Лëд и снег (Mar 2015)
Assessment of the economic risk for the ski resorts of changes in snow cover duration
Abstract
Winter tourism that is intensively developed in the Russian Federation in recent years strongly depends on the snow availability and properties in the region. Climate changes exert significant influence on the functioning of mountain ski resorts, especially if they are located in areas with relatively high air temperatures in winter season. At the present time, a snowy cluster of mountain ski resorts is intensively progressing in vicinity of Krasnaya Polyana. This region in the West Caucasus (Russia) is characterized by relatively warm climate conditions. The snow cover thickness (of 1% insurance) in area of the Aibga mountain range may reach 8.1 m. But the snow cover thickness is not the only characteristic of the mountain skiing attractiveness. According to the Swiss standards a mountain ski resort can be considered reliable if during seven seasons of ten ones the snow cover with minimal thickness of 30–50 cm exists for a time not shorter than 100 days during a period from 1st December till 15th April.According to the forecast, during future decades the calculated amount of solid precipitation should reduce by 25–30% in mountain regions on the south macro-slope of the Great Caucasus. As the calculations show, by 2041–2050 the maximal decade thickness of snow cover will decrease by 29–35% while a number of days with snow – by 35–40%. If this is the case, artificial snow will be needed in addition to the natural one. But, under warm climate conditions using of plants for artificial snow production will require a certain perfecting of the nowadays technologies, and very likely, with use of chemicals. That is why a shadowing of existing mountain ski routes by means of the tree planting along them could be ecologically more promising. As for the mountain ski resorts of the West Caucasus, we should mention a possible weakening of the avalanche activity as a potential positive effect of the climate warming predicted by models.
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