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774 results for “glacier”
Advance and retreat of glaciers during the end of the Little Ice Age in Europe
<p>Input data and model results of Huss&Förster (2019), L'avancée et le recul des glaciers pendant le Petit Age Glaciaire. Actes du colloque du Giétro, 14./15.juin, 2018. In: Annales Valaisannes, publication of the SHVR.</p> <p>Refer to file header for the description of all variables.</p> <p> </p>
Data used in meta-analysis of debris-covered glacier melt
<p>The data used in the study Accuracy of Empirical Models of Debris-Covered Glaciers, A Winter-Billington, RD Moore and R Dadic, in prep. for submission to the Journal of Glaciology.</p>
Vanishing glaciers: a cause of sea-level rise and a threat to water supply
<p><span>The video discusses the contribution of glaciers to sea-level rise and their importance for humans. Due to their proximity to 0°C temperature, glaciers respond much faster to global warming than ice sheets, making their mass loss a significant contributor to sea-level rise during the 20th century and beyond. To determine the health state of glaciers and their contribution to sea-level rise, glaciologists calculate their mass budget, which has been largely negative for several decades now, indicating that glaciers are losing mass year after year, causing them to retreat. The video emphasizes the need for immediate reductions of greenhouse gas emissions to preserve these crucial and vulnerable water resources and natural heritage.</span></p>
Drone-based glacier mapping of Nordenskiöldbreen and Tunabreen in Svalbard, 2024
<p>This database contains drone-based mapping data of two glaciers, Svalbard, Norway. The glaciers are Tunabreen in Templefjord and Nordenskiöldbreen in Billefjord. The dataset was generated using a structure-from-motion (SfM) method using drone-based imagery. The data was processed with Agisoft Metashape and the processed data consists of digital elevation models (DEMs) in georeferenced .TIF file format, orthomosaic maps in georeferenced .TIF file format, and textured 3D models in .STL and .JPG file format. In addition, a process report in .PDF file format is included for each dataset. Mapping was conducted with a DJI Mavic 3 Pro Enterprise. The mapping area covers the crevassed glacier fronts. Data collection was conducted on Nordenskiöldbreen in Spring 2024 (2024-04-29) and Tunabreen in Summer 2024 (2024-09-04). </p> <p> </p> <p>00_ReadMe.txt <br>01_2024_Tunabreen_Images.zip - Contains all raw images. <br>01_2024_Nordenskiöldbreen_Images.zip- Contains all raw images. <br>02_2024_Tunabreen_DEM.tif - Contains the digital elevation model.<br>02_2024_Nordenskiöldbreen_DEM.tif- Contains the digital elevation model.<br>03_2024_Tunabreen_Ortho.tif - Contains the orthomosaic map.<br>03_2024_Nordenskiöldbreen_Ortho.tif - Contains the orthomosaic map.<br>04_2024_Tunabreen_Report.pdf - Contains the post-processing report. <br>04_2024_Nordenskiöldbreen_Report.pdf- Contains the post-processing report. <br>05_2024_Tunabreen_Model.stl - Contains the 3D model. <br>05_2024_Nordenskiöldbreen_Model.stl - Contains the 3D model. <br>06_2024_Tunabreen_Texture.jpg - Contains the 3D model texture. <br>06_2024_Nordenskiöldbreen_Texture.jpg - Contains the 3D model texture. </p>
Drone-based glacier mapping of Borebreen in Svalbard, 2024
<p>This database contains drone-based glacier mapping data of Borebreen, Svalbard, Norway. The glacier is located in Isfjorden. The dataset was generated using a structure-from-motion (SfM) method using drone-based imagery. The data was processed with Agisoft Metashape and the processed data consists of digital elevation models (DEMs) in georeferenced .TIF file format, orthomosaic maps in georeferenced .TIF file format, and textured 3D models in .STL and .JPG file format. In addition, a process report in .PDF file format is included for each dataset. Mapping was conducted with a DJI Mavic 3 Pro Enterprise. The mapping area covers the crevassed glacier fronts. Data collection was conducted over three days in August/September 2024. The three collection dates were 2024-08-14, 2024-08-28, and 2024-09-25. </p>
Modeling of Greenland Peripheral Glaciers - Supporting data
<p>This data is related to section 4.9 (Figure 12) in the "Kanzow, T., Humbert, A., Mölg, T., Scheinert, M., Braun, M., Burchard, H., Doglioni, F., Hochreuther, P., Horwath, M., Huhn, O., Kusche, J., Loebel, E., Lutz, K., Marzeion, B., McPherson, R., Mohammadi-Aragh, M., Möller, M., Pickler, C., Reinert, M., Rhein, M., Rückamp, M., Schaffer, J., Shafeeque, M., Stolzenberger, S., Timmermann, R., Turton, J., Wekerle, C., and Zeising, O.: The atmosphere-land/ice-ocean system in the region near the 79N Glacier in Northeast Greenland: Synthesis and key findings from GROCE, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2024-757, 2024."</p>
Area, volume and ELA changes of West Greenland local glaciers and ice caps over the last 35 years
<p>This dataset refers to:</p> <p><em>Area, volume and ELA changes of West Greenland local glaciers and ice caps over the last 35 years</em><br><em>Securo Andrea, Del Gobbo Costanza, Citterio Michele, Machguth Horst, Marcer Marco, Korsgaard Niels J., Colucci Renato R.</em></p> <p>This study analyses the cumulative area, ice mass and Equilibrium Line Altitude changes that occurred on more than 4000 glaciers and ice caps in West Greenland (outside of the Greenland Ice Sheet) from 1985 to 2020, using remotely sensed data and including glaciers smaller than 1 km2 in the calculations.</p> <p>This dataset contains:</p> <ol> <li><strong>1_Area_1985_2020.csv</strong> - Comma Separated Value file with the following data for all Glaciers and ice caps involved in the study:<br>Area Loss from 1985 to 2020 (km2), Total Area of 1985 (km2), Relative Area Loss from 1985 to 2020 (%), Longitude, Latitude</li> <li><strong>2_Area_Loss_1985_2020.gpkg</strong> - Geopackage file contanining the same information as (1.) but including the centroids positions. EPSG 4326 WGS84</li> <li><strong>3_Volume_and_ELA_1985_2020.csv</strong> - Comma Separated Value file with the following data for all Glaciers and ice caps involved in the study: GLIMS Glacier ID, minimum elevation (m a.s.l.), mean elevation (m a.s.l.), maximum elevation (m a.s.l.), Surface elevation change 1985-Present (m), Glacier Area from RGI (km2), Ice Mass Loss (Gt), 1985 mean ice thickness from Millan and others (m), Relative volume loss from 1985 to Present (%), Longitude, Latitude, Equilibrium Line Altitude* (m)</li> <li><strong>4_Volume_and_ELA_1985_2020.gpkg</strong> - Geopackage file contanining the same information as (3.) but including the centroids positions. EPSG 4326 WGS84</li> </ol> <p>* note that not all glaciers and ice caps have ELA value for present.</p>
Linked collectors and determiners for: Bitentaculate Cirratulidae (Annelida, Polychaeta) collected chiefly during cruises of the R / V Anton Bruun, USNS Eltanin, USCG Glacier, R / V Hero, RVIB Nathaniel B. Palmer, and R / V Polarstern from the Southern Ocean, Antarctica, and off Western South America.
Natural history specimen data linked to collectors and determiners held within, "Bitentaculate Cirratulidae (Annelida, Polychaeta) collected chiefly during cruises of the R / V Anton Bruun, USNS Eltanin, USCG Glacier, R / V Hero, RVIB Nathaniel B. Palmer, and R / V Polarstern from the Southern Ocean, Antarctica, and off Western South America". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/897dc544-e3e9-46db-b36e-4aa995caabc4">https://bionomia.net/dataset/897dc544-e3e9-46db-b36e-4aa995caabc4</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/897dc544-e3e9-46db-b36e-4aa995caabc4">https://gbif.org/dataset/897dc544-e3e9-46db-b36e-4aa995caabc4</a>. Formatted as a Frictionless Data package.
Surface Evolution of the Belvedere Glacier from aerial and UAV orthophotos from 1951 to 2023 - glacier outlines dataset
Open the record for dataset details and reuse information.
Timeseries of simulated glacier meltwater runoff for primary hydrological regions in Svalbard
<p>Timeseries of annual cumulative glacier meltwater runoff for 14 primary hydrological regions of Svalbard, as well as one subregion, for the period September 2003 to September 2013. Regional glacier meltwater runoff are extracted from climatic mass balance simulations for all glaciers in Svalbard published by Aas et al., 2016, "The climatic mass balance of Svalbard glaciers: a 10-year simulation with a coupled atmosphere–glacier mass balance model", doi:10.5194/tc-10-1089-2016) and used in a manuscript by Dunse et al., 2021, "Regional-scale phytoplankton dynamics and their association with glacier meltwater runoff in Svalbard", submitted to the EGU journal Biogeosciences in July 2021</p>
Density maps of seismic localization with a low phase coherence at 13 Hz on Argentière glacier
<p>This dataset is associated with the paper:</p> <p>----------</p> <p><strong>Dynamic imaging of glacier structures at high-resolution using source localization: a dense seismic array experiment.</strong></p> <p> </p> <p><em>Ugo Nanni<sup>1,*</sup>, Philippe Roux<sup>2</sup>, Florent Gimbert<sup>1</sup> and Albanne Lecointre<sup>2</sup></em></p> <p> </p> <p><em><sup>1</sup> IGE, Univ. Grenoble Alpes, CNRS, IRD, Grenoble, France</em></p> <p><em><sup>2</sup> ISTerre, Univ. Grenoble Alpes, Univ. Savoie Mont Blanc, CNRS, IRD, IFSTTAR, Grenoble, France</em></p> <p><em>---------</em></p> <p>It contains 32 maps of seismic localization associated with low phase coherence and frequency of 13 Hz, and represents the events originating from transverse crevasses (see details in the paper).</p> <p>An image of the glacier is also provided to compare the localization to the glacier structure.</p> <p>Dataset is in .mat format</p> <p> </p>
Surface elevation (2012-2020) and ice thickness (2014-2020) datasets measured at Artesonraju Glacier, Cordillera Blanca, Perú
<p>We present a set of data of interpreted ice thickness and ice surface elevation of Artesonraju glacier.<br> The ice thickness was obtained by means of Ground Penetrating Radar (GPR) which was measured by Instituto Nacional de Investigación en Glaciares y Ecosistemas de Montaña (INAIGEM) and Autoridad Nacional de Agua (ANA). The years of ice thickness records include 2013, 2014, 2015, 2017, 2018, and 2020. On the other hand, the surface elevation points were obtained by means of automated total stations and mass balance stakes, integrally measured by ANA. The years of measurements are 2012, 2014, 2015, 2017, 2018, 2019, and 2020.<br> The results from GPR data show a maximum depth of 235±18 m and a decreasing mean depth of ranging from 134±18 m in 2013 to 110±18 m in 2020. Additionally, we estimate a mean ice thickness change rate of 4.2±3.2 m yr<sup>-1</sup> between 2012 and 2020 with GPR data alone, which is in agreement with the elevation change in the same period. The latter was estimated with the more accurate surface elevation data, yielding a change rate of -3.2±0.2 m yr<sup>-1</sup>, and hence, confirming a negative glacier mass balance. The datasets can be valuable for further analysis when combined with other data types, and as input for glacier dynamics modeling, ice volume estimations, and GLOF risk assessment.</p>
Fracture maps and calving fronts for Thwaites Glacier western terminus 2015-2021
<p>These data comprise observations of severe crevassing and calving front position over the Thwaites Glacier Ice Tongue (TGIT) between 2015 and 2021 in geotiff form, along with bitmap versions of Sentinel-1 backscatter images from which the observations were derived. A version of UNet was used to create the data from the backscatter images.<br> These data were collected in 2021 for the study of structural change on the TGIT.</p> <p>File information: tgit_cfs.tar.gz is a gz-compressed directory of binary calving front segmentations of the Thwaites Glacier Ice Tongue in geotiff format.<br> tgit_fms.tar.gz is a gz-compressed directory of binary fracture segmentations of the Thwaites Glacier Ice Tongue in geotiff format.</p>
Long-term snow chemical composition monitoring - Hansbreen glacier (Hornsund) - raw data
<p>During the accumulation season, snow samples were taken on Hansbreen Glacier. Several times per season. Snow samples were collected in polyethylene sterile bags and transported to the Polish Polar Station Hornsund. After melting at room temperature, the pH, conductivity and chemical composition (major ions) were analysed in the chemical laboratory of the Polish Polar Station.<br> Snow chemical composition: major ions, HCO3-, pH, conductivity</p> <p>Presented data from 2015 to 2019</p> <p>The data has not been checked, which means that it is raw data.</p> <p>Principal Investigator (PI) Adam Nawrot</p>
SfM-MVS derived orthomosaics of the Otemma glacier forefield (2020)
<p><strong>Orthomosaics (2020) of the Otemma glacier forefield derived from SfM-MVS photogrammetry</strong></p> <p>This dataset includes the orthomosaics of the Otemma glacier forefield generated through SfM-MVS photogrammetry. This dataset is based upon the images collected during summer 2020. Details on the image acquisition and image processing can be found in Roncoroni et al. (2022) at this URL: https://doi.org/10.1080/01431161.2022.2079963</p> <p>Details:</p> <ul> <li>Format: .tif </li> <li>Name format: mmddyyyy<em>x</em>m_Orthomosaic (where mm is the month, dd the day, yyyy the year, and xm is AM or PM)</li> <li>Coordinate system: CH1903+ LV95 (EPSG:2056)</li> <li>Spatial resolution: 0.05 m</li> </ul> <p> </p>
Digital elevation models, ortho images and outlines of Yala Glacier, Langtang Valley, Nepal Himalaya
<p>Datasets related to article "Up-glacier propagation of surface lowering of Yala Glacier, Langtang Valley, Nepal Himalaya". The data includes three digital elevation models (DEM), two ortho images and five outlines of Yala Glacier between 1981 and 2015.<br> </p> <p>Description of files:<br> 1) DEM and ortho image</p> <p>- 1981Yala_dem_20_-10_bias_cor.tif: 10 m resolution digital elevation model derived from a map that was generated using ground phogogrammetry images that were acquired in 1981 (Yokoyama, 1984; Fujita and Nuimura).</p> <p>- 2007Yala_dem_0_-4_bias_cor.tif: 2 m resolution digital elevation model derived from 14 oblique photographs that were acquired by a private jet with handheld cameras in 2007.<br> - 2007Yala_ortho.tif: an ortho images derived by the same data in 2007.</p> <p>- 2015Yala_dem_0_0_bias_cor.tif: 1 m resolution digital elevation model derived from 519 photographs that were acquired by a UAV-based photogrammetric survey in 2015.<br> - 2015Yala_ortho.tif: an ortho images derived by the same data in 2015.<br> <br> 2) Glacier boundary (shapefile Files)</p> <p>- Yala_area_1981: <br> - Yala_area_2007:<br> - Yala_area_2009:<br> - Yala_area_2012:<br> - Yala_area_2015:<br> <br> <br> Please see the related journal article for details on datasets.<br> <br> Sunako, S., Fujita, K., Izumi, T., Yamaguchi, S., Sakai, A., & Kayastha, R. (2023). Up-glacier propagation of surface lowering of Yala Glacier, Langtang Valley, Nepal Himalaya. Journal of Glaciology, 69(274), 425-432. doi:10.1017/jog.2022.118<br> </p>
Data for 'Controls on Ice Cliff Distribution and Characteristics on Debris-Covered Glaciers'
<p>This dataset contains ice cliff, pond, debris, glacier and crevasse zone outlines (as shapefiles) derived for the study 'Controls on Ice Cliff Distribution and Characteristics on Debris-Covered Glaciers'.</p> <p>They were outlined from 14 Pléiades multi-spectral images (see Methods and tables in corresponding manuscript and SI) acquired at the dates indicated in the corresponding folder names.</p> <p>The majority of the Pléiades stereo-pairs used were provided by Etienne Berthier via the Pléiades Glacier Observatory (PGO) initiative of the French Space Agency (CNES). The remaining images were acquired through the CNES ISIS Programme. </p>
Output of several experiments with Fenics_ice over Smith, Pope, and Kohler Glaciers
<p>Output of several experiments with <a href="https://github.com/EdiGlacUQ/fenics_ice">Fenics_ice</a> over Smith, Pope, and Kohler Glaciers. The code to produce this output can be found in the following repository; <a href="https://github.com/bearecinos/smith_glacier">Smith_glacier</a></p> <p>More information on how to read and plot this data can be found in the smith glacier repository <a href="https://github.com/bearecinos/smith_glacier/wiki">wiki</a>: </p> <p>Citation for the smith glacier repository used to produce this data:</p> <pre>https://zenodo.org/badge/latestdoi/101511241</pre> <p>Citation for the Fenics_ice version used to produce this data:</p> <pre>https://zenodo.org/badge/latestdoi/417440075</pre> <p> </p>
The MAMMAMIA project: A multi-scale multi-method approach to understand runoff-induced changes in the subglacial environment and consequences for surge dynamic in Kongsvegen glacier, Svalbard
<p> </p> <p>Data set at a 3h resolution of all the data used in the study ''The MAMMAMIA project: A multi-scale multi-method approach to<br> understand runoff-induced changes in the subglacial environment<br> and consequences for surge dynamic in Kongsvegen glacier,<br> Svalbard', Coline bouchayer, Ugo Nanni, Pierre-Marie Lefeuvre, John Hulth, Louise Schmidt, Jack Kohler, Francois Renard and Thomas V. Schuler. The mansucript is in prepartaion to be submitted to The Crysophere (@ Add DOI when submitted).</p>
dataset of: "Glacier projections sensitivity to temperature-index model choices and calibration strategies"
<p>data for <strong><em>Schuster, L., Rounce, D. R., and Maussion, F.: Glacier projections sensitivity to temperature-index model choices and calibration strategies, Annals of Glaciology, 2023, <a href="https://doi.org/10.1017/aog.2023.57" target="_blank" rel="noopener"><span>https://doi.org/10.1017/aog.2023.57 </span></a></em></strong><br><br><strong>When using this dataset, please refer to the original publication in addition to this Zenodo repository.</strong></p> <p>You can find the code to create the data and plot the figures in the <a href="https://github.com/lilianschuster/oggm_mb_sandbox_option_intercomparison">oggm_mb_sandbox_option_intercomparison GitHUB repository</a>.<br> </p>
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International Brain Laboratory public data
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OpenNeuro
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