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Climate change exacerbates snow-water-energy challenges for European ski tourism

Nature Climate Change volume 13, pages 935–942 (2023)Cite this article

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Abstract

Ski tourism is a substantial component of the economy of mountainous regions in Europe and is highly vulnerable to snow scarcity, which is increasing due to climate change. However, the climate change snow supply risk to ski tourism has not been quantified in a consistent way throughout Europe, including the influence and environmental footprint of snowmaking. Here we show that the snow supply risk to ski tourism increases with global warming level, heterogeneously within and across mountain areas and countries. Without snowmaking, 53% and 98% of the 2,234 ski resorts studied in 28 European countries are projected to be at very high risk for snow supply under global warming of 2 °C and 4 °C, respectively. By contrast, assuming a snowmaking fractional coverage of 50% leads to corresponding proportions of 27% and 71%, but with increasing water and electricity demand (and related carbon footprint) of snowmaking. While it represents a modest fraction of the overall carbon footprint of ski tourism, snowmaking is an inherent part of the ski tourism industry and epitomizes some of the key challenges at the nexus between climate change adaptation, mitigation and sustainable development in the mountains, with their high social-ecological vulnerability.

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Fig. 1: Snow supply risk to ski tourism in the context of climate change.
Fig. 2: Distribution of ski resorts in mountain areas in Europe.
Fig. 3: Burning ember representation of the snow supply risk to ski tourism in European mountain areas.
Fig. 4: Water and electricity demand, and associated carbon footprint for electricity production due to snowmaking.

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Data availability

The MTMSI dataset is available on the Copernicus Climate Data Store at https://doi.org/10.24381/cds.2fe6a082 under the Copernicus licence. We used the OpenSkiMap (https://openskimap.org) global database of ski resorts, which is based on the OpenStreetMap (https://www.openstreetmap.org) collaborative geospatial database. Description of the 15 European Environmental Agency (EEA) European mountain areas is available at https://www.eea.europa.eu/data-and-maps/data/european-mountain-areas. The European Digital Elevation Model (EU-DEM) (v.1.1, spatial resolution of 25 m) is available at http://land.copernicus.eu/pan-european/satellite-derived-products/eu-dem/eu-dem-v1.1/view. The global warming level dataset is available from the Santander Meteorology Group (UC-CSIC) (https://github.com/SantanderMetGroup/ATLAS/blob/main/warming-levels/CMIP5_Atlas_WarmingLevels.csv). The carbon footprints were calculated using hourly amounts of electricity generation by type of production (for example hard coal, run-of-river hydropower and so on) and country as recorded on the ENTSO-E Transparency Platform for 2019 (https://transparency.entsoe.eu/dashboard/show).

Code availability

The Crocus snow cover model used for this work was developed inside the open-source SURFEX project (http://www.umr-cnrm.fr/surfex/, accessed 7 June 2020). For reproducibility of results, the version used in this work is tagged as ‘C3S-European-Tourism-MTMSI-2019’ on the SURFEX git repository. The computer code necessary to reproduce the main results is provided on Zenodo at https://doi.org/10.5281/zenodo.8047168.

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Acknowledgements

We are grateful to all organizations and individuals involved in the production and distribution of the datasets used for this work. Work toward this publication has benefitted from funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 730203. We thank N. Ribot (Geospatial Solutions) for support on database management and E. Maldonado and F. Bray (INRAE) for operating the DBMS tools. Figures 2 and 3 and Extended Data Figs. 1 and 8 use images from flaticon.com.

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Author notes

  1. These authors contributed equally: Hugues François and Raphaëlle Samacoïts.

Authors and Affiliations

  1. Univ. Grenoble Alpes, INRAE, LESSEM, Grenoble, France

    Hugues François & Raphaëlle Samacoïts

  2. Météo-France, Direction de la Climatologie et des Services Climatiques, Toulouse, France

    Raphaëlle Samacoïts

  3. JOANNEUM RESEARCH Forschungsgesellschaft mbH, Institute for Climate, Energy Systems and Society, Graz, Austria

    David Neil Bird, Judith Köberl & Franz Prettenthaler

  4. Univ. Grenoble Alpes, Météo-France, CNRS, Centre National de Recherches Météorologiques, Grenoble, France

    Samuel Morin

  5. Université de Toulouse, Météo-France, CNRS, Centre National de Recherches Météorologiques, Toulouse, France

    Samuel Morin

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  1. Hugues François
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Contributions

H.F. and S.M. conceived the original idea for the paper. R.S. and H.F. carried out the calculations, with input from D.N.B. regarding carbon intensity calculations. S.M., H.F. and R.S. led the analysis. All authors contributed to the analysis, drafting, reviewing and editing of the paper.

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Correspondence to Samuel Morin.

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Nature Climate Change thanks Daniel Farinotti, Christoph Marty, Paul Peeters and Daniel Scott for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Methodological approach to compute the snow conditions on ski pistes and related externalities.

The method brings together advanced spatial modelling of the ski resorts (left), modelling of snow conditions in ski resorts taking into account grooming and snowmaking (middle) and estimates of the carbon intensity of snowmaking (right). Results, primarily computed for individual ski resorts and providing annual values, are aggregated at the scale of EEA mountain areas and countries, for past climate conditions and for various global warming levels.

Extended Data Fig. 2 Methodological approach for the spatial modelling of ski resorts.

Using the European Digital Elevation Model (EU-DEM), version 1.1, with a spatial resolution of 25 m and the ski-lift catalogue from OpenSkiMap (a), we compute, for each ski lift in each ski resort, the geographical domain accessible downhill from the top of all the ski lifts (b) and reaching the bottom of one of the ski lifts (c). Combining these two areas results in the gravitational envelope of the ski resort (d and e). Further, we apply a geospatial modelling framework to infer the location where snowmaking is applied within a given ski resort, depending on the snowmaking fractional coverage value (0%, 25%, 50% and 75%) (f). Each pixel of the ski resort gravitational domain is associated to a (NUTS-3, elevation) pair from the climate and snow cover dataset, by steps of 100 m elevation and for flat terrain conditions (g). The modelling approach is illustrated here using the Obergugl ski resort in Austria (European Alps), in the NUTS-3 region ‘Tiroler Oberland’ (h).

Extended Data Fig. 3 Methodological approach employed to determine whether snow conditions are snow scarce or not, in a given ski resort, when snowmaking is taken into account.

The reference snow reliability index Q\({}_{20}^{ref}\) is computed based on groomed natural snow simulations for the reference period 1961-1990, bounding the 20% worst values of the snow reliability index. A snow reliability index value for a given year is considered snow scarce if it falls below a threshold value Qthreshold, equal to the maximum between Q\({}_{20}^{ref}\) and the snowmaking fractional coverage. In case A (top), Q\({}_{20}^{ref}\) (here 23%) is lower than the snowmaking fractional coverage (here 50%). If for a given year the snow reliability index value is larger than both, it is not considered snow scarce. If it is lower than both, it is considered snow scarce. In the intermediate case (here 33%), it is considered snow scarce, because it is lower than the snowmaking fractional coverage. In case B (bottom), Q\({}_{20}^{ref}\) (here 67%) is higher than the snowmaking fractional coverage (here 50%). There are then only two possible situations, simply comparing the snow reliability index with the Q\({}_{20}^{ref}\).

Extended Data Fig. 4 Illustration of the results of the simulations for a given ski resort, here the Métabief ski resort (French Jura mountain region).

All panels display past simulations (based on the UERRA reanalysis), and climate projections (historical from 1950 to 2005 and future projections for RCP2.6, RCP4.5 and RCP8.5 for 2006 to 2100). Panels are organized horizontally as a function of snowmaking fractional coverage (0% on the top row, panels a and b; 25% for panels c, d and e; 50% for panels f, g, and h; 75% for panels i, j and k). Panels on the left hand side (a, c, f and i) display the time series of the snow reliability index values. Individual annual values are displayed for UERRA reanalysis. For climate projections, the panels show the median (solid line) and 20/80 percentile values (colored area) for all simulations of the same RCP, on 15-years sliding windows. The black solid line displays the median value based on UERRA reanalysis. Panels in the middle column (b, d, g and j) display the frequency of years below the snow reliability threshold Qthreshold (computed based on the values obtained using the UERRA reanalysis from 1961 to 1990). Here, the solid line refers to the mean of the frequency values obtained for each GCM/RCM pair for each 15-years periods, and the colored area displays the standard deviation around the mean. Panels on the right side (e, h and k) display the computed water demand for snowmaking. The solid line refers to the median and the colored area spans the 20/80 percentile range, on 15-years sliding windows.

Extended Data Fig. 5 Illustration of the results of the simulations for a given ski resort in the Austrian Alps.

Same as Extended Data Figure 4 but for a ski resort located in the Austrian Alps.

Extended Data Fig. 6 Illustration of the results of the simulations for a given ski resort in Norway.

Same as Extended Data Figure 4 but for a ski resort located in Norway.

Extended Data Fig. 7 Illustration of the results of the simulations for a given ski resort in the British Isles.

Same as Extended Data Figure 4 but for a ski resort located in the British Isles.

Extended Data Fig. 8 Numerical values of the snow supply risk levels displayed on the burning ember figure, including the proportion of individual ski resorts belonging to the ‘very high risk’ category for each mountain area, for various global warming levels and snowmaking fractional coverage values and changes in water demand for snowmaking.

Numerical values of the snow supply risk levels from Fig. 3, including the proportion of individual ski resorts belonging to the ‘very high risk’ category for each mountain area, for various global warming levels and snowmaking fractional coverage values (a) and changes in water demand for snowmaking (b), used in the burning embers diagram displayed in Fig. 3. The uncertainty shown in panel (b) is calculated based on the standard deviation around the mean of the water demand calculated for each global warming level with respect to the reference mean value (mean water demand for a snowmaking fractional coverage of 25% during the reference time period 1961-1990).

Extended Data Fig. 9 Water demand, corresponding electricity demand and associated carbon footprint for electricity production, due to snowmaking only, assuming a uniform snowmaking fractional coverage of 25% and 75%, for the main 12 countries.

Same as Fig. 4 but for 25% snowmaking fractional coverage (a and b) and 75% snowmaking fractional coverage (c and d).

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François, H., Samacoïts, R., Bird, D.N. et al. Climate change exacerbates snow-water-energy challenges for European ski tourism. Nat. Clim. Chang. 13, 935–942 (2023). https://doi.org/10.1038/s41558-023-01759-5

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