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DK1
No full textThe .zip files contains simulated recharge from DK-model HIP 100m, as the MIKE SHE resultfile: .._2DUZ_AllCells.dfs2, with daily timestep, unit mm/day, for the period 1989 – 2024. Large files (>16GB) are packed with 7-zip into several .zip file, please unpack them using 7-zip in order to get the singe .dfs2 file
Earthquake Catalogue of The Faroe Islands
No full textParametric data of the earthquake catalogue of The Faroe Islands, compiled, processed and analysed at the Geological Survey of Denmark and Greenland – GEUS, using data from earthquake monitoring networks on and around the Faroe Islands. The data set is extracted from GEUS earthquake database.
The file ‘Earthquake-Catalouge-Faroe-Islands.nor’ includes the parametric data in Nordic format. The format is described in appendix A of the SEISAN manual (Ottemöller et al. 2021).
The file ‘2023-nordic-fareo-islands-poster-peter-voss.pdf’ is a poster describing the catalogue, the poster was presented at the 54th Nordic Seismology Seminar in Herdla, Bergen, 12-14 June, 2023 https://nss2023.w.uib.no/ .
Reference:
Ottemöller, L., Voss, P.H. and Havskov J. (2021). SEISAN Earthquake Analysis Software for Windows, Solaris, Linux and Macosx, Version 12.0. 607 pp. University of Bergen. ISBN 978-82-8088-501-2. http://seisan.info
DK4
No full textThe .zip files contains simulated recharge from DK-model HIP 100m, as the MIKE SHE resultfile: .._2DUZ_AllCells.dfs2, with daily timestep, unit mm/day, for the period 1989 – 2024. Large files (>16GB) are packed with 7-zip into several .zip file, please unpack them using 7-zip in order to get the singe .dfs2 file
DK3
No full textThe .zip files contains simulated recharge from DK-model HIP 100m, as the MIKE SHE resultfile: .._2DUZ_AllCells.dfs2, with daily timestep, unit mm/day, for the period 1989 – 2024. Large files (>16GB) are packed with 7-zip into several .zip file, please unpack them using 7-zip in order to get the singe .dfs2 file
Depth of subsurface water on the Greenland ice sheet from machine learning and multifrequency passive microwave measurements (2010-2023)
No full text
Multi-frequency passive microwave emissions from the Greenland Ice Sheet are known to be sensitive to the presence of liquid water at different depths. Here we derive the upper depth of the wet layer on the ice sheet—henceforth referred to as the depth of liquid water (DLW)—from brightness temperature (BT) observations at 1.4, 6.9, 10.7, and 18.7 GHz from the Soil Moisture and Ocean Salinity (SMOS) satellite and Advanced Microwave Scanning Radiometers (AMSR-E, AMSR2), combined with snow and radiative transfer modeling and machine learning.
First, to understand the response of multi-frequency TB to the presence of liquid water in the snow, we build the following simulation catalogue. The GEUS snow model (Vandecrux et al.,
2018,
2020a,
2020b)
was run at 19 sites in the accumulation area of the Greenland Ice Sheet using the Copernicus Arctic Regional Reanalysis (CARRA) as forcing. The Snow Microwave Radiative Transfer (SMRT) model from
Picard et al. (2018)
calculated the daily (6 AM) brightness temperature (TB) at four frequencies using as input the simulated profiles of snow temperature, density, and grain size from the GEUS snow model. The coupling between these two models was optimized through the adjustment of two parameters for each site and each year. First, the number of pure ice layers to be considered in the SMRT input—based on the ice content simulated by the GEUS snow model—was optimized to maximize the match between observed and simulated winter vertically polarized TB at 1.4 GHz. Then, a multiplicative correction factor applied to the GEUS snow model's simulated grain diameter was also optimized each year and at each site to maximize the match between observed and simulated vertically polarized TB at 6.9, 10.7, and 18.7 GHz.
Then we train an ensemble of 14 random forest (RF) models on this simulation catalogue, each RF model leaving one year out of the data. As input, the RF models take the vertically polarized BT at our four frequencies of interest, along with the same TB normalized between the preceding winter TB and 273.15 K (i.e., TB_norm = 0 if TB = TB_winter and TB_norm = 1 if TB = 273.15). Once trained, the RF model ensemble provides a prediction of the DLW and an evaluation of its uncertainty: DLW_std, the ensemble's standard deviation for a given prediction. We recommend discarding retrievals with DLW_std > 1. To ensure that values are only retrieved when water is detected on the ice sheet, we only consider times and pixels when the surface wetness maps from Zeiger et al.
(2024)
derived from both vertically and horizontally polarized TB at 1.4 GHz, indicate water at or below the surface.
Please refer to, and cite:
Vandecrux, B., Picard, G., Zeiger, P., Leduc-Leballeur, M., Colliander, A., Hossan, A., & Ahlstrøm, A. (submitted). Estimating the depth of subsurface water on the Greenland Ice Sheet using multi-frequency passive microwave remote sensing, radiative transfer modeling, and machine learning. Remote Sensing of Environment.
This dataset was produced under the European Space Agency Climate Change Initiative research fellowship
Water Under Snow Cover.
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Compositional data of biochar samples
No full textData from analysis of ten biochar samples produced from wood, straw and nut shells. Data include H/C molar ratios, mean random reflectance (%Ro) values, carbon fractions, and Fperm
Estimating potential groundwater contribution of nickel, copper, cadmium, lead, and zinc concentrations to surface water catchments in Denmark
No full textThis dataset includes estimates of the typical groundwater contribution of nickel, copper, cadmium, lead, and zinc to the ID15 catchments. The methodology is described in detail and illustrated for nickel (see pdf file). The data is provided as a shapefile and csv file.
This work was funded by the Danish EPA (Miljøstyrelsen) in relation to "Videreudvikling og klargøring af MetalStat for landsdækkende beregninger." (Sørensen et al., 2024) and "Unifying monitoring and modelling of water concentration levels in surface waters (Version 2)" (Sorensen et al., 2025)
PROMICE-2022 Ice Mask
No full textPROMICE-2022 Ice Mask
The PROMICE-2022 Ice Mask is a high-resolution outline of the contiguous ice masses of the Greenland Ice Sheet. The dataset is derived from a true-colour, multi-band mosaic of Sentinel-2 satellite images at 10 m resolution, compiled using the SentinelHub Cloud Processing API. The mosaic was generated using the most recent valid pixels from August 2022, ensuring high temporal and geometric accuracy.
Manual editing and mapping was conducted at a scale of around 1:25,000, after which quality assessment was performed independently of the mapping operator, before finally being merged into one coherent dataset. The manual mapping process is further supported by data from the Danish Agency for Climate Data (KDS), including mosaics of Sentinel-2 and SPOT 6/7 imagery, as well as recent vector data from topographical mapping.
Issues with the dataset
We welcome feedback from the scientific glaciology community regarding the PROMICE-2022 Ice Mask dataset. Issues, suggestions, or corrections can be submitted via the project’s
GitHub repository.
Terms of use
If the data are presented or used to support results of any kind, please include an acknowledgement and references to the applicable publications:
Include a reference to the peer-reviewed article presenting the PROMICE-2022 Ice Mask:
Luetzenburg G. et al. (preprint) PROMICE-2022 Ice Mask: A High-Resolution Outline of the Greenland Ice Sheet from August 2022. Earth Syst. Sci. Data.
https://doi.org/10.5194/essd-2025-415
Include a reference to the data itself (see citation and DOI at the top of this page).
If the data are crucial to the main conclusions of a manuscript or presentation of any kind, please contact a relevant member of the PROMICE team at GEUS and include them in the manuscript author list.
Detailed description
The PROMICE-2022 Ice Mask is provided as line and polygon vector layers in the GeoPackage format (.gpkg), with coordinates referenced in the WGS NSIDC Sea Ice Polar Stereographic North projection (EPSG:3413). The 10 m datasets are provided as sparse rasters, meaning only the pixels along the ice margin and nunatak outlines are stored. This reduces file size by several orders of magnitude while preserving the high-resolution geometry needed to mask or classify remote-sensing imagery. Users may fill the raster inward if needed.
The ice sheet boundary is classified into land-terminating and marine-terminating sectors. The dataset includes all contiguous ice that forms part of the Greenland Ice Sheet, as well as local glaciers and ice caps that are physically connected to it. Debris-covered ice, including lateral and medial moraines, stagnant or dead ice, snowfields, and supraglacial lakes are included. Ice-marginal lakes and unconnected peripheral glaciers or ice caps are excluded.
Dataset contents
The PROMICE-2022 Ice Mask is derived from a true-colour Sentinel-2 mosaic from August 2022 and includes the following files:
00-README-PROMICE-2022-IceMask.md: Dataset README file
01-PROMICE-2022-IceMask-line.gpkg: Ice sheet outline (line features)
02-PROMICE-2022-IceMask-polygon.gpkg: Ice sheet outline (polygon features)
03-PROMICE-2022-Nunatak-line.gpkg: Nunatak outlines (line features)
04-PROMICE-2022-Nunatak-polygon.gpkg: Nunatak outlines (polygon features)
05-PROMICE-2022-IceMask-Nunatak-line.gpkg: Combined ice sheet + nunatak outlines (line features)
06-PROMICE-2022-IceMask-Nunatak-polygon.gpkg: Ice sheet with nunataks removed (polygon)
07-PROMICE-2022-IceMask-CL1-polygon.gpkg: Ice sheet with CL1 glaciers following Rastner et al. 2012
08-PROMICE-2022-IceMask-basins-polygon.gpkg: Ice sheet mask by drainage basins (Mouginot et al. 2019) with CL1 delineations
09-PROMICE-2022-IceMask-Nunatak-basins-polygon.gpkg: Ice sheet without nunataks, basin-divided and intersected by CL1
10-PROMICE-2022-IceMask-raster-10m.tif: Sparse raster (10 m)
11-PROMICE-2022-Nunatak-raster-10m.tif: Nunatak sparse raster (10 m)
12-PROMICE-2022-IceMask-Nunatak-raster-10m.tif: Combined ice + nunatak sparse raster (10 m)
13-PROMICE-2022-IceMask-raster-150m.gpkg: Raster (150 m) aligned to BedMachine v4 (Morlighem et al. 2017)
14-PROMICE-2022-DOY-polygon.gpkg: Sentinel-2 mosaic DOY footprints
15-PROMICE-2022-DOY-line.gpkg: Ice mask/nunatak outlines attributed with DOY
Each file name is followed by its version number. Please consult the README for further metadata.
Related Datasets
PROMICE-2022 Ice Mask Sentinel-2 RGB Mosaic (August 2022)
This companion dataset provides a Greenland-wide Sentinel-2 RGB mosaic from August 2022, used as the primary reference layer during manual delineation of the ice mask.
Access it here:
https://doi.org/10.22008/FK2/OUKHBW
PROMICE-2022 Ice Mask QGIS Bundle
This bundle provides a ready-to-use QGIS project containing the PROMICE-2022 ice mask, WMS layers (Sentinel-2, SPOT 6/7, topographic maps), layer styles, and preconfigured map layouts for immediate use in a GIS environment.
Access it here:
https://doi.org/10.22008/FK2/ARQ2L1
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