Abstract
Abstract. Mountain permafrost is invisible, and mapping it is still a challenge. Available permafrost distribution maps often overestimate the permafrost extent and include large permafrost-free areas in their permafrost zonation. In addition, the representation of the lower belt of permafrost consisting of ice-rich features such as rock glaciers or ice-rich talus slopes can be challenging. These problems are caused by considerable differences in genesis and thermal characteristics between ice-poor permafrost, occurring for example in rock walls, and ice-rich permafrost. While ice-poor permafrost shows a strong correlation of ground temperature with elevation and potential incoming solar radiation, ice-rich ground does not show such a correlation. Instead, the distribution of ice-rich ground is controlled by gravitational processes such as the relocation of ground ice by permafrost creep or by ground ice genesis from avalanche deposits or glacierets covered with talus. We therefore developed a mapping method which distinguishes between ice-poor and ice-rich permafrost and tested it for the entire Swiss Alps. For ice-poor ground we found a linear regression formula based on elevation and potential incoming solar radiation which predicts borehole ground temperatures at multiple depths with an accuracy higher than 0.6 ∘C. The zone of ice-rich permafrost was defined by modelling the deposition zones of alpine mass wasting processes. This dual approach allows the cartographic representation of permafrost-free belts, which are bounded above and below by permafrost. This enables a high quality of permafrost modelling, as is shown by the validation of our map. The dominating influence of the two rather simple connected factors, elevation (as a proxy for mean annual air temperature) and solar radiation, on the distribution of ice-poor permafrost is significant for permafrost modelling in different climate conditions and regions. Indicating temperatures of ice-poor permafrost and distinguishing between ice-poor and ice-rich permafrost on a national permafrost map provides new information for users.
Highlights
Maps of potential permafrost distribution are useful products applied in different fields of practice and research because permafrost is an invisible subsurface phenomenon
For ice-poor ground we found a linear regression formula based on elevation and potential incoming solar radiation which predicts borehole ground temperatures at multiple depths with an accuracy higher than 0.6 ◦C
The ground temperatures were calculated based on a multiple linear regression analysis using the explanatory variables potential incoming solar radiation (PISR) and elevation
Summary
Maps of potential permafrost distribution are useful products applied in different fields of practice and research because permafrost is an invisible subsurface phenomenon. Such maps are used to plan construction work in alpine terrain, to evaluate local slope instability or to estimate largescale permafrost occurrence for scientific purposes. Mapping permafrost in the highly variable alpine landscape is challenging, on a global scale for which ground temperature data or climate and terrain datasets are rare (Fiddes et al, 2015; Gruber, 2012). Developing a method appropriate to model mountain permafrost requires test areas with a dense set of reference and validation data, as well as highly resolved digital terrain models. Many authors have used the Swiss dataset to calibrate or validate their permafrost distribution model
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