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774 results for “glacier”
Stream water chemistry data for Green Lake 5 Rock Glacier, 1998 - ongoing.
This is a summary of major ion concentrations for stream water samples collected at the Green Lake 5 Rock Glacier, near Green Lake 5 in the Boulder County Watershed Green Lakes Valley.
Supraglacial features of debris covered glaciers in the Himalaya from Landsat-8 spectral umixing and Pleiades
<p>This dataset contains the spectral unmixing output files for the debris covered glacier surfaces based on Landsat-8 OLI imagery and Pleiades imagery of 2015. Files are provided for two domains, the Khumbu reference region of Nepal and the greater Himalaya region (76.3 to 92.6° W and 26.3 to 34.2° N), which covers covering most area from Himachal/Jammu and Kashmir border to Bhutan Himalaya. </p> <ul> <li>Landsat surface reflectance : Himalaya_L8_6S_surface_reflectance_scenes_2015 .zip <ul> <li>Contains surface reflectance images of Landsat-8 OLI scenes mostly from 2015 (two images are from 2014 and 2016 due to clouds in 2015) </li> <li>Collection 1 Level 1 (L1TP)</li> <li>Atmospherically and topographically corrected using the ARCSI routine, supplied in .kea format. These can be converted to GeoTifs using the GDAL command.</li> <li>Naming structure: LS8_yyyymmdd_latYYlongXXXX_rRRpPPP_vmsk_topshad_rad_srefdem_stdsref.kea</li> <li>Projection is UTM (zones depending on the image), from the original Landsat L1TP files</li> <li>The file naming convention, which is a standard output from ARCSI routine, include the image date ("yyyy" = year, mm = "month", "dd" = day), latitude ("YY") and longitude ("XXXX") of the image center, path/row ("PPP" = path, "RRR" = row), and the output products generated by ARCSI ("rad" = radiation, "topshad" = topographic shadows, "srefdem" indicates the use of elevation data, "stdsref" = standardized surface reflectance)</li> </ul> </li> <li>Fractional maps for the Khumbu: LS8_20150930_r41p140_frac_files. zip <ul> <li>Raster format (GeoTiffs) </li> <li>Non-normalized fractional water, light and dark debris and vegetation maps for the Khumbu reference image (Sept 30, 2015, path 140 row 40)</li> <li>Output from the linear mixing model routine used to produce binary maps of surfaces with values ranging from 0 to 1 (0% to 100% pixel coverage)</li> </ul> </li> <li>Binary surface maps for the Himalaya: Himalaya_L8_raw_binary_surface_maps.zip <ul> <li>Vector format (ArcGIS shapefiles)</li> <li>Raw, unprocessed binary maps of ponds, vegetation debris, ice and clouds over the debris covered glacier tongues in the Himalaya around the year 2015 (binary files) </li> <li>Derived from tresholding the fractional maps using a variable threshold (see publication)</li> <li>Maps in this pre-release version have not been manually corrected for misclassified areas due to confusion of classes, and the ice and cloud classes are not highly accurate</li> <li>These are not the final coverages of these surfaces over the domain and should not be used as such</li> <li>The supraglacial pond maps will undergo manual corrections and the datasets will be updated on this page</li> </ul> </li> <li>Dataset for analysis, glacier-by-glacier: Himalaya_SDC_LS_for_analysis_gt1km2_with_frac_and_debris_attributes.txt <ul> <li>original data from the SupraGlacial Debris Cover dataset (Sherler et al 2018)</li> <li>updated with the preliminary fractional cover of each surface (in %) on a glacier-by-glacier basis</li> <li>contains only debris covered tongues >1 km2 </li> <li>debris covered attributes were calculated from the ALOS Global Digital Surface Model (AW3D30 DEM) for each debris covered tongue <ul> <li>DC_area_km2 = recalculated debris covered area</li> <li>DCmin = minimum debris cover elevation (meters)</li> <li>DCmax = maximum debris cover elevation (meters)</li> <li>DCrange = altitudinal range (meters)</li> <li>DCmed = median elevation (meters)</li> <li>SLmean = mean slope (degrees)</li> <li>SLrange = slope range (degrees)</li> <li>SLmin = min slope (degrees)</li> <li>SLmax = max slope (degrees)</li> </ul> </li> </ul> </li> </ul>
Belvedere Glacier long-term monitoring Open Data
<p><strong>Introduction </strong></p> <p>This dataset contains extensive, long-term monitoring data on the Belvedere Glacier, a debris-covered glacier located on the east face of Monte Rosa in the Anzasca Valley of the Italian Alps. The data is derived from photogrammetric 3D reconstruction of the full Belvedere Glacier and includes:</p> <ul> <li><strong>dense point clouds</strong> obtained with UAV-based MVS covering the entire glacier body</li> <li>high-resolution<strong> </strong><strong>orthophotos</strong></li> <li>high-resolution<strong> </strong><strong>DEMs</strong></li> </ul> <p>Since 2015, in-situ survey of the glacier have been conducted annually using fixed-wing UAVs until 2020 and quadcopters from 2021 to 2022 to remotely sense the glacier and build high-resolution photogrammetric models. A set of ground control points (GCPs) were materialized all over the glacier area, both inside the glacier and along the moraines, and surveyed (nearly-) yearly with topographic-grade GNSS receivers (Ioli et al., 2022).</p> <p>For the period from 1977 to 2001, historical analog images, digitalized with photogrammetric scanners and acquired from aerial platforms, were used in combination with GCPs obtained from recent photogrammetric models (De Gaetani et al., 2021).</p> <p>Before downloading them, you can explore the photogrammetric point clouds of the Belvedere Glacier within web app based on Potree from <a href="https://thebelvedereglacier.it/" target="_blank" rel="noopener">https://thebelvedereglacier.it/</a> (use a web browser from a desktop/laptop for the best experience). Additionally, from here you can also visualize and download the coordinates of the GCPs measured by GNSS every year since 2015.</p> <p> </p> <p><strong>Belvedere Glacier </strong></p> <p>The Belvedere Glacier is an important temperate alpine glacier located on the east face of Monte Rosa in the Anzasca Valley of Italy. The Belvedere Glacier is of particular importance among alpine glaciers because it is a debris-covered glacier and it reaches its lowest elevation at about 1800 m a.s.l. Over the last century, the Belvedere Glacier has experienced extraordinary dynamics, such as a surge-like movement or the formation of a supraglacial lake, which seriously threatened the nearby community of Macugnaga.</p> <p> </p> <p><strong>Data organization</strong></p> <p>The data are organized by year in compressed zip folders named <em>belvedere_YYYY.zip</em>, which can be downloaded independently. Each folder contains all data available for that year (i.e. photogrammetric point clouds, orthophotos, and DEMs) and the corresponding metadata. Metadata is provided as a .json file which contains all the main information for data usage. Point clouds are saved in compressed las format (<em>.laz</em>)<em> </em>and they can be inspected e.g., with CloudCompare. Orthophotos and DEMs are georeferenced images (<em>.tif</em>) that can be inspected with any GIS software (e.g., <em>QGIS</em>).</p> <p>Large point clouds are subdivided into regular tiles, which are numbered in a progressive row-wise order from the bottom-left corner of the point cloud bounding box.</p> <p>All the files are named according to the following naming schema:</p> <p>"belv_YYYY_surveyplatform_datatype[_resolution][vertical_datum][-tile_number].extension"</p> <p>where: </p> <ul> <li>YYYY: is the year of the survey</li> <li>surveyplatform: can be either "uav" for the UAV-based photogrammetry survey or "histo" for the historical aerial datasets.</li> <li>datatype: can be either "pcd" for point clouds, "orthophoto" for orthophotos and "dsm" for DSMs. </li> <li>resolution: on-ground resolution of each pixel in meters. This applies only to raster data (orthophoto and DSMs)</li> <li>vertical_datum: if the DSM is given in orthometric coordinates, the label "ortho" is present in the filename, otherwise the height of the dataset is supposed to be ellipsoidal.</li> <li>tile: tile number, if the data is tiled to avoid large files.</li> </ul> <p><strong>Data Usage</strong></p> <p>This dataset can be used to estimate glacier velocities, volume variations, study geomorphological processes such as the process of moraine collapse, or derive other information on glacier dynamics. If you have any requests on the data provided, data acquisition, or the raw data themselves, you are encouraged to contact us.</p> <p> </p> <p><strong>Contributions</strong></p> <p>The monitoring activity carried out on the Belvedere Glacier was designed and conducted jointly by the Department of Civil and Environmental Engineering (DICA) of Politecnico di Milano and the Department of Environment, Land and Infrastructure Engineering (DIATI) of Politecnico di Torino. The DREAM projects (DRone tEchnnology for wAter resources and hydrologic hazard Monitoring), involving teachers and students from Alta Scuola Politecnica (ASP) of Politecnico di Torino and Milano, contributed to the campaign from 2015 to 2017.</p> <p> </p> <p><strong>Acknowledgements</strong></p> <div>The authors thank CGR SpA for digitizing the historical images (1977, 1991, 2001, 2009) and making them available to the authors for the photogrammetric processing.</div> <div>The authors thank all students and collaborators contributing to the Alta Scuola Politecnica projects DREAM 1, DREAM 2, and DREAM 3 (DRone tEchnnology for wAter resources and hydrologic hazard Monitoring). </div> <div> </div> <div> </div> <p><strong>If you use the data, please, cite these our pubblications:</strong></p> <p>Ioli, F., Dematteis, N., Giordan, D., Nex, F., Pinto, L., Deep Learning Low-cost Photogrammetry for 4D Short-term Glacier Dynamics Monitoring. <em>PFG</em> (2024). <a href="https://doi.org/10.1007/s41064-023-00272-w" target="_blank" rel="noopener">https://doi.org/10.1007/s41064-023-00272-w</a></p> <p>Ioli, F.; Bianchi, A.; Cina, A.; De Michele, C.; Maschio, P.; Passoni, D.; Pinto, L. Mid-Term Monitoring of Glacier’s Variations with UAVs: The Example of the Belvedere Glacier. Remote Sensing, 14, 28 (2022). <a href="https://doi.org/10.3390/rs14010028" target="_blank" rel="noopener">https://doi.org/10.3390/rs14010028</a></p> <p>De Gaetani, C.I.; Ioli, F.; Pinto, L. Aerial and UAV Images for Photogrammetric Analysis of Belvedere Glacier Evolution in the Period 1977–2019. Remote Sensing, 13, 3787 (2021). <a href="https://doi.org/10.3390/rs13183787" target="_blank" rel="noopener">https://doi.org/10.3390/rs13183787</a></p>
UAV-based colour-infrared orthomosaics and digital elevation models of basalts and rock glaciers on Disko Island, West Greenland
<p><span>This data set contains multispectral surveys conducted with an unoccupied aerial vehicle over rock glaciers and steep mafic outcrops (intrusive and flood volcanics) near the coastline of Disko Island.</span></p> <ul> <li><span>Acquisition date: 07.08.2019 – 10.08.2019</span></li> <li><span>Location: Illukunnguaq, Disko Island, Greenland</span></li> <li><span>UAV: SenseFly eBee Plus</span></li> <li><span>Flight altitude above ground level: >100m</span></li> <li><span>Image Overlap forward/side: various</span></li> <li><span>Camera: Parrot Sequoia multispectral</span></li> <li><span>EPSG: 32622</span></li> <li><span>Center coordinates: 69.885277°N, -52.577724°E</span></li> <li><span>Flight mode: automatic flight plan</span></li> </ul> <p><span>Data products: </span></p> <ul> <li><span>Orthomosaic colour-infrared, 10-16 cm pixel resolution</span></li> <li><span>Colour-infrared spectral bands: 790nm, 660nm, 550nm</span></li> <li><span>DEM, 20-30cm pixel resolution</span></li> <li><span>Data coverage: approx. 5500 x 2500 m</span></li> <li><span>Elevation profile: 20-680m </span></li> <li><span>Processing in Agisoft Metashape</span></li> </ul> <p><span>Additional data supplement for article:<br>Barnes, E. (2020). Assessment of Drone-Borne Multispectral Mapping in the Exploration of Magmatic Ni-Cu Sulphides–an Example from Disko Island, West Greenland. <br><em>URN: urn:nbn:se:uu:diva-418858</em></span></p> <p>MULSEDRO field campaign was conducted under scientific survey licence (VU-00158-2019) within mineral exploration licence MEL 2018-16 by Blue Jay Mining PLC. This research has been supported by the project MULSEDRO, funded by HZDR-HIF & EITRawMaterials (project ID 16193) and the European Union.</p>
Hydrochemical Data from a Tropical Andean Glacierized Catchment: δ18O, electrical conductivity, maximum fluorescence intensity, and dissolved organic carbon concentrations from short-term sampling campaigns, Ecuador (2022 and 2024)
Fluorescent dissolved organic matter (FDOM) quality, dissolved organic carbon (DOC) concentration, electrical conductivity (EC), and stable water isotopes (δ¹⁸O and δ2H) were determined in water, snow, and ice samples from a tropical glacierized catchment in the Ecuadorian Andes. The sampling locations were selected to capture the major hydrologic inputs to the main stream channel (glacial melt, tributaries, wetlands, and groundwater springs) and constrain the in-stream spatiotemporal variation in DOM quality and other hydrochemical characteristics. Two sets of high-resolution time series were collected on Oct 13, 2022 and Jun 14, 2024. Time series samples were collected at various upper catchment locations and the outlet simultaneously. DOM quality was characterized via fluorescence spectroscopy and processed using parallel factor analysis (PARAFAC). The DOM quality data are expressed as %FMax values obtained through a 4-component PARAFAC model, where %Fmax 1– 4 are interpreted as terrestrial humic-like, tyrosine-like, tryptophan-like, and microbial humic-like fluorescent components, respectively. DOC concentrations were quantified using high-temperature catalytic combustion, stable water isotopes were analyzed using laser-based spectroscopy, and EC was measured in situ with handheld multiparameter water quality probes.
High-frequency, hourly, and daily measurements from Canada Glacier Meteorological Station (CAAM), McMurdo Dry Valleys, Antarctica (1994-2025, ongoing)
As part of the McMurdo Dry Valleys Long-Term Ecological Research program, a spatially distributed, long-term climate monitoring network was established across the McMurdo Dry Valleys region of Antarctica, consisting of fourteen research-grade weather stations that continuously measure a standard suite of environmental parameters. Ecosystem processes in this region are strongly regulated by climatic drivers that exhibit high variability across both time and space, making accurate measurement of environmental variables at high temporal and spatial resolution essential to understanding the biophysical dynamics of this polar desert ecosystem. This data package includes measurements from the Canada Glacier Meteorological Station (CAAM), which was established in 1994 within the ablation zone of Canada Glacier, between the Hoare and Fryxell Basins of Taylor Valley, McMurdo Dry Valleys, Antarctica. Parameters include air temperature, relative humidity, incoming and outgoing shortwave radiation, wind speed and direction, and surface (snow/ice) temperature. Data are provided at high frequency (typically 15-minute intervals), along with hourly and daily summaries. Users should note that summary statistics may be affected by periods of missing data. Since there is no universally accepted standard for handling gaps in time-series data, users are encouraged to work with the high-frequency data and establish their own criteria for acceptable data completeness to minimize any potential bias.
High-frequency, hourly, and daily measurements from Commonwealth Glacier Meteorological Station (COHM), McMurdo Dry Valleys, Antarctica (1993-2025, ongoing)
As part of the McMurdo Dry Valleys Long-Term Ecological Research program, a spatially distributed, long-term climate monitoring network was established across the McMurdo Dry Valleys region of Antarctica, consisting of fourteen research-grade weather stations that continuously measure a standard suite of environmental parameters. Ecosystem processes in this region are strongly regulated by climatic drivers that exhibit high variability across both time and space, making accurate measurement of environmental variables at high temporal and spatial resolution essential to understanding the biophysical dynamics of this polar desert ecosystem. This data package includes measurements from the Commonwealth Glacier Meteorological Station (COHM), which was established in 1993 within the ablation zone of Commonwealth Glacier in the Fryxell Basin of Taylor Valley, McMurdo Dry Valleys, Antarctica. Parameters include air temperature, relative humidity, incoming and outgoing shortwave and longwave radiation, wind speed and direction, surface (snow/ice) temperature, and surface distance. Data are provided at high frequency (typically 15-minute intervals), along with hourly and daily summaries. Users should note that summary statistics may be affected by periods of missing data. Since there is no universally accepted standard for handling gaps in time-series data, users are encouraged to work with the high-frequency data and establish their own criteria for acceptable data completeness to minimize any potential bias.
High-frequency, hourly, and daily measurements from Howard Glacier Meteorological Station (HODM), McMurdo Dry Valleys, Antarctica (1993-2025, ongoing)
As part of the McMurdo Dry Valleys Long-Term Ecological Research program, a spatially distributed, long-term climate monitoring network was established across the McMurdo Dry Valleys region of Antarctica, consisting of fourteen research-grade weather stations that continuously measure a standard suite of environmental parameters. Ecosystem processes in this region are strongly regulated by climatic drivers that exhibit high variability across both time and space, making accurate measurement of environmental variables at high temporal and spatial resolution essential to understanding the biophysical dynamics of this polar desert ecosystem. This data package includes measurements from the Howard Glacier Meteorological Station (HODM), which was established in 1993 within the ablation zone of Howard Glacier in the Fryxell Basin of Taylor Valley, McMurdo Dry Valleys, Antarctica. Parameters include air temperature, relative humidity, incoming and outgoing shortwave radiation, wind speed and direction, and surface distance. Data are provided at high frequency (typically 15-minute intervals), along with hourly and daily summaries. Users should note that summary statistics may be affected by periods of missing data. Since there is no universally accepted standard for handling gaps in time-series data, users are encouraged to work with the high-frequency data and establish their own criteria for acceptable data completeness to minimize any potential bias.
High-frequency, hourly, and daily measurements from Taylor Glacier Meteorological Station (TARM), McMurdo Dry Valleys, Antarctica (1994-2025, ongoing)
As part of the McMurdo Dry Valleys Long-Term Ecological Research program, a spatially distributed, long-term climate monitoring network was established across the McMurdo Dry Valleys region of Antarctica, consisting of fourteen research-grade weather stations that continuously measure a standard suite of environmental parameters. Ecosystem processes in this region are strongly regulated by climatic drivers that exhibit high variability across both time and space, making accurate measurement of environmental variables at high temporal and spatial resolution essential to understanding the biophysical dynamics of this polar desert ecosystem. This data package includes measurements from the Taylor Glacier Meteorological Station (TARM), which was established in 1994 on the lower section of Taylor Glacier in the Bonney Basin of Taylor Valley, McMurdo Dry Valleys, Antarctica. Parameters include air temperature, relative humidity, incoming and outgoing shortwave radiation, wind speed and direction, surface (snow/ice) temperature, and surface distance. Data are provided at high frequency (typically 15-minute intervals), along with hourly and daily summaries. Users should note that summary statistics may be affected by periods of missing data. Since there is no universally accepted standard for handling gaps in time-series data, users are encouraged to work with the high-frequency data and establish their own criteria for acceptable data completeness to minimize any potential bias.
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>
Observed and WRF-simulated near-surface meteorological parameters on selected James Ross Island glaciers during heatwaves in summer 2022/23
<p>The files contain time series of near-surface meteorological conditions observed on Triangular Glacier and Davies Dome on James Ross Island, Antarctica and simulated time series for these glaciers based on the Weather Research and Forecasting (WRF) model output. Observations of 2-m air temperature, 2-m wind speed, net radiation and glacier surface height are available from 01 November 2022 to 16 January 2023 (net radiation is available only on Triangular Glacier). Simulated values of 2-m air temperature, 2-m wind speed, net radiation, sensible and latent heat fluxes are available from 08 November 2022 to 16 January 2023.</p>
Weather dataset from Otemma glacier forefield, Switzerland (from 14 July 2019 to 18 November 2021)
<p>Weather data collected in the Otemma forefield (Switzerland) from 14 July 2019 to 18 November 2021.<br> Data were collected by the research teams of Bettina Schaefli<sup>2</sup> and Stuart N. Lane<sup>1</sup>.</p> <p><sup>1</sup> Institute of Earth Surface Dynamics (IDYST), University of Lausanne, 1015 Lausanne, Switzerland</p> <p><sup>2</sup> Institute of Geography (GIUB), University of Bern, 3012 Bern, Switzerland</p> <p>For further information, please contact:<br> tom.muller.1@unil.ch<br> bettina.schaefli@giub.unibe.ch</p> <p><strong>Description of data : </strong>WeatherData.csv</p> <p>Time span of data : 14 July 2019 to 18 November 2021<br> Time step : homogenized 10 minutes-averaged data</p> <p>Location of data (Coordinate in SWISS LV95 (EPSG:2056)<br> - Glacier snout Station : 2598615 / 1087375<br> - Glacier center Station : 2600495 / 1088631<br> - Floodplain Station : 2598096 / 1087087</p> <p>STRUCTURE OF DATA : tidy dataframe with following headers :<br> - <em>date </em>: local date (UTC+01 with daylight saving time)<br> - <em>variable </em>: parameter of interest, with following classes :<br> Air_humidity : Air humidity in percent of air saturation [%]<br> Air_temperature : Air temperature in [°C]<br> Atm_pressure : Atmospheric pressure in [hPa]<br> Incoming_radiation : Incoming shortwave radiation in [W/m2]<br> Precipitation : Liquid precipitation measured [mm]<br> - <em>name </em>: location of data (see coordinates above)<br> - <em>dateUTC </em>: date with UTC timezone</p> <p>Device used for data acquisition :<br> - Air_humidity/Air_temperature/Atm_pressure : Decagon VP-4<br> - Incoming_radiation : Apogee Instruments SP-11<br> - Precipitation : Double tipping buckets rain gauge from Davis Instruments (resolution 0.2 mm)</p> <p> </p> <p><strong>Description of data : </strong>RainComposite_Otemma_Arolla.csv</p> <p>Time span of data : 01 July 2019 to 18 November 2021<br> Time step : homogenized 10 minutes-averaged data</p> <p>The dataset compiles the measured rain during summer at the closest weather station (Glacier snout).<br> For the winter period (and few gaps during the summer), the solid precipitations (snow) from the closest MeteoSwiss weather stations (SwissMetNet) were used.<br> Gaps where filled with 1) MeteoSwiss Otemma and if data were still missing filled with 2) MeteoSwiss Arolla<br> <br> Location of data (Coordinate in SWISS LV95 (EPSG:2056)<br> - Glacier snout Station : 2598615 / 1087375<br> - Otemma camp Station : 2597508 / 1086653<br> - MeteoSwiss Station : 2596476 / 1085864<br> - MeteoSwiss Arolla : 2603507 / 1095832</p> <p>STRUCTURE OF DATA : tidy dataframe with following headers :<br> - date : local date (UTC+01 with dst)<br> - variable : parameter of interest<br> Precipitation : Liquid and solid precipitation [mm]. Composite dataset composed of melted snow (snow Water Equivalent, in mm, from MeteoSwiss station) and Rain (in mm from Glacier station).<br> - Location : location of data (see above)<br> - dateUTC : date in UTC timezone<br> <br> Device used for data acquisition :<br> - Glacier snout Station : Double tipping buckets rain gauge from Davis Instruments (resolution 0.2 mm)<br> - Otemma camp Station : Double tipping buckets rain gauge, Spectrum WatchDog 1120 (resolution 0.25 mm)<br> - MeteoSwiss : see <a href="https://www.meteoswiss.admin.ch/home/measurement-and-forecasting-systems/land-based-stations/automatisches-messnetz.html">SwissMetNet</a> project</p> <p> </p> <p><strong>Description of data :</strong> Otemma_weather_Plot_alldata.html</p> <p>An interactive plot generated with python plotly (open in web browser) containing all above described data.</p>
Stream discharge, stage, electrical conductivity & temperature dataset from Otemma glacier forefield, Switzerland (from July 2019 to October 2021)
<p>Stream data collected in the Otemma forefield (Switzerland) from July 2019 to Ocober 2021.<br> Data were collected by the research teams of Bettina Schaefli<sup>2</sup> and Stuart N. Lane<sup>1</sup>.</p> <p><sup>1</sup> Institute of Earth Surface Dynamics (IDYST), University of Lausanne, 1015 Lausanne, Switzerland</p> <p><sup>2</sup> Institute of Geography (GIUB), University of Bern, 3012 Bern, Switzerland</p> <p>For further information, please contact:</p> <ul> <li>tom.muller.1@unil.ch</li> <li>floreana.miesen@unil.ch</li> </ul> <p><strong>Description of data </strong></p> <p>A detailed description of the dataset is provided in the <strong>data_description_analysis.pdf</strong> file. In particular, the methodology and stage-discharge rating curves are provided in this file. Stream data were measured in three locations from glacier snout (Station 1); after the outwash plain (Station 2) and at the end of the glacier forefield (Station 3) (<strong>see overview_GS.png</strong>). A <strong>shapefile </strong>is also provided (coordinate system LV95).</p> <p>2 datasets are available in the data.zip file:</p> <ul> <li> <p><strong>River_2019_2021_10T.csv</strong> : contains the measured River Electrical conductivity (EC) [μS/cm], Stage [meters] and Temperature [°C] data for all stations in a tidy data format (see pdf for detailed description), with a 10 minutes timestep.</p> </li> <li> <p><strong>Discharge2020_10T.csv </strong>&<strong> Discharge2021_10T.csv </strong>: contains the estimated discharge [m<sup>3</sup>/s] at Station 1 and Station 2 from July 2020 to October 2021 and estimated error (2 standard deviations) in a tidy data format (see pdf for detailed description), with a 10 minutes timestep.</p> </li> </ul> <p>Additionally, the point discharge measurements covering peak summer discharge to minimal winter baseflow are provided in the <strong>Point_discharge_measurements_2020_2021.xlsx</strong> file.</p> <p>Plots of river parameters and discharge are also provided in data.zip for vizualisation.</p> <p> </p>
Electrical Resistivity Tomography (ERT) datasets from the Otemma glacier forefield and outwash plain
<p><strong>Electrical Resistivity Tomography (ERT) datasets collected in the Otemma forefield (Switzerland) from 2019 to 2021.</strong><br> Data were collected by the research teams of Bettina Schaefli<sup>1,2</sup>, Stuart N. Lane<sup>1</sup> and James Irving<sup>3</sup>.</p> <p><sup>1</sup> Institute of Earth Surface Dynamics (IDYST), University of Lausanne, 1015 Lausanne, Switzerland</p> <p><sup>2</sup> Institute of Geography (GIUB), University of Bern, 3012 Bern, Switzerland</p> <p><sup>3</sup> Institute of Earth Sciences (ISTE), University of Lausanne, 1015 Lausanne, Switzerland</p> <p>For further information, please contact:</p> <ul> <li>tom.muller.1@unil.ch</li> </ul> <p><strong>This dataset is first referenced and discussed in the research paper by Müller et al., 2022.</strong></p> <p>------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p><strong>Data Description</strong></p> <p>Electrical Resistivity Tomography (ERT) profiles were collected around the outwash plain of the Otemma glacier forefield (WGS84 : 45.93434 / 7.41209). All data were collected with a <a href="http://www.iris-instruments.com/syscal-pro.html">Syscal Pro</a> Switch 48 from Iris Instruments, using an array of maximum 48 electrodes with a spacing between 1 and 10 meters. For each line, measurements were performed using a Dipole-Dipole (dd) and a Wenner-Schlumberger (ws) electrode configuration.</p> <p>All ERT lines locations can be visualized in <em><strong>ERT_map_lines_2019-2021.jpg</strong>.</em></p> <p>A result overview can be vizualized in <em><strong>ERT_allResults_3Doverview.png</strong>.</em></p> <p><strong>Data Structure</strong></p> <p>Two <a href="https://jupyter.org/">Juypter Notebook</a> files are provided and can be used to reproduce all inversion analyses.</p> <ul> <li><em><strong>1_createInput_prosys_to_pygimli.ipynb</strong></em> : Transforms the raw data from Syscal Pro (exported with <a href="http://www.iris-instruments.com/download.html">ProsysII</a> software as .csv) to a processed .dat file formated for inversion using the <a href="https://www.pygimli.org/">pyGIMLi</a> library.</li> <li><em><strong>2_ERT_inversion.ipynb</strong></em> : Reads the processed .dat file and performs a 2D robust inversion for a set of regularization parameters for the selected line.</li> </ul> <p>In <strong>ERT_data.zip</strong>, 3 folders with similar structure contain all data for year 2019, 2020 and 2021. Each folder contains :</p> <ol> <li><strong>GPS </strong>: folder with electrodes coordinates for each ERT line</li> <li><strong>inputGiMLi</strong> <ul> <li><strong>prosys_csv</strong>: contains the raw field measurements (downloaded from the Syscal device using ProsysII)</li> <li><strong>input_ERT </strong>: stores the processed .dat file. (created from notebook 1)</li> <li><strong>results_lambda</strong> : contains a .png image with the inversion results using different values of the regularization parameter lambda used to assess the sensitivity of the inversion results (over/underfitting). Analysis is performed for each line and each electrode configuration (dd or ws). (created from notebook 2)</li> <li><strong>results_final</strong> : contains a .png image with the final inversion results for each line and electrode configuration (dd or ws) using the optimal lambda parameter only (all arrays are shown from East to West). (created from notebook 2)</li> <li><strong>vtk</strong> : contains a .vtk file for each final results for 3D vizualization in the <a href="https://www.paraview.org/">Paraview</a> software.</li> </ul> </li> <li><strong><em>ERT_line_description_yyyy.csv</em> </strong>: a file describing the ERT arrays characteristics (read in notebook 1 and 2)</li> </ol> <p>The <strong>results </strong>folder contains :</p> <ul> <li><strong>ERT_3Dview_paraview</strong> folder : contains Paraview state files (.pvsm) for 3D vizualization of all results, as well as image files.</li> <li><em><strong>ERT_results_all.pdf</strong></em> : A summary of all final results for all years (similar content as <em>ERT/inputGiMLi</em><strong>/</strong><em>results_final</em> folders)</li> <li><em><strong>ERT_results_bedrock.pdf</strong></em> : Contains the vizualization of specific ERT profiles in the outwash plain and their field location . The separation between a surface layer of water-saturated sediments (resistivity <2500 Ωm) and the underlying bedrock is delimited. The likely presence of buried ice blocks (isolated blocks with resistivity >5000-10000 Ωm) is also highlighted.</li> <li><em><strong>ERT_timelapse_salt_tracer.gif</strong></em> : results of a time-lapse ERT measurement performed on 9 August 2019 to track the movement of a salt plume injected at 06 am, 9.38 meters upslope (see paper by<em> Müller et al., 2022</em> for detailed analysis). The tracer starts to appear at 12:45 at a distance of 30m on the array. Minimum resistivity is reached at between 16:45 and 17:45.</li> </ul>
Water table elevation and groundwater temperature from the outwash plain of the Otemma glacier forefield (Switzerland) from 2019 to 2021
<p><strong>Water table elevation and groundwater temperature from the outwash plain of the Otemma glacier forefield (Switzerland) from 2019 to 2021.</strong><br> Data were collected by the research teams of Bettina Schaefli<sup>1,2</sup>, Stuart N. Lane<sup>1</sup>.</p> <p><sup>1</sup> Institute of Earth Surface Dynamics (IDYST), University of Lausanne, 1015 Lausanne, Switzerland</p> <p><sup>2</sup> Institute of Geography (GIUB), University of Bern, 3012 Bern, Switzerland</p> <p>For further information, please contact:</p> <ul> <li>tom.muller.1@unil.ch</li> </ul> <p><strong>This dataset is first referenced and discussed in the research paper by Müller et al., 2022.</strong></p> <p>------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p><strong>Data Description</strong></p> <p>9 piezometers consisting of fully screened plastic tubes were installed at an averaged depth of 1.5 to 2m in the outwash plain of the Otemma glacier forefield (WGS84 : 45.93434 / 7.41209). They cover four transects perpendicular to the stream from downstream (A) to upstream to (D).</p> <p>Water table elevation and temperature were recorded in each well at a 10 minute interval using SparkFun MS5803-14BA pressure sensors. Sensor resolution is 1 mm and 0.01 °C, sensor accuracy is ± 2 cm and ± 0.8°C. Sensor bias was verified and corrected by bi-monthly manual groundwater stage measurements. Water temperature was not manually corrected and may be subject to some unidentified bias.</p> <p>Piezometer location can be visualized in<strong><em> overview_piezo.jpg</em> </strong></p> <p>Piezometer coordinates are available in<strong> </strong>shapefile <strong><em>GPS_piezometers.zip</em></strong> (coordinate system: swiss LV95 (EPSG:2056))</p> <p>Piezometers name matches the labelling used in Müller et al. 2022 (A1,A2 to D1,D2). Two additionnal piezometers (BinjUp & BinjDown) used for a specific salt tracing analysis (see <em>ERT_timelapse_salt_tracer.gif </em>at <a href="https://zenodo.org/record/6342767#.YjBRmTXjJlh">https://zenodo.org/record/6342767#.YjBRmTXjJlh</a>) are also available. Finally an additional piezometer (B1-2) located between B1 and B2 is also available although it was not used in Müller et al. 2022.</p> <p><strong>Data Structure</strong></p> <p><strong>df_piezometers.csv</strong> is formated as a tidy dataframe with 10 minute interval and following headers :<br> - <em>date </em>: local date (UTC+01 with daylight saving time)<br> - <em>variable </em>: parameter of interest, with following classes :<br> w_elevation: Water table elevation [m. asl]<br> w_temperature : Groundwater temperature [°C]<br> - <em>name </em>: name of piezometer (see coordinates in GPS_piezometers.zip)<br> - <em>dateUTC </em>: date with UTC timezone</p> <p>Data can be quickly vizualized in<strong> plot_piezo.png</strong> or interactively in a browser using <strong>plot_piezo_interactive.html</strong></p>
Data supporting 'Empirical correction of systematic orthorectification error in Sentinel-2 velocity fields for Greenlandic outlet glaciers'
<p><strong>Note: An updated dataset covering the majority of Greenland's marine-terminating glaciers is available as part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) project through the National Snow and Ice Data Center (NSIDC) at <a href="https://doi.org/10.5067/B28FM2QVVYWY">https://doi.org/10.5067/B28FM2QVVYWY</a>. </strong></p> <p>Data supporting the paper:</p> <blockquote> <p>Chudley, T. R., Howat, I. M., Yadav, B. N., & Noh, M. J. (2022). Empirical correction of systematic orthorectification error in Sentinel-2 velocity fields for Greenlandic outlet glaciers. <em>The Cryosphere. </em>16, 2629–2642, https://doi.org/10.5194/tc-16-2629-2022</p> </blockquote> <p>Dataset consists of four netCDF files containing stacked Sentinel-2 velocity data of four Greenlandic outlet glaciers (Helheim Glacier, Jakobshavn Isbræ, Store Glacier, and Kangerlussuaq) between 2017 and 2021. Velocity data are derived and corrected following the methods outlined in Chudley <em>et al.</em> (2022). </p> <p>NetCDF files are created by, and tested to be readable by, Python's xarray package.</p> <p>The dimensions of the netCDF file are as follows:</p> <ul> <li><strong>X</strong> - <em>x </em>coordinates in NSDIC Sea Ice Polar Stereographic North (EPSG:3413).</li> <li><strong>Y</strong> - <em>y</em> coordinates in NSDIC Sea Ice Polar Stereographic North (EPSG:3413).</li> <li><strong>time</strong> - temporal midpoint of velocity field.</li> </ul> <p>The variables of the netCDF file are as follows:</p> <ul> <li><strong>dmag</strong> - the absolute magnitude of the velocity, in metres per day.</li> <li><strong>dx</strong> - the velocity in the <em>x</em> direction, in metres per day.</li> <li><strong>dy</strong> - the velocity in the <em>y</em> direction, in metres per day.</li> <li><strong>date1</strong> - the date and time of the first scene acquisition.</li> <li><strong>date2</strong> - the date and time of the second scene acquisition.</li> <li><strong>baseline</strong> - the temporal baseline, in days, between scene acquisitions.</li> <li><strong>orbit_pair</strong> - the combination of orbital pathways in the string format 'RXXX_RYYY', where XXX is relative orbit number of the first scene and YYY the relative orbit number of the second scene.</li> <li><strong>mag_rmse</strong> - the root mean square error of the absolute velocity of the off-ice area. </li> <li><strong>dx_mean</strong> - the mean velocity of the off-ice area in the <em>x</em> direction.</li> <li><strong>dx_sd</strong> - the standard deviation of the velocity of the off-ice area in the <em>x</em> direction.</li> <li><strong>dy_mean</strong> - the mean velocity of the off-ice area in the <em>x</em> direction.</li> <li><strong>dy_sd</strong> - the standard deviation of the velocity of the off-ice area in the <em>y</em> direction.</li> </ul>
Abramov glacier firn data
<p>Historical and recent firn data for Abramov glacier, Pamir Alay compiled and measured in the context of the PhD Thesis 'Changing glacier firn in Central Asia and its impact on glacier mass balance' by Marlene Kronenberg (2022).</p> <p>This repository contains firn data measured in 2018 consisting of GPR measurements (zip folder), subsurface temperature measurements (zip folder) and firn core data from three drill sites as well as historical firn profile data digitized from Kislov (1982) and Suslov and Krenke (1980). Refer to abra_firn_cores_overview.xlsx for an overview of the core data.</p> <p>The data are described in Kronenberg et al. (2021) and Kronenberg (2022). Please refer to these studies when using the data.</p> <p>References:</p> <p>Kislov BV (1982) Formation and regime of the firn-ice stratum of a mountain glacier [in Russian] (Ph.D. thesis). SARNIGMI Tashkent.</p> <p>Kronenberg M, Machguth H, Eichler A, Schwikowski M, Hoelzle M (2021). Comparison of historical and recent accumulation rates on Abramov Glacier, Pamir Alay. Journal of Glaciology 67(262), 253–268. https://doi.org/10.1017/jog.2020.103</p> <p>Kronenberg Marlene. (2022). Changing glacier firn in Central Asia and its impact on glacier mass balance. PhD thesis, University of Fribourg.</p> <p>Suslov VF and Krenke AN (1980) Abaramov Glacier (Alay Range) [in Russian]. St. Petersburg: Gidrometeoizdat.</p>
Dataset and codes for 'Climatic control on seasonal variations of glacier surface velocity'
<p><strong>This repository contains the codes and processed data used to retrieve 10-day changes in glacier surface velocity over the Western Pamir.</strong></p> <p>The supp_CODES.zip contains all details and codes to use COSI-CORR (<a href="http://www.tectonics.caltech.edu/slip_history/spot_coseis/">http://www.tectonics.caltech.edu/slip_history/spot_coseis/</a>) to process a large batch of satellite images. The images can be downloaded directly via <a href="https://earthexplorer.usgs.gov/">https://earthexplorer.usgs.gov/</a> or <a href="https://scihub.copernicus.eu/">https://scihub.copernicus.eu</a>. Please read the Methods and Data section of the associated manuscript for details.</p> <p> </p> <p>The Matrix_velocities.zip contains, for each of the 48 investigated glaciers, the DEM, X, Y (NANNI_2022_supp_glacier_centreline_DEM_XY_1px_30m_1.txt) as well as a matrix of n*m with m the distance along flow and n the number of time step over which the velocity is calculated (NANNI_2022_supp_glacier_centreline_vel_matrix_1px_30m_1.txt), ans the associated figure that show the multi year velocity changes together with the one year average and the along centreline profiles. An example is shown in the two figures for glacier 48 in the main repository.</p> <p> </p> <p>The NANNI_2022_supp_glacier_characteristics file contains the glacier characteristics (48*8), as shown in the associated figures.</p> <p> </p> <p>The NANNI_2022_supp_pickedpoints_migration_AUTUMN/SPRING contains the automatically picked points for the onset of the acceleration in Spring and Autmun for each glacier. The headers contains the information, and the files contains is shown in the associated figure.</p> <p>the temperature profiles used to calculate the Iso 0C are in NANNI_2022_supp_temp_perday_fedchenko_2400m</p> <p>The position of each 48 glacier is shown in the associated figure.</p> <p> </p> <p>You can also find the processed velocity fields (velocity magnitude) under the different path an row: p151r33.zip and p152r33.zip for Landsat8, T42SYJ.zip and T43SBD.zip for Sentinel 2. In these folder you will a find a .tif file names similar to:</p> <p><em>Working_cosicorr_windows_FCorr_16days_p152r33_159_175_AB_1101110_Filtered_correlations_p152r33_filtered_abs.tif</em></p> <p>The name of the files gives information about the time span used (16days), the path and raw (p152r33), the data of the slave in DOY from 2013 (159) and of the master (175).</p> <p>The Statistics.zip file contains for each path and row the associated DEM, glacier mask (RGI), median magnitude (ABS), median NS displacement (NS), median EW displacemnt (EW), with the associated median absolute deviation (MAD). The files containing 'bflt' corresponds to the values computed before the filtering procedure, and the one without, after the filtering procedure. </p> <p>The .tif files are not georeferenced, but are all projected on the same grid with a 30m square pixel size on a UTM 33 42N projection.</p> <p> </p> <p> </p> <p>Please contact me for any question.</p> <p> </p> <div class="notranslate"> </div>
The potential of low-cost UAVs and open-source photogrammetry software for high-resolution monitoring of alpine glaciers: A case study from the Kanderfirn (Swiss Alps)
<p>This dataset contains high-resolution orthophotos (5 x 5 cm) and digital surface models (25 x 25 cm) of the Kandernfirn Glacier located in the Swiss Alps. Aerial images were aquired with a self-developed fixed-wing Unmanned Aerial Vehicle during ten surveys on five different days in 2017 and 2018. The open-source photogrammetry software OpenDroneMap (version 0.4.1) was used for image processing.</p> <p>The orthophotos and digital surface models were validated through dGNSS point measurements of ground control points. Please refer to the corresponding paper for information on the horizontal and vertical accuracy of the files.</p>
Rock glaciers in the Himalaya
<p><span>This dataset consists in rock glacier inventoried over the Himalayas, described briefly in Jones et al. (2021) and in more detail in the associated paper Harrison et al. (2024). </span></p> <p> </p> <p><span>Data description:</span></p> <p> </p> <p><span>1) Point-based inventory for the Greater Himalayas (24,968 landforms)</span></p> <p><span> Rock glacier identification for the Greater Himalaya was done in Google Earth Pro based on geomorphic indicator</span></p> <p><span> (surface flow structure, rock glacier body and frontal slope). For consistency, each pin point was digitised at the elevation at which the base of the frontal slope met the slope downstream. </span></p> <p> </p> <p><span>2) Sampled rock glacier points</span></p> <p><span> Randomly selected sample (5%, n= 2070 landforms) from the Western, Central, and Eastern Himalaya regions, provided as point shapefile.</span></p> <p> </p> <p><span>3) Sampled rock glacier polygons </span></p> <p><span><em> </em></span><span>Randomly selected sample (5%, n= 2070 landforms) from the Western, Central, and Eastern Himalaya regions digitised on Google Earth. The extended geomorphological footprint was used, in agreement with RGIK (2022) guidelines. </span><span><em>In the GH rock glacier inventory both intact and relict rock glaciers were digitised. Data are provided as polygon shape file and excel sheet.</em></span></p> <p><span><em>Data are provided in<span> </span>Geographic Coordinate System: GCS_WGS_1984</em></span></p> <p><span><em>Datum: D_WGS_1984</em></span></p> <p> </p>
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