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

Maggioni, Margherita; Godone, Danilo; Höller, Peter; Oppi, L.; Stanchi, Silvia; Frigo, Barbara; Freppaz, Michèle

doi:10.1080/17445647.2016.1167785

Cited by 5 publications

(4 citation statements)

References 21 publications

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“…This distinction is based on the origin of liquid water at the snow-soil interface: in a cold event, the nec-essary wet snow-soil interface originates either from snow melting at basal layers of the snowpack or from water suction from the soil; in a warm event, the water originates from melting processes at the snow surface, percolates through the snowpack and ponds at the snow-soil interface. However, Margueritat (2016) recently registered cases of snow gliding in wintertime (cold events) without a basal layer of wet ground grains, but in certain cases with a basal layer made of faceted particles.…”

Section: Introductionmentioning

confidence: 99%

Snow gliding and glide-snow avalanches: recent outcomes from two experimental test sites in Aosta Valley (northwestern Italian Alps)

Maggioni

Godone

Frigo

et al. 2019

Nat. Hazards Earth Syst. Sci.

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Abstract. Snow gliding and glide-snow avalanches are gaining importance among scientists as global warming might induce conditions favourable to those phenomena. Our aim is to analyse such processes with a particular focus on the potential driving factors associated with the soil conditions. We equipped two experimental test sites in the Aosta Valley region (NW Italy) with glide-snow shoes, temperature and volumetric liquid water content (VLWC) sensors in the soil and in the basal snowpack layer; snow and weather parameters were also collected by automatic weather stations and at manual snow measuring sites. In the two monitoring seasons 2013–2014 and 2014–2015 we registered nine glide-snow avalanches, two cold and seven warm events, which were characterized by different snow and soil conditions. In the only warm glide-snow avalanche event, which presented a continuous gliding before, the daily glide rate showed a significant exponential relationship with the soil VLWC. We also found, though without a general trend, that gliding and non-gliding periods (either considering warm and cold periods separately or together) were characterized by significantly different predisposing factors. This study contributes to the assessment of the importance of soil VLWC, which seems to be one of the most important driving factors for gliding processes. Therefore, it supports the need, already suggested by other scientists, for analysing such processes with an interdisciplinary approach which integrates snow and soil sciences.

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Section: Introductionmentioning

confidence: 99%

Snow gliding and glide-snow avalanches: recent outcomes from two experimental test sites in Aosta Valley (northwestern Italian Alps)

Maggioni

Godone

Frigo

et al. 2019

Nat. Hazards Earth Syst. Sci.

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“…The lithological units in a region can affect heat absorption and transfer, which may lead to snow mass movement and avalanches (Choubin et al, 2020). Considering the land use and land cover (LULC) and the avalanche relationship, several researchers emphasized that avalanches frequently occur on grassland and bare land (Bergua et al, 2018;Maggioni et al, 2016;Suk and Klimánek, 2011). Even though some avalanches were observed in forests, it was concluded in some studies that the forests were also effective in reducing the avalanche risk (Varol, 2022).…”

Section: Input Datasets and Featuresmentioning

confidence: 99%

Snow Avalanche Susceptibility Mapping for Davos, Switzerland

Cetinkaya

Kocaman

2022

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Snow avalanches are among destructive hazards occurring in mountainous regions and spatial distribution (susceptibility) of their occurrences needs to be considered for spatial planning and disaster risk mitigation efforts. The susceptibility assessment is the first step in avalanche disaster management and can be carried out using high resolution geospatial data and machine learning (ML) algorithms. In this study, we have assessed the snow avalanche susceptibility in Davos, Switzerland using an inventory delineated on satellite imagery in a previous study. The conditioning factors used for the avalanche susceptibility assessment include elevation, slope, plan curvature, profile curvature, aspect, topographic position index, topographic ruggedness index, topographic wetness index, land use and land cover, lithology, distance to road, and distance to the river. Two ML algorithms, the logistic regression (LR) and the random forest (RF), were comparatively assessed using validation data split from the training data (30/70). The prediction performances of both models were assessed based on the area under the receiver operating characteristic curve (ROC-AUC) value. Although the AUC value obtained from the LR method was relatively low (0.74), the value obtained from the RF (0.96) demonstrated high performance and usability of this approach. The results indicate that the RF method can successfully produce an avalanche susceptibility map for the region, although potential improvements may be possible by investigating various input features and ML algorithms as well as by classifying the starting and runout zones of the avalanche data separately. Furthermore, the accuracy is expected to increase by using a larger training dataset.

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“…Published by Copernicus Publications on behalf of the European Geosciences Union. The slope angle, the microrelief, and the hydrological properties of the slope influence the glide velocity (Ceaglio et al, 2017;McClung and Schaerer, 1999;Margreth, 2007). Friction originated by the vegetation depends on its composition and height (Höller et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

Determining the drivers for snow gliding

Fromm¹,

Baumgärtner²,

Leitinger³

et al. 2018

Nat. Hazards Earth Syst. Sci.

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Abstract. Snow gliding is a key factor for snow-glide avalanche formation and soil erosion. This study considers atmospheric and snow variables, vegetation characteristics, and soil properties and determines their relevance for snow gliding at a test site (Wildkogel, Upper Pinzgau, Austria) during winter 2014/2015. The time-dependent data were collected at a high temporal resolution. In addition to conventional sensors, a “snow melt analyzer” was used. The analysis shows that the soil temperature 10 cm below the surface, the phytomass of mosses, the liquid water content in the snowpack, and the static friction coefficient of the glide shoes had significant influence on snow gliding during the whole winter. In the first period (October to January) the soil moisture at the surface and 1.5 cm below the surface and the length of the slope uphill of the glide shoes affected the snow gliding, too. In the second period (February to May) the soil temperature at the surface, the soil moisture 10 cm below the surface, and the slope angle had additional influence on snow gliding. The role of the vegetation in the snow-glide process is determined by the influence on the static friction coefficient caused by its composition and characteristics and by moss-rich and short-stemmed canopies being seemingly more interconnected with the snowpack. In addition to the soil and snow properties, the topography and the vegetation characteristics, further investigations may be focused on the freezing and melting processes in the uppermost soil layers and at the soil surface.

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

Cited by 5 publications

References 21 publications

Snow gliding and glide-snow avalanches: recent outcomes from two experimental test sites in Aosta Valley (northwestern Italian Alps)

Snow gliding and glide-snow avalanches: recent outcomes from two experimental test sites in Aosta Valley (northwestern Italian Alps)

Snow Avalanche Susceptibility Mapping for Davos, Switzerland

Determining the drivers for snow gliding

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