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

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TITLE: Snow gliding susceptibility maps for ski resort management: the case study of the Monterosa Ski in the NW Italian Alps

AUTHORS: M. Maggioni1,2, D. Godone1,2, P. Höller3, L. Oppi2,4, S. Stanchi1,2 , B. Frigo2,5 and M. Freppaz1,2

1 Research Centre on Natural Risks in Mountain and Hilly Enviroments (NatRisk) and Department of Agriculture, Forest and Food Sciences (DISAFA), University of Torino, Largo P. Braccini 2, 10095 Grugliasco (TO), Italy,

margherita.maggioni@unito.it, d.godone@gmail.com,

michele.freppaz@unito.it

2 Mountain Risk Research Team - MRR Team, Via L. Barone 8, 11029 Verrès (AO), Italy

3 Department Natural Hazard, Federal Research Centre for Forests (BFW), 6020 Innsbruck, Austria, peter.hoeller@uibk.ac.at

4 Université Paul-Valéry Montpellier, Route de Mende – F34199 MONTPELLIER CEDEX 5, France, oppi.leonardo@gmail.com

5 Department of Structural, Geotechnical and Building Engineering (DISEG), Politecnico di Torino, C.so Duca degli Abruzzi, 24, I-10129 Torino, Italy,

barbara.frigo@gmail.com

ABSTRACT

The slow snow gliding process could be considered as much as the faster snow avalanche flows, as it can produce severe damages to buildings and infrastructures. It depends on snow, land cover and terrain parameters. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

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Among those driving factors, in this work, we focus on the stationary ones, i.e. 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-(GIS) based procedure to create a snow gliding susceptibility index (SI) and to produce a related snow gliding susceptibility map. We tested this procedure in the Monterosa Ski resort located in Aosta Valley (NW Italian Alps), where snow gliding data were available. Though validated only in a relatively small area and only with a qualitative method, we found good agreement between the areas classified with a high value of SI and the historical data. The map covers an area of about 338 km2; the map scale is 1:50’000. The proposed procedure is seen as a valuable tool to help security personnel of the ski resorts, but even in other contest (e.g. road manager), in the identification of the areas most prone to snow gliding phenomena.

1. INTRODUCTION

Snow gliding is a downhill motion of snow on the ground (In der Gand and Zupancic, 1966). It is dependent on physical snow characteristics, overall depth, density and water content, and on morphological features, overall slope angle and surface roughness.

The most recent review on snow gliding and glide avalanches (Höller, 2014a) summarizes all the research done since the ’30 of the last Century on the predisposing factors to these processes. Studies of several authors (see Höller, 2014a for extensive literature) prove that snow gliding is closely related to the morphology of the terrain. The intensity of gliding depends on slope 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48

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inclination, aspect, temperature and water content of the snow, temperature at the snow /soil interface and roughness of the surface.

Snow gliding, though a slow process, should be considered within ski resorts as much as avalanches, as it can produce damaging effects larger than the faster snow avalanche flows. In fact, snow gliding can develop high pressure on buildings, trees and masts and produce large damages (Höller, 2009; Margreth, 2007a). Guidelines exists for building of structures on areas prone to snow gliding (Margreth, 2007b), however a homogeneous and well-defined procedure to identify those areas does not exist yet.

In this work, among all the predisposing factors to snow gliding, we focus on stationary parameters, i.e. 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. We propose a geographical information system-(GIS) based procedure to create a snow gliding susceptibility index and to produce a related snow gliding susceptibility map in a ski resort located in Aosta Valley (NW Italian Alps).

2. STUDY AREA

The Monterosa Ski resort is located in the North-western Italian Alps, in the Aosta Valley and Piemonte regions, and develops among three different valleys south of Monte Rosa massif, from an elevation of 1500 m up to about 3500 m asl. The area considered in this work is located in Aosta Valley and covers an extension of about 140 km2 (Fig. 1. Location of the study site). The long-term yearly mean precipitation recorded at the manual weather station of Lago Gabiet (2340 m asl, property of Servizio Idrografico e Mareografico Nazionale 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73

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– Ufficio Idrografico del Po) is 1066 mm yr−1 (period 1919–2001) and the mean annual air temperature is -0.2 °C (period 1978–2001). The average annual cumulated snowfall is 631 cm (period 1928–2001) with 49 days of snowfall and 228 days of snow cover on average (Mercalli et al., 2003).

3. METHODOLOGY

The procedure was developed within a Geographical Information System and uses as a main input the Digital Elevation Model. From the DEM, different parameters, known to be favourable to snow gliding, were derived and later combined to produce a susceptibility index, which characterizes each pixel of the DEM. The resolution of the DEM was 10 m, but the procedure could be applied to any grid resolution. In the following, we show the classification and the weighting criterion of the different parameters with respect to their driving importance to snow gliding, following mainly the indication of Leitinger et al. (2008) and Newesely et al. (2000). We finally describe the procedure to generate the susceptibility index used to build the snow gliding susceptibility map.

3.1. PREDISPOSING FACTORS

Slope angle

Glide only occurs when slope angle is at least 15° (McClung and Schaerer, 2006): above this angle, the downslope component of the gravitational force acting on the snowpack is larger than the combined frictional forces from the snow/ground interface and the internal frictional forces within the snowpack (Jones, 2004). 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96

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In the proposed procedure, the slope angle is derived from the DEM with the function “slope” of QGIS (QGIS, 2014), using the algorithm described in Burrough and McDonell (1998). The range 0-90° is divided into 5 classes: the class 35-60° is considered the most prone to snow gliding, while the classes 0-15° and 60-90° are not considered as snow gliding cannot occur, either due to the insufficient inclination (0-15°) or to the impossibility for snow to accumulate (60-90°).

The criterion for weighting the parameter “slope angle” is derived from the fact that the intensity of gliding is depending on sin ψ, where ψ in the inclination of the slope. We first calculated sin ψ for seven classes (Tab. 1) and then determine the weighting factors for the slope angle taking into consideration these values and the existing literature. We set a weighting factor of 10 for the range 35-45° (the class where snow gliding is most frequent) and calculated the remaining factors by using the corresponding ratios of the relative extent of gliding (sin ψ). We corrected these values according to literature (McClung and Schaerer, 2006) and finally get to the definitive values of the weighting factors (Tab. 1).

Table 1. Classes and weights for the parameter “slope angle”.

slope angle *

sin ψ (relative extent of

gliding) weighting factors

definitive used weighting factors 00-15° (7.5°) 0.130 2.0 0.0 15-25° (20.0°) 0.342 5.3 5.3 25-35° (30.0°) 0.500 7.8 7.8 35-45° (40.0°) 0.643 10.0 10.0 45-60° (52.5°) 0.793 12.3 10.0 60-75° (67.5°) 0.924 14.4 0.0 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114

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75-90°

(82.5°) 0.991 15.5 0.0

* Values in the brackets were used to calculate sin ψ Land cover

Snow gliding, and glide snow avalanches, mostly occur on smooth surfaces, smooth rocks and grassland (Mitterer and Schweizer, 2013). Long grass are most favourable than short grass (Newesely et al., 2000).

In the proposed procedure, the land use map of the Aosta Valley Region (ISPRA, 2007) is first clipped on the study area and the original 56 classes reclassified into 6 classes to create a land cover map: the class “grassland” is considered the most prone to snow gliding, while in the class “other” (which include villages, caves, rivers, glaciers) the process cannot occur.

The criterion for weighting the land cover was based on the calculation of the relative glide intensity starting from the equation N = (1 + 3 n)1/2 where N is the glide factor and n the relative glide velocity (Haefeli, 1948; Salm, 1977). Considering the values of N reported by Margreth (2007b) for different land covers, we calculated the relative glide velocity n. We set a weighting factor of 10 for the class grassland (the class where snow gliding is most frequent) and calculated the remaining factors by using the corresponding ratios of the relative glide velocity n. (Tab. 2). To consider also forested areas, where also snow gliding can though rarely occur, according to Höller (2014b) we set a weighting factor of 0.3 for those areas. The definitive values of the weighting factors are shown in Table 2.

Table 2. Classes and weights for the parameter “land cover”.

Land cover relative glide velocity

weighti ng factors definitive used weighting factors 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136

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grassland 3.08 (S) or1.92 (N) 10.0 10.00 dwarf shrubs (e.g.

rhododendron, …) 1.59 (S) or1.00 (N) 5.16 5.2

bushes (e.g. alnus viridis, pinus mugo,

…) 0.75 (S) or0.52 (N) 2.43 2.4 coarse scree, terrain covered by smaller or larger boulder 0.23 (S) or0.15 (N) 0.74 0.8 forests 0.3 other 0.00

* N assumes different values on aspect North (N) and South (S) Roughness

Smooth surfaces are most favourable to snow gliding (In der Gand and Zupancic, 1966; Mitterer and Schweizer, 2012) as the basal friction is low. McClung and Clarke (1987) reported a linear relationship between surface roughness and glide velocity.

In the proposed procedure, the roughness is derived from the DEM implementing in R (R, 2014) the method proposed by Sappington (2007). Three classes are defined: the class with lower values for the roughness (smoother terrain) is the most predisposing to the snow gliding.

The criterion for weighting the parameter “roughness” was based on the work of Höller (2012) who determined the intensity of gliding for different terrain roughness starting from stagnation depth and height of mounds. We set a weighting factor of 10 for the smoothest ground surface (0.1 m terrain roughness for Höller, 2012) and calculated the remaining factors by using the corresponding ratios of the relative extent of gliding (Tab. 3).

Table 3. Classes and weights for the parameter “roughness”.

roughnes s

relative extent of

gliding weighting factors

low 1.00 10.0 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153

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medium 0.25 2.5

high 0.10 1.0

Aspect

Mitterer and Schweizer (2013) state that results on prevailing aspects for snow gliding areas are inconclusive, as most studies did not cover all aspects. However, southern aspects were found to be associated with larger gliding (Leitinger at al., 2008).

In the proposed procedure, the aspect is derived from the DEM with the function “aspect” of QGIS (QGIS, 2014), using the algorithm described in Burrough and McDonell (1998). The range 0-360° is divided into 3 classes: the southern aspects (SE-S-SW: 112.5°-247.5°) are the most favourable ones, while on northern aspects (NW-N-NE: 292.5-67.5°) the snow gliding process is expected to rarely occur.

The criterion for weighting the parameter “aspect” was based on the work of Höller (2012) who determined the intensity of gliding for different aspects using the snowpack structure. We set a weighting factor of 10 for south-facing slopes (where snow gliding is most frequent) and calculated the remaining factors by using the corresponding ratios of the relative extent of gliding (Tab. 4).

Table 4. Classes and weights for the parameter “aspect”.

aspect relative extent of gliding weighti ng factors South (SE-S-SW) 1.0 10.0 East, West 0.6 6.0 North (NW-N-NE) 0.2 2.0 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172

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3.2. SUSCEPTIBILITY INDEX

The layers of the four different parameters were weighted and reclassified according to the classification shown in Table 5, using the function “r.reclass” of QGIS (Shapiro et al., 1993), in order to generate four new layers with values ranging between the minimum and maximum values of the weighting factors. As done in previous applications for land evaluation (e.g. Stanchi et al., 2012) the four layers were finally merged, using map algebra, summing cell values, to obtain the snow gliding susceptibility index (SI) layer: higher the value of SI, higher is the possibility of occurrence of snow gliding processes.

Table 5. Classification and weighting factors of the different parameters for the determination of the susceptibility index.

Slope angle

weig ht

Land use weig

ht Roughn ess weig ht Aspect weig ht

0° – 15° 0.0 grassland 10.0 low 10.0 South 10.0 15° – 25° 5.3

dwarf

schrubs 5.2 medium 2.5 East,

West 6.0 25° – 35° 7.8 bushes 2.4 high 1.0 North 2.0 35° – 60° 10.0 coarse scree 0.8 60° – 90° 0.0 forests 0.3 other 0.0 RESULTS 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 194

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The resulting map was compared with data provided by the Monterosa Ski resort personnel to assess the quality of the results: a simple qualitative comparison with historical and more recent photos was made. We were able to identify several areas known to be affected by snow gliding and glide snow avalanches (e.g. the experimental test sites described by Frigo et al., 2014) and also areas where typically those phenomena do not occur (e.g. the Seehore avalanche test site described by Barbero et al., 2013 and Maggioni et al., 2013). These areas are shown as examples in the four small boxes reported in the map.

CONCLUSIONS

In this work we propose a procedure for the determination of a snow gliding susceptibility index (SI) and we realized a snow gliding susceptibility map for the Monterosa Ski resort in Aosta Valley. Though validated only in a relatively small area and only with a qualitative method, we found good agreement between the areas classified with a high value of SI and the historical data.

These maps are seen as useful tools for the ski resort management: e.g. masts of new cableways could be placed in the areas identified as less prone to snow gliding or be properly dimensioned. As glide snow avalanches might release where snow gliding is intense, this map could also be used for avalanche risk management: ski runs below areas prone to snow gliding and glide snow avalanches might be protected with simple protective measures and/or monitored with specific monitoring system (e.g. Frigo et al., 2014).

The proposed procedure could be extended also to other applications: in villages it might be useful for the identification of areas where buildings will 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218

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be endangered by snow gliding; for viability it might be useful to identify areas at the road sides that might slowly fall over the road.

SOFTWARE

For the creation of the snow gliding susceptibility index we used the software QGIS (QGIS, 2014) and the programming language R (R, 2014). Figure 2 (Fig.

2. Flow chart of the procedure for the creation of the snow gliding susceptibility map) show a flow chart that summarizes the procedure.

AKNOWLEDGEMENTS

This work was done within the project Risk, Research and Innovation of the Mountain Risk Research Team, a research unit build in the frame of the “Bando per la creazione e lo sviluppo di Unità di Ricerca” (D.G.R. 1988/2011 e s.m.i.), in the Operational Program 2007-2013 of the FSE and FESR – Regione Autonoma Valle d’Aosta. We thank the other partners of the Research Unit, in particular Monterosa Ski for the avalanche data and Fondazione Montagna sicura and Politecnico of Torino for discussions.

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FIGURES

Figure 1. Location of the study site.

Figure 2. Flow chart of the procedure for the creation of the snow gliding susceptibility map. 320 321 322 323 324 325

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