Theoretical and Applied Climatology

, Volume 127, Issue 3–4, pp 875–889 | Cite as

Impact of climate change in Switzerland on socioeconomic snow indices

  • Edgar Schmucki
  • Christoph Marty
  • Charles Fierz
  • Rolf Weingartner
  • Michael Lehning
Original Paper

Abstract

Snow is a key element for many socioeconomic activities in mountainous regions. Due to the sensitivity of the snow cover to variations of temperature and precipitation, major changes caused by climate change are expected to happen. We analyze the evolution of some key snow indices under future climatic conditions. Ten downscaled and postprocessed climate scenarios from the ENSEMBLES database have been used to feed the physics-based snow model SNOWPACK. The projected snow cover has been calculated for 11 stations representing the diverse climates found in Switzerland. For the first time, such a setup is used to reveal changes in frequently applied snow indices and their implications on various socioeconomic sectors. Toward the end of the twenty-first century, a continuous snow cover is likely only guaranteed at high elevations above 2000 m a.s.l., whereas at mid elevations (1000–1700 m a.s.l.), roughly 50 % of all winters might be characterized by an ephemeral snow cover. Low elevations (below 500 m a.s.l.) are projected to experience only 2 days with snowfall per year and show the strongest relative reductions in mean winter snow depth of around 90 %. The range of the mean relative reductions of the snow indices is dominated by uncertainties from different GCM-RCM projections and amounts to approximately 30 %. Despite these uncertainties, all snow indices show a clear decrease in all scenario periods and the relative reductions increase toward lower elevations. These strong reductions can serve as a basis for policy makers in the fields of tourism, ecology, and hydropower.

Keywords

Snow Cover Snow Depth Snow Water Equivalent Future Climatic Condition Scenario Period 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

This study was funded by the Swiss National Science Foundation (Grant No. 200021_132200). We strongly acknowledge MeteoSwiss for allocating the meteorological data and the Center for Climate Systems Modeling (C2SM) for providing the CH2011 data.

Supplementary material

704_2015_1676_MOESM1_ESM.pdf (98 kb)
ESM 1 (PDF 98.1 kb)

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Copyright information

© Springer-Verlag Wien 2015

Authors and Affiliations

  • Edgar Schmucki
    • 1
    • 2
    • 3
  • Christoph Marty
    • 1
  • Charles Fierz
    • 1
  • Rolf Weingartner
    • 2
    • 3
  • Michael Lehning
    • 1
    • 4
  1. 1.WSL Institute for Snow and Avalanche Research SLFDavos DorfSwitzerland
  2. 2.Institute of GeographyUniversity of BernBernSwitzerland
  3. 3.Oeschger Centre for Climate Change ResearchUniversity of BernBernSwitzerland
  4. 4.CRYOS, School of Architecture, Civil and Environmental EngineeringEPFLLausanneSwitzerland

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