Find research datasets worth reusing

opencc-by-4.0Feb 2022View details →

Weather dataset from Otemma glacier forefield, Switzerland (from 14 July 2019 to 18 November 2021)

Weather data collected in the Otemma forefield (Switzerland) from 14 July 2019 to 18 November 2021. Data were collected by the research teams of Bettina Schaefli2 and Stuart N. Lane1. 1 Institute of Earth Surface Dynamics (IDYST), University of Lausanne, 1015 Lausanne, Switzerland 2 Institute of Geography (GIUB), University of Bern, 3012 Bern, Switzerland For further information, please contact: tom.muller.1@unil.ch bettina.schaefli@giub.unibe.ch Description of data : WeatherData.csv Time span of data : 14 July 2019 to 18 November 2021 Time step : homogenized 10 minutes-averaged data Location of data (Coordinate in SWISS LV95 (EPSG:2056)     - Glacier snout Station : 2598615 / 1087375     - Glacier center Station : 2600495 / 1088631     - Floodplain Station : 2598096 / 1087087 STRUCTURE OF DATA : tidy dataframe with following headers :     - date : local date (UTC+01 with daylight saving time)     - variable : parameter of interest, with following classes :             Air_humidity : Air humidity in percent of air saturation [%]             Air_temperature : Air temperature in [°C]             Atm_pressure : Atmospheric pressure in [hPa]             Incoming_radiation : Incoming shortwave radiation in [W/m2]             Precipitation : Liquid precipitation measured  [mm]     - name : location of data (see coordinates above)     - dateUTC : date with UTC timezone Device used for data acquisition :     - Air_humidity/Air_temperature/Atm_pressure : Decagon VP-4     - Incoming_radiation : Apogee Instruments SP-11     - Precipitation : Double tipping buckets rain gauge from Davis Instruments (resolution 0.2 mm)   Description of data : RainComposite_Otemma_Arolla.csv Time span of data : 01 July 2019 to 18 November 2021 Time step : homogenized 10 minutes-averaged data The dataset compiles the measured rain during summer at the closest weather station (Glacier snout). For the winter period (and few gaps during the summer), the solid precipitations (snow) from the closest MeteoSwiss weather stations (SwissMetNet) were used. Gaps where filled with 1) MeteoSwiss Otemma and if data were still missing filled with 2) MeteoSwiss Arolla          Location of data (Coordinate in SWISS LV95 (EPSG:2056)     - Glacier snout Station : 2598615 / 1087375     - Otemma camp Station : 2597508 / 1086653     - MeteoSwiss Station : 2596476 / 1085864     - MeteoSwiss Arolla : 2603507 / 1095832 STRUCTURE OF DATA : tidy dataframe with following headers :     - date : local date (UTC+01 with dst)     - variable : parameter of interest             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).     - Location : location of data (see above)     - dateUTC : date in UTC timezone      Device used for data acquisition :     - Glacier snout Station : Double tipping buckets rain gauge from Davis Instruments (resolution 0.2 mm)     - Otemma camp Station : Double tipping buckets rain gauge, Spectrum WatchDog 1120 (resolution 0.25 mm)     - MeteoSwiss : see <a href="https://www.meteoswiss.admin.ch/home/measurement-and-forecasting-systems/land-based-stations/automatisches-messnetz.html">SwissMetNet</a> project   Description of data : Otemma_weather_Plot_alldata.html An interactive plot generated with python plotly (open in web browser) containing all above described data.

opencc-by-4.0Feb 2022View details →

Stream discharge, stage, electrical conductivity & temperature dataset from Otemma glacier forefield, Switzerland (from July 2019 to October 2021)

Stream data collected in the Otemma forefield (Switzerland) from July 2019 to Ocober 2021. Data were collected by the research teams of Bettina Schaefli2 and Stuart N. Lane1. 1 Institute of Earth Surface Dynamics (IDYST), University of Lausanne, 1015 Lausanne, Switzerland 2 Institute of Geography (GIUB), University of Bern, 3012 Bern, Switzerland For further information, please contact: <ul> <li>tom.muller.1@unil.ch</li> <li>floreana.miesen@unil.ch</li> </ul> Description of data A detailed description of the dataset is provided in the data_description_analysis.pdf 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) (see overview_GS.png). A shapefile is also provided (coordinate system LV95). 2 datasets are available in the data.zip file: <ul> <li> River_2019_2021_10T.csv : 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. </li> <li> Discharge2020_10T.csv & Discharge2021_10T.csv : contains the estimated discharge [m3/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. </li> </ul> Additionally, the point discharge measurements covering peak summer discharge to minimal winter baseflow are provided in the Point_discharge_measurements_2020_2021.xlsx file. Plots of river parameters and discharge are also provided in data.zip for vizualisation.

opencc-by-4.0Mar 2022View details →

Electrical Resistivity Tomography (ERT) datasets from the Otemma glacier forefield and outwash plain

Electrical Resistivity Tomography (ERT) datasets collected in the Otemma forefield (Switzerland) from 2019 to 2021. Data were collected by the research teams of Bettina Schaefli1,2, Stuart N. Lane1 and James Irving3. 1 Institute of Earth Surface Dynamics (IDYST), University of Lausanne, 1015 Lausanne, Switzerland 2 Institute of Geography (GIUB), University of Bern, 3012 Bern, Switzerland 3 Institute of Earth Sciences (ISTE), University of Lausanne, 1015 Lausanne, Switzerland For further information, please contact: <ul> <li>tom.muller.1@unil.ch</li> </ul> This dataset is first referenced and discussed in the research paper by Müller et al., 2022. ------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Data Description 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. All ERT lines locations can be visualized in ERT_map_lines_2019-2021.jpg. A result overview can be vizualized in ERT_allResults_3Doverview.png. Data Structure Two <a href="https://jupyter.org/">Juypter Notebook</a> files are provided and can be used to reproduce all inversion analyses. <ul> <li>1_createInput_prosys_to_pygimli.ipynb : 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>2_ERT_inversion.ipynb : Reads the processed .dat file and performs a 2D robust inversion for a set of regularization parameters for the selected line.</li> </ul> In ERT_data.zip, 3 folders with similar structure contain all data for year 2019, 2020 and 2021. Each folder contains : <ol> <li>GPS : folder with electrodes coordinates for each ERT line</li> <li>inputGiMLi <ul> <li>prosys_csv: contains the raw field measurements (downloaded from the Syscal device using ProsysII)</li> <li>input_ERT : stores the processed .dat file. (created from notebook 1)</li> <li>results_lambda : 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>results_final : 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>vtk : 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>ERT_line_description_yyyy.csv : a file describing the ERT arrays characteristics (read in notebook 1 and 2)</li> </ol> The results folder contains : <ul> <li>ERT_3Dview_paraview folder : contains Paraview state files (.pvsm) for 3D vizualization of all results, as well as image files.</li> <li>ERT_results_all.pdf : A summary of all final results for all years (similar content as ERT/inputGiMLi/results_final folders)</li> <li>ERT_results_bedrock.pdf : 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>ERT_timelapse_salt_tracer.gif : 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 Müller et al., 2022 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>

opencc-by-4.0Oct 2022View details →

Dataset and codes for 'Climatic control on seasonal variations of glacier surface velocity'

This repository contains the codes and processed data used to retrieve 10-day changes in glacier surface velocity over the Western Pamir. 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.   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.   The NANNI_2022_supp_glacier_characteristics file contains the glacier characteristics (48*8), as shown in the associated figures.   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. the temperature profiles used to calculate the Iso 0C are in NANNI_2022_supp_temp_perday_fedchenko_2400m The position of each 48 glacier is shown in the associated figure.   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: Working_cosicorr_windows_FCorr_16days_p152r33_159_175_AB_1101110_Filtered_correlations_p152r33_filtered_abs.tif 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). 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.  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.     Please contact me for any question.   <div class="notranslate"> </div>

opencc-by-4.0Apr 2023View details →

CLM/CTSM glacier input datasets used for study on evaluation variable-resolution CESM2 in High-Mountain Asia

General Info This data archive contains the updated glacier-cover and glacier regions for the Community Land Model version 5 (CLM5)/Community Terrestrial Systems Model (CTSM). The updated glacier-cover and glacier regions are used for a study on the evaluation of variable-resolution (VR) CESM2 in High Mountain Asia (<a href="https://tc.copernicus.org/preprints/tc-2022-256/">https://tc.copernicus.org/preprints/tc-2022-256/</a>). The data archive also contains the model scripts and input files that have been used to create the glacier-cover dataset. The global glacier outlines used for the glacier-cover dataset were retrieved from the Randolph Glacier Inventory version 6 (RGI-Consortium, 2017). The vector data for the Greenland and Antarctic ice sheets were retrieved from the masks of Bedmachine version 4 (Morlighem et al., 2017, 2021) and version 2 (Morlighem et al., 2020; Morlighem, 2020), respectively.  Contact René Wijngaard (<a href="mailto:r.r.wijngaard.uu@gmail.com">r.r.wijngaard.uu@gmail.com</a> / <a href="mailto:r.r.wijngaard@uu.nl">r.r.wijngaard@uu.nl</a>)  Dataset Contents  <pre><code>mksrf_glacier_3x3min_simyr2000.c210708.nc</code></pre> The updated glacier-cover dataset, encompassing three 3-minute datasets: 1) fractional land ice coverage, including both glaciers and ice sheets (PCT_GLACIER), 2) distributions of areal glacier coverage by elevation (PCT_GLC_GIC), and 3) distributions of areal ice-sheet coverage by elevation (PCT_GLC_ICESHEET). <pre><code>mksrf_GlacierRegion_10x10min_nomask_c200813.nc</code></pre> The updated glacier regions, encompassing five different glacier regions (0 - Other regions, 1 - Inside standard CISM grid but outside Greenland itself, 2 - Greenland, 3 - Antarctica, and 4 - High Mountain Asia (new)), used to set the ice melt and runoff behaviour in CLM5/CTSM (more detailed information can be found in the CLM5 Documentation, <a href="https://escomp.github.io/ctsm-docs/">https://escomp.github.io/ctsm-docs/</a>) <pre><code>model_scripts.tar</code></pre> Model scripts used for creating the glacier-cover dataset. A README file is included that lists instructions on how to make the glacier-cover dataset.  <pre><code>glacier_final.tar</code></pre> Input files used to create the glacier-cover dataset. The following files are included: a global 30-arcsec merged BedMachine/GMTED2010 elevation dataset (gmted_bedmachine_stitched.nc) and land-sea mask (gmted2010_modis-rawdata-lonshift.nc), Antarctica land mask (BedMachineAntarcticaRotate2RotateBack_2020-07-15_v02_lonshift.map_TO_30arcsec.nc), Greenland land mask (BedMachineGreenland-2021-04-20.map_TO_30arcsec.nc), and 30-arcsec datasets encompassing glacier-cover (30arcsec_00_rgi60_World.nc) and ice-sheet cover (30arcsec_00_BM_World.nc).

opencc-by-4.0Sep 2023View details →

Calving Front Dataset for Marine-Terminating Glaciers in Svalbard 1985-2023

Svalbard has experienced increased climate variability as a result of global warming, leading to significant mass loss in its marine-terminating glaciers over recent decades. Nevertheless, the mechanisms driving this mass loss remain less understood, primarily due to a limited understanding of calving dynamics. Here we present a new high-resolution calving front dataset of 149 marine-terminating glaciers in Svalbard, comprising 124919 glacier calving front positions during the period of 1985-2023. This dataset was generated using a novel automated deep learning framework and multiple optical and SAR satellite images from Landsat, Terra-ASTER, Sentinel-2, and Sentinel-1 satellite missions. The information regarding the glacier calving front terminal traces, glacier centrelines, glacier domains, fjord masks and the along-centreline glacier calving front change time series is consolidated into a single Geopackage file named "Svalbard_Calving_Front_Product.gpkg." The specific file structure for this data file is detailed in Table 1, and the feature attribute table for the different data layers recorded in this data file can be found in Table 2. Furthermore, we have included spatial distribution map plots of the glacier calving front traces and line plots depicting the time series of calving front changes for each individual glacier. These plots are provided in .PNG file format and can be accessed within the Figures folder. Table 1. The layer structure of the Svalbard calving front data product. <table> <tbody> <tr> <td> Layer Name </td> <td> Details </td> </tr> <tr> <td> traces </td> <td> Line geometries recording the terminal traces of all the glaciers (EPSG:3995). </td> </tr> <tr> <td> centrelines </td> <td> Line geometries recording the glacier centrelines used in calving front change estimation (EPSG:3995). </td> </tr> <tr> <td> domains </td> <td> Polygon geometries recording the glacier domains (EPSG:3995). </td> </tr> <tr> <td> fjord_masks </td> <td> Polygon geometries recording the fjord masks (EPSG:3995). </td> </tr> <tr> <td> front_change_time_series </td> <td> Point geometries recording the along-centreline glacier calving front change time series (EPSG:4326). </td> </tr> </tbody> </table>   Table 2. The feature attribute table of the data layer. <table> <tbody> <tr> <td> Data Field </td> <td> Description </td> </tr> <tr> <td> Glacier </td> <td> The Randolph Glacier Inventory (RGI) version 6 (RGI Consortium, 2017) glacier id. </td> </tr> <tr> <td> Sensor </td> <td> The satellite platform used in mapping glacier calving front, including “Landsat”, “Terra-ASTER”, “Sentinel2” and “Sentinel1”. </td> </tr> <tr> <td> ImageId </td> <td> The image id of the satellite image used in mapping the glacier calving front. </td> </tr> <tr> <td> DateString </td> <td> The datetime string of the satellite image in the format of “YYYYMMDD”. </td> </tr> <tr> <td> CFL_Change </td> <td> The calving front location (CFL) changes in meters along the glacier centreline in relation to the earliest calving front location in the time series. </td> </tr> <tr> <td> glacier_lat </td> <td> The latitude of the glacier location (WGS84 coordinate system). </td> </tr> <tr> <td> glacier_lon </td> <td> The longitude of the glacier location (WGS84 coordinate system). </td> </tr> </tbody> </table>

opencc-by-4.0Jun 2022View details →

Dataset: Halving of Swiss glacier volume since 1931 observed from terrestrial image photogrammetry

This is supplementary data for the article currently in review for The Cryosphere, titled "Halving of Swiss glacier volume since 1931 observed from terrestrial image photogrammetry". <a href="https://doi.org/10.5194/tc-2022-14">See the preprint here</a>

opencc-by-4.0May 2024View details →

New glacier thickness and bed topography maps for Svalbard - Dataset

The dataset includes three Geotiff files that include: 1) A bed topography map of Svalbard (heights in m a.s.l.) [Bed_map.tiff] 2) An ice thickness map of Svalbard (in m) [Thickness_map.tiff] 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).  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.

opencc-by-4.0Dec 2022View details →

A new inventory of High Mountain Asia surging glaciers derived from multiple elevation datasets since the 1970s

Glacier surging is an unusual undulation instability of ice flow and complete surging glacier inventories are important for regional mass balance studies and assessing glacier-related hazards. Glacier surge events in High Mountain Asia (HMA) are widely reported. Through the estimated elevation changes from multiple DEMs sources that acquired from 1970s to 2020, and morphologic changes from 1986 to 2021, here we present a new surging glacier inventory across HMA. The inventory has incorporated 890 surging and 336 surge-like glaciers, each glacier is assigned with indicators of surging feature and surge possibility. Compared to previous surging glacier inventory in HMA, our inventory is theoretically more complete because of the much longer observation period. This data repository contains the surging glacier inventory and glacier elevation change maps. The inventory is stored in the format of GeoPackage (.gpkg) and ESRI Shapefile format (.shp), which is represented by glacier polygon (from GAMDAM2) or surface point with geometric attributes. The multi-temporal elevation change maps of identified surging glaciers were divided into 1×1° tiles, storing in the format of GeoTiff(*.tif). Detailed description of the dataset including the file contents and attributes information can be found in the metadata file (README.txt).

opencc-by-4.0Nov 2023View details →

Dataset of micro-roughness, Schmidt hammer and reflectance spectra obtained at Hallstaetter Glacier foreland

The files contain data of micro-roughness (Ra and Rz), raw data of Schmidt hammer rebound-values, and reflectance spectra obtained at Hallstaetter Glacier foreland in July 2022. The data was use in a publication: Dąbski M, Badura I, Kycko M, Grabarczyk A, Matlakowska R, Otto J-C. The Development of Limestone Weathering Rind in a Proglacial Environment of the Hallstätter Glacier. Minerals. 2023; 13(4):530. https://doi.org/10.3390/min13040530. Funding provided by National Science Centre, Poland (Preludium Bis-2 2020/39/O/ST10/01068).Micro-roughness, rock strength (Schmidt hammer rebound values), and spectral reflectance were obtained in-situ on glacially abraded rock surfaces along a transect from the glacial snout  to the outermost moraines from the Little Ice Age, covering circa 172 years of subaerial weathering in the proglacial alpine environment. UAV surveys of the studied area were performed to obtain Digital Elevation Models (DEMs) and allow for detailed comparative studies in the future.Test site 1 was very close to the glacier (undergoes weathering for 1–2 years), site 2 was in the zone c. 10 years old, site 3 was in the zone c. 50–51 years old, site 4 was in the zone c. 105–106 years old, and the last one (site 5) was on the LIA moraines, where the duration of weathering is c. 167–172 years. The sites were located on bedrock or boulders embedded in the moraines with distinct traces of glacial abrasion, allowing us to infer that older weathering rind (developed before glacial accumulation) has been eroded. The sites were selected based on their age, homogenous petrography, accessibility, and suitability for micro-roughness measurements. Within each test site, we selected ten specific rock surfaces (c. 100 cm2 each), with clear signs of glacial abrasion, for the measurements of micro-roughness, Schmidt hammer rebound (rock strength), and spectral reflectance.

opencc-by-4.0Apr 2022View details →

A 21st century high-resolution glacier and ice sheet fractional area dataset: Code and Data

This resource contains code and input data to develop a high-resolution (0.1°) gridded global glacier and ice sheet fractional area dataset, which is also available in this resource. The dataset is developed from Randolph Glacier Inventory v6.0 shapefiles and supplementary shapefiles for the Antarctic and Greenland ice sheets. The approach is adapted from Li et al., (2021; <a href="https://doi.org/10.1017/jog.2021.28">https://doi.org/10.1017/jog.2021.28</a>). The dataset provides estimates of the fraction (0 to 1) of land cover that is glaciated in each 0.1° x 0.1° grid cell. The dataset was designed for use in the SPEAR model (<a href="https://www.gfdl.noaa.gov/spear/">https://www.gfdl.noaa.gov/spear/</a>) from the NOAA Geophysical Fluid Dynamics Laboratory (GFDL), but may be useful for other applications as well. This work is documented in a NOAA Technical Memorandum (citation information to follow).

opencc-by-4.0May 2021View details →

Dataset - Warming-induced monsoon precipitation phase change intensifies glacier mass loss in the southeastern Tibetan Plateau

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: "Warming-induced monsoon precipitation phase change intensifies glacier mass loss in the southeastern Tibetan Plateau". A. Jouberton, T. E. Shaw, E. Miles, M. McCarthy, S. Fugger, S. Ren, A. Dehecq, W. Yang and F. Pellicciotti 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. --------------- Contents : Folder : "Matlab_scripts"     'Climate_import.m' : Organizes meteorological forcing and generates Figure S9     'TOPKAPI_result_import.m' : Imports TOPKAPI's reference run outputs and prepares them for analysis     'Experiment_analysis.m' : Analyses the results of the forcing experiments, generates Figure 4 and Figure S25     'Main_text_results.m' : Analyses the results of TOPKAPI's reference runs, generates Figure 1D, FIgure 2 and Figure 3     'TOPKAPI_validation.m' : Compares TOPKAPI's reference run results with several validation datasets, generates the figures and performance metrics of the model calibration and validation procedure.     'Parlung_albedo_regional_analysis.m': 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.    'Parlung_GMB_regional_analysis.m': 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.    'Precipitation_phase_sensitivity_analysis.m': Performs a sensitivity analysis on the simulated monsoon snowfall ratio per elevation band and on the attribution of glacier mass loss to precipitation phase change using Monte Carlo simulations.    'TOPKAPI_MODIS_validation.m': Compares the snow cover at Parlung No.4 catchment simulated by TOPKAPI-ETH and observed by MODIS, generates Figure S19.   Folder : "Remote_sensing" :    Sub-Folder: 'Hugonnet' = Glacier mass balance averaged over 2000-2020 covering the Southeastern Tibetan Plateau, 100m resolution, derived from Hugonnet et al. 2021    Sub-Folder: 'MODIS' = contains the snow cover at Parlung No.4 derived from the daily product MOD10A1 version 61, for the period 2000-2018    Sub-Folder: 'Regional_glacier_albedo' = contains the annual glacier surface albedo from 2000 to 2020, covering the Southeastern Tibetan Plateau, 500m resolution.    Sub-Folder: 'Shapefiles' = contains the Parlung No.4 glacier outlines in 1974 and from the RGI 6.0    'ASTER_Nyainqentanglha_15m_utm.tif' = ASTER Digital elevation model at 15m resolution covering the Southeastern Tibetan Plateau    'parlung_mask_1974.mat' = Parlung No.4 glacier mask as a matlab file    'dh_ASTER_SRTM_30m.tif' = Mean elevation change rate from 2000 to 2016 at Parlung No.4 catchment.    'Geodetic_map.mat' = Elevation change maps for the periods 1974-2000 and 1974-2014, as a matlab file    'GMB_geodetic.mat' = Geodetic mass balance (glacier-wide mean and profile per elevation band) used in Figure S13    'parlung_30m_catchment_mask.tif' = Parlung No.4 catchment mask    'parlung_1974_30m_dem.tif' = DEM of Parlung No.4 catchment, 30 m resolution    'parlung_1974_30m_gla.tif' = Parlung No. 4 glacier mask, 30m resolution    'parlung_1974_30m_glah.tif' = Reconstructed ice thickness of 1975 for Parlung No.4 glacier    'parlung_1974_2000_diff_24m.tif' = Elevation change from DEM differencing at Parlung No.4 catchment for 1974-2000    'parlung_1974_2014_diff_24m.tif' = Elevation change from DEM differencing at Parlung No.4 catchment for 1974-2014    'Parlung_1974_bedrock_dem_30m.tif' = Bedrock surface digital elevation model of the catchment, 30m spatial resolution    'RGI_KangriKarpo_100m_utm_id.tif' = Glacier mask covering the Kangri Karpo mountain region, 100m resolution, with glacier IDs in the attribute table    'RGI_Nyainqentanglha_100m_utm_id.tif' = = Glacier mask covering the Southeastern Tibetan Plateau, 100m resolution, with glacier IDs in the attribute table   Folder : "TOPKAPI_forcing" :     CCT_AWS4600_extended.csv : Hourly cloud cover transmissivity from 1975 to 2018 reconstructed at AWSoff location      Climate.mat : Organizes meteorological forcings, output from the matlab script 'Climate_import.m'     LR_AWS4600_extended.csv : Hourly temperature lapse-rates from 1975 to 2018 reconstructed at AWSoff location      Precipitation_AWS4600_extended.csv : Hourly precipitation from 1975 to 2018 reconstructed at AWSoff location      Ta_AWS4600_extended.csv : Hourly air temperature from 1975 to 2018 reconstructed at AWSoff location    Sub-Folder: 'National_meteorological_stations' = Contains the daily air temperature and precipitation measured at the national meteorological stations of Bomi, Zayu,    Zuogong and Basu    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.   Folder : "TOPKAPI_output":    Sub-Folder : "Forcing experiment" = organized TOPKAPI outputs from the forcing experiment    Sub-Folder :" Reference_run_outputs" = raw TOPKAPI outputs from the reference run (catchment average, spatial and grid cells)    Sub-Folder : "Reference_run_results" = organized TOPKAPI outputs from the reference run    Sub-Folder : "Snow_ice_cover" = contains TOPKAPI-ETH derived snow cover maps (daily map outputs)    Sub-Folder : "Regional_analysis" =        'Alb'= Table containing the mean glacier albedo (2000-2020) per normalized elevation band, for each glacier in the SETP (RGI 6.0)        'GMB'= Table containing the mean glacier mass balance (2000-2020) per normalized elevation band, for each glacier in the SETP (RGI 6.0)        '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        'NormEl_100m' = Table containing the elevation per normalized elevation band, for each glacier in the SETP (RGI 6.0), resolution of 100/500m    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   Folder : "Validation_data"    'topkapi.out_reference_discharge2016' =  raw TOPKAPI outputs run in 2016 with AWSoff air temperature    'master_file_parlung.mat' = matlab structure containing AWS measurements, necessary for running 'TOPKAPI_validation.m'    'Qdigit.mat' = Discharge measured at the Parlung No.4 glacier outlet, from Li et al., (2016)    'Parlung_Q_1970.mat'  = 'Discharge time-series used to run TOPKAPI-ETH (goes back to 1975, but filled with 0 when no measurements are available)   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).

opencc-by-4.0Jul 2024View details →

Dataset for five lake-terminating glaciers in Iceland 2008-2020

Data for five lake-terminating glaciers in Iceland for the period 2008-2020, incuding: (1) Surface velocity rasters at different time periods. (2) Shapefiles of glacier terminus position at different time periods. (3) Shapefiles of proglacial lake area at different time periods. Velocity data were generated using select TerraSAR-X scenes and the offset tracking toolbox in SNAP (Sentinel Application Platform). Glacier teminus position and proglacial lake area were digitised from select Landsat 7, Landsat 8 and Sentinel-2 scenes. Full methodology is given in the accompanying paper that is currently in review (preprint DOI to follow).  Glaciers of study are Skaftafellsjökull, Svínafellsjökull, Kvíárjökull, Fjallsjökull and Breiðamerkurjökull, all of which are found on the southern side of the Vatnajökull Ice Cap in southeast Iceland. Data for Fjallsjökull and Breiðamerkurjökull are for the period 2008-2020, whereas data for Skaftafellsjökull, Svínafellsjökull, Kvíárjökull are for the period 2010-2020 (there was no TerraSAR-X data from 2008 for these three glaciers).

opencc-by-4.0Mar 2018View details →

Datasets associated with "Quantifying debris thickness of debris-covered glaciers in the Everest region of Nepal through inversion of a sub-debris melt model"

Datasets that accompany "Quantifying debris thickness of debris-covered glaciers in the Everest region of Nepal through inversion of a sub-debris melt model".  These datasets include the debris thickness estimates derived including and excluding ponds (denoted as wponds and noponds, respectively), flux divergences, the shapefile of the 600 m boxes, the master 10 m DEM, the median x and y velocities, and the change in elevation for each pair of DEMs used in the study for Ngozumpa, Khumbu, and Imja-Lhotse Shar Glaciers.  Debris thickness is in units of meters.  Flux divergence is in units of meters per year.  A negative flux divergence means the box is gaining mass and is called the emergence velocity; while positive flux divergence means the box is losing mass and is called the submergence velocity.

opencc-by-4.0Nov 2024View details →

Surface Evolution of the Belvedere Glacier from aerial and UAV orthophotos from 1951 to 2023 - glacier outlines dataset

Open the record for dataset details and reuse information.

opencc-by-4.0Oct 2021View details →

Surface elevation (2012-2020) and ice thickness (2014-2020) datasets measured at Artesonraju Glacier, Cordillera Blanca, Perú

We present a set of data of interpreted ice thickness and ice surface elevation of Artesonraju glacier. The ice thickness was obtained by means of Ground Penetrating Radar (GPR) which was measured by Instituto Nacional de Investigación en Glaciares y Ecosistemas de Montaña (INAIGEM) and Autoridad Nacional de Agua (ANA). The years of ice thickness records include 2013, 2014, 2015, 2017, 2018, and 2020. On the other hand, the surface elevation points were obtained by means of automated total stations and mass balance stakes, integrally measured by ANA. The years of measurements are 2012, 2014, 2015, 2017, 2018, 2019, and 2020. The results from GPR data show a maximum depth of 235±18 m and a decreasing mean depth of ranging from 134±18 m in 2013 to 110±18 m in 2020. Additionally, we estimate a mean ice thickness change rate of 4.2±3.2 m yr-1 between 2012 and 2020 with GPR data alone, which is in agreement with the elevation change in the same period. The latter was estimated with the more accurate surface elevation data, yielding a change rate of -3.2±0.2 m yr-1, and hence, confirming a negative glacier mass balance. The datasets can be valuable for further analysis when combined with other data types, and as input for glacier dynamics modeling, ice volume estimations, and GLOF risk assessment.