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270 results for “permafrost”
Decomposition of soil and permafrost organic matter eroding into the Beaufort Sea near Drew Point, Alaska
Arctic coastal erosion mobilizes large quantities of permafrost organic matter to the Arctic Ocean, where it may be decomposed, releasing carbon dioxide. To quantify the biodegradability of this eroding material, we designed an aerobic bottle incubation experiment to measure CO2 production from coastal soils/sediments submerged in seawater. Seasonally thawed active layer soils and permafrost were sampled near Drew Point along the Alaska Beaufort Sea coast. Cores were taken from three surface geomorphic classifications common in this area: primary surface that has not been reworked by thaw-lake cycles, a young drained lake basin, and an ancient drained lake basin. Core subsamples were chosen to represent three distinct horizons present in eroding bluffs at Drew Point: seasonally thawed active layer soils near the tundra surface, Holocene-age terrestrial soils and/or lake sediments, and late-Pleistocene age relict marine sediments. Soil/sediment subsamples were mixed with Beaufort Sea surface water and incubated in triplicate at 4C and 16C for 40 days. In addition, a subset of soil/sediment samples were incubated with and without seawater at 16C for 40 days. The data reported here summarizes the results of the incubation experiment for each soil sample: cumulative CO2-C production over 40 days normalized to dry weight and to organic carbon content (OC), average rate of CO2 production normalized to dry weight and to TOC, and the percent of OC remineralized over 40 days.
Composition and biodegradability of dissolved organic matter in supra-permafrost groundwater and surface waters near Simpson Lagoon, Alaska
Supra-permafrost groundwater (SPGW) is an important source of terrestrial dissolved organic matter (DOM) to the Arctic Ocean, yet few studies have investigated the quality or characteristics of this DOM. We sampled fresh SPGW, run-off, and rivers near Simpson Lagoon, Alaska during spring ice break-up (mid-June), summer open water (late July), and fall freeze-up (late September - early October). We measured dissolved organic carbon (DOC) concentrations in these samples and analyzed the composition of DOM using high-resolution mass spectrometry (Fourier transform ion cyclotron resonance mass spectrometry; FT-ICR MS). To measure biodegradable dissolved organic carbon (BDOC), we conducted an aerobic incubation experiment following the methods suggested by Vonk et al. (2015). Briefly, water samples were incubated at 20C for 28 days to measure DOC loss due to remineralization by in-situ microbial communities.
Composition and biodegradability of dissolved organic matter leached from eroding coastal soils and permafrost in seawater, from Drew Point, Alaska
Eroding permafrost coastlines export significant quantities of organic carbon (OC) to the marine environment, similar in magnitude to riverine particulate OC fluxes to the Arctic Ocean. Moreover, erosion rates are predicted to increase due to warming temperatures, declines in sea ice, and increasing waves. While erosion primarily mobilizes organic matter in the particulate form, this material can be leached to dissolved organic matter (DOM). This DOM may be incorporated by microbial communities and fuel marine food webs or decomposed to form greenhouse gases like carbon dioxide and methane. Many studies show that permafrost-derived organic matter can be rapidly decomposed in soils and freshwater, but few studies examine the fate of permafrost organic matter in seawater. To address this knowledge gap, we designed a laboratory experiment to leach coastal soils and permafrost in seawater and examine the composition and biodegradability of leached DOM. Coastal soil/sediment was cored near Drew Point, Alaska in 2019, representing three horizons found within rapidly eroding permafrost bluffs: seasonally thawed active layer soils, Holocene terrestrial soils and/or lacustrine sediments, and late-Pleistocene relict marine sediments. To measure dissolved organic carbon (DOC) leaching yields, we placed soil/sediments in Beaufort Sea seawater for 24 hours before filtering to remove particulates. To measure biodegradable dissolved organic carbon (BDOC), we conducted an aerobic incubation experiment following the methods suggested by Vonk et al. (2015). Briefly, leachates were incubated at approximately room temperature for 26 and 90 days to measure DOC loss due to remineralization and/or incorporation into microbial biomass. Additionally, we used chromophoric dissolved organic matter (CDOM) measurements and ultra-high resolution mass spectrometry (FT-ICR MS) to examine the initial leachate DOM composition. References: Vonk, J. E., Tank, S. E., Mann, P. J., Spencer, R. G. M., Tre
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>
Pan‐Arctic Coastal Settlements and Infrastructure Vulnerable to Coastal Erosion, Sea‐Level Rise, and Permafrost Thaw
<p>The datasets are issued from the combination of records of the ESA EO4PAC and Permafrost_cci and HORIZON 2020 Nunataryuk projects. The EO4PAC project aimed to develop a new generation of geospatial products for the observation of permafrost and associated changes from space with a special focus on the coastal Arctic. Four components were considered in the creation of the datasets:</p> <p>(1) Landsat-7/8 for the detection of coastline changes over the 2000-2020 period (Tanguy et al., 2024).</p> <p>(2) Sentinel-1/2 for the detection and mapping of coastal infrastructures (Bartsch et al. 2024), updating Wang et al. (2021).</p> <p>(3) Permafrost_cci timeseries for retrieval of trends of ground temperature and active layer thickness for the 2000-2020 period (Obu et al. 2021a,b), evaluated based on Martin et al (2023) and CALM et al. (2024).</p> <p>(4) Sea level rise by 2100 (Garner et al. 2022).</p> <p>The respective output provides a consistent mapping of settlements along arctic and permafrost-dominated coasts (2), and associated coastline and permafrost conditions changes during the last 20 years (1, 3). Combined together, an assessment of Arctic infrastructures at risk due to permafrost change (GT, ALT) and coastline erosion was possible, the latter with projections for the years 2030, 2050 and 2100.<a name="_heading=h.jkogrw14ymt"></a></p> <p>References</p> <p>Bartsch, Annett, Pointner, Georg, & Nitze, Ingmar. (2023). Sentinel-1/2 derived Arctic Coastal Human Impact dataset (SACHI) (Version 2) [Data set]. Zenodo. https://zenodo.org/records/10160636.</p> <p>CALM, GTN-P, Wieczorek, M., Heim, B., Streletskiy, D., Bartsch, A., 2024, GTN-P CALM: 34 years of Active Layer Thickness (ALT) across latitudinal and elevational gradients in the Northern Hemisphere [dataset]. PANGAEA, https://doi.pangaea.de/10.1594/PANGAEA.972777</p> <p>Garner, G. G., Hermans, T., Kopp, R. E., Slangen, A. B. A., Edwards, T. L., Levermann, A., et al. (2022). IPCC AR6 sea level projections [Dataset]. Zenodo. <a href="https://doi.org/10.5281/zenodo.6382554">https://doi.org/10.5281/zenodo.6382554</a></p> <p>Martin, Julia; Boike, Julia; Chadburn, Sarah; Zwieback, Simon; Anselm, Norbert; Goldau, Maybrit; Hammar, Jennika; Abramova, Ekatarina N; Lisovski, Simeon; Coulombe, Stéphanie; Dakin, Brampton; Wilcox, Evan James; Giamberini, Mariasilvia; Rader, Fieke; Suominen, Otso; Rudd, Daniel Alexander; Mastepanov, Mikhail; Young, Amanda (2023): T-MOSAiC 2021 myThaw data set [dataset]. PANGAEA, https://doi.org/10.1594/PANGAEA.956039, In: Boike, Julia; Hammar, Jennika; Goldau, Maybrit; Miesner, Frederieke; Anselm, Norbert (2024): Circumarctic seasonal measurements of permafrost parameters (thaw depth, snow depth, vegetation and tree height, water level and soil properties) [dataset publication series]. PANGAEA, https://doi.org/10.1594/PANGAEA.971787</p> <p>Obu, J., Westermann, S., Barboux, C., Bartsch, A., Delaloye, R., Grosse, G., Heim, B., Hugelius, G., Irrgang, A., Kääb, A. M., Kroisleitner, C., Matthes, H., Nitze, I., Pellet, C., Seifert, F. M., Strozzi, T., Wegmüller, U., Wieczorek, M., and Wiesmann, A.: ESA Permafrost Climate Change Initiative (Permafrost_cci): Permafrost active layer thickness for the Northern Hemisphere, v3.0, CEDA, 2021. <a href="https://doi.org/10.5285/29C4AF5986BA4B9C8A3CFC33CA8D7C85">https://doi.org/10.5285/29C4AF5986BA4B9C8A3CFC33CA8D7C85</a></p> <p>Obu, J., Westermann, S., Barboux, C., Bartsch, A., Delaloye, R., Grosse, G., Heim, B., Hugelius, G., Irrgang, A., Kääb, A. M., Kroisleitner, C., Matthes, H., Nitze, I., Pellet, C., Seifert, F. M., Strozzi, T., Wegmüller, U., Wieczorek, M., and Wiesmann, A.: ESA Permafrost Climate Change Initiative (Permafrost_cci): Permafrost active layer thickness for the Northern Hemisphere, v3.0, CEDA, 2021. <a href="https://doi.org/10.5285/29C4AF5986BA4B9C8A3CFC33CA8D7C85">https://doi.org/10.5285/29C4AF5986BA4B9C8A3CFC33CA8D7C85</a></p> <p>Tanguy, R., Bartsch, A., Nitze, I., Irrgang, A., Petzold, P., Widhalm, B., von Baeckmann, C., Boike, J., Martin, J., Efimova, A., Vieira, G., Whalen, D., Heim, B., Wieszorek, M., Grosse, G.: Pan‐Arctic Assessment of Coastal Settlements and Infrastructure Vulnerable to Coastal Erosion, Sea‐Level Rise, and Permafrost Thaw, Earth’s Future, 10.1029/2024EF005013.</p> <p>Wang, S., Ramage, J., Bartsch, A., & Efimova, A. (2021). Population in the Arctic Circumpolar Permafrost Region at settlement level (Version 2) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.4529610" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.4529610</a></p>
Permafrost Thaw Settlement Dataset
<p>The Permafrost Thaw Settlement Dataset comprises four data files containing measurements, properties and derived parameters related to permafrost thaw settlement tests. This dataset is intended to support research on mechanical properties of permafrost, particularly in understanding the relationship between soil properties and thaw settlement behavior.</p> <h3><strong>Data Paper Reference</strong></h3> <p>A detailed description of the dataset, including methodology, data sources, and analysis, is provided in the accompanying data paper submitted to <em>Earth System Science Data</em>:</p> <div> <div>Mohammadi, Z., Hayley, J.L., 2025. Compilation and Analysis of Thaw Settlement Test Results: Implications for Prediction Tools and Stress-Strain Characterization in Permafrost. <a href="https://doi.org/10.5194/essd-2024-514">https://doi.org/10.5194/essd-2024-514</a></div> </div> <p>An <strong>updated GitHub repository</strong> containing fully reproducible R code and figures: → <a href="https://github.com/Zucchii/ThawSettlement_DataPaper/releases/tag/essd-pts-final">https://github.com/Zucchii/ThawSettlement_DataPaper/releases/tag/essd-pts-final</a></p> <p> </p> <p>The GitHub repository includes:</p> <ul> <li> <p>R scripts for data processing, analysis, and figure generation</p> </li> <li> <p>Pre-generated figures and processed datasets for convenience</p> </li> <li> <p>Detailed instructions for reproducing all results</p> </li> </ul>
CO₂ and related biogeochemical data from permafrost rivers on the Qinghai-Tibet Plateau (2016–2018, 2023)
This dataset includes four years of direct measurements from 50 permafrost rivers in the Qinghai-Tibet Plateau’s major Asian headwaters (Yellow, Yangtze, Lancang, Nu, Derung, Yarlung Tsangpo, Marja Tsangpo, and Indus Rivers), collected during the ice-free seasons (April–October) of 2016–2018 and 2023. It includes CO₂ partial pressure (pCO₂) and emission rates, concentrations of DOC, DIC, and major dissolved ions, carbon (δ¹³C), sulfur (δ³⁴S), and oxygen (δ¹⁸O) isotopes, alongside site location and other physiochemical data. Given its scarcity and scientific significance, this dataset will greatly support updates to global river CO2 flux estimates.
Geochemical characterization and material properties of coastal permafrost near Drew Point, Alaska
Permafrost cores (4.5-7.5 m long) were collected April 10th-19th, 2018, along a geomorphic gradient near Drew Point, Alaska to characterize active layer and permafrost geochemistry and material properties. Cores were collected from a young drained lake basin, an ancient drained lake basin, and primary surface that has not been reworked by thaw lake cycles. Measurements of total organic carbon (TOC) and total nitrogen (TN) content, stable carbon isotope ratios (δ13C) and radiocarbon (14C) analyses of bulk soils/sediments were conducted on 45 samples from 3 permafrost cores. Porewaters were extracted from these same core sections and used to measure salinity, dissolved organic carbon (DOC), total dissolved nitrogen (TDN), anion (Cl-, Br-, SO4 2-, NO3 -), and trace metal (Ca, Mn, Al, Ba, Sr, Si, and Fe) concentrations. Radiogenic strontium (87Sr/86Sr) was measured on a subset of porewater samples. Cores were also sampled for material property measurements such as dry bulk density, water content, and grain size fractions.
Eight Mile Lake Research Watershed, Carbon in Permafrost Experimental Heating Research (CiPEHR): Half-hourly growing season, chamber-based, CO2 flux data, 2009-2021
The Carbon in Permafrost Experimental Heating Research (CiPEHR) project addresses the following questions: 1) Does ecosystem warming cause a net release of C from the ecosystem to the atmosphere?, 2) Does the decomposition of old C, that comprises the bulk of the soil C pool, influence ecosystem C loss?, and 3) How do winter and summer warming alone, and in combination, affect ecosystem C exchange? We are answering these questions using a combination of field and laboratory experiments to measure ecosystem carbon balance and radiocarbon isotope ratios at a warming experiment located in an upland tundra field site near Healy, Alaska in the foothills of the Alaska Range. This data contains CO2 fluxes measured using an automated chamber system that measures net ecosystem CO2 exchange (NEE). Measurements are made every ~1.5 hours and modeled half-hourly. Half hour ecosystem respiration is modeled using an exponential Q10 relationship when light conditions are low (PAR<5umol/m2/s) and using a hyperbolic light relationship when PAR>5umol/m2/s. GPP is calculated as the difference between NEE and Reco.
Eight Mile Lake Research Watershed, Carbon in Permafrost Experimental Heating and Drying Research (DryPEHR): Growing season, chamber-based, CO2 flux data, 2009-2021
This drying and warming experiment addresses the following questions: 1) Does ecosystem drying, warming and permafrost thaw cause a net release or uptake of C from the ecosystem to the atmosphere?, 2) Does the decomposition of old C that comprises the bulk of the soil C pool influence ecosystem C loss? 3) How do drying and warming affect plant communities and ecosystem properties? We are answering these questions using a combined warming and drying experiment (DryPEHR), which is situated with the Carbon in Permafrost Experimental Heating Research (CiPEHR) project and located in an upland tundra field site near Healy, Alaska in the foothills of the Alaska Range. Warming treatment here refers to growing season air temperature warming (~1C) using open top chambers (OTC) combined with soil 'warming' using snow fences during the snow covered months. Drying is achieved using an automated pumping system that lowers the water table in the dry plots. Soil warming began in 2008; OTCs and drying in 2011. This data set includes measured values of CO2 fluxes during the growing season.
Eight Mile Lake Research Watershed, Carbon in Permafrost Experimental Heating Research (CiPEHR): Seasonal water table depth data, 2012-2024
The Carbon in Permafrost Experimental Heating Research (CiPEHR) project addresses the following questions: 1) Does ecosystem warming cause a net release of C from the ecosystem to the atmosphere?, 2) Does the decomposition of old C, that comprises the bulk of the soil C pool, influence ecosystem C loss?, and 3) How do winter and summer warming alone, and in combination, affect ecosystem C exchange? We are answering these questions using a combination of field and laboratory experiments to measure ecosystem carbon balance and radiocarbon isotope ratios at a warming experiment located in an upland tundra field site near Healy, Alaska in the foothills of the Alaska Range. This data includes water table depth measurements collected from winter warming and control treatment plots at CiPEHR for the ice-free period of 2024. Note that the experimental warming portion of this experiment concluded in 2022. These data are a continuation of measurements taken at previously warmed plots but plots were not actively manipulated in 2023 and 2024.
Bonanza Creek LTER: Active Layer Depth or Permafrost Presence for the Regional Site Network
The initial goal (2000-2013) of these data was to define the presence/absence of permafrost within 2.5m of the surface in the regional site network. Efforts were focused mainly on sites where this was not easily deduced. The final subset of sites (2015 � present) are distributed across the 3 ecoregions of the RSN and primarily in older aged wet sites. The permafrost distribution in interior Alaska is discontinuous and dynamic; susceptible to fire and climate disturbances. Therefore, sites included in this long-term monitoring dataset may cease to be monitored as permafrost degrades and disappears or may be monitored again if permafrost is reestablished.
APEX beta greenhouse gas flux data from both the permafrost plateau area and the active thaw margin, from 2017 on
This dataset contains greenhouse gas flux data for the bog and permafrost plateau sites at the Alaskan Peatland Experiment (APEX) from 2017 on. Includes some environmentals, raw gas concentrations, and derived gas flux rates.
Data on ground ice, organic carbon and soluble cations in tundra permafrost and active-layer soils near Lac de Gras in the Slave Geological Province, N.W.T., Canada
<p>Data and computer code for producing figures for the manuscript:</p> <p>Subedi, R., Kokelj, S. V., and Gruber, S.: Ground ice, organic carbon and soluble cations <br> in tundra permafrost soils and sediments near a Laurentide ice divide in the Slave <br> Geological Province, N.W.T., Canada. The Cryosphere, accepted for publication in October 2020. </p> <p>Discussion paper and final version: https://doi.org/10.5194/tc-2020-33</p> <p> </p> <p>==========================================================================================<br> CONTENT OF DIRECTORIES<br> ==========================================================================================<br> -– data [input data to produce plots]<br> |–– BoreholesMeta.csv<br> |–– brackets_photos_ice.csv<br> |–– brackets_photos_thawed.csv<br> |–– Lac_de_Gras_permafrost_20200612.csv<br> |–– NordicanaD<br> <br> |–– ds_000582159 [authoritative copy at doi: 10.5885/45558XD-EBDE74B80CE146C6]<br> |–– Cored_Drill_TCR.csv<br> |–– Cored_Drill_TCR.csv_ReadMe.txt<br> <br> |–– ds_000582163 [authoritative copy at doi: 10.5885/45558XD-EBDE74B80CE146C6]<br> |–– Cored_Drill_Logs.csv_ReadMe.txt<br> |–– Cored_Drill_Logs.csv</p> <p>–– plot [R scripts write plots into this subdirectory]</p> <p>–– src [R scripts to generate plots]<br> |–– Combined_Plots.R [produces Figures 3–6]<br> |–– Eskers.R [helper function called by Combined_Plots.R]<br> |–– Organics.R [helper function called by Combined_Plots.R]<br> |–– plot_boreholes_DD_single.R [produces Figures S3]<br> |–– plot_boreholes_DD.R [produces raw Figure S2 for further graphic processing]<br> |–– Till.R [helper function called by Combined_Plots.R]<br> |–– Valley.R [helper function called by Combined_Plots.R]</p> <p><br> ==========================================================================================<br> RUNNING SCRIPTS<br> ==========================================================================================</p> <p>Adjust the variable 'path' in these scrips, then run: <br> Combined_Plots.R<br> plot_boreholes_DD_single.R<br> plot_boreholes_DD.R </p> <p>Tested with R version 3.6.3 (2020-02-29) -- "Holding the Windsock"</p> <p> </p> <p>==========================================================================================<br> REFRERENCE<br> ==========================================================================================<br> Please note that the data contained in data/NordicanaD is published as Gruber et al. (2018)<br> and only included here for convenience. The full reference for the authoritative copy is: <br> <br> Gruber, S., Brown, N., Stewart-Jones, E., Karunaratne, K., Riddick, J., Peart, C., <br> Subedi, R., Kokelj, S. 2018. Drill logs, visible ice content and core photos from 2015 <br> surficial drilling in the Canadian Shield tundra near Lac de Gras, Northwest Territories, <br> Canada, v. 1.0 (2015-2015). Nordicana D38, doi: 10.5885/45558XD-EBDE74B80CE146C6. <br> http://www.cen.ulaval.ca/nordicanad/dpage.aspx?doi=45558XD-EBDE74B80CE146C6 </p>
Gridded active layer thickness across northern permafrost regions from 2003 to 2020
<ol><li>This dataset provides the annual gridded active layer thickness (ALT) across northern permafrost regions (NPR) at 1 km resolution for the period 2003–2020. The ALT in permafrost regions refers to the upper layer of soil that thaws and refreezes annually as a result of seasonal temperature variations. It is a critical parameter in permafrost studies because it determines the depth to which plant roots can penetrate and influences various ecological and engineering processes. This dataset was produced based on the relationship between available ALT site measurements (2966 site-years) and satellite observations of annual gridded predictors, including vegetation, temperature, soil, and topography, using a Random Forest (RF) approach. The annual ALT map for the NPR was generated using the ensemble mean of ALT from the ten best RF predictions. Extensive uncertainty analysis was also conducted. </li><li>The dataset can be viewed at https://liuzh833.users.earthengine.app/view/altv1</li></ol>
Dataset to: Organic carbon stocks, quality and prediction in permafrost-affected forest soils in North Canada (CATENA) - Version 2 (Corrected)
<p><strong>Version update: Coordinates were not correct in previsous version and have been corrected now in version 2</strong></p> <p> </p> <p>Dataset to the manuscript: Schiedung et al. (2022, Catena) Organic carbon stocks, quality and prediction in permafrost-affected forest soils in North Canada ( <a href="https://doi.org/10.1016/j.catena.2022.106194">https://doi.org/10.1016/j.catena.2022.106194</a> )</p> <p>Data files, variables and parameter are described in <em>Var_names_dd_all.csv</em> for all data on each sample and <em>Var_names_dd_composites.csv </em>for all data on composited samples per site and depth. DRIFT data and corresponding explenation are in <em>Schiedung_CATENA_DRIFT_v1.1.zip.</em></p> <p> </p> <p><strong> </strong></p>
Laboratory-measured and X-ray CT-derived volumetric composition of a permafrost core
<p>This dataset contains data on the volumetric composition of a permafrost core which has been drilled in a Yedoma upland in northeast Siberia (72.36613 N, 126.27272 E) in September 2017. This dataset supplements a research article to be submitted to the scientific journal <em>The Cryosphere</em>. It contains the following files:</p> <p><strong><em>volumetric_contents_sampleRes_lab+CT.csv</em> </strong><br> Contains the volumetric contents of total ice, organic, and mineral measured in the laboratory at AWI Potsdam at a coarse resolution. It further contains the volumetric contents of gas, excess ice, and two sediment phases (A,B) derived from a CT scan at UFZ Halle, downsampled to the resolution of the laboratory samples.</p> <p><em><strong>volumetric_contents_highRes_CT.csv</strong></em><br> Contains the volumetric contents of gas, excess ice, and two sediment phases (A,B) derived from a CT scan at UFZ Halle at the original resolution of 50µm.</p> <p><em><strong>regression analysis_paper.py</strong></em><br> This pyhton script uses the above listed input files to perform and evaluate a regression analysis<strong><em> </em></strong>of the CT data against the laboratory data. The regression result is the composition of the CT-derived sediment phases (A,B) in terms of pore ice, organic, and mineral. The script furthermore computes evaluation metrics of the lab-CT comparison, and computes volumetric contents of pore ice, total ice, organic, and mineral at the high resolution of the original CT data.</p> <p><em><strong>volumetric_contents_sampleRes_all.csv</strong></em><br> This file can be reproduced by the files listed above and contains, in addition to the data contained in <em>volumetric_contents_sampleRes_lab+CT.csv</em>, the volumetric contents of pore ice, total ice, mineral, and organic as predicted by the regression model at the same (coarse) resolution as the laboratory samples.</p> <p><em><strong>volumetric_contents_highRes_all.csv</strong></em><br> This file can be reproduced by the files listed above and contains, in addition to the data contained in <em>volumetric_contents_highRes_CT.csv</em>, the volumetric contents of pore ice, total ice, mineral, and organic as predicted by the regression model at the same (high) resolution as the original CT data.</p> <p>More details can be found in the article describing the study.</p>
Ground ice content predictions for the Northern Hemisphere permafrost region at 1-km resolution, version 1.1
<p>Ground ice content is one of the least known characteristics of the permafrost-affected soils in the Northern Hemisphere. At the same time, ground ice content exerts a crucial effect on the thermal response of permafrost to changing climate and environmental conditions, and dictates the permafrost degradation-related geomorphic, hydrologic, and ecological processes, including thermokarst. This dataset presents numerical estimates of volumetric ice content over the permafrost region at a 1-km spatial resolution. The predictions are representative of pore and segregated ice contents in the topmost five meters of permafrost. We use compilations of field measurements of ground ice contents from across the permafrost region to train statistical models and to predict volumetric ice content with the aid of high-resolution geospatial data on climatic, soil and topography conditions. The dataset facilitates assessments of conditions of changing permafrost landscapes at an improved spatial and thematic resolution.</p>
Dataset to Manuscript: Schiedung et al. (2023; SBB) Enhanced loss but limited mobility of pyrogenic and organic matter in continuous permafrost-affected forest soils.
<p>Dataset to Schiedung et al. (2023; SBB) Enhanced loss but limited mobility of pyrogenic and organic matter in continuous permafrost-affected forest soils.</p> <p>All published data is provided in the files "<strong>dd_</strong>". This includes:</p> <ul> <li>dd_cores: All data of soil cores and with depth</li> <li>dd_fractions: All data obtained from fractionation of the 0-3cm core layers</li> <li>dd_teabag: All data and mass losses of incubated teabags</li> <li>dd_temperature: All data and recorded soil temperatures</li> </ul> <p>All parameters and names are described in the corresponding file starting with "<strong>Var_names_</strong>". Details on methods and calculations are given in the manuscript and supporting information.</p> <p>NanoSIMS data is provided in the folder "<strong>dd_NanoSIMS.zip</strong>". This contains a file with descriptions of the provided tif-files "<strong>dd_NanoSIMS</strong>". Descriptions of the variables and parameters as well as further instructions are given in the file "<strong>Var_names_description_dd_NanoSIMS</strong>". Images and additional data can be requested by the corresponding author (marcusschiedung@gmail.com).</p> <p> </p> <p> </p>
Eight Mile Lake Research Watershed, Carbon in Permafrost Experimental Heating and Drying Research (DryPEHR): Peak growing season aboveground biomass 2011-2017. (Reformatted to the ecocomDP Design Pattern)
This data package is formatted as an ecocomDP (Ecological Community Data Pattern). For more information on ecocomDP see https://github.com/EDIorg/ecocomDP. This Level 1 data package was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-bnz/502/16. The abstract below was extracted from the Level 0 data package and is included for context: This drying and warming experiment addresses the following questions: 1) Does ecosystem drying, warming and permafrost thaw cause a net release or uptake of C from the ecosystem to the atmosphere?, 2) Does the decomposition of old C that comprises the bulk of the soil C pool influence ecosystem C loss? 3) How do drying and warmign affect plant communities and ecosystem properties? We are answering these questions using a combined warming and drying experiment (DryPEHR), which is situated with the Carbon in Permafrost Experimental Heating Research (CiPEHR) project and located in an upland tundra field site near Healy, Alaska in the foothills of the Alaska Range. Warming treatment here refers to growing season air temperature warming (~1C) using open top chambers (OTC) combined with soil 'warming' using snow fences during the snow covered months. Drying is achieve using an automated pumping system that lowers the water table in the dry plots. Soil warming began in 2008; OTCs and drying in 2011. Above ground plant biomass was surveyed non-destructively using a point-intercept method for all vascular and moss species at peak growing season.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.