Publication Date:
2015-09-10
Description:
Permafrost is a widespread phenomenon in the European Alps. Many important topics such as the future evolution of permafrost related to climate change and the detection of permafrost related to potential natural hazards sites are of major concern to our society. Numerical permafrost models are the only tools which facilitate the projection of the future evolution of permafrost. Due to the complexity of the processes involved and the heterogeneity of Alpine terrain, models must be carefully calibrated and results should be compared with observations at the site (borehole) scale. However, a large number of local point data are necessary to obtain a broad overview of the thermal evolution of mountain permafrost over a larger area, such as the Swiss Alps, and the site-specific model calibration of each point would be time-consuming. To face this issue, this paper presents a semi-automated calibration method using the Generalized Likelihood Uncertainty Estimation (GLUE) as implemented in a 1-D soil model (CoupModel) and applies it to six permafrost sites in the Swiss Alps prior to long-term permafrost evolution simulations. We show that this automated calibration method is able to accurately reproduce the main thermal condition characteristics with some limitations at sites with unique conditions such as 3-D air or water circulation, which have to be calibrated manually. The calibration obtained was used for RCM-based long-term simulations under the A1B climate scenario specifically downscaled at each borehole site. The projection shows general permafrost degradation with thawing at 10 m, even partially reaching 20 m depths until the end of the century, but with different timing among the sites. The degradation is more rapid at bedrock sites whereas ice-rich sites with a blocky surface cover showed a reduced sensitivity to climate change. The snow cover duration is expected to be reduced drastically (between −20 to −37 %) impacting the ground thermal regime. However, the uncertainty range of permafrost projections is large, resulting mainly from the broad range of input climate data from the different GCM-RCM chains of the ENSEMBLES data set.
Print ISSN:
1994-0432
Electronic ISSN:
1994-0440
Topics:
Geography
,
Geosciences
Permalink