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774 results for “glacier”
New glacier thickness and bed topography maps for Svalbard - Dataset
<p>The dataset includes three Geotiff files that include:</p> <p>1) A bed topography map of Svalbard (heights in m a.s.l.) [Bed_map.tiff]</p> <p>2) An ice thickness map of Svalbard (in m) [Thickness_map.tiff]</p> <p>3) A mask file that with values from 0-3 indicating non-glacier areas (0), glaciers modelled with the Parallel Ice Sheet model (1), glaciers modelled with the Instructed Glacier Model (2), and surging glaciers (3). </p> <p>For a description of the methods used to generate the datasets, we refer to the manuscript "A new glacier thickness and bed map for Svalbard", to be submitted to The Cryosphere Discussions.</p>
Data: Projecting the Response of Greenland's Peripheral Glaciers to Future Climate Change: Glacier Losses, Sea Level Impact, Freshwater Contributions, and Peak Water Timing
<p>The dataset contains supporting data for the paper submitted to The Cryosphere "Projecting the Response of Greenland's Peripheral Glaciers to Future Climate Change: Glacier Losses, Sea Level Impact, Freshwater Contributions, and Peak Water Timing".<br><br>OGGM_area_projections.nc contains data for Figure 3.<br>OGGM_volume_projections contains data for Figure 4.</p> <p>OGGM_MassLoss_SLR_projections_regions.nc contains data for Figure 5.</p> <p>OGGM_solid_ice_discharge_regions.nc contains data for Figure 6.</p> <p>OGGM_freshwater_runoff_magnitude_composition_timings_projections.nc & OGGM_freshwater_runoff_projections_regions.nc contain data for Figure 7.</p> <p>OGGM_PeakWaterYear_projections_regions.nc contains data for Figure 8.</p>
Arctic glaciers mass balance from satellite gravimetry only
<p>This document describes the LEGOS-Magellium mass balance dataset for Arctic glacier regions based on satellite gravimetry only. A similar dataset using an a priori based on DEM differencing has been submitted to GLAMBIE (Pfeffer et al., 2024). previously. The dataset submitted here does not use an a priori, but uses only satellite gravimetry data. </p> <p>The total mass balance is evaluated for five regions of the 6th version of the Randolph Glacier Inventory , namely the Arctic Canada North, Arctic Canada South, Iceland, Svalbard, and, Russian Arctic. The total mass balance of Arctic glaciers is evaluated mainly based on satellite gravimetry measurements. An ensemble approach updated from Blazquez et al. (2018) is adopted to evaluate uncertainties associated with the processing and post-processing of GRACE (Gravity Recovery And Climate Experiment) and GRACE-FO (GRACE-Follow On) data. The effect of land hydrology is estimated for each region, but not corrected in the total mass balance dataset, because of the small water mass balance values and large errors inherent to hydrological models. Total mass changes expressed in Gt are estimated from April 2002 to September 2022 for five RGI regions. The uncertainty on total mass changes is provided with a confidence interval of 95%. The dataset is provided in the GlaMBIE CSV file format.</p> <p>The data product has been developed in collaboration between LEGOS and Magellium within the scope of the hybridation<br>challenge funded by the CNES (R&T Hybrid Spatial Gravimetry 2022/2023).</p> <p><strong>Reference</strong>:</p> <ul> <li>Blazquez, A., Meyssignac, B., Lemoine, J., Berthier, E., Ribes, A., & Cazenave, A. (2018). Exploring the uncertainty in GRACE estimates of the mass redistributions at the Earth surface : Implications for the global water and sea level budgets. Geophysical Journal International, 215(1), 415‑430. https://doi.org/10.1093/gji/ggy293</li> <li>Pfeffer, J., Coupry, B., Berthier, E., Blazquez, A., & Barnoud, A. (2024). Arctic Glaciers Mass Balance from satellite gravimetry and DEM differencing [Jeu de données]. Zenodo. https://doi.org/10.5281/ZENODO.13134559</li> </ul> <p> </p>
Glacier inventory of the upper Huasco valley, Norte Chico, Chile
<p>Shapefile of the glacier inventory of the Upper Huasco catchment, Chile, that was generated for the following research article:</p> <p>Nicholson, L. I., Marín, J., Lopez, D., Rabatel, A., Bown, F. and Rivera, A.: Glacier inventory of the upper Huasco valley, Norte Chico, Chile: glacier characteristics, glacier change and comparison with central Chile, Ann. Glaciol., 50(53), 111–118, doi:10.3189/172756410790595787, 2010.</p> <p>Glaciers were mapped on the basis of ASTER imagery from 2004, and classifed for the study as clean ice glaciers (1), debris covered glaciers (2) and rock glaciers (3). Additionally, classification following the GLIMS gudeilines (https://www.glims.org/.../GLIMS_Glacier-Classification-Manual_V1_2005-02-10.pdf ) was applied where possible to glacier features on the basis of additional ASTER imagery from 2002 and 2003, and older air photographs.</p>
Brewster Glacier AWS data 2010-2012
<p>Meteorological data collected over Brewster Glacier, New Zealand for the period October 2010-September 2012.</p> <p>The data sets have undergone quality control and are described in:</p> <p>Cullen, N. J. and Conway, J. P.: A 22 month record of surface meteorology and energy balance from the ablation zone of Brewster Glacier, New Zealand, Journal of Glaciology, 61, 931-946, 2015.<br> Conway, J. P. and Cullen, N. J.: Cloud effects on surface energy and mass balance in the ablation area of Brewster Glacier, New Zealand, The Cryosphere, 10, 313-328, 2016.</p> <p>The data is the same as that submitted to the ESM SNOWMIP project in 2018. </p> <p>The readme file gives a brief description of the data in each column.</p> <p>Please get in touch if you would like more details - jono.conway@niwa.co.nz or nicolas.cullen@otago.ac.nz<br> </p>
DEM and orthophoto argentiere glacier - 13/09/2019
<p>DEM at 10 cm and 100 cm resolution / ortho-image at 5 and 10cm resolution made by drone flight on 13/09/2019 on the Argentière glacier (2350m on average - profil 4 Glacioclim SNO)</p>
Displacements on selected rock glaciers in the United States
<p>Lateral displacements between two years on selected rock glaciers in the United States (Rocky Mountains and westwards). Displacements were measured using image correlation on repeat orthoimages from the United States Geological Survey archive. Correlation software used: CIAS (http://mn.uio.no/icemass).</p> <p><em><strong>The data are the raw data behind the following publication. This publication contains more details about the measurements. </strong></em></p> <p><a href="https://www.nature.com/articles/s41467-024-52093-z">Kääb, A., Røste, J. Rock glaciers across the United States predominantly accelerate coincident with rise in air temperatures. Nat Commun 15, 7581 (2024). https://doi.org/10.1038/s41467-024-52093-z</a></p> <h3>Detailed rock glacier positions [lat°, lon°]:</h3> <p>Star Peak [44.253,-120.417]<br>Galena creek [44.645,-109.791]<br>Sulphur creek [44.617,-109.756]<br>Crater Mtn [44.023,-109.627]<br>Old Hyndman [43.743, -114.106]<br>Ferguson ranch [39.270, -107.194]<br>Thomas lake [39.270,-107.155]<br>Arapaho [40.020,-105.641]<br>Mt Mears [38.018,-107.871]<br>Mt Sneffels [38.010,-107.781]<br>Teakettle Mtn [38.011,-107.768]<br>Twin Sisters [37.762,-107.803]<br>Pine creek [37.078,-118.450]<br>Birch creek [37.065,-118.431]<br>Cardinal Mtn N [37.011,-118.414]<br>Cardinal Mtn S [37.008,-118.409].</p> <h3>Names of individual displacement files:</h3> <p>Short-name-of-rock-glacier_year1_year2_correlation-window-size_search-window-size_xxx.txt/.csv<br>xxx is either 'helm' indicating that the two orthoimages have been coregistered using Helmert transformation, or 'filt' indicating Helmert transformation and filtering of grid-based measurements for outliers. </p> <h3>Data columns of each file:</h3> <p>X: UTM coordinate, Easting of displacement measurement point <br>Y: UTM coordinate, Northing of displacement measurement point<br>dx: displacement component in east<br>dy: displacement component in north<br>length: pythagoras of dx and dy (vector length)<br>direction: azimuth of vector (from North, clock-wise)<br>max_corrcoeff: correlation coefficient of displacement match<br>avg_corrcoeff: background correlation coefficent at a matching poistion. <br><em><strong> </strong></em></p> <p> </p>
Model data for "Melt sensitivity of irreversible retreat of Pine Island Glacier"
<p>Model inputs and outputs for the experiments in Reed et al., 2024 "Melt sensitivity of irreversible retreat of Pine Island Glacier".</p>
Atmospheric sounding of the boundary layer over alpine glaciers using fixed-wing UAVs
<p>Additional code and data for the paper by Groos et al. entitled "Atmospheric sounding of the boundary layer over alpine glaciers using fixed-wing UAVs"</p> <p>Correspondence: Alexander R. Groos (alexander.groos@fau.de)</p> <p><br>The repository contains:<br>(1) The raw data (log files) for each UAV-based atmospheric sounding<br>(2) The postprocessed and reformatted data for each sounding and vertical profile<br>(3) The commented R-Scripts for data processing, analysis and visualisation<br>(4) A subset of the meteorological data from the nearby weather stations</p> <p><br>Description of sub-folders:</p> <p>-aws_data<br>-- aws_fisistock.txt # meteorological data from AWS Fisistock for the period of the campaign<br>-- aws_gandegg.txt # meteorological data from AWS Gandegg for the period of the campaign<br>-- aws_sackhorn.txt # meteorological data from AWS Sackhorn for the period of the campaign</p> <p>- processed_data<br>-- kanderfirn_2021-06-16_10:45_p1_pprz.tab # meteorological data for first profile/descent at about <br>-- kanderfirn_2021-06-16_10:45_p2_fr.tab # flight recorder data for second profile/descent at about 10:45 CEST<br>-- kanderfirn_2021-06-16_10:45_p2_pprz.tab # meteorological data for second profile/descent at about 10:45 CEST<br>-- kanderfirn_2021-06-16_10:45_pprz.tab # meteorological data for the entire sounding (first and second profile/descent) at about 10:45 CEST<br>-- .<br>-- .<br>-- .<br>-- kanderfirn_2021-06-16_16:50_p1_pprz.tab # meteorological data for first profile/descent at about 16:50 CEST<br>-- kanderfirn_2021-06-16_16:50_p2_fr.tab # flight recorder data for second profile/descent at about 16:50 CEST<br>-- kanderfirn_2021-06-16_16:50_p2_pprz.tab # meteorological data for second profile/descent at about 16:50 CEST<br>-- kanderfirn_2021-06-16_16:50_pprz.tab # meteorological data for the entire sounding (first and second profile/descent) at about 16:50 CEST<br>-- kanderfirn_soundings_2021-06-16.csv # summary table of vertical profiles (1 m height intervals): one column for each profile/descent and variable<br>-- kanderfirn_turbulence_2021-06-16.csv # summary table of vertical turbulence profiles (1 m height intervals): one column for each profile/descent</p> <p>- raw_data<br>-- fr_kanderfirn_2021-06-16_10:45.LOG # flight recorder data from the sounding at about 10:45 CEST (binary file)<br>-- .<br>-- .<br>-- .<br>-- fr_kanderfirn_2021-06-16_16:50.LOG # flight recorder data from the sounding at about 16:50 CEST (binary file)<br>-- pprz_kanderfirn_2021-06-16_10:45.LOG # meteorological data from the sounding at about 10:45 CEST (human readable text file)<br>-- .<br>-- .<br>-- .<br>-- pprz_kanderfirn_2021-06-16_16:50.LOG # meteorological data data from the sounding at about 16:50 CEST (human readable text file)</p> <p>- R_scripts<br>-- figures.R # Script to create Figures 5, 6, 8, 9, 10, 11, 12<br>-- lapse_rate.R # Script to calculate lapse rates and surface-based inversions (includes code for Figures 7 and B1)<br>-- postprocessing.R # Script to reformat preprocessed and preselected pprz-files<br>-- turbulence.R # Script for the calculation of the turbulence proxy from the recorded roll rate</p>
Width-averaged terminus position of Helheim Glacier, 2002-2019
<p>Terminus position of Helheim Glacier (2002-2019) in terms of a width-averaged distance [km] from an upstream flux gate. Positions are identified from satellite imagery---primarily Moderate Imaging Spectroradiometer (MODIS) imagery, but incorporating Landsat and Sentinel-2 imagery when available. Terminus positions are derived manually until 2010 (Schild & Hamilton, 2013) and using a semi-automated technique thereafter (Foga, Stearns & van der Veen, 2014). </p>
Model output from Inverting ice surface elevation and velocity for bed topography and slipperiness beneath Thwaites Glacier
<p>This model output dataset accompanies the draft paper 'Inverting ice surface elevation and velocity for bed topography and slipperiness beneath Thwaites Glacier'.</p>
Microfossildataset "Alpine glacier reveals ecosystem impacts of Europe's prosperity and peril over the last millennium"
<p>Full palynological dataset from Colle Gnifetti glacier in the Monte Rosa Massif (Swiss Alps; 45°55'45.7'' N, 7°52'30.5'' E; 4450m a.s.l.)</p> <p>The dataset contains raw counts for pollen, ferns, NPP (Non-pollen palynomorphs), microscopic charcoal concentrations.</p> <p>Ice core depths are given as m weq = meters waterequivalent.</p> <p>Chronology is based on Jenk et al. (2009) and Sigl et al. (2018). "yr. BP" indicates "years before present" with "present" defined as 1950 CE.</p> <p>Further information on the dataset is given in the paper "Alpine glacier reveals ecosystem impacts of Europe’s prosperity and peril over the last millennium" by Brugger et al. published in Geophysical Research Letters.</p> <p> </p>
Vanishing_Glaciers_Epilithic_Biofilms_Data
<p>Metagenomic data from epilithic biofilms obtained from Glacer-fed streams as part of the Vanishing Glaciers Project, funded by NOMIS</p>
WP3-CT2 Alpine Glaciers Disappearance Tipping Point
<p>Glaciers Length changes starting from small, medium, and large glaciers.</p>
Historical digital elevation models (DEMs) and orthoimage mosaics for North American Glacier Aerial Photography (NAGAP) program, version 1.0
<p>This data archive contains digital elevation models (DEMs) and orthoimages generated from scanned historical aerial photographs from the North American Glacier Aerial Photography program available from the NSF Arctic Data Center (ADC, arcticdata.io). </p> <p>The scanned images were preprocessed using the <a href="https://github.com/friedrichknuth/hipp">Historical Image Pre-Processing</a> v0.1 software. Photogrammetric processing was performed with the <a href="https://github.com/friedrichknuth/hsfm">Historical Structure from Motion</a> v0.1 software. </p> <p>All DEM and orthoimage products are provided in the UTM Zone 10N (EPSG:32610) projected coordinate system. Elevation values are in meters above the WGS84 ellipsoid. </p> <p>See <a href="https://www.sciencedirect.com/science/article/pii/S0034425722004850">manuscript</a> and <a href="https://ars.els-cdn.com/content/image/1-s2.0-S0034425722004850-mmc1.pdf">supplement</a> for processing details and further dataset description.</p> <p>This release contains data products for two study sites in Washington state, USA:</p> <p><strong>Mount Baker</strong><br> 1970-09-09<br> 1970-09-29<br> 1974-08-10<br> 1977-09-27<br> 1979-10-06<br> 1987-08-21<br> 1990-09-05<br> 1991-09-09<br> 1992-09-15<br> 1992-09-18</p> <p><strong>South Cascade</strong><br> 1967-09-21<br> 1970-09-29<br> 1974-08-10<br> 1977-10-03<br> 1979-08-20<br> 1979-10-06<br> 1984-08-14<br> 1986-09-05<br> 1987-08-21<br> 1990-09-05<br> 1991-09-09<br> 1992-07-28<br> 1992-09-15<br> 1992-09-18<br> 1992-10-06<br> 1994-09-06<br> 1996-09-10<br> 1997-09-23</p> <p>The 00_thumbnails.jpg provides a quicklook overview at both sites.</p> <p><strong>The DEM and ortho file names are structured as follows:</strong><br> hsfm_NAGAP_[site-name]_[date]_[type].tif</p> <p><strong>For example:</strong><br> hsfm_NAGAP_south-cascade_19670921_ortho.tif</p> <p><strong>Where:</strong><br> [site-name] = Either mount-baker or south-cascade<br> [date] = Image acquisition date in YYYYMMDD format<br> [type] = File type</p> <p><strong>For each DEM and ortho pair, we provide the following:</strong><br> _1m_dem.tif = Digital elevation model posted at 1 m resolution <br> _ortho.tif = Orthoimage mosaic posted at the median image ground sample distance, rounded up to the nearest second decimal place.<br> _metadata.tar.gz = Metadata tarball containing:<br> _ortho_footprints.geojson = Orthoimage mosaic footprint polygons provided in GeoJSON format (EPSG:4326)<br> _dem_footprints.geojson = DEM footprint polygons provided in GeoJSON format (EPSG:4326)<br> _cameras.csv = Image file names, positions, and orientations</p>
On-Glacier Meteorological Data for Haut Glacier d'Arolla, Switzerland
<p>The compiled dataset is a series of summer meteorological observations on the Swiss Haut Glacier d'Arolla (45.97°N, 7.52°E) <br>to support the analysis presented in the manuscript:</p><p>%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%<br> "The Decaying Near-Surface Boundary Layer of a Retreating Alpine Glacier", <br>submitted to Geophysical Research Letters. </p><p>Thomas E. Shaw1, Pascal Buri1, Michael McCarthy1, Evan S. Miles1, Álvaro Ayala2, Francesca Pellicciotti1</p><p>1 Swiss Federal Institute, WSL, Birmensdorf, Switzerland<br>2 Centro de Estudios Avanzados en Zonas Áridas, La Serena, Chile</p><p>%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%</p><p>The following files are provided:<br>1) 6 x xlsx files "Arolla_Meteorological_Data_[YEAR].xlsx"<br> contains within are tabs for:<br> i) The station locations and elevations (tab "[YEAR]_Info").<br> ii) All AWS/Tlogger data in hourly format (tab "[YEAR]_Met_Data").<br> iii) Only the hourly air temperature data for on-glacier sites (tab "[YEAR]_Ta").</p><p>2) Glacier outlines (.shp) for years 1850 (GLAMOS), 1973 (GLAMOS), 1994 (Carenzo, 2012), 1999 (Carenzo et al., 2012), 2010 (GLAMOS), 2022 (Digitised from PlanetScope imagery).<br>3) Debris cover area (.shp) derived by applying an NDSI classification of cloud filtered, summer Landsat scenes in Google Earth Engine following the approach of Scherler et al. (2018).</p><p>For the meteorological data in 1), the following variables are provided:<br>"TA" = near surface air temperature (°C).<br>"RH" - relative humidity (%).<br>"SWIN" - Shortwave incoming radiation (Wm^-2).<br>"SWOUT" - Shortwave outgoing radiation (Wm^-2).<br>"LWIN" - Longwave incoming radiation (Wm^-2).<br>"LWOUT" - Longwave outgoing radiation (Wm^-2).<br>"FF" - Wind speed (m s-1).<br>"DIR" - Wind direction (°).<br>"PP" - precipitation (mm).<br>"DEW" - dew point temperature (°C).</p><p>The term "OG" refers to an off-glacier station, which are numbered accordingly. If no variable names are given as a header in the "Met_Data" tab, then the data are air temperature values. </p><p>Data are filtered for obvious errors and errors are then removed. Data are not gap-filled as this would affect the analysis presented about patterns in air temperature data. </p><p>Data were checked and compiled by Thomas Shaw (WSL) - thomas.shaw@wsl.ch<br>Data were measured by ETH (2001-2010) and WSL as part of the Marie-Curie Project 'TEMPEST' (2021-2022).</p><p>Details of data collection and analysis can be found in:<br>Strasser et al. (2004) - 2001.<br>Carenzo (2012) - 2001-2010.<br>Shaw et al. (N.D.) 2021-2022. </p><p>%% CITED WORK</p><p>Carenzo, M. (2012). Distributed modelling of changes in glacier mass balance and runoff (Issue 20616). ETH Zurich.</p><p>Scherler, D., Wulf, H., & Gorelick, N. (2018). Global Assessment of Supraglacial Debris-Cover Extents. Geophysical Research Letters, 45(21), 11,798-11,805. https://doi.org/10.1029/2018GL080158</p><p><strong>Shaw, T. E.,</strong> Buri, P., McCarthy, M., Miles, E. S., Ayala, Á., & Pellicciotti, F. (2023). The Decaying Near-Surface Boundary Layer of a Retreating Alpine Glacier. <i>Geophysical Research Letters</i>, <i>50</i>, 1–12. <a href="https://doi.org/10.1029/2023GL103043">https://doi.org/10.1029/2023GL103043</a></p><p>Strasser, U., Corripio, J. G., Pellicciotti, F., Burlando, P., Brock, B. W., & Funk, M. (2004). Spatial and temporal variability of meteorological variables at Haut Glacier d'Arolla (Switzerland) during the ablation season 2001: Measurements and simulations. Journal of Geophysical Research, 109, D03103. https://doi.org/10.1029/2003JD003973</p><p><br> -------------</p><p> </p><p>This work was funded by the EU Horizon 2020 Marie Skłodowska-Curie Actions Grant 101026058.</p>
Ground Penetrating Radar survey of San Quintin glacier, Northern Patagonia Icefield
<p>Measured ice thickness, Residual Bedrock Reflection Power (BRP) and Internal Reflection Power (IRP) of San Quitin glacier, Northern Patagonia Icefield.</p> <p> </p> <p>Data for the preprint "Frontal collapse of San Quintín glacier (Northern Patagonia Icefield), the last piedmont glacier lobe in the Andes" in review for The Cryosphere (https://doi.org/10.5194/tc-2023-10)</p> <p> </p>
Water depth observed in front of lake-terminating glaciers in Patagonia
<p>This is the dataset of water depth observed in front of O'Higgins, Upsala, Viedma, and Tyndall glaciers in southern Patagonia.</p> <p> </p> <p>Data format:</p> <p>Latitude [deg], Longitude [deg], Depth [m]</p>
Rutor glacier fronts footprints
<p>Footprints of the various glacial fronts obtained from the elaborated cartographic products: orthophoto from August 2017 satellite imagery, September 2020 aerial orthophoto, July 2021 UAV orthophoto, and September 2021 aerial orthophoto.</p>
Validation of MODIS11A2 LST and glacier surface heatwave during 2001-2020 over Tibetan Plateau
<p>1,Validation of MODIS11A2 LST in 2019 using AWS temperature on the glacier</p> <p>2,Validation of MODIS11A2 LST during 2001-2020 using CMA station temperature over the Tibetan Plateau</p> <p>3,Glacier surface heatwave during 2001-2020 over the Tibetan Plateau glacier </p>
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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)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
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.