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270 results for “permafrost”
InSAR measured permafrost degradation of palsa peatlands in northern Sweden Datasets
<p>Datasets used in the writing of "InSAR measured permafrost degradation of palsa peatlands in northern Sweden" published in The Cryosphere. </p> <p>The processed interferometric data and deformation maps are commercially sensitive and<br>may be made available upon reasonable request (by email) from the corresponding author.</p>
Microbial iron(III) reduction during palsa collapse promotes greenhouse gas emissions before complete permafrost thaw
<p>Data associated with publication "Microbial iron(III) reduction during palsa collapse promotes greenhouse gas emissions before complete permafrost thaw". The data contained within this data set is arranged according to the main text and the supplementary information of this publication.</p> <p><strong>Background information</strong></p> <p>Field site: Stordalen mire, Abisko, Sweden (68 22ʹ N, 19 03ʹ E)</p> <p>Thaw stages: Palsa, bog and fen</p> <p>Type of samples: Gas samples, porewater samples, soil core samples</p>
Data related to "Emissions of atmospherically reactive gases nitrous acid and nitric oxide from arctic permafrost peatlands"
<p>The data file contains the individual (each replicate) values of the soil variables and gas fluxes obtained from the study. It contains data shown in the both main text and supplementary files. </p>
Dataset: Consistent release of volatile organic compounds across an actively degrading permafrost peatland
<p>Here, we conducted in situ measurements of soil and pond VOC emissions across an actively degrading permafrost peatland in subarctic Norway. We used a permafrost thaw gradient that covered bare soil and vegetated palsa plateaus, underlain by intact permafrost, and increasingly degraded permafrost landscapes: thaw slumps, thaw ponds, and vegetated thaw ponds.</p> <p>This dataset includes two excel files: 1) the first one "Finnmark_source_data" is the source data for figures in the publication <a href="https://doi.org/10.1016/j.geoderma.2023.116355">https://doi.org/10.1016/j.geoderma.2023.116355</a>. ii) the second one "Rawdata_of_emission_rate" is the emission rate of the 210 VOC species identified in this study.</p> <p>Results showed that every peatland landscape type was an important and consistent source of atmospheric VOCs, with a large variety species, such as methanol, acetone, monoterpenes, sesquiterpenes, isoprene, hydrocarbons, oxygenated VOCs, etc. VOC composition varied considerably across the measurement period and across the permafrost thaw gradient. We observed enhanced terpenoid emissions following thaw slump degradation, highlighting the potential atmospheric impact of permafrost thaw, due to the high chemical reactivities of terpenoid compounds. Overall, our study demonstrates that VOCs are being emitted in significant quantities and with largely similar composition upon permafrost thawing, inundation, and subsequent vegetation development, despite major differences in microclimate, hydrological regime, vegetation, and permafrost occurrence.</p> <p>Should you have any questions regarding the dataset, please free feel to contact Yi jiao at yi.jiao@bio.ku.dk or the PI of this project Prof. Rinnan at riikkar@bio.ku.dk</p>
Permafrost-thaw lake development in Central Yakutia: sedimentary ancient DNA and element analyses from a Holocene sediment record
<p>In Central Yakutia (Siberia) livelihoods of local communities depend on alaas (thermokarst depression) landscapes and the lakes within. Development and dynamics of these alaas lakes are closely connected to climate change, permafrost thawing, catchment conditions, and land use. To reconstruct lake development throughout the Holocene we analyze sedimentary ancient DNA (sedaDNA) and biogeochemistry from a sediment core from Lake Satagay, spanning the last c. 10,800 calibrated years before present (cal yrs BP). SedaDNA of diatoms and macrophytes and microfossil diatom analysis reveal lake formation earlier than 10,700 cal yrs BP. The sedaDNA approach detected 42 amplicon sequence variants (ASVs) of diatom taxa, one ASV of Eustigmatophyceae (Nannochloropsis), and 12 ASVs of macrophytes. We relate diatom and macrophyte community changes to climate-driven shifts in water level and mineral and organic input, which result in variable water conductivity, in-lake productivity, and sediment deposition. We detect a higher lake level and water conductivity in the Early Holocene (c. 10,700–7000 cal yrs BP) compared to other periods, supported by the dominance of Stephanodiscus sp. and Stuckenia pectinata. Further climate warming towards the Mid-Holocene (7000–4700 cal yrs BP) led to a shallowing of Lake Satagay, an increase of the submerged macrophyte Ceratophyllum, and a decline of planktonic diatoms. In the Late Holocene (c. 4700 cal yrs BP–present) stable shallow water conditions are confirmed by small fragilarioid and staurosiroid diatoms dominating the lake. Lake Satagay has not yet reached the final stage of alaas development, but satellite imagery shows an intensification of anthropogenic land use, which in combination with future warming will likely result in a rapid desiccation of the lake.</p>
Geochemical and isotopic compositions of lake waters, creek and permafrost of Central Yakutia from 2017, 2018 and 2019
<p>The file contains a table of all data of geochemical and isotopic compositions of lake waters, creek and permafrost of Central Yakutia from 2017, 2018 and 2019.</p>
Additional greenhouse gas emissions under different scenarios of permafrost melt'
<p>This dataset contains the underlying data for the following publication Significant implications of permafrost thawing for climate change control, Climatic Change, DOI: 10.1007/s10584-016-1666-5. </p> <p>This data set contains the permafrost emissions used as inputs for the DICE model. These are estimates of the emissions release from permafrost under the RCP 2.6 scenario (GtCO 2 -eq y −1. Three inputs were used: the median, 16th percentile and 84th percentile pathway.</p>
Data on spatiotemporal thermokarst pond characteristics from a permafrost peatland, northern Sweden
<p>Data related to the article: <span><span>Seemann</span><span>, </span><span>F.</span></span><span> & </span><span><span>Sannel</span><span>, </span><span>A.B.K.</span></span><span> (</span><span>2024</span><span>) </span><span>Morphology and dynamics of thermokarst ponds in a subarctic permafrost peatland, northern Sweden</span><span>. </span><span>Earth Surf. Process. Landforms</span><span>, Available from: </span><a href="https://doi.org/10.1002/esp.6021" target="_blank" rel="noopener">https://doi.org/10.1002/esp.6021</a><span>.</span></p> <p>Each file contains metadata information. Detailed information on data aquisition can be found in the article. </p> <p>Study area: Dávvavuopmi, northern Sweden (68°28'N, 20°54'E)</p> <p>Fieldwork was conducted 24 August – 3 September 2021.</p> <p> </p> <p> </p> <p> </p>
The role of catchment characteristics, discharge, and active layer thaw on seasonal stream chemistry across ten permafrost catchments
<p>Data used for the paper: The role of catchment characteristics, discharge, and active layer thaw on seasonal stream chemistry across ten permafrost catchments. Contains water quality and discharge data. See paper for more details.</p>
Data related to article 'Thawing Yedoma permafrost is a neglected nitrous oxide source'
<p>Data on nitrous oxide (N<sub>2</sub>O) fluxes with related process, soil and microbial data from two thawing Yedoma exposures in Northeast Siberia.</p> <p>Metadata:</p> <p>Study site 1:Kurungnakh<br> Location 1:N 72°20', E 126°17'</p> <p>Study site 2:Duvanny Yar<br> Location 2:68°38' N, 159°09' E</p> <p>Contact:Maija Marushchak (maija.marushchak@uef.fi); Christina Biasi (christina.biasi@uef.fi)</p> <p>Ecosystem type:Yedoma exposure; retrogressive permafrost thaw slump</p> <p>Duration:July 2016, July 2017</p> <p>Data creation date:1 September 2021</p> <p>File origin:Created at University of Eastern Finland/University of Jyväskylä by Maija Marushchak (maija.marushchak@uef.fi)<br> Data policy:Kindly inform Maija Marushchak and Christina Biasi if you are going to use the data and of any publication plans.<br> If they think that they should be acknowledged or offered participation as authors they will let you know.</p> <p>Questions about this file should be addressed to Maija Marushchak (maija.marushchak@uef.fi).</p> <p> </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>
The Potential of UAV Imagery for the Detection of Rapid Permafrost Degradation: Assessing the Impacts on Critical Arctic Infrastructure
<p>Dataset and Python code complementing the publication </p> <p>Kaiser, S.; Boike, J.; Grosse, G.; Langer, M. The Potential of UAV Imagery for the Detection of Rapid Permafrost Degradation: Assessing the Impacts on Critical Arctic Infrastructure. <em>Remote Sens.</em> <strong>2022</strong>, <em>14</em>, 6107. https://doi.org/10.3390/rs14236107</p> <ul> <li><strong>AROSICS.zip</strong> contains the orthomosaic of 2018 shifted to 2019 with the AROSICS algorithm. The .txt file contains the x-/y-shift in map units [m].</li> <li><strong>CC_DistancePointClouds.zip</strong> contains the distance point clouds as calculated via Multiscale Model to Model Comparison (M3C2 after Lague et. al, 2013) at each post-processing level (I-IV) and the validation.</li> <li><strong>CC_PointCloudProcessing.zip</strong> contains the point clouds at post-processing levels II-IV.</li> <li><strong>ODM_Orthomosaics.zip</strong> contains the orthomosaics of 2018 and 2019 as processed in WebODM (based on OpenDroneMap).</li> <li><strong>ODM_PointClouds.zip</strong> contains the raw point clouds of 2018 and 2019 (post-processing level I) as processed in WebODM (based on OpenDroneMap).</li> <li><strong>PointCloudStatistics.zip</strong> contains the M3C2 distance statistics at each post-processing level (I-IV) and the validation for the whole point cloud and the two subsets.</li> <li><strong>Python_ChangeDetection.zip</strong> contains the Python (v 3.6) script for calculating the displacement vectors Dx, Dy, Dz for each distance point cloud, rasterizing the attribute "vertical displacement (Dz)" of the distance point cloud with the highest accuracy (post-processing level IV), applying a Sobel edge detection filter to highlight high image gradients and clustering the image into two categories: change (high image gradient) and no change (low image gradient). Needed data input is <strong>CC_DistancePointClouds.zip.</strong></li> <li><strong>Subsets.zip </strong>contains shapefiles of the two subsets.</li> </ul>
Permafrost in Spitsbergen, Svalbard
<p>The data used in the study of ground ice loss over permafrost in Spitsbergen, Svalbard during 2018-2020.</p>
Permafrost Thaw and its Impact on Arctic Infrastructure: A Site Selection Bibliography
<p>Project Bibliography for DRP Task 1.2.1. Cited sources were used in the site selection process. </p>
Carbon and mineral data of organic matter fractions in Siberian Yedoma permafrost
<p>This file contains carbon and mineral data of organic matter fractions obtained from two permafrost drill cores L14-02 (73.33616° N; 141.32776° E) and L14-05 (73.34994° N; 141.24156° E) from Bol’shoy Lyakhovsky Island in NE Siberia in 2014. The datasets contain mass fractions of different size and density fractions, OC concentrations, OC/N ratios, data on organic matter composition based on <sup>13</sup>C-NMR, radiocarbon (<sup>14</sup>C) data, as well as data on iron (Fe) mineral phases and CO<sub>2</sub> production rates of mineral-associated organic matter. Further, carbon and organic biomarker data (<em>n</em>-alkanes) of the bulk sediment are included. The data were created to study mass partitioning of Pleistocene permafrost OC among different organic matter fractions to assess the bioavailability and stability of the organic matter. Please refer to the publication listed below for more information.</p>
Modeling archive of How does humidity data impact land surface modeling of hydrothermal regimes at a permafrost site in Utqiaġvik, Alaska?
<p>Modeling archive contains the meteorological forcings, model input files, and Jupyter notebooks used to generate model meshes and figures for the paper entitled "How does humidity data impact land surface modeling of hydrothermal regimes at a permafrost site in Utqiaġvik, Alaska?"</p>
SIRIUS - Synthesized Inventory of CRitical Infrastructure and HUman-Impacted Areas in Permafrost Regions of AlaSka
<p>The SIRIUS inventory integrates data from (i) the Sentinel-1/2 derived Arctic coastal human impact dataset (SACHI) (Bartsch et al., 2021), (ii) OpenStreetMap dataset for the infrastructure and land use information (OpenStreetMap Contributors and Geofabrik GmbH, 2018), (iii) the pan-Arctic catchments summary database (ARCADE) for the watersheds (Speetjens et al., 2022), (iv) the modeled Northern Hemisphere permafrost map by Obu et al. (2018), and (v) the contaminated sites database and reports by the State of Alaska Department of Environmental Conservation (2023) (DEC) to create a unified new dataset of critical infrastructure and human-impacted areas as well as permafrost and watershed information for Alaska.</p> <p>The dataset is deployed as a GeoPackage and can be imported to spatial databases (e.g. PostgreSQL/PostGIS), a Geographic Information System (e.g. QGIS), and used within geospatial processing libraries (e.g. Python's GeoPandas). All layers can be queried either in dependence or combination with one another.</p> <p>Each GeoPackage contains the following layers:</p> <ul> <li>ARCADE_WatershedsDB</li> <li>DEC_ContaminatedSitesAK</li> <li>OSM_Point_InfrastructureHIElements</li> <li>SACHI_OSM_InfrastructureHIElements</li> <li>SACHI_OSM_InfrastructureHIElements_RRNetwork</li> <li>UiO_MAGT</li> <li>UiO_PermafrostProbability</li> <li>UiO_PermafrostZones</li> </ul> <p>A corresponding manuscript, including application examples and a thorough description of the individual components, was submitted to be published in an open-access journal.</p> <p><strong>Download Data</strong></p> <ul> <li><strong>Python Scripts</strong> <ul> <li>01_InfrastructureDataETL: reprojects the input Shapefiles and raster datasets to a common coordinate system (EPSG:5936) and then clips datasets to the boundary of Alaska. It also includes a step for filtering the permafrost probability raster dataset based on a minimum probability threshold of 50% and rounds the values in the mean annual ground temperature raster dataset.</li> <li>02_OSM-aggregation: processes the OpenStreetMap (OSM) geospatial data. It imports and merges OSM polygon and point data, cleans and extracts unique values of "fclass" and "osm_type", and aggregates these values for manual categorization, based on the OSM key-value-scheme. The script assigns Land Use/Cover Area frame Statistical Survey (LUCAS) categories to the data, filters out natural objects and places, and resolves unknown categories by identifying intersections between datasets.</li> <li>03_SACHI-aggregation: assigns LUCAS categories to the SACHI dataset based on the 'Use' column.</li> <li>04_SACHI-OSM_decisiontree: performs a series of geospatial operations to determine the overlap between polygonal OSM features and SACHI features and assigns LUCAS categories to the overlapping features based on certain criteria and dissolves them. The overlapping and non-overlapping features are then combined into a single dataset: the harmonized critical infrastructure and human-impacted areas dataset.</li> <li>05_TextMiningNLTK-CSSites: performs text mining and data preprocessing on the reports of the DEC contaminated sites database. It extracts dates, calculates cleanup times for inactive sites, identifies contaminants based on abbreviations and text entries, and extracts information related to contaminants and the medium they are found in.</li> </ul> </li> <li><strong>GeoPackages</strong> <ul> <li>PermaRisk_RRNetworkLine_v01_r00.gpkg contains the rail and road network as line geometries.</li> <li>PermaRisk_RRNetworkPolygonal_v01_r00.gpkg contains the rail and road network as polygon geometries.</li> </ul> </li> </ul> <p> </p> <ul> </ul> <p> </p>
Geospatial Analysis of Road Conditions and Hazardous Factors in Communities on Continuous vs. Sporadic Permafrost in Greenland
<p>Road conditions and hazardous factors were surveyed in two permafrost-affected communities of West Greenland, Ilulissat (underlain by continuous ice-rich permafrost) and Sisimiut (underlain by sporadic permafrost). Pavement damages, repairs, embankment structural elements, artificial drainage systems, water accumulations and preferential snow ploughing deposits were notably mapped and georeferenced in a geographic information system to form high-resolution spatial databases. In total, respectively 66 and 76 \% of the paved road networks of Ilulissat and Sisimiut were surveyed. Manual in-situ mapping took place in September 2020 and September 2021 in Ilulissat, while Global Navigation Satellite System (GNSS) equipment was used to map road conditions in Sisimiut in September 2020. The severity of the pavement damages was assessed according to the ASTM D 6433–07, Standard Practice for Roads and Parking Lots Pavement Condition Index Surveys, by ASTM International (2008). The drainage conditions were characterized following the recommendations in Cold Regions Pavement Engineering, by Doré, G. and Zubeck, H. K. (2009).</p> <p>This dataset comprises the geospatial layers of the road damage and hazard inventories created for the settlements of Ilulissat and Sisimiut. Each settlement’s inventory is provided in a ZIP-folder, containing the geospatial layers as geopackages and sorted following a thematic structure. Further information about each layer and its attributes can be found in the metadata PDF document.</p>
Data for: Sedimentary ancient DNA and pollen reveal the composition of plant organic matter in Late Quaternary permafrost sediments of the Buor Khaya Peninsula (north-eastern Siberia)
Open the record for dataset details and reuse information.
Permafrost-thaw lake development in Central Yakutia: sedimentary ancient DNA and element analyses from a Holocene sediment record
Open the record for dataset details and reuse information.
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OpenNeuro
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