Snow gliding on a south-facing slope covered with larch trees

Höller, Peter

doi:10.1007/s13595-013-0333-5

Cited by 9 publications

(3 citation statements)

References 8 publications

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“…The supporting force of tree stems and the heterogeneity of the forest snowpack, influenced by crown interception, reduce the release of slab avalanches (Bebi et al, 2009;Mc-Clung and Schaerer, 2006;Schneebeli and Bebi, 2004;Teich et al, 2012b). The anchoring effect of the vegetation in snow gliding has been demonstrated in several studies, and the density, height and heterogeneity of vegetation cover are crucial characteristics (Endo, 1983;Feistl et al, 2014;Höller, 2001Höller, , 2013. Furthermore, surface roughness has a critical impact on the flow path and runout distance of avalanches (Bühler et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

Multiscale analysis of surface roughness for the improvement of natural hazard modelling

Brožová

Baggio

D’Agostino

et al. 2021

Nat. Hazards Earth Syst. Sci.

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Abstract. Surface roughness influences the release of avalanches and the dynamics of rockfall, avalanches and debris flow, but it is often not objectively implemented in natural hazard modelling. For two study areas, a treeline ecotone and a windthrow-disturbed forest landscape of the European Alps, we tested seven roughness algorithms using a photogrammetric digital surface model (DSM) with different resolutions (0.1, 0.5 and 1 m) and different moving-window areas (9, 25 and 49 m2). The vector ruggedness measure roughness algorithm performed best overall in distinguishing between roughness categories relevant for natural hazard modelling (including shrub forest, high forest, windthrow, snow and rocky land cover). The results with 1 m resolution were found to be suitable to distinguish between the roughness categories of interest, and the performance did not increase with higher resolution. In order to improve the roughness calculation along the hazard flow direction, we tested a directional roughness approach that improved the reliability of the surface roughness computation in channelised paths. We simulated avalanches on different elevation models (lidar-based) to observe a potential influence of a DSM and a digital terrain model (DTM) using the simulation tool Rapid Mass Movement Simulation (RAMMS). In this way, we accounted for the surface roughness based on a DSM instead of a DTM, which resulted in shorter simulated avalanche runouts by 16 %–27 % in the two study areas. Surface roughness above a treeline, which in comparison to the forest is not represented within the RAMMS, is therefore underestimated. We conclude that using DSM-based surface roughness in combination with DTM-based surface roughness and considering the directional roughness is promising for achieving better assessment of terrain in an alpine landscape, which might improve the natural hazard modelling.

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Section: Introductionmentioning

confidence: 99%

Multiscale analysis of surface roughness for the improvement of natural hazard modelling

Brožová

Baggio

D’Agostino

et al. 2021

Nat. Hazards Earth Syst. Sci.

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“…We set a weighting factor of 10 for the class 'grassland' (the class where snow gliding is most frequent) and calculated the remaining weighting factors (forth column in Table 2) by using the corresponding ratios of the relative glide velocity n. Although not considered in Margreth (2007b) we also took into account forested areas, where snow gliding, though to a minor degree, is also possible. We set a weighting factor of 0.3 for those areas, taken from the work of Höller (2014b). The definitive values of the weighting factors are shown in the last column of Table 2.…”

Section: Land Covermentioning

confidence: 99%

Snow gliding susceptibility: the Monterosa Ski resort, NW Italian Alps

Maggioni¹,

Godone²,

Höller

et al. 2016

Journal of Maps

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Snow gliding, though a slow process, should be considered as important as the faster snow avalanche flows, as it can similarly produce severe damage to buildings and infrastructure. Snow gliding depends on snowpack properties, land cover and terrain parameters. Among these driving factors, in this work, we focus on stationary factors, that is, those that are considered features related to terrain and land cover, in particular those that could be derived from a Digital Elevation Model or land use/cover maps: slope angle, aspect, roughness and land cover. We propose a geographical information system-based procedure to create a snow gliding susceptibility index and to produce a related snow gliding susceptibility map. We tested this procedure in the Aosta Valley (NW Italian Alps), where the Monterosa Ski resort is located. The map covers an area of about 338 km 2 at a scale of 1:50,000. The proposed procedure is seen as a valuable tool to help safety personnel at ski resorts as well as in other scenarios (e.g. road management) in the identification of areas most prone to snow gliding. ARTICLE HISTORY

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“…The potential sources of liquid water include melt-water percolation (Lackinger, 1987;Clarke and McClung, 1999), geothermal heat (McClung, 1987;Newesely et al, 2000;Höller, 2001), and capillary suction (Mitterer and Schweizer, 2012). Whether the loss in friction causes the formation of a tensile crack or a full-depth avalanche release depends on the stauchwall, which is the supporting snow cover located at the lower edge of the gliding zone.…”

Section: Introductionmentioning

confidence: 99%

Glide-snow avalanches: A mechanical, threshold-based release area model

Fees,

van Herwijnen,

Lombardo

et al. 2024

Preprint

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Abstract. Glide-snow avalanches release at the ground-snow interface due to a loss in basal friction. They pose a threat to infrastructure because of the combination of unreliable mitigation measures, limited forecasting capabilities, and a lack of understanding of the release process. The aim of this study was to investigate the influence of spatial variability in basal friction and snowpack properties on the avalanche release area distribution and the release location. We developed a pseudo-3D, mechanical, threshold-based model that consists of many interacting snow columns on a uniform slope. Parameterizations in the model are based on our current understanding of glide-snow avalanche release. The model can reproduce the power law glide-snow avalanche release area distribution as observed on Dorfberg (Davos, Switzerland). A sensitivity analysis of the input parameters showed that the avalanche release area distribution was mostly influenced by the homogeneity (correlation length and variance) of the basal friction and whether the basal friction was reduced suddenly or in small increments. Larger release areas were modeled for a sudden decrease and a more homogeneous basal friction. The spatial variability of the snowpack parameters had little influence on the release area distribution. Extending the model to a real-world slope showed that the modeled location of avalanche releases qualitatively matched the observed locations. The model can help narrow down the length- and time-scales for field investigations. Simultaneously, it can grow in complexity with our increasing knowledge on glide-snow avalanche release processes. Input parameters such as the basal friction or snowpack parameters could potentially all be connected to the liquid water content. This would allow for the use of meteorological measurements to drive the model. The model has the potential to help identify potentially dangerous conditions for large or numerous avalanches which would help improve glide-snow avalanche forecasting.

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Snow gliding on a south-facing slope covered with larch trees

Cited by 9 publications

References 8 publications

Multiscale analysis of surface roughness for the improvement of natural hazard modelling

Multiscale analysis of surface roughness for the improvement of natural hazard modelling

Snow gliding susceptibility: the Monterosa Ski resort, NW Italian Alps

Glide-snow avalanches: A mechanical, threshold-based release area model

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