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7 results for “mountain permafrost”
Ground temperature time series in European mountain permafrost
<p>RELATED PUBLICATION</p> <p>This dataset is related to the following publication:</p> <p><strong>Noetzli J., Isaksen, K., Barnett, J., Chrisitiansen, H.H., Delaloye, R., Etzelmueller, B., Farinotti, D., Gallemann, T., Guglielmin, M., Hauck, C., Hilbich, C., Hoelzle, M., Lambiel, C., Magnin, F., Oliva, M., Paro, L, Pogliotti, P., Riedl, C., Schoeneich, P., M., Valt, M., Vieli A., Philliips, M. (2024). Enhanced permafrost warming in Euro­pean mountains in the 21st century. Nature Communications, 15, 10508, <a href="https://doi.org/10.1038/s41467-024-54831-9">https://doi.org/10.1038/s41467-024-54831-9</a>.</strong></p> <p><strong>==> </strong></p> <p><strong>For information on the measurements, selection criteria, processing information and data providers please refer to the methods, data availability and acknowledgements sections of the related publication ! </strong></p> <p> </p> <p>---------------------------------------------------------------------------------------------------------------------------</p> <p>CONTENT</p> <p>The dataset includes monthly and annual time series of ground temperatures measured in 64 boreholes in European mountain permafrost areas and corresponding metadata.</p> <p>Temporal coverage: at least 10 years until 2022</p> <p>Spatial coverage: European mountain regions (Svalbard, Scandinavia, Iceland, European Alps, Sierra Nevada)</p> <p>Depth of measurements: at least 10 m; for all boreholes data of the sensors closest to 5, 10 and 20 m depth are included</p> <p>Monthly means are calculated from daily values and annual values are derived from monthly mean values.</p> <p> </p> <p>---------------------------------------------------------------------------------------------------------------------------</p> <p>DATA COMPILATION</p> <p>The data were compiled to derive 10-year and 20-year warming rates in European mountain permafrost in the study by Noetzli et al. (in review, see above). Data were collected from national permafrost observation networks as well as from individual institutions (e.g, universities, environmental agencies).</p> <p>The aquisition of long time series over decades requires long-term committment from the responsible institutions to maintain instruments and to collect and curate the data. Details on the data source for each time series can be found in the metadata file as well as in the related publication. The main data sources by country are given in the list below.</p> <table> <tbody> <tr> <td><strong>Country</strong></td> <td><strong>Data source (institution or national network)</strong></td> </tr> <tr> <td>Austria</td> <td>GeoSphere Austria</td> </tr> <tr> <td>France</td> <td>Réseau français d'observation du permafrost (PermaFrance, <a href="https://wslch365-my.sharepoint.com/personal/jeannette_noetzli_slf_ch/Documents/PermafrostEurope/permafrance.osug.fr">permafrance.osug.fr</a>)</td> </tr> <tr> <td>Germany</td> <td>Bavarian Environment Agency</td> </tr> <tr> <td>Iceland</td> <td>University of Oslo</td> </tr> <tr> <td>Italy</td> <td>ARPA Piemonte, ARPA Valle d'Aosta, ARPA Veneto, University of Insubria</td> </tr> <tr> <td>Norway</td> <td>Norwegian Permafrost Monitoring Network (<a href="https://cryo.met.no/">cryo.met.no</a> and <a href="http://sios-svalbard.org/">sios-svalbard.org</a>)</td> </tr> <tr> <td>Spain</td> <td>Universitat de Barcelona</td> </tr> <tr> <td>Svalbard</td> <td>Norwegian Permafrost Monitoring Network (<a href="https://cryo.met.no/">cryo.met.no</a> and <a href="http://sios-svalbard.org/">sios-svalbard.org</a>)</td> </tr> <tr> <td>Sweden</td> <td>University of Stockholm</td> </tr> <tr> <td>Switzerland</td> <td>Swiss Permafrost Monitoring Network PERMOS (<a href="http://www.permos.ch">http://www.permos.ch</a>)</td> </tr> </tbody> </table> <p> </p> <p>---------------------------------------------------------------------------------------------------------------------------</p> <p>FILES AND FORMAT</p> <p>This data set includes three csv-files: <br>1) metadata with information on the measurement location and data provider<br>2) monthly ground temperature time series and <br>3) annual ground temperature time series. </p> <p>The variables in the three files are described below. Data files are in long data format.</p> <p><strong>File 1 – borehole_overview.csv<br></strong>Key information on the boreholes, responsible institutions and contact persons.</p> <table> <tbody> <tr> <td><strong>Variable</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>Name</td> <td>Name of the borehole (as used in the related study)</td> </tr> <tr> <td>Country</td> <td>Alpha-2 code</td> </tr> <tr> <td>Region</td> <td>Larger region</td> </tr> <tr> <td>First_year</td> <td>First year of data</td> </tr> <tr> <td>Elevation [m asl.]</td> <td>Elevation of the borehole</td> </tr> <tr> <td>Lat [° N]</td> <td>Latitude</td> </tr> <tr> <td>Lon [° E]</td> <td>Longitude</td> </tr> <tr> <td>Depth [m]</td> <td>Total depth of the borehole</td> </tr> <tr> <td>DZAA [m]</td> <td>Depth of the Zero Annual Amplitude (uppermost sensor with annual amplitude ≤0.1)</td> </tr> <tr> <td>Phase lag</td> <td>Phase lag at 10 m depth compared to surface in months</td> </tr> <tr> <td>Morphology</td> <td>Main morphology of the site</td> </tr> <tr> <td>Surface_cover</td> <td>Main surface cover at the site</td> </tr> <tr> <td>Lithology</td> <td>Main lithology of the site</td> </tr> <tr> <td>Ice_content</td> <td>Basic classification by ground ice content at the site (no ice, ice-poor, ice-bearing, ice-rich), see publication for details</td> </tr> <tr> <td>Institution</td> <td>Responsible institution (in the year 2024)</td> </tr> <tr> <td>Contact_person</td> <td>Contact person (in the year 2024)</td> </tr> <tr> <td>Special_remarks</td> <td>Remarks on location, e.g. horizontal borehole</td> </tr> </tbody> </table> <p> </p> <p><strong>File 2 – permafrost_temperatures_european_mountains_monthly_2022.csv<br></strong>Time series of monthly mean ground temperatures at ca. 5, 10 and 20 m depth for 64 boreholes in European mountain permafrost until 2022.</p> <table> <tbody> <tr> <td><strong>Variable</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>bh</td> <td>Name of the borehole</td> </tr> <tr> <td>time [YYYY-MM-DD]</td> <td>Date</td> </tr> <tr> <td>depth [m]</td> <td>Depth of measurement</td> </tr> <tr> <td>temp [°C]</td> <td>Monthly mean ground temperature (aggregated from daily values)</td> </tr> <tr> <td>t_min [°C]</td> <td>Minimum daily ground temperature of the year</td> </tr> <tr> <td>t_max [°C]</td> <td>Maximum daily ground temperature of the year</td> </tr> <tr> <td>count</td> <td>Number of daily values available to calculate monthly mean values</td> </tr> <tr> <td>dclass [5, 10 or 20 m]</td> <td>Depth class defined for analyses in related study</td> </tr> </tbody> </table> <p> </p> <p><strong>File 3 – permafrost_temperatures_european_mountains_annual_2022.csv<br></strong>Time series of annual mean ground temperatures at ca. 5, 10 and 20 m depth for 64 boreholes in European mountain permafrost until 2022.</p> <table> <tbody> <tr> <td><strong>Variable</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>bh</td> <td>Name of the borehole</td> </tr> <tr> <td>time [YYYY]</td> <td>Year</td> </tr> <tr> <td>depth [m]</td> <td>Depth of measurement</td> </tr> <tr> <td>temp [°C]</td> <td>Annual mean ground temperature (aggregated from monthly values)</td> </tr> <tr> <td>t_min [°C]</td> <td>Minimum monthly ground temperature of the year</td> </tr> <tr> <td>t_max [°C]</td> <td>Maximum monthlyground temperature of the year</td> </tr> <tr> <td>count</td> <td>Number of monthly values available to calculate annual mean values</td> </tr> <tr> <td>dclass [5, 10 or 20 m]</td> <td>Depth class defined for analyses in related study</td> </tr> </tbody> </table> <p> </p> <p>---------------------------------------------------------------------------------------------------------------------------</p> <p>CONTACT</p> <p>For question related to this dataset please contact the corresponding author: jeannette.noetzli@slf.ch. <br>For questions related to a specific time series, see metadata for contact information.</p>
Identifying mountain permafrost degradation by repeating historical ERT-measurements - supplement
<p>Ongoing global warming affects the degradation of mountainous permafrost. Permafrost thawing impacts landform evolution, reduces fresh water resources, enhances the potential of natural hazards, and thus has significant socio-economic impact. Electrical resistivity tomography (ERT) has been widely used to map the ice-containing permafrost by its resistivity contrast compared to the surrounding non-frozen medium. We analyse the temporal changes in the resistivity distribution by comparing historical with recently measured ERT profiles. Three periglacial landforms (two rock glaciers and one talus slope) are surveyed in the Swiss and Austrian Alps by repeating historical field campaigns after periods of 10, 12, and 16 years, respectively. The resistivity values have been significantly reduced concerning ice-poor permafrost at all study sites. Interestingly, resistivity values related to ice-rich permafrost in the studied active rock glacier partly increased during the studied time period. To explain this apparent contradictory (in view of observed increase) observation, geomorphological circumstances, such as the relief and creeping behaviour of the active rock glacier, are discussed. Additional remote sensing data indicates an increased velocity in and around the active part with increased resistivity. The present study highlights alpine permafrost degradation resulting from ever-accelerating global warming.</p>
Data from: Driving factors on greenhouse gas emissions in permafrost region of Daxing'an Mountains, Northeast China
<p>Permafrost regions are an important source of greenhouse gases. However, the effects of different permafrost wetland types on greenhouse gas emissions and the driving factors are still unclear in the permafrost region. Here, we selected three typical permafrost wetlands from the Daxing'an Mountains to investigate the effects of permafrost wetland types on greenhouse gas emissions. <span class="fontstyle71"><span>The cumulative </span></span>N<sub>2</sub>O, CO<sub>2</sub>, and CH<sub>4</sub> emissions were 84–122, 657,942–1,446,121, and 173–16,924 kg km<sup>−2</sup>, respectively. The linear mixed effects model indicated that N<sub>2</sub>O emissions were significantly affected by the NO<sub>3</sub><sup>−</sup>-N content, whereas CO<sub>2</sub> emissions were mainly driven by soil temperature, water table level, and NO<sub>3</sub><sup>−</sup>-N content. CH<sub>4</sub> emissions were affected by soil temperatue and water table level. Permafrost wetland types significantly affected the average and cumulative N<sub>2</sub>O, CO<sub>2</sub>, and CH<sub>4</sub> emissions. The cumulative N<sub>2</sub>O emissions were highest in the <i>Larix gmelinii - Carex</i> <i>appendiculata </i>(<i>LC</i>) wetland and lowest in the <em>Betula fruticosa Pall. </em>(<em>B</em>) wetland<span class="fontstyle71"><span>, driven by </span></span>NO<sub>3</sub><sup>−</sup>-N content. The cumulative CO<sub>2</sub> emissions were highest in the (<em>B</em>) wetland and lowest in the <em>L. gmelinii</em> - Ledum palustre var. dilatatum (<em>LL</em>) wetland. The cumulative CH<sub>4</sub> emissions from <span class="fontstyle71"><span><i>B</i></span></span><span class="fontstyle71"><span> wetland were significantly higher than those from </span></span><i>LL</i> and <i>LC</i> wetlands. The differences in cumulative CO<sub>2</sub> and CH<sub>4 </sub>emissions were driven by the water table level. Our findings indicate that NO<sub>3</sub><sup>−</sup>-N content affect the spatial-temporal variation of N<sub>2</sub>O emissions, whereas water table level influence the spatial-temporal variation of CO<sub>2</sub> and CH<sub>4</sub> emissions in the permafrost region of the Daxing'an Mountains.</p>
Data from: Driving factors on greenhouse gas emissions in permafrost region of Daxing’an Mountains, Northeast China
Open the record for dataset details and reuse information.
Map of Natural (Landscape) and Permafrost Zones and the Net of Soil Temperature Meteorological Stations in Russia and Middle Asian Mountains, Version 1
This data set is a vector coverage of the Map of Natural Landscape and Permafrost Zones and the Net of Soil Temperature Meteorological Stations in Russia and Middle Asian Mountains (scale 1:4000000), published by the Russian Academy of Sciences Earth Cryosphere Institute and Institute for Phytochemical and Biological Problems in Earth Science The original map was compiled by E. Melnikov and D.A. Gilichinsky. The digital map was created in Geograph, which is a Geographic Information System developed by the GIS Research Center, Institute of Geography, Russian Academy of Sciences. Data are available through FTP.
Borehole temperatures from mountain permafrost monitoring, Mongolia, Version 1
Location and description of some geocryological boreholes in Mongolia. Data include latitude, longitude, location, depth of permafrost top and bottom, and mean annual soil temperature. These data are presented on the CAPS Version 1.0 CD-ROM, June 1998.
High Mountain Asia 1 km MODIS-AIRS Gap-Filled Ground Temperatures and Permafrost Probability Maps, 2003-2016 V001
This data set consists of 1 km resolution monthly land surface temperatures (MLSTs); mean annual ground temperatures (MAGTs); and estimates of permafrost extent (PE) in the High Mountain Asia region from 1 Jan 2003 – 31 Dec 2016. The data were generated by gap-filling daily MODIS Terra/Aqua Land surface temperatures (LSTs) with downscaled Atmospheric Infra-Red Sounder (AIRS) skin surface temperatures.
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