Pôle Alpin Risques Naturels (PARN) Alpes–Climat–Risques Avec le soutien de la Région Rhône-Alpes (2007-2014)

Fiche bibliographique


Réf. Boeckli & al. 2012

Référence bibliographique
BOECKLI L., BRENNING A, GRUBER S., and NOETZLI J. Permafrost distribution in the European Alps: calculation and evaluation of an index map and summary statistics. The Cryosphere, 6, 807–820

Abstract: The objective of this study is the production of an Alpine Permafrost Index Map (APIM) covering the entire European Alps. A unified statistical model that is based on Alpine-wide permafrost observations is used for debris and bedrock surfaces across the entire Alps. The explanatory variables of the model are mean annual air temperatures, potential incoming solar radiation and precipitation. Offset terms were applied to make model predictions for topographic and geomorphic conditions that differ from the terrain features used for model fitting. These offsets are based on literature review and involve some degree of subjective choice during model building. The assessment of the APIM is challenging because limited independent test data are available for comparison and these observations represent point information in a spatially highly variable topography. The APIM provides an index that describes the spatial distribution of permafrost and comes together with an interpretation key that helps to assess map uncertainties and to relate map contents to their actual expression in the terrain. The map can be used as a first resource to estimate permafrost conditions at any given location in the European Alps in a variety of contexts such as research and spatial planning. Results show that Switzerland likely is the country with the largest permafrost area in the Alps, followed by Italy, Austria, France and Germany. Slovenia and Liechtenstein may have marginal permafrost areas. In all countries the permafrost area is expected to be larger than the glacier-covered area.


Organismes / Contact
  • Department of Geography, University of Zurich, Switzerland
  • Department of Geography and Environmental Management, University of Waterloo, Ontario, Canada
  • Corresponding author: lorenz.boeckli@geo.uzh.ch

(1) - Paramètre(s) atmosphérique(s) modifié(s)
(2) - Elément(s) du milieu impacté(s)
(3) - Type(s) d'aléa impacté(s)
(3) - Sous-type(s) d'aléa
mean annual air temperatures, potential incoming solar radiation and precipitation spatial distribution of permafrost    

Pays / Zone
Massif / Secteur
Site(s) d'étude
Période(s) d'observation
Europe European Alps        

(1) - Modifications des paramètres atmosphériques

Informations complémentaires (données utilisées, méthode, scénarios, etc.)

(2) - Effets du changement climatique sur le milieu naturel
 The topographic and climatic variables that are required to apply APMOD are calculated according to Boeckli et al. (2012). In the following, data and methods are combined to derive an Alpine-wide surface cover that is considered in APIM (Sect. 3.1), and to prepare evaluation data for APIM (Sect. 3.2). Section 3.3 describes the method to derive Alpine-wide summary statistics

Sensibilité du milieu à des paramètres climatiques
Informations complémentaires (données utilisées, méthode, scénarios, etc.)

(3) - Effets du changement climatique sur l'aléa

Calculated permafrost index areas provide an indication of possible permafrost extents in different subregions of the Alps. The relative area of permafrost occurrence in relation to the total area of the Alps is estimated to be 3% when considering an index  0.5.

Results show that Switzerland likely is the country with the largest permafrost area in the Alps, followed by Italy, Austria, France and Germany. Slovenia and Liechtenstein may have marginal permafrost areas. In all countries the permafrost area is expected to be larger than the glacier-covered area.


Paramètre de l'aléa
Sensibilité du paramètres de l'aléa à des paramètres climatiques
Informations complémentaires (données utilisées, méthode, scénarios, etc.)

We suggest that the index represents an indicator of the probability for permafrost occurrence, the spatial percentage of permafrost per cell and/or the thickness of the permafrost body for current climatic conditions. The index can also be interpreted as a proxy of the mean annual ground temperature

However, permafrost extent, thickness or temperature cannot be allocated directly with the values of the index, because various local and regional processes are neglected or only approximated by the model.

 The statistical model that is applied in this study, APMOD, is described in detail by Boeckli et al. (2012). APMOD is based on an Alpine-wide evidence collection (Cremonese et al., 2011) and uses mean annual air temperatures (MAAT), potential incoming solar radiation (PISR) and the mean annual sum of precipitation (PRECIP) as explanatory variables. APMOD involves two sub-models for two different land cover classes: The debris model has been calibrated using rock glacier inventories and predicts the probability of rock glaciers being intact as opposed to relict. The rock model is based on mean annual rock surface temperatures (MARST) and predicts the probability of finding MARST  0 °C in steep bedrock. Both models are combined based on fuzzy membership (linear function depending on slope angle. Sect. 3.1) to the land cover types rock and debris, and allow the inclusion of temperature offset terms. These offset terms are required to generalize APMOD to other surface characteristics than those used for model calibration. When applied to digital elevation models (DEMs) of differing resolution, scaling functions improve the coherence and comparability of the results. The probabilities of permafrost occurrence derived from APMOD are translated into permafrost index values

(4) - Remarques générales

(5) - Syntèses et préconisations

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