Decreasing Snow Reliability and Ski Tourism
What do the results tell us?
For the decades ahead, climate models predict a decrease in snow cover in Europe's ski areas. Hence, significant reductions in the number of winter overnight stays are expected. Climate-induced net gains in summer tourism will not fully compensate for this reduction. However, if future tourists do not abandon skiing because of less favourable snow conditions, more snow reliable regions might profit.
When assessing the impacts of climate change on snow-based winter tourism, we consider two different strategies of how tourists might adapt to altered climatic conditions: (i) sticking to the winter season and alpine skiing, but changing month and/or destination of their holidays (upper figure, left side) and (ii) sticking to the winter season, but changing month, destination and/or holiday type (upper figures, right side).
When tourists stick to alpine skiing, ski areas in the southern part of the Alps (e.g. in France and Italy) lose more overnight stays than those in the northern parts of the Alps and Scandinavia. Some areas even benefit from climate change. When tourists also respond by changing the holiday type, all skiing areas face a reduction in overnight stays.
An often named adaptation strategy for ski areas is to invest in all year tourism so also the effects of climate change to summer tourism (lower figures). In this case, most skiing areas will see a rise in summer overnight stays due to improved climatic conditions. However, the increase in the summer season cannot compensate for the losses during winter.
What can we do with the results?
Besides technical adaptation strategies to increase snow reliability (e.g artificial snow making or slope development) the most often cited adaptation strategy is a change to all year tourism. The results of the modelling show how the climate potential for a considered region changes over the course of the year.
How are the results obtained?
In the core of our model we use functions that relate given climate conditions (snow cover and temperature) to the utility of skiing tourists. These functions are derived from observed data. A change in climate conditions is then translated to a change in utility. The changed utilities are used to describe the change in tourism demand due to climate change.
For the estimation of parameters we use monthly counts of overnight stays from national statistic agencies. Historical and future climate data, including snow depth, is taken from the FP7-Project IMPACT2C. Climate change impacts are assessed for the periods 2015-2045 and 2035-2065. Three combinations of Representative Concentration Pathways (RCPs) and Shared Socioeconomic Pathways (SSPs) are considered: RCP2.6/SSP1, RCP4.5/SSP4 and RCP8.5/SSP5. Results shown in the maps on this page are for RCP4.5/SSP4. Climate change impacts are measured relative to a baseline, which simulates overnight stays according to the supposed socio-economic scenario, but based on the climate of 1979-2009.
One source of uncertainty refers to the future climatic conditions. Especially in mountainous regions, there are significant small-scale differences in the regional climate that affect precipitation parameters, such as snow and resulting snow cover. This makes the prediction of future snow cover quite uncertain.
A further source of uncertainty concerns the reaction of tourists to less reliable snow conditions; will they stop skiing at all or rather concentrate on more reliable ski areas.
What are the broader applications?
Potential broader applications include the extension of the tourism model to further climatic indices or other types of tourism (e.g. city tourism, hiking tourism, beach tourism, etc.). The basic approach is to combine data on tourism demand, expressed e.g. by overnight stays, and climate data to evaluate the utility of different climatic conditions. In addition, we can take competition into account. The methodology can be used for other economic sectors, but also for smaller entities or time scales.
Within ToPDAd climate change impact assessment is carried out on NUTS 3 level. A possible extension is to apply the model to a smaller spatial scale, such as a specific ski area or a set of ski areas, provided that appropriate data is available.
Key Messages and Conclusions
Tourists will shift to more snow reliable regions or different holiday types.
ToPDAd model simulations show that, when tourists adapt by changing the month (within the winter season) and/or destination, but stick to alpine skiing, ski areas in the southern part of the Alps (e.g. in France and Italy) lose more overnight stays than those in the northern parts of the Alps and Scandinavia. Some areas even benefit from climate change. When tourist also respond by changing the holiday type, but still stick to the winter season, all skiing areas face a reduction in overnight stays.
Average percentage change in winter overnight stays (2035-2065 vs. baseline) in skiing dominated regions for RCP4.5/SSP4, when tourists adapt by changing month and/or destination, but stick to alpine skiing (left) or when tourists adapt by changing month, destination and/or holiday type (right); average over all climate models.
Summer season gains do not compensate for winter season losses
ToPDAd model simulations show that most skiing-dominated areas will see a rise in summer overnight stays due to climate change. However, the increase in summer overnight stays cannot compensate for the losses during winter.
Average percentage change in summer overnight stays (left) and average percentage change in annual overnight stays (right) (2035-2065 vs. baseline) in skiing dominated regions for RCP4.5/SSP4, when tourists adapt by changing timing and activity of holidays; average over all climate models.