Climatic Change

, Volume 111, Issue 3–4, pp 705–721 | Cite as

Long-term changes in annual maximum snow depth and snowfall in Switzerland based on extreme value statistics

  • Christoph MartyEmail author
  • Juliette Blanchet


Mountain snow cover is an important source of water and essential for winter tourism in Alpine countries. However, large amounts of snow can lead to destructive avalanches, floods, traffic interruptions or even the collapse of buildings. We use annual maximum snow depth and snowfall data from 25 stations (between 200 and 2,500 m) collected during the last 80 winters (1930/31 to 2009/2010) to highlight temporal trends of annual maximum snow depth and 3-day snowfall sum. The generalized extreme value (GEV) distribution with time as a covariate is used to assess such trends. It allows us in particular to infer how return levels and return periods have been modified during the last 80 years. All the stations, even the highest one, show a decrease in extreme snow depth, which is mainly significant at low altitudes (below 800 m). A negative trend is also observed for extreme snowfalls at low and high altitudes but the pattern at mid-altitudes (between 800 and 1,500 m) is less clear. The decreasing trend of extreme snow depth and snowfall at low altitudes seems to be mainly caused by a reduction in the magnitude of the extremes rather than the scale (variability) of the extremes. This may be caused by the observed decrease in the snow/rain ratio due to increasing air temperatures. In contrast, the decreasing trend in extreme snow depth above 1,500 m is caused by a reduction in the scale (variability) of the extremes and not by a reduction in the magnitude of the extremes. However, the decreasing trends are significant for only about half of the stations and can only be seen as an indication that climate change may be already impacting extreme snow depth and extreme snowfall.


Return Period Snow Depth Generalize Extreme Value Return Level Generalize Extreme Value Distribution 
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.



We thank MeteoSwiss for providing access to the daily snow data. Seraina Grob is acknowledged for her devotion in digitizing old snow data. We are indebted to Jean-Pierre Dulex, who pursued the long-term snow measurements in Leysin without support from either governmental organization. Special thanks go to all the observers: the measurement of extreme snow amounts often has to occur during adverse weather conditions.


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Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  1. 1.WSL Institute for Snow and Avalanche Research SLF DavosDavosSwitzerland
  2. 2.Institute of Mathematics, Ecole Polytechnique Fédérale de LausanneLausanneSwitzerland

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