Can we infer avalanche–climate relations using tree-ring data? Case studies in the French Alps
- 342 Downloads
Dendrogeomorphology is a powerful tool to determine past avalanche activity, but whether or not the obtained annually resolved chronologies are sufficiently detailed to infer avalanche–climate relationships (in terms of temporal resolution) remains an open question. In this work, avalanche activity is reconstructed in five paths of the French Alps and crossed with a set of snow and weather variables covering the period 1959–2009 on a monthly and annual (winter) basis. The variables which best explain avalanche activity are highlighted with an original variable selection procedure implemented within a logistic regression framework. The same approach is used for historical chronologies available for the same paths, as well as for the composite tree-ring/historical chronologies. Results suggest that dendrogeomorphic time series allow capturing the relations between snow or climate and avalanche occurrences to a certain extent. Weak links exist with annually resolved snow and weather variables and the different avalanche chronologies. On the contrary, clear statistical relations exist between these and monthly resolved snow and weather variables. In detail, tree rings seem to preferentially record avalanches triggered during cold winter storms with heavy precipitation. Conversely, historical avalanche data seem to contain a majority of events that were released later in the season and during episodes of strong positive temperature anomalies.
KeywordsDendrogeomorphology Snow avalanche Avalanche–climate relations Logistic regression Hazard assessment French Alps
The authors gratefully acknowledge Louis Manière and Matthieu Schläppy for their assistance in the field. They also want to thank the Office National des Forêts (ONF) for sampling permissions. This study has been realized within the framework of the MOPERA (MOdélisation Probabiliste pour l’Évaluation du Risque d’Avalanche) program funded by the French National Research Agency (ANR-09-RISK-007-01). SC data have been provided by CNRM-GAME/CEN (Météo-France-CNRS) within the framework of the ECANA (Étude Climatologique de l’Activité Avalancheuse NAturelle) Project, and feedback/comments from S. Morin and G. Giraud are acknowledged. Finally, R.S. acknowledges financial support from the Swiss National Science Foundation (Project P1SKP2_148492).
- Alestalo J (1971) Dendrochronological interpretation of geomorphic processes. Fennia 105:1–139Google Scholar
- Brun E, David P, Sudul M, Brunot G (1992) A numerical model to simulate snow-cover stratigraphy for operational avalanche forecasting. J Glaciol 38:13–22Google Scholar
- Corona C, Lopez Saez J, Stoffel M et al (2012) How much of the real avalanche activity can be captured with tree rings? An evaluation of classic dendrogeomorphic approaches and comparison with historical archives. Cold Reg Sci Technol 74–75:31–42. doi: 10.1016/j.coldregions.2012.01.003 CrossRefGoogle Scholar
- Durand Y, Giraud G, Brun E et al (1999) A computer-based system simulating snowpack structure as a tool for regional avalanche forecasting. J Glaciol 45:469–484Google Scholar
- Eckert N, Coleou C, Castebrunet H et al (2010b) Cross-comparison of meteorological and avalanche data for characterising avalanche cycles: the example of December 2008 in the eastern part of the French Alps. Cold Reg Sci Technol 64:119–136. doi: 10.1016/j.coldregions.2010.08.009 CrossRefGoogle Scholar
- Eckert N, Parent E, Kies R, Baya H (2010c) A spatio-temporal modelling framework for assessing the fluctuations of avalanche occurrence resulting from climate change: application to 60 years of data in the northern French Alps. Clim Change 101:515–553. doi: 10.1007/s10584-009-9718-8 CrossRefGoogle Scholar
- Jamard A-L, Garcia S, Bélanger L (2002) L’Enquête Permanente sur les Avalanches (EPA) - Statistique descriptive générale des événements et des sitesGoogle Scholar
- McClung DM, Tweedy J (1993) Characteristics of avalanching: Kootenay Pass. J Glaciol 39:316–322Google Scholar
- R Development Core Team (2011) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org/
- Saporta G (2011) Probabilités, analyse des données et statistique, 3rd edn. France, ParisGoogle Scholar