in forested terrain: previous simulations of our data set with RAMMS with alternating ξ values for forested areas (100-1000 m s −2 ) showed that runout distances of 31 out of the 40 avalanches were still overestimated when applying the smallest chosen ξ value of 100 m s −2 (Teich et al, 2012b). Moreover, simulating small-scale avalanches with a model based on Voellmy-type frictional relationships only is generally questionable (Sailer et al, 2008), since the avalanche will not stop as long as the slope angle is larger than the friction angle, i.e., tan φ > µ. Therefore, including physical processes within the avalanche flow such as snow entrainment (mass uptake) and detrainment (mass extraction) is important, for example, modeling the mass removal by trees, remnant stumps or dead wood, as realized in this study.…”

Section: Discussionmentioning

confidence: 99%

Computational snow avalanche simulation in forested terrain

Teich

Fischer

Feistl

et al. 2014

Nat. Hazards Earth Syst. Sci.

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Abstract. Two-dimensional avalanche simulation software operating in three-dimensional terrain is widely used for hazard zoning and engineering to predict runout distances and impact pressures of snow avalanche events. Mountain forests are an effective biological protection measure against avalanches; however, the protective capacity of forests to decelerate or even to stop avalanches that start within forested areas or directly above the treeline is seldom considered in this context. In particular, runout distances of smallto medium-scale avalanches are strongly influenced by the structural conditions of forests in the avalanche path. We present an evaluation and operationalization of a novel detrainment function implemented in the avalanche simulation software RAMMS for avalanche simulation in forested terrain. The new approach accounts for the effect of forests in the avalanche path by detraining mass, which leads to a deceleration and runout shortening of avalanches. The relationship is parameterized by the detrainment coefficient K [kg m −1 s −2 ] accounting for differing forest characteristics. We varied K when simulating 40 well-documented smallto medium-scale avalanches, which were released in and ran through forests of the Swiss Alps. Analyzing and comparing observed and simulated runout distances statistically revealed values for K suitable to simulate the combined influence of four forest characteristics on avalanche runout: forest type, crown closure, vertical structure and surface cover, for example, values for K were higher for dense spruce and mixed spruce-beech forests compared to open larch forests at the upper treeline. Considering forest structural conditions within avalanche simulations will improve current applications for avalanche simulation tools in mountain forest and natural hazard management.

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Section: Discussionmentioning

confidence: 99%

Computational snow avalanche simulation in forested terrain

Teich

Fischer

Feistl

et al. 2014

Nat. Hazards Earth Syst. Sci.

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“…More recently, new surveying techniques like photogrammetry and laser scanning have allowed studying the mass balance of large snow avalanches as well (Vallet et al 2001;Sailer et al 2008;Sovilla et al 2010). However, there are significant sources of error: (i) These measurements determine only the net change of surface elevation, but not the separate snow-depth changes due to erosion and deposition at the same location.…”

Section: Steps Towards Practical Applicationsmentioning

confidence: 99%

“…However, there are significant sources of error: (i) These measurements determine only the net change of surface elevation, but not the separate snow-depth changes due to erosion and deposition at the same location. (ii) The snow depth measurements need to be complemented by manual measurements of the densities of the undisturbed snow pack, the deposits and the remaining snow cover (substrate) underneath the deposit at sufficiently many locations (Issler et al 1996;Sailer et al 2008;Issler et al 2008).…”

Section: Steps Towards Practical Applicationsmentioning

confidence: 99%

Dynamically consistent entrainment laws for depth-averaged avalanche models

Issler

2014

J. Fluid Mech.

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The bed entrainment rate in a gravity mass flow is uniquely determined by the properties of the bed and the flow. In depth-averaging, however, critical information on the flow variables near the bed is lost and empirical assumptions usually are made instead. We study the interplay between bed and flow assuming a perfectly brittle bed, characterized by its shear strength τ c , and erosion along the bottom surface of the flow; frontal entrainment is neglected here. The brittleness assumption implies that the shear stress at the bed surface cannot exceed τ c . For quasi-stationary flows in a simplified setting, analytic solutions are found for Bingham and frictional-collisional (FC) fluids. Extending this theory to non-stationary flows requires some assumptions for the velocity profile. For the Bingham fluid, the profile of a "proxy" quasi-stationary eroding flow is used; the rheological parameters are chosen to match the instantaneous velocity and shear-layer depth of the non-stationary flow. For the FC fluid, a two-parameter family of functions that closely match the profiles obtained in depth-resolved numerical simulations is assumed; the boundary conditions determine the instantaneous parameter values and allow computation of the erosion rate. Preliminary tests with the FC erosion formula incorporated in a simple slab model indicate that the non-stationary erosion formula matches the depthresolved simulations asymptotically, but differs in the start-up phase. The non-stationary erosion formulas are valid only up to a limit velocity (and to a limit flow depth if there is Coulomb friction). This appears to mark the transition to another erosion regime-to be described by a different model-where chunks of bed material are intermittently ripped out and gradually entrained into the flow.

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“…In past years, a number of avalanche runout modelling studies are performed with statistical models such as alpha-beta (α-β) and runout ratio models (Sinickas and Jamieson 2014), dynamic models such as Voellmy-Salm model (Salm 1993), Voellmy-fluid model (Bartelt, Salm, and Gruber 1999), two-parameter model (Perla, Cheng, and McClung 1980), particle model (Perla 1984), leading edge model (McClung and Mears 1995), AVAL-1D (Oller et al 2010), erosion and deposition model (Naaim, Faung, and Naaim-Bouvet 2003), Snow Avalanche MOdelling and Simulation (SAMOS) model (Sailer, Rammer, and Sample 2002), the modified version of SAMOS, that is, SamosAT (Sailer et al 2008) and rapid mass movement simulation (Christen, Kowalski, and Bartlet 2010). Extensive research has been performed on the identification and probabilistic assessment of weather and snow cover conditions influencing avalanche activity (Hendrikx et al 2005;McClung and Schaerer 2006;Jomelli et al 2007;Schweizer, Mitterer, and Stoffel 2009).…”

Section: Introductionmentioning

confidence: 99%

Fuzzy–frequency ratio model for avalanche susceptibility mapping

Kumar

Snehmani

Srivastava

et al. 2016

International Journal of Digital Earth

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Avalanche activities in the Indian Himalaya cause the majority of fatalities and responsible for heavy damage to the property. Avalanche susceptibility maps assist decision-makers and planners to execute suitable measures to reduce the avalanche risk. In the present study, a probabilistic data-driven geospatial fuzzy-frequency ratio (fuzzy-FR) model is proposed and developed for avalanche susceptibility mapping, especially for the large undocumented region. The fuzzy-FR model for avalanche susceptibility mapping is initially developed and applied for Lahaul-Spiti region. The fuzzy-FR model utilized the six avalanche occurrence factors (i.e. slope, aspect, curvature, elevation, terrain roughness and vegetation cover) and one referent avalanche inventory map to generate the avalanche susceptibility map. Amongst 292 documented avalanche locations from the avalanche inventory map, 233 (80%) were used for training the model and remaining 59 (20%) were used for validation of the map. The avalanche susceptibility map is validated by calculating the area under the receiver operating characteristic curve (ROC-AUC) technique. For validation of the results using ROC-AUC technique, the success rate and prediction rate were calculated. The values of success rate and prediction rate were 94.07% and 91.76%, respectively. The validation of results using ROC-AUC indicated the fuzzy-FR model is appropriate for avalanche susceptibility mapping. ARTICLE HISTORY

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Snow avalanche mass-balance calculation and simulation-model verification

Cited by 31 publications

References 16 publications

Computational snow avalanche simulation in forested terrain

Computational snow avalanche simulation in forested terrain

Dynamically consistent entrainment laws for depth-averaged avalanche models

Fuzzy–frequency ratio model for avalanche susceptibility mapping

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