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### Overview The SACHI (Sentinel-1/2 derived Arctic Coastal Human Impact) dataset has been developed as part of the HORIZON2020 project Nunataryuk by b.geos (www.bgeos.com). V1 covered a 100km buffer from the Arctic Coast (land area), for areas with permafrost near the coast. V2 covers additional selected areas extending the coverage to the south. It is based on Sentinel-1 and Sentinel-2 data from 2016-2020 using the algorithms described in Bartsch et al. (2020). It is a supplement to Bartsch et al. (2023). This dataset contains detected coastal infrastructure separated into seven different categories: linear transport infrastructure (asphalt), linear transport infrastructure (gravel), linear transport infrastructure (undefined), buildings (and other constructions such as bridges), other impacted area (includes gravel pads, mining sites), airstrip, and reservoir or other water body impacted by human activities. This SACHI version 2 dataset was post-processed by the Permafrost Discovery Gateway visualization pipeline. This workflow cleaned, standardized, and visualized the data as two Tile Matrix Sets per year. One Tile Matrix Set is the data in the form of GeoPackages, or staged tiles, and the other Tile Matrix Set is the staged tiles in the form of GeoTIFF tiles. The highest resolution tiles were resampled to produce GeoTIFFs for lower resolutions. This data is visualized on the Permafrost Discovery Gateway portal: https://arcticdata.io/catalog/portals/permafrost/Imagery-Viewer ### References Bartsch, A., Widhalm, B., von Baeckmann, C., Efimova, A., Tanguy, R., and Pointner, G. (2023). Sentinel-1/2 derived Arctic Coastal Human Impact dataset (SACHI) (v2.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.10160636 Bartsch, A., G. Pointner, I. Nitze, A. Efimova, D. Jakober, S. Ley, E. Högström, G. Grosse, P. Schweitzer (2021): Expanding infrastructure and growing anthropogenic impacts along Arctic coasts. Environmental Research Letters. https://doi.org/10.1088/1748-9326/ac317 Bartsch, A., Pointner, G., Ingeman-Nielsen, T. and Lu, W. (2020), ‘Towards circumpolar mapping of Arctic settlements and infrastructure based on Sentinel-1 and Sentinel-2’, Remote Sensing 12(15), 2368. ### Access Data files output from the visualization workflow are available for download at: [http://arcticdata.io/data/10.18739/A21J97929](http://arcticdata.io/data/10.18739/A21J97929) To download all files in the command line, run the following command in a terminal: `wget -r -np -nH --cut-dirs=3 -R '\?C=' -R robots.txt https://arcticdata.io/data/10.18739/A21J97929/` To download a subdirectory of the archived files, add the subdirectories to the end of the URL above.
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Pan-Arctic Simulated Wave Heights (2020-2070)
### Overview This dataset contains simulated significant wave height data generated from the WaveWatch III model run from 2020 up to 2070. It was produced to predict future environmental hazards threatening maritime navigation within the Arctic. Four unique simulations were produced using different Coupled Model Intercomparison Project Phase 6 (CMIP6) climate models' wind and sea ice projections along the shared socioeconomic pathways 5-8.5 (SSP5-8.5) future emissions scenario. The climate models used include: CNRM-CM6-1-HR, EC-Earth3, MPI-ESM1-2-HR, and MRI-ESM2-0. For each climate model, data is organized into yearly files written to NetCDF format. The data is contained on a spatially-varying unstructured triangular mesh which spans from 50° North (N) to 89.9°N and 180° West (W) to 180° East (E). The 'hs' variable presents the significant wave height (highest one thirds of wave heights) to occur for each node during the simulation in 6 hour intervals. ### Access Data files can be accessed via: [https://arcticdata.io/data/10.18739/A2ST7DZ74/](https://arcticdata.io/data/10.18739/A2ST7DZ74/)
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- Award ID(s):
- 1927785
- PAR ID:
- 10639412
- Publisher / Repository:
- NSF Arctic Data Center
- Date Published:
- Subject(s) / Keyword(s):
- Waves Maritime Simulation Data
- Format(s):
- Medium: X Other: text/xml
- Sponsoring Org:
- National Science Foundation
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### Access All files can be accessed and downloaded from the directory via: [http://arcticdata.io/data/10.18739/A2MW28G9D](http://arcticdata.io/data/10.18739/A2MW28G9D). ### Overview Storm surge extremes are intensifying across Arctic coastlines, yet limited observational records hamper detailed spatial and temporal characterization of these events. To address that, this data is a 45-year hydrodynamic hindcast of storm-driven water levels across Northern and Western Alaska. We utilize ADCIRC+SWAN to simulate interactions between the ocean, land, sea ice, and atmosphere, focusing on the period from 1979 to 2024 for Western to Northern Alaska coasts. Data from the European Centre for Medium-Range Weather Forecasts Re-Analysis (ERA5), including sea ice concentration and atmospheric forcing were utilized to support these simulations, which investigate annual conditions in the Alaskan Arctic. The Processed_DATA dataset contains extracted parameters for communities located in western to northern Alaska. For other areas in the state not included here, please refer to the Raw_DATA file. ### Goal The goal of this study's data is to attribute long-term changes in Arctic storm surge extremes to evolving physical drivers—primarily the transition from sea-ice-dominated to wind-driven surge regimes. Furthermore, to fill in the gap in observed water levels and wave conditions throughout Alaska. ### Methods This study’s hindcast model framework is to evaluate the storm driven water levels from 1979 to 2024. The framework integrates a coupled hydrodynamic–wave model driven by time-varying boundary inputs representing atmospheric, oceanic, tidal, and sea ice conditions. We used the coupled Advanced CIRCulation and Simulating WAves Nearshore model (ADCIRC+SWAN) to simulate water levels and wave conditions.more » « less
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The Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) produced a wealth of observational data along the drift of the R/V Polarstern in the Arctic Ocean from October 2019 to September 2020. These data can further process-level understanding and improvements in models. However, the observational records contain temporal gaps and are provided in different formats. One goal of the MOSAiC Single Column Model Working Group (MSCMWG: https://mosaic-expedition.org/science/cross-cutting_groups/) is to provide consistently-formatted, gap-filled, merged datasets representing the conditions at the MOSAiC Central Observatory (the intensively studied region within a few km of R/V Polarstern) that are suitable for driving models on this spatial domain (e.g., single column models, large eddy simulations, etc). The MSCMWG is an open group, please contact the dataset creators if you would like to contribute to future versions of these merged datasets (including new variables). This dataset contains version 1 of these merged datasets, and comprises the variables necessary to force a single column ice model (e.g., Icepack: https://zenodo.org/doi/10.5281/zenodo.1213462). The atmospheric variables are primarily derived from Met City (~66 percent (%) of record, https://doi.org/10.18739/A2PV6B83F), with temporal gaps filled by bias and advection corrected data from Atmospheric Surface Flux Stations ( https://doi.org/10.18739/A2XD0R00S, https://doi.org/10.18739/A25X25F0P, https://doi.org/10.18739/A2FF3M18K). Some residual gaps in shortwave radiation were filled with ARM ship-board radiometer data. Three different options for snowfall precipitation rate (prsn) are provided, based on in-situ observations that precipitation greatly exceeded accumulation on level ice, and accumulation rates varied on different ice types. MOSAiC_kazr_snow_MDF_20191005_20201001.nc uses 'snowfall_rate1' derived from the vertically-pointing, ka-band radar on the vessel (https://doi.org/10.5439/1853942). MOSAiC_Raphael_snow_fyi_MDF_20191005_20201001.nc and MOSAiC_Raphael_snow_syi_MDF_20191005_20201001.nc use snow accumulation measurements from manual mass balance sites (https://doi.org/10.18739/A2NK36626) to derived a pseudo-precipitation. MOSAiC_Raphael_snow_fyi_MDF_20191005_20201001.nc is based on the First Year Ice (fyi) sites. MOSAiC_Raphael_snow_syi_MDF_20191005_20201001.nc is based on the Second Year Ice (syi) sites. The other atmospheric variables for these files are identical. Oceanic variables are in MOSAiC_ocn_MDF_20191006_20200919.nc and are derived from https://doi.org/10.18739/A21J9790B. The data are netCDF files formatted according to the Merged Data File format (https://doi.org/10.5194/egusphere-2023-2413, https://gitlab.com/mdf-makers/mdf-toolkit). The code 'recipes' that were used to produce these data are available at: https://doi.org/10.5281/zenodo.10819497. If you use these datasets, please also cite the appropriate publications: Meteorological variables (excluding precipitation): Cox et al., 2023 (https://doi.org/10.1038/s41597-023-02415-5) Oceanographic variables: Schulz et al., 2023 (https://doi.org/10.31223/X5TT2W) KAZR-derived precipitation: Matrosov et al., 2022 (https://doi.org/10.1525/elementa.2021.00101) Accumulation-derived pseudo-precipitation: Raphael et al., in review. The following are known issues that will be addressed in future dataset releases: 1. Residual gaps occupy approximately 20% of the data record (see addendum) 2. Some transitions to shiprad downwelling shortwave are unreasonable abrupt 3. MDF format does not currently include a field for point-by-point data source Addendum: For atmospheric variables, below indicates the percentage sourced from each dataset (and the amount missing a.k.a NaN) Air Temperature metcity 0.661943 NaN 0.193333 asfs30 0.134910 asfs40 0.008607 asfs50 0.001207 Specific Humidity metcity 0.658890 NaN 0.196298 asfs40 0.008695 Wind Velocity metcity 0.666334 NaN 0.255003 asfs30 0.068828 asfs40 0.008630 asfs50 0.001205 Downwelling Longwave metcity 0.549417 asfs30 0.241502 NaN 0.209081 Downwelling Shortwave metcity 0.674166 NaN 0.158814 asfs30 0.140794 shipradS1 0.026226 Note that the 21 day gap from the end of Central Observatory 2 to the start of Central Observatory 3 occupies 5.8% of the record.more » « less
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### Overview This data release includes surface nuclear magnetic resonance (sNMR) data collected as part of the SUN-SPEARS project. The project is funded by the National Science Foundation (Award number 2015329) and is concerned with studying soil evolution in high Arctic environments post glacial retreat. Within SUN-SPEARS, data are collected on a chronosequence from very recently deglaciated to older locations which have been exposed for decades to centuries. In this data release, sNMR data from two sites are included: site 1 which was collected approximately 15 meters (m) from the snout of the glacier, and site 2 which was located approximately 1000 m from the snout of the glacier, Global Positioning System (GPS) coordinates are included for more precise locations. ### Access Data files can be accessed via: [https://arcticdata.io/data/10.18739/A23X83N25](https://arcticdata.io/data/10.18739/A23X83N25)more » « less
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{"Abstract":["The intended use of this archive is to facilitate meta-analysis of the Data Observation Network for Earth (DataONE, [1]). <\/p>\n\nDataONE is a distributed infrastructure that provides information about earth observation data. This dataset was derived from the DataONE network using Preston [2] between 17 October 2018 and 6 November 2018, resolving 335,213 urls at an average retrieval rate of about 5 seconds per url, or 720 files per hour, resulting in a data gzip compressed tar archive of 837.3 MB . <\/p>\n\nThe archive associates 325,757 unique metadata urls [3] to 202,063 unique ecological metadata files [4]. Also, the DataONE search index was captured to establish provenance of how the dataset descriptors were found and acquired. During the creation of the snapshot (or crawl), 15,389 urls [5], or 4.7% of urls, did not successfully resolve. <\/p>\n\nTo facilitate discovery, the record of the Preston snapshot crawl is included in the preston-ls-* files . There files are derived from the rdf/nquad file with hash://sha256/8c67e0741d1c90db54740e08d2e39d91dfd73566ea69c1f2da0d9ab9780a9a9f . This file can also be found in the data.tar.gz at data/8c/67/e0/8c67e0741d1c90db54740e08d2e39d91dfd73566ea69c1f2da0d9ab9780a9a9f/data . For more information about concepts and format, please see [2]. <\/p>\n\nTo extract all EML files from the included Preston archive, first extract the hashes assocated with EML files using:<\/p>\n\ncat preston-ls.tsv.gz | gunzip | grep "Version" | grep -v "deeplinker" | grep -v "query/solr" | cut -f1,3 | tr '\\t' '\\n' | grep "hash://" | sort | uniq > eml-hashes.txt<\/p>\n\nextract data.tar.gz using:<\/p>\n\n~/preston-archive$$ tar xzf data.tar.gz <\/p>\n\nthen use Preston to extract each hash using something like:<\/p>\n\n~/preston-archive$$ preston get hash://sha256/00002d0fc9e35a9194da7dd3d8ce25eddee40740533f5af2397d6708542b9baa\n<eml:eml xmlns:eml="eml://ecoinformatics.org/eml-2.1.1" xmlns:xs="http://www.w3.org/2001/XMLSchema" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:stmml="http://www.xml-cml.org/schema/stmml_1.1" packageId="doi:10.18739/A24P9Q" system="https://arcticdata.io" scope="system" xsi:schemaLocation="eml://ecoinformatics.org/eml-2.1.1 ~/development/eml/eml.xsd">\n <dataset>\n <alternateIdentifier>urn:x-wmo:md:org.aoncadis.www::d76bc3b5-7b19-11e4-8526-00c0f03d5b7c</alternateIdentifier>\n <alternateIdentifier>d76bc3b5-7b19-11e4-8526-00c0f03d5b7c</alternateIdentifier>\n <title>Airglow Image Data 2011 4 of 5</title>\n...<\/p>\n\nAlternatively, without using Preston, you can extract the data using the naming convention:<\/p>\n\ndata/[x]/[y]/[z]/[hash]/data<\/p>\n\nwhere x is the first 2 characters of the hash, y the second 2 characters, z the third 2 characters, and hash the full sha256 content hash of the EML file.<\/p>\n\nFor example, the hash hash://sha256/00002d0fc9e35a9194da7dd3d8ce25eddee40740533f5af2397d6708542b9baa can be found in the file: data/00/00/2d/00002d0fc9e35a9194da7dd3d8ce25eddee40740533f5af2397d6708542b9baa/data . For more information, see [2].<\/p>\n\nThe intended use of this archive is to facilitate meta-analysis of the DataONE dataset network. <\/p>\n\n[1] DataONE, https://www.dataone.org\n[2] https://preston.guoda.bio, https://doi.org/10.5281/zenodo.1410543 . DataONE was crawled via Preston with "preston update -u https://dataone.org".\n[3] cat preston-ls.tsv.gz | gunzip | grep "Version" | grep -v "deeplinker" | grep -v "query/solr" | cut -f1,3 | tr '\\t' '\\n' | grep -v "hash://" | sort | uniq | wc -l\n[4] cat preston-ls.tsv.gz | gunzip | grep "Version" | grep -v "deeplinker" | grep -v "query/solr" | cut -f1,3 | tr '\\t' '\\n' | grep "hash://" | sort | uniq | wc -l\n[5] cat preston-ls.tsv.gz | gunzip | grep "Version" | grep "deeplinker" | grep -v "query/solr" | cut -f1,3 | tr '\\t' '\\n' | grep -v "hash://" | sort | uniq | wc -l<\/p>\n\nThis work is funded in part by grant NSF OAC 1839201 from the National Science Foundation.<\/p>"]}more » « less
