Abstract
Abstract. Mountain permafrost is sensitive to climate change and is expected to gradually degrade in response to the ongoing atmospheric warming trend. Long-term monitoring of the permafrost thermal state is a key task, but problematic where temperatures are close to 0 ∘C because the energy exchange is then dominantly related to latent heat effects associated with phase change (ice–water), rather than ground warming or cooling. Consequently, it is difficult to detect significant spatio-temporal variations in ground properties (e.g. ice–water ratio) that occur during the freezing–thawing process with point scale temperature monitoring alone. Hence, electrical methods have become popular in permafrost investigations as the resistivities of ice and water differ by several orders of magnitude, theoretically allowing a clear distinction between frozen and unfrozen ground. In this study we present an assessment of mountain permafrost evolution using long-term electrical resistivity tomography monitoring (ERTM) from a network of permanent sites in the central Alps. The time series consist of more than 1000 datasets from six sites, where resistivities have been measured on a regular basis for up to 20 years. We identify systematic sources of error and apply automatic filtering procedures during data processing. In order to constrain the interpretation of the results, we analyse inversion results and long-term resistivity changes in comparison with existing borehole temperature time series. Our results show that the resistivity dataset provides valuable insights at the melting point, where temperature changes stagnate due to latent heat effects. The longest time series (19 years) demonstrates a prominent permafrost degradation trend, but degradation is also detectable in shorter time series (about a decade) at most sites. In spite of the wide range of morphological, climatological, and geological differences between the sites, the observed inter-annual resistivity changes and long-term tendencies are similar for all sites of the network.
Highlights
Mountain permafrost is sensitive to climate change and has been affected by a significant warming trend in the European Alps for the last 2 decades (Noetzli et al, 2016)
Mean resistivity values obtained for all sites span over 3 orders of magnitude as shown in Fig. 3, and highlight the considerable variability of alpine permafrost conditions ranging from fine-grained debris and weathered bedrock at SCH (∼ 1 k m) to massive ice in a coarse-blocky rock glacier MCO (∼ 1000 k m)
This study presents the analysis of a network of six electrical resistivity tomography monitoring (ERTM) sites in the central Alps, where at least one measurement per year at the end of summer was conducted for more than 1 decade
Summary
Mountain permafrost is sensitive to climate change and has been affected by a significant warming trend in the European Alps for the last 2 decades (Noetzli et al, 2016). The warming trend is not uniformly distributed (both spatially and temporally), as permafrost occurs in a large variety of complex landform settings. To understand the site-specific and regional evolution of mountain permafrost, detailed information about the spatio-temporal permafrost distribution and its monitoring is necessary. A better understanding of freeze and thaw processes is critical as thawing permafrost will affect mountain slope stability and trigger rock falls (Ravanel et al, 2017). The thermal definition of permafrost (ground remaining at or below 0 ◦C during at least 2 consecutive years) led the scientific research into ground temperature monitoring, at both regional – such as the well-established borehole networks in Switzerland (PERMOS, 2019) and Norway (NORPERM, Juliussen et al, 2010; Isaksen et al, 2011) – and global scales (within the Global Terrestrial Network of Permafrost, GTNP; Biskaborn et al, 2015). The permafrost definition does not necessarily imply the presence of ground ice or even any freezing and thawing processes: saline permafrost or dry permafrost do not necessarily contain any ice and the 0 ◦C limit does not take a freezing point depression into account (Harris et al, 1988)
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