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A Statistical Permafrost Distribution Model for the European Alps : Volume 5, Issue 3 (09/05/2011)

By Boeckli, L.

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Book Id: WPLBN0003992055
Format Type: PDF Article :
File Size: Pages 41
Reproduction Date: 2015

Title: A Statistical Permafrost Distribution Model for the European Alps : Volume 5, Issue 3 (09/05/2011)  
Author: Boeckli, L.
Volume: Vol. 5, Issue 3
Language: English
Subject: Science, Cryosphere, Discussions
Collections: Periodicals: Journal and Magazine Collection, Copernicus GmbH
Publication Date:
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications


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Gruber, S., Noetzli, J., Brenning, A., & Boeckli, L. (2011). A Statistical Permafrost Distribution Model for the European Alps : Volume 5, Issue 3 (09/05/2011). Retrieved from

Description: Department of Geography, University of Zurich, Switzerland. Permafrost distribution modeling in densely populated mountain regions is an important task to support the construction of infrastructure and for the assessment of climate change effects on permafrost and related natural systems. In order to analyze permafrost distribution and evolution on an Alpine-wide scale, one consistent model for the entire domain is needed.

We present a statistical permafrost model for the entire Alps based on rock glacier inventories and rock surface temperatures. Starting from an integrated model framework, two different sub-models were developed, one for debris covered areas (debris model) and one for steep rock faces (rock model). For the debris model a generalized linear mixed-effect model (GLMM) was used to predict the probability of a rock glacier being intact as opposed to relict. The model is based on the explanatory variables mean annual air temperature (MAAT), potential incoming solar radiation (PISR) and the mean annual sum of precipitation (PRECIP), and achieves an excellent discrimination (area under the receiver-operating characteristic, AUROC = 0.91). Surprisingly, the probability of a rock glacier being intact is positively associated with increasing PRECIP for given MAAT and PISR conditions. The rock model was calibrated with mean annual rock surface temperatures (MARST) and is based on MAAT and PISR. The linear regression achieves a root mean square error (RMSE) of 1.6 °C. The final model combines the two sub-models and accounts for the different scales used for model calibration. Further steps to transfer this model into a map-based product are outlined.

A statistical permafrost distribution model for the European Alps

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