Abstract
Abstract. The production of Machine Made (MM) snow is now generalized in ski resorts and represents the most common method of adaptation for mitigating the impact of a lack of snow on skiing. Most investigations of correlations between snow conditions and the ski industry's economy focus on the production of MM snow though not one of these has taken into account the efficiency of the snowmaking process. The present study consists of observations of snow conditions (depth and mass) using a Differential GPS method and snow density coring, following snowmaking events and seasonal snow accumulation in Les Deux Alpes ski resort (French Alps). A detailed physically based snowpack model accounting for grooming and snowmaking was used to compute the seasonal evolution of the snowpack and compared to the observations. Our results show that approximately 30 % of the water mass can be recovered as MM snow within 10 m from the center of a MM snow pile after production and 50 % within 20 m. Observations and simulations on the ski slope were relatively consistent with 60 % (±10 %) of the water mass used for snowmaking within the limits of the ski slope. Losses due to thermodynamic effects were estimated in the current case example to be less than 10 % of the total water mass. These results suggest that even in ideal conditions for production a significant fraction of the water used for snowmaking can not be found as MM snow within the limits of the ski slope with most of the missing fraction of water. This is due to site dependent characteristics (e.g. meteorological conditions, topography).
Subject
Earth-Surface Processes,Water Science and Technology
Reference38 articles.
1. Armstrong, R. and Brun, E.: Snow and climate: physical processes, surface energy exchange and modeling, Polar Res., 29, 461–462, https://doi.org/10.3402/polar.v29i3.6091, 2008.
2. Bergstrom, K. and Ekeland, A.: Effect of trail design and grooming on the incidence of injuries at alpine ski areas, Brit. J. Sport. Med., 38, 264–268, https://doi.org/10.1136/bjsm.2002.000270, 2004.
3. Bevington, P. R. and Robinson, D. K.: Data reduction and error analysis, McGraw-Hill, 3rd Edn., available at: http://experimentationlab.berkeley.edu/sites/default/files/pdfs/Bevington.pdf (last access: 4 April 2017), 2003.
4. Brun, E., David, P., Sudul, M., and Brunot, G.: A numerical model to simulate snow-cover stratigraphy for operational avalanche forecasting, J. Glaciol., 38, 13–22, 1992.
5. Damm, A., Koeberl, J., and Prettenthaler, F.: Does artificial snow production pay under future climate conditions? – A case study for a vulnerable ski area in Austria, Tourism Manage., 43, 8–21, https://doi.org/10.1016/j.tourman.2014.01.009, 2014.
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