<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:creator>Mollaret, Coline</dc:creator>
  <dc:creator>Hilbich, Christin</dc:creator>
  <dc:creator>Pellet, Cécile</dc:creator>
  <dc:creator>Flores-Orozco, Adrian</dc:creator>
  <dc:creator>Delaloye, Reynald</dc:creator>
  <dc:creator>Hauck, Christian</dc:creator>
  <dc:date>2019-09-30</dc:date>
  <dc:description xmlns:ns0="xml" ns0:lang="en">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.</dc:description>
  <dc:format>application/pdf</dc:format>
  <dc:identifier>https://folia.unifr.ch/global/documents/308063</dc:identifier>
  <dc:identifier>https://folia.unifr.ch/documents/308063/files/hau_mpd.pdf</dc:identifier>
  <dc:language>eng</dc:language>
  <dc:relation>info:eu-repo/semantics/altIdentifier/doi/10.5194/tc-13-2557-2019</dc:relation>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>License undefined</dc:rights>
  <dc:source>The Cryosphere. - 2019, vol. 13, no. 10, p. 2557–2578</dc:source>
  <dc:subject>info:eu-repo/classification/udc/551</dc:subject>
  <dc:title xmlns:ns1="xml" ns1:lang="en">Mountain permafrost degradation documented through a network of permanent electrical resistivity tomography sites</dc:title>
  <dc:type>http://purl.org/coar/resource_type/c_6501</dc:type>
</oai_dc:dc>
