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48 results for “glacier change”
High-mountain Asia glacier elevation change trend (dh/dt) map for the period spanning 2000 to 2018
<p>See manuscript for methodology and dataset description:</p> <p>Shean DE, Bhushan S, Montesano P, Rounce DR, Arendt A and Osmanoglu B (2020) A Systematic, Regional Assessment of High-Mountain Asia Glacier Mass Balance. Front. Earth Sci. 7:363. DOI: 10.3389/feart.2019.00363</p> <p>https://www.frontiersin.org/articles/10.3389/feart.2019.00363/full</p> <p>GeoTiff header contains relevant metadata and georeferencing information (30 m pixel size, Albers Equal Area projection). Proj string is '+proj=aea +lat_1=25 +lat_2=47 +lat_0=36 +lon_0=85 +x_0=0 +y_0=0 +ellps=WGS84 +datum=WGS84 +units=m +no_defs'</p> <p>External overview file (.ovr) contains pyramidal overviews for improved visualization performance at different zoom levels.</p>
Global and regional glacier mass changes from 1961 to 2016
<p>Supplementary data tables with the results from Zemp et al. (2019) entitled "<em><strong>Global glacier mass changes and their contributions to sea-level rise from 1961 to 2016</strong></em>", Nature:</p> <p><strong>Data Tables 1a-t | Temporal variabilities for glaciological clusters based on variance decomposition model. </strong>Annual results are made available as csv-files for all 20 cluster: Zemp_etal_results_clusters.zip</p> <p><strong>Data Tables 2a-t | Regional and global mass balance and mass change results from 1961-2016. </strong>Annual results are made available as csv-files for all 19 regions (cf. RGI 6.0) as well as for the global sum: Zemp_etal_results_regions_global.zip</p> <p><strong>Data Table 3 | Glacier mass changes for Central Europe from 1961-2016. </strong>Annual results as in Data Table 2 but with examples for multi-year error calculations are made available as Excel-file: Zemp_etal_results_region-CEU_errorcalcs.xlsx</p> <p><strong>Note</strong>: The corresponding full sample of glaciological and geodetic observations for individual glaciers are publicly available from the World Glacier Monitoring Service: http://doi.org/10.5904/wgms-fog-2018-11</p> <p><strong>Version 1.1</strong><br> This version provides the results from the glaciological clusters (Data Tables 1a-t) as used by Zemp et al. (2019). In addition, it contains corrected results for region Iceland (Data Table 2, region_6_ISL) and correspondingly for the global sum (Data Table 2, global); the results for the other regions remain unchanged. For more details on the correction of the regional results for Iceland, see Zemp et al. (2019, Nature), Author Correction.</p> <p><strong>Version 1.0</strong><br> Data tables containing the results for the 20 glaciological clusters (Data Tables 1a-t) as well as for the 19 regions and global sums (Data Tables 2a-t) related to the publication by Zemp et al. (2019, Nature). We note that this version erroneously contains pre-release versions of results for most of the glaciological clusters that were not used in Zemp et al. (2019, Nature).</p> <p> </p>
Topography extraction and topographic change measurements using PlanetScope data: Shisper Glacier (Pakistan)
<p>The study area is located in the north flank of Hunza Valley in the Central Karakoram. Shisper glacier covers ~53.7 km2 at an elevation range of 2567-611 m a.s.l. It is a surge-type glacier, which has recently gained the attention of the scientific community and disaster response agencies during its surge. In 2018, the glacier surged beyond the confluence with the outlet stream of Mochwar glacier. The blockage resulted in the creation of a lake, which then drained causing a GLOF (Glacial Lake Outbreak Flood) and has recently begun reforming. The melt water from the two glaciers feeds hydropower plants in the Hunza valley and is a major source of fresh water for agriculture. Glacier-related hazards threaten both the town of Hassanabad and the Karakoram Highway, the only paved road through the mountain range. Here we show the potential of CubSat data to monitor such glaciers, which are not easily accessible to field observation and their potential impact on power generation, water resources and infrastructure.</p> <p>We use multi-date L1B DOVE-C PlanetScope data to extract two DEMs in 2017 and 2019 over the study area in order to compute the elevation difference caused by the glacier surge.</p> <p>Supplementary material for our paper: Optimization of optical image geometric modeling, application to topography extraction and topographic change measurements using PlanetScope and SkySat imagery.</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>
Columbia River headwaters - hydroclimate and glacier change, 1977-2017
<p>These data files were used in the production of a manuscript titled "Detecting the effects of sustained glacier wastage on streamflow in variably glacierized catchments" by R.D. Moore, B. Pelto, B. Menounos and D. Hutchinson.</p> <p>cb_shapes - polygons of catchment boundaries for the catchments included in the analysis</p> <p>cq.csv - streamflow and climatic data used in regression and trend analyses within the script "cbt_aug_flow_analysis.r"</p> <p>good_stn.csv - contains station IDs for Water Survey of Canada stations that met selection criteria</p> <p>stn_info.csv - catchment characteristics for Water Survey of Canada stations included in the analysis</p> <p>cbt_metadata.xlsx - full metadata for catchments used in the analysis</p> <p>*.r - R scripts used to conduct analyses and generate figures</p> <p> </p>
dh/dt data of the manuscript "Surface Elevation Change of Glaciers Along the Coast of Prudhoe Land, Northwestern Greenland from 1985 to 2018 "
<p>This is a dataset including dt/dt data for periods of T0–T2, T0–T1, and T1–T2, which used in the manuscript "Surface Elevation Change of Glaciers Along the Coast of Prudhoe Land, Northwestern Greenland from 1985 to 2018".</p>
Multi-temporal elevation changes of Fedchenko Glacier (Tajikistan) from 1928 to 2021
<p>This dataset contains rasters of elevation changes on and around Fedchenko Glacier. All the elevation change maps are provided relatively to a Pléiades DEM acquired on 2021-09-20. Rasters are georeferenced in UTM43, and are provided in the form of *.tif files.</p> <p>For methodological details, please refer to the final publication of the article "Multi-temporal elevation changes of Fedchenko Glacier (Tajikistan) from 1928 to 2021" by Brun and others, or refer to the pre-print available at <a href="https://doi.org/10.31223/X5CX1H">https://doi.org/10.31223/X5CX1H</a></p>
Supplementary data for: "Historical glacier change on Svalbard predicts doubling of mass loss by 2100"
<p>Supplementary datasets for:</p> <p>Geyman, E.C., van Pelt, W.J.J., Maloof, A.C., Faste Aas, H., and Kohler, J., 2022. "Historical glacier change on Svalbard predicts doubling of mass loss by 2100." Nature.</p> <p>Abstract:</p> <p>The melting of glaciers and ice caps accounts for about one-third of current sea-level rise, exceeding the mass loss from the more voluminous Greenland or Antarctic Ice Sheets. The Arctic archipelago of Svalbard, which hosts spatial climate gradients that are larger than the expected temporal climate shifts over the next century, is a natural laboratory to constrain the climate sensitivity of glaciers and predict their response to future warming. Here we link historical and modern glacier observations to predict that twenty-first century glacier thinning rates will more than double those from 1936 to 2010. Making use of an archive of historical aerial imagery from 1936 and 1938, we use structure-from-motion photogrammetry to reconstruct the three-dimensional geometry of 1,594 glaciers across Svalbard. We compare these reconstructions to modern ice elevation data to derive the spatial pattern of mass balance over a more than 70-year timespan, enabling us to see through the noise of annual and decadal variability to quantify how variables such as temperature and precipitation control ice loss. We find a robust temperature dependence of melt rates, whereby a 1°C rise in mean summer temperature corresponds to a decrease in area-normalized mass balance of -0.28 m yr<sup>-1</sup> of water equivalent. Finally, we design a space-for-time substitution8 to combine our historical glacier observations with climate projections and make first-order predictions of twenty-first century glacier change across Svalbard.</p> <p> </p> <p>Dataset description: </p> <p><br> This dataset contains the digital elevation models (DEMs), elevation change maps, point clouds, orthophotos, and vector outlines of glacier extents based on the Norwegian Polar Institute's collection of 5,507 high-oblique aerial images captured over Svalbard in 1936/1938. The photographs were analyzed through structure-from-motion (SfM) photogrammetry to generate 3D models. We also provide an .xlsx spreadsheet containing glacier-by-glacier statistics of ice loss and climate fields. Note that all of the raster and point cloud files listed below have been georeferenced in Metashape using the ground control points (GCPs) illustrated in Main Text, Fig. 2e, but have not undergone the co-registration and bias-correction following the methods of Nuth & Kaab (2011), which was done on a glacier-by-glacier basis. However, the glacier change budgets in the .xlsx file [#5 below] do reflect the values from the glacier-by-glacier co-registered and bias-corrected DEMs. See below for descriptions of each dataset (each number below corresponds to a different zipped folder).</p> <p>------------------------------------------------------------------------------------- </p> <p><strong>Svalbard-wide datasets [all georeferenced Svalbard-wide datasets are in the coordinate system UTM 33N]: </strong></p> <p><br> 1. Svalbard-wide 1936 DEM (20 m and 50 m resolution) [georeferenced .tif file] </p> <p>2. Svalbard-wide 1936 orthophotomosaic (20 m resolution) [georeferenced .tif file] </p> <p>3. Svalbard-wide dh (1936-2010) (20 m and 50 m resolution) [georeferenced .tif file] </p> <p>4. Shapefile of 1936 glacier extents [ESRI .shp file] </p> <p>5. Glacier-by-glacier statistics [.xlsx file] </p> <p>------------------------------------------------------------------------------------- </p> <p><strong>Regional-datasets: </strong></p> <p><em>Due to file size limitations, the high-resolution (5 m) datasets are split into the 8 regions illustrated in Main Text, Fig. 2d: </em></p> <p><em>Zone 1 - South Spitsbergen</em></p> <p><em>Zone 2 - Barentsoya-Edgeoya</em></p> <p><em>Zone 3 - Austfonna</em></p> <p><em>Zone 4 - Vestfonna</em></p> <p><em>Zone 5 - Northeast Spitsbergen</em></p> <p><em>Zone 6 - Central Spitsbergen</em></p> <p><em>Zone 7 - Northwest Spitsbergen</em></p> <p><em>Zone 8 - North Spitsbergen</em></p> <p><br> 6. Regional 1936 DEMs (5 m resolution) [georeferenced .tif files] </p> <p>7. Regional dh (1936-2010) (5 m resolution) [georeferenced .tif files] </p> <p>8. Local 1936 orthomosaics (5 m resolution) [georeferenced .tif files] </p> <p>9. Unprocessed point clouds [.laz files]. These files represent the raw 3D point clouds (x,y,z) generated in Agisoft Metashape for each of the 17 local models described in Extended Data Figure 3.</p> <p>10. Thumbnail-sized copies of the 5,507 historical aerial images (1936 and 1938) analyzed in this study, along with a .csv file labeling the approximate location of each photograph.</p>
Dataset - Warming-induced monsoon precipitation phase change intensifies glacier mass loss in the southeastern Tibetan Plateau
<p>Materials and data results needed to reproduce the findings of the study published in (PNAS) Proceedings of the National Academy of Sciences of the United States of America:</p> <p>"<em>Warming-induced monsoon precipitation phase change intensifies glacier mass loss in the southeastern Tibetan Plateau"</em>. A. Jouberton, T. E. Shaw, E. Miles, M. McCarthy, S. Fugger, S. Ren, A. Dehecq, W. Yang and F. Pellicciotti</p> <p>It includes the meteorological forcing time-series, an exhaustive list of the model parameters, the outputs of TOPKAPI-ETH, and Matlab scripts allowing to reproduce the figures and compute the numbers given in the main manuscript as well as in the Supplementary Information.</p> <p>---------------</p> <p><strong>Contents</strong> :</p> <p>Folder : "Matlab_scripts"<br> '<strong>Climate_import.m</strong>' : Organizes meteorological forcing and generates Figure S9<br> <strong> 'TOPKAPI_result_import.m' :</strong> Imports TOPKAPI's reference run outputs and prepares them for analysis<br> <strong> 'Experiment_analysis.m' : </strong>Analyses the results of the forcing experiments, generates Figure 4 and Figure S25<br> <strong> 'Main_text_results.m' : </strong>Analyses the results of TOPKAPI's reference runs, generates Figure 1D, FIgure 2 and Figure 3<br> <strong> 'TOPKAPI_validation.m' : </strong>Compares TOPKAPI's reference run results with several validation datasets, generates the figures and performance metrics of the model calibration and validation procedure.<br> <strong> 'Parlung_albedo_regional_analysis.m'</strong>: Computes the mean glacier albedo per elevation band for each glacier within the Southeastern Tibean Plateau and compares it to the albedo of Parlung No.4 glacier.<br> <strong> 'Parlung_GMB_regional_analysis.m'</strong>: Computes the mean glacier mass balance per elevation band for each glacier within the Southeastern Tibean Plateau and compares it to the glacier mass balance of Parlung No.4 glacier.<br> <strong> 'Precipitation_phase_sensitivity_analysis.m'</strong>: Performs a sensitivity analysis on the simulated monsoon snowfall ratio per elevation band and on the attribution of glacier mass loss to precipitation<br> phase change using Monte Carlo simulations.<br> <strong> 'TOPKAPI_MODIS_validation.m'</strong>: Compares the snow cover at Parlung No.4 catchment simulated by TOPKAPI-ETH and observed by MODIS, generates Figure S19.</p> <p> </p> <p>Folder : "Remote_sensing" :</p> <p> Sub-Folder: 'Hugonnet' = Glacier mass balance averaged over 2000-2020 covering the Southeastern Tibetan Plateau, 100m resolution, derived from Hugonnet et al. 2021<br> Sub-Folder: 'MODIS' = contains the snow cover at Parlung No.4 derived from the daily product MOD10A1 version 61, for the period 2000-2018<br> Sub-Folder: 'Regional_glacier_albedo' = contains the annual glacier surface albedo from 2000 to 2020, covering the Southeastern Tibetan Plateau, 500m resolution.<br> Sub-Folder: 'Shapefiles' = contains the Parlung No.4 glacier outlines in 1974 and from the RGI 6.0<br> <strong>'ASTER_Nyainqentanglha_15m_utm.tif'</strong> = ASTER Digital elevation model at 15m resolution covering the Southeastern Tibetan Plateau<br> <strong> 'parlung_mask_1974.mat' </strong>= Parlung No.4 glacier mask as a matlab file<br> <strong> 'dh_ASTER_SRTM_30m.tif' </strong>= Mean elevation change rate from 2000 to 2016 at Parlung No.4 catchment.<br> <strong> 'Geodetic_map.mat'</strong> = Elevation change maps for the periods 1974-2000 and 1974-2014, as a matlab file<br> <strong> 'GMB_geodetic.mat' </strong>= Geodetic mass balance (glacier-wide mean and profile per elevation band) used in Figure S13<br> <strong> 'parlung_30m_catchment_mask.tif'</strong> = Parlung No.4 catchment mask<br> <strong>'parlung_1974_30m_dem.tif' </strong>= DEM of Parlung No.4 catchment, 30 m resolution<br> <strong> 'parlung_1974_30m_gla.tif'</strong> = Parlung No. 4 glacier mask, 30m resolution<br> <strong>'parlung_1974_30m_glah.tif' </strong>= Reconstructed ice thickness of 1975 for Parlung No.4 glacier<br> <strong>'parlung_1974_2000_diff_24m.tif' </strong>= Elevation change from DEM differencing at Parlung No.4 catchment for 1974-2000<br> <strong> 'parlung_1974_2014_diff_24m.tif' </strong>= Elevation change from DEM differencing at Parlung No.4 catchment for 1974-2014<br> <strong> 'Parlung_1974_bedrock_dem_30m.tif' </strong>= Bedrock surface digital elevation model of the catchment, 30m spatial resolution<br> <strong>'RGI_KangriKarpo_100m_utm_id.tif' </strong>= Glacier mask covering the Kangri Karpo mountain region, 100m resolution, with glacier IDs in the attribute table<br> <strong> 'RGI_Nyainqentanglha_100m_utm_id.tif' </strong>= = Glacier mask covering the Southeastern Tibetan Plateau, 100m resolution, with glacier IDs in the attribute table</p> <p> </p> <p>Folder : "TOPKAPI_forcing" :<br> <strong> CCT_AWS4600_extended.csv : </strong>Hourly cloud cover transmissivity from 1975 to 2018 reconstructed at AWSoff location <br> <strong> Climate.mat : </strong>Organizes meteorological forcings, output from the matlab script '<strong>Climate_import.m</strong>'<br> <strong> LR_AWS4600_extended.csv :</strong> Hourly temperature lapse-rates from 1975 to 2018 reconstructed at AWSoff location <br> <strong> Precipitation_AWS4600_extended.csv : </strong>Hourly precipitation from 1975 to 2018 reconstructed at AWSoff location <br> <strong> Ta_AWS4600_extended.csv :</strong> Hourly air temperature from 1975 to 2018 reconstructed at AWSoff location<br> Sub-Folder: 'National_meteorological_stations' = Contains the daily air temperature and precipitation measured at the national meteorological stations of Bomi, Zayu, Zuogong and Basu<br> Sub-Folder: 'Reference_run_inputs' = Contains the input files necessary to run TOPKAPI-ETH to obtain the outputs from which the results of this study are based on.</p> <p> </p> <p>Folder : "TOPKAPI_output":<br> <strong> </strong> Sub-Folder : "Forcing experiment" = organized TOPKAPI outputs from the forcing experiment<br> Sub-Folder :" Reference_run_outputs" = raw TOPKAPI outputs from the reference run (catchment average, spatial and grid cells)<br> Sub-Folder : "Reference_run_results" = organized TOPKAPI outputs from the reference run<br> Sub-Folder : "Snow_ice_cover" = contains TOPKAPI-ETH derived snow cover maps (daily map outputs)<br> Sub-Folder : "Regional_analysis" =<br> 'Alb'= Table containing the mean glacier albedo (2000-2020) per normalized elevation band, for each glacier in the SETP (RGI 6.0)<br> 'GMB'= Table containing the mean glacier mass balance (2000-2020) per normalized elevation band, for each glacier in the SETP (RGI 6.0)<br> 'Hypso_xxm' = Table containing the percentage of glacier area per normalized elevation band, for each glacier in the SETP (RGI 6.0), resolution of 100/500m<br> 'NormEl_100m' = Table containing the elevation per normalized elevation band, for each glacier in the SETP (RGI 6.0), resolution of 100/500m<br> Sub-Folder : "Semi_distributed_outputs" = Precipitation phase and amounts resulting from TOPKAPI-ETH simulation per elevation band, for the reference run and for the Monte Carlo sensitivity analysis</p> <p> </p> <p>Folder : "Validation_data"<br> <strong> 'topkapi.out_reference_discharge2016' </strong>= <strong> </strong>raw TOPKAPI outputs run in 2016 with AWSoff air temperature<br> <strong> </strong><strong> 'master_file_parlung.mat' </strong>=<strong> </strong>matlab structure containing AWS measurements, necessary for running <strong>'TOPKAPI_validation.m'</strong><br> <strong> 'Qdigit.mat' </strong>= Discharge measured at the Parlung No.4 glacier outlet, from Li et al., (2016)<br> <strong> 'Parlung_Q_1970.mat'</strong> = 'Discharge time-series used to run TOPKAPI-ETH (goes back to 1975, but filled with 0 when no measurements are available)</p> <p> </p> <p>In order to run the Matlab scripts, it is recommended to download all folders and gather them into the same folder. Any request about data or questions on how to run the Matlab scripts can be asked to the author of the paper (at achille.jouberton@wsl.ch).</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>
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>
10-year glacier mass changes from CryoSat-2 radar altimetry
<p>We generate a record of ice loss across glaciers from CryoSat-2 swath interferometric radar altimetry. We resolve changes at 500m resolution for Arctic Canada North and South, Greenland periphery glaciers, Iceland, Svalbard, Franz Josef Land, Novaya Zemlya, Severnaya Zemlya, Southern Andes and Antarctic periphery glaciers between 2010 and 2020.</p>
Projected 21st-century mass change for Northern Hemisphere glaciers including frontal ablation
<p>Glacier mass change projections for Nothern Hemisphere glaciers including frontal ablation (2000 to 2100) used in the publication "Exploring the impact of a frontal ablation parameterization on projected 21st-century mass change for Northern Hemisphere glaciers" (doi: 10.1017/jog.2023.19).</p> <p>Generated using a modified version of the Open Global Glacier Model (OGGM, https://github.com/jmalles/oggm/tree/jog23)</p>
Glaciers elevation change over Tibetan Plateau's endorheic basin during 1975-2000
<p>Glacier elevation changes over the endorheic basin of the Tibetan plateau with KH-9 in 1975-2000.</p>
Regional Assessments of Glacier Mass Change (RAGMAC) experiment dataset
<p>This repository contains the dataset that was distributed to the participants of the Regional Assessments of Glacier Mass Change (RAGMAC) working<br>group of the International Association of Cryospheric Science (IACS, 2023).</p> <p>In short, the dataset is divided in 5 study sites: Hintereisferner (Austrian Alps - HEF_AT), Grosser Aletschgletscher (Swiss Alps - ALE_CH), Vestre Svartisen ice cap (Scandes, Norway - VES_NO), Baltoro glacier (Karakoram, Pakistan - BAL_PK), and the Northern Patagonian Icefield (Andes, Chile - NPI_CL).</p> <p>For each site, we provide series of Digital Elevation Models (DEMs) derived from SRTM, ASTER and TanDEM-X sensors. Additionally, we provide glacier outlines extracted from the Randolph Glacier Inventory version 6, for all glaciers in the study area, and selected glaciers for which participants were required to calculate the geodetic mass balance.</p> <p>Additionally, we provide the airborne DEMs that were used as reference data to evaluate the spaceborne DEMs. These are available for the cases HEF_AT, ALE_CH and VES_NO, all included in the single zip file “validation_data.zip”.</p> <p>For details on the dataset, experiment and any reference to this dataset, please refer to the following publication: Piermattei et al. (2024) "Observing glacier elevation changes from spaceborne optical and radar sensors – an inter-comparison experiment using ASTER and TanDEM-X data" The Cryosphere, DOI: <a href="https://doi.org/10.5194/egusphere-2023-2309">10.5194/egusphere-2023-2309</a> (to be updated upon final acceptance).</p> <p><strong>NOTE: </strong>As of July 2024, the TanDEM-X DEMs cannot be publicly shared due to license restrictions. We are discussing future opportunities to share the data in a future version of this repository.</p> <p> </p>
Glacier Inventory and Elevation Change Data for the Shule River, Heihe River, and Shiyang River Basins in the Qilian Mountains (1990-2020)
<p>This dataset contains comprehensive glacier inventory data and elevation change measurements for the Qilian Mountains, specifically covering the Shule River, Heihe River, and Shiyang River basins. The dataset includes:</p> <ol> <li> <p>Glacier inventory data for the years 1990, 1995, 2000, 2005, 2010, 2015, and 2020. The inventory provides detailed information on glacier attributes such as area, length, orientation, and other essential characteristics for each of the target years.</p> </li> <li> <p>Elevation change data for glaciers within the Qilian Mountains during the periods 2000-2010 and 2010-2020. This data tracks changes in glacier surface elevation, providing valuable insights into glacier mass balance and regional climate change impacts.</p> </li> </ol> <p>The data is critical for understanding the response of glaciers to climate change over the past three decades and will support ongoing research in glaciology, hydrology, and environmental monitoring.</p>
High spatial resolution thickness changes of Pyrenean glaciers from 2011 to 2020
<p>Pyrenean glaciers are the largest in southern Europe. Because their very survival is threatened by climate change in the coming decades, it is urgently necessary to monitor their evolution. This dataset presents high spatial resolution observation of Pyrenean glaciers during the period 2011–2020. In 2011, the glacier surface was retrieved with an airborne lidar and in 2020 data were obtained with three different UAVs (fixed-wing or quad copters). Comparing these observations, glacier surface changes were computed for 17 out of the 24 remaining glaciers. Using high-resolution optical satellite imagery and field observations, glacier outline changes between 2011 and 2020 were also obtained. On average the glacierized area has shrunk by 23.2% and thickness has decreased on average by 6.3 m.</p>
Data of "Inferring the Basal Friction Law from long term observations of Glacier Length, Thickness and Velocity changes on an Alpine Glacier"
<p>This dataset contains the surface velocity and elevation of Argentière Glacier in 2003 and 2018 used in:</p> <p>Gilbert, A., Gimbert, F., Gagliardini, O., & Vincent, C. (2023). Inferring the Basal Friction Law From Long Term Changes of Glacier Length, Thickness and Velocity on an Alpine Glacier. <em>Geophysical Research Letters</em>, <em>50</em>(16), e2023GL104503. <a href="https://doi.org/10.1029/2023GL104503">https://doi.org/10.1029/2023GL104503</a></p> <p>Surface DEM files contain elevation Z on a 20X20 meter grid (geotif files, coordinnate are in Lambert 2E ( EPSG:27572 ) )</p> <p>Horizontal Velocity files contain measured horizontal velocities Vh in m/yr on a 20X20 meter grid (geotif files, coordinnate are in Lambert 2E ( EPSG:27572 ) )</p>
Toy glacier elevation change dataset
<p>Dataset for conference paper: 'Beyond Intuition, a Framework for Applying GPs to Real-World Data' at the ICML Workshop tructured Probabilistic Inference and Generative Modelling, 2023 (Honolulu, Hawaii, USA).</p> <p><a href="https://arxiv.org/abs/2307.03093">https://arxiv.org/abs/2307.03093</a></p>
Unveiling Transformation: Glacier Area Changes in Changra-Bhaga and Suru-Zanskar Basins (2000-2022), Western Himalaya
<p>The dataset encompasses the temporal variations in glacier extents within the Suru-Zanskar Basin and the Chandra-Bhaga Basin, both integral parts of the Western Himalayan region. Within this dataset, a total of 45 representative glaciers were manually digitized for the years 2000, 2007, 2016, and 2022, using satellite imagery for the Suru-Zanskar Basin. Similarly, temporal glacier areas were calculated for 51 selected glaciers spanning the years 2000, 2009, 2016, and 2022 within the Chandra-Bhaga Basin. For the year 2000, the dataset comprises a recorded area of 913.77 km² for the Suru-Zanskar Basin and 608.95 km² for the Chandra-Bhaga Basin, respectively. This dataset forms an integral component of the manuscript titled "Divergent Temporal Glacier Responses in the Chandra-Bhaga and Suru-Zanskar Basins of the Indian Western Himalaya."</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.