Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
10
datasets available to search
ShareScore release 0.9.0
Dataset results
10 results for “global river discharge”
Global River BankFull Discharge (GQBF) - Siberia(SI) & South Pacific/Australia(SP)
<p>The GQBF is the estimated bankfull discharge across ~2.87 million km (length) of global river reaches. The bankfull discharge here is defined as the maximum flow rate contained within a river just before inundation occurs in the surrounding floodplain. We based our river bankfull discharge estimation on a newly developed river network, Global RIver Topology (GRIT), using GRIT’s river reaches as the spatial scale to represent the variation in bankfull discharge. We included all GRIT river reaches that coincided with the Global River Width from Landsat (GRWL) river masks (with overlapping ratio >=0.5). This selects river reaches with satellite-derived width measurements >=30 m, resulting in a total length of ~2.87 million km. Here, the GQBF represents the time-averaged bankfull discharge at <1 km (river length) spatial resolution.</p> <p><strong>Regions</strong></p> <p>Added regions SI, SP Vector files.</p> <ul> <li>SI - Siberia</li> <li>SP - South Pacific/Australia</li> </ul> <p>The subcontinental catchment groups (vector, polygons) can be found at <a href="https://zenodo.org/records/11219313">GRIT domain polygon</a> (GRITv06_domain_GLOBAL.gpkg.zip). They allow for more fine-grained subsetting of data .</p> <p>Vector files are provided in geographic WGS84 coordinates (EPSG:4326).</p> <p><strong>Change log</strong></p> <ul> <li>v0.1 - 2024-09-29<br> <ul> <li>First globally complete dataset published</li> </ul> </li> <li>v0.1 - 2024-11-19 <ul> <li>Add vector files for regions SI, SP</li> </ul> </li> </ul>
Datasets for manuscript: Global River Discharge and Floods in the Warmer Climate of the Last Interglacial
<p>This datasets contains results of the global hydrological and hydrodynamic modeling presented in the paper referenced in the title (doi: 10.1029/2020GL089375). The dataset comprises results for one set of simulations, based on Global Climate Model CESM1.2, out of the eight sets of simulations for eight GCMs included in the paper. The corresponding results for the other seven sets of simulations (based on GCMs CESM2, EC‐EARTH3.2, HadGEM3‐GC3.1, IPSL‐CM6‐LR, MPI‐ESM 1.2.01p1‐LR, NorESM1‐F, and NUIST‐CSM) can be obtained by writing to the corresponding author at paolo.scussolini@vu.nl.</p> <p>Files description:</p> <p>fldare_yearmean_timmean_CEM1.2_LIG.nc : Annual average flood area for the Last Interglacial simulation with GCM CESM1.2, hydrological model PCR-GLOBWB and hydrodynamic model CaMa-Flood.<br> <br> fldare_yearmean_timmean_CESM1.2_PI.nc 4 Mb : Annual average flood area for the Pre-Industrial simulation with GCM CESM1.2, hydrological model PCR-GLOBWB and hydrodynamic model CaMa-Flood.<br> <br> fldsto_yearmean_timmean_CESM1.2_LIG.nc 4 Mb : Annual average flood volume for the Last Interglacial simulation with GCM CESM1.2, hydrological model PCR-GLOBWB and hydrodynamic model CaMa-Flood.<br> <br> fldsto_yearmean_timmean_CESM1.2_PI.nc 4 Mb : Annual average flood volume for the Pre-Industrial simulation with GCM CESM1.2, hydrological model PCR-GLOBWB and hydrodynamic model CaMa-Flood.<br> <br> outflw_yearmean_timmean_CESM1.2_LIG.nc 4 Mb : Annual average river discharge for the Last Interglacial simulation with GCM CESM1.2, hydrological model PCR-GLOBWB and hydrodynamic model CaMa-Flood.<br> <br> outflw_yearmean_timmean_CESM1.2_PI.nc 4 Mb : Annual average river discharge for the Pre-Industrial simulation with GCM CESM1.2, hydrological model PCR-GLOBWB and hydrodynamic model CaMa-Flood.<br> <br> runoff_annuaTot_output_mergetime_timmean_CESM1.2_LIG.nc : Annual average runoff for the Last Interglacial simulation with GCM CESM1.2 and hydrological model PCR-GLOBWB.<br> <br> runoff_annuaTot_output_mergetime_timmean_CESM1.2_PI.nc : Annual average runoff for the Pre-Industrial simulation with GCM CESM1.2 and hydrological model PCR-GLOBWB.</p> <p> </p>
Global River Discharge Reanalysis dataset (GRDR)
<p>The Global River Discharge Reanalysis dataset (GRDR) is a global river discharge product that contains daily flows in ~2.9 million vectorized river reaches for 1984-2018.</p> <p>For more details about GRDR, please refer to the following publication: Feng D. and Gleason C.J., 2024, More flow upstream and less flow downstream: The changing form and function of global rivers, <em>Science</em></p>
A Synthetic Global Spatiotemporal Sampled River Discharge Database for Different Satellite Altimetry Mission Orbits
<p><strong>Corresponding peer-reviewed publication</strong></p> <p>This dataset corresponds to all the RRR input and output files that were used in the study reported in:</p> <ul> <li> <p>Sikder, Md. S., Bonnema, M., Emery, C. M., David, C. H., Lin, P., Pan, M., et al. (2021). A Synthetic Data Set Inspired by Satellite Altimetry and Impacts of Sampling on Global Spaceborne Discharge Characterization. <em>Water Resources Research</em>, <em>57</em>(2), e2020WR029035. <a href="https://doi.org/10.1029/2020WR029035">https://doi.org/10.1029/2020WR029035</a></p> </li> </ul> <p>When making use of any of the files in this dataset, please cite both the aforementioned article and the dataset herein. </p> <p>Note that this dataset makes extensive use of the river network and RAPID simulations that were produced in the following study, and the paper is gratefully acknowledged here:</p> <ul> <li> <p>Lin, P., Pan, M., Beck, H. E., Yang, Y., Yamazaki, D., Frasson, R., et al. (2019). Global Reconstruction of Naturalized River Flows at 2.94 Million Reaches. <em>Water Resources Research</em>, <em>55</em>(8), 6499–6516. <a href="https://doi.org/10.1029/2019WR025287">https://doi.org/10.1029/2019WR025287</a></p> </li> </ul> <p><strong>Version of record and details of this version</strong></p> <p>The version of record for this dataset (i.e. the one used in the aforementioned paper) is version V1.1 available at <a href="https://doi.org/10.5281/zenodo.4064188">https://doi.org/10.5281/zenodo.4064188</a>. This version V2.1 was produced to facilitate testing of the RRR software (<a href="https://github.com/c-h-david/rrr">https://github.com/c-h-david/rrr</a>). Notable details regarding this version compared to V2.0 are as follows:</p> <ul> <li>The temporal sequence files (seq_TIM*.csv) of observations for regular temporal sampling now all have a sampling mean time of 0 second for every river reach instead of the previous value which corresponded to the cycle of observations (e.g. 259,200 seconds for a three-day regular temporal sampling). This allows to start sampling at the onset of each simulation instead of at the end of the first cycle. This change does impact the findings of the study.</li> <li>The sampled discharge files (Qout*.nc) where produced with an updated version of rrr_anl_spl_mod.py which now selects the time step at which a sample is retained using a slightly different approach. The update only impacts sampling results when the sampling time matches the river model output time step exactly, and is more accurate now. This change does impact the findings of the study.</li> </ul>
A framework for estimating global river discharge from the Surface Water and Ocean Topography satellite mission example data
<p>These files contain the Confluence pipeline outputs, prior information (SOS) and Simulated SWOT shape files from the example in the "A framework for estimating global river discharge from the Surface Water and Ocean Topography satellite mission" manuscript. </p>
Compound flood potential from river discharge and storm surge extremes at the global scale
<p>This dataset presents the results presented in <a href="https://doi.org/10.5194/nhess-20-489-2020">Couasnon et al. (2019) - Measuring compound flood potential from river discharge and storm surge extremes at the global scale</a>. For more information about the methods, please refer to the paper. This dataset was created using as input <a href="https://zenodo.org/record/3552820#.XmIdoVxKhaQ">time series of discharge and maximum storm surge at river mouths globally from 1980 - 2014</a>.</p> <p>If using this data, please cite: </p> <p>Couasnon, A., Eilander, D., Muis, S., Veldkamp, T. I. E., Haigh, I. D., Wahl, T., Winsemius, H. C., and Ward, P. J.: Measuring compound flood potential from river discharge and storm surge extremes at the global scale, Nat. Hazards Earth Syst. Sci., 20, 489–504, https://doi.org/10.5194/nhess-20-489-2020, 2020.</p>
Global Daily River Discharge product at 0.5° resolution from ISBA-CTRIP simulations based on ERA5-GPCC forcing from 1950 to 2023
<p>This product provides <strong>global daily river discharge at a resolution of 0.5 degree from 1950 to 2023</strong>. It is derived from an offline simulation conducted with the <a href="http://www.umr-cnrm.fr/spip.php?article1092&lang=en" target="_blank" rel="noopener">ISBA-CTRIP</a> global land surface modeling system (<a href="https://doi.org/10.1029/2018MS001545" target="_blank" rel="noopener"><em>Decharme et al. </em>2019</a>) embedded in the <a href="http://www.umr-cnrm.fr/spip.php?article145&lang=en" target="_blank" rel="noopener">SURFEX</a> version 8 modeling platform. It was designed for use in large-scale hydrological applications, as well as in the the <a href="http://www.umr-cnrm.fr/cmip6/spip.php?rubrique8" target="_blank" rel="noopener">CNRM climate models</a> that participate in <a href="https://www.wcrp-climate.org/wgcm-cmip/wgcm-cmip6" target="_blank" rel="noopener">CMIP6</a> but also in large scale hydrological applications. The model is driven by meteorological forcing data (temperature, precipitation, humidity, winds, etc) derived from the <a href="https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5" target="_blank" rel="noopener">ERA5</a> global atmospheric reanalysis, but where montly precipitation is corrected to align with the <a href="https://www.dwd.de/EN/ourservices/gpcc/gpcc.html" target="_blank" rel="noopener">GPPC</a> observed precipitation product. GPCC <a href="https://opendata.dwd.de/climate_environment/GPCC/html/fulldata-monthly_v2022_doi_download.html">Full Data Monthly Product Version 2022</a> at 0.5 degree resolution are used from 1950 to 2020. For the last three years (2021 to 2023), we used the GPCC <a href="https://opendata.dwd.de/climate_environment/GPCC/html/gpcc_monitoring_v2022_doi_download.html" target="_blank" rel="noopener">Monitoring Product Version 2022</a>, which is only available at 1 degree resolution, and then interpolated at 0.5 degree using a conservative remapping.</p>
Global database of river width, slope, catchment area, meander wavelength, sinuosity, and discharge
<p><strong>1.Summary</strong></p> <p>This document describes the database that accompanies the article written by the authors of this dataset and accepted by Geophysical Research Letters (doi: 10.1029/2019GL082027).The database is distributed as a set of shapefiles, containing polylines that define the geometry of river centerlines located between 60°N and 56°S, with attributes described below. The shapefiles are organized according to continent and further broken into major basins to allow for manageable file sizes. A more complete dataset is available in the netCDF format upon request (please email Renato Frasson at renato.prata.de.moraes.frasson@jpl.nasa.gov).</p> <p>This database was partially funded by the Algorithm Definition Team contract to the Ohio State University, University of North Carolina at Chapel Hill, and Remote Sensing Solutions, Inc.</p> <p><strong>2.Polyline geometry</strong></p> <p>The centerline geometry is defined by sets of points located approximately every 30 m based on the Global River Widths from Landsat (GRLW) database (Allen & Pavelsky, 2015; 2018). Each line describes a meander and features the following attributes.</p> <p><strong>3.Attribute description</strong></p> <ul> <li><strong>SegmentID:</strong> identification number of the river segment (segments are parts of a river delimited by confluences).</li> <li><strong>lakeFlag:</strong> 0 – river, 1 – lake, 2 – river under the influence of tide, 3 – canal, 4 – unable to connect GRWL with HydroSHEDs, 5 – dam, -9999 – no data.</li> <li><strong>Width:</strong> average width in the meander, disregarding small river widths assigned to locations undetected by Landsat but known to be inundated. Locations where no width could be produced are marked as -9999.</li> <li><strong>Elevation:</strong> mean elevation from SRTM (90m) per river meander in meters. SRTM pixels are assigned to equally spaced points (every ~30m) over the river centerlines using the nearest neighbor approach. The average elevation of all valid points per meander is reported here. Locations where no elevation could be produced are marked as -9999.</li> <li><strong>Slope:</strong> water surface slope in centimeter per kilometer. Slope is initially computed over 10 km reaches, then used to compute optimum reach lengths using a modified version of the equation proposed by LeFavour and Alsdorf (2005) in the form of RL=2σ /S, where RL is the optimum reach length, σ is the height uncertainty (5.51 m from LeFavour and Alsdorf, 2005) and S the initial slope estimate. Final slopes are computed over the optimum reach lengths using elevations assigned to the 30 m river points using either classic linear regression or the Theil-Sen estimator depending on which method produces the best coefficient of determination. Locations where no slope could be produced are marked as -9999.</li> <li><strong>Meandwave:</strong> Meander wavelength in meters. This is computed by first smoothing the 30 m resolution river centerlines using a 5-point moving average and then identifying inflection points on the smoothed river centerlines. Finally, the meander wavelength takes the value of twice the distance between consecutive inflection points according to the definition given by Leopold and Wolman (1960).</li> <li><strong>Sinuosity:</strong> Dimensionless sinuosity of each river meander computed the ratio of the length between meander endpoints measured along the river centerline to half the meander wavelength as defined by Leopold and Wolman (1960).</li> <li><strong>catch_area:</strong> Catchment area was derived from flow direction and corresponding flow accumulation grids based on HydroSHEDS (Lehner<em> et al.</em>, 2008). The flow accumulation grid describes, for any location (i.e. pixel), the number of upstream raster pixels that drain to that particular location. We translated flow accumulation given in number of pixels into catchment area (in m<sup>2</sup>) by multiplying the number of pixels flowing to a location by the average area of SRTM pixels according to the latitude of the centroid of the river segment.</li> <li><strong>QWBM:</strong> mean annual flow estimated with the water balance model WBMsed (Cohen<em> et al.</em>, 2014).</li> <li><strong>Strpwr_len:</strong> stream power normalized by width (W/m).</li> <li><strong>Strpwr_are:</strong> stream power normalized by area (W/m<sup>2</sup>).</li> </ul> <p><strong>Acknowledgements</strong></p> <p>Use of this database should be acknowledged appropriately.</p> <p>The WBM data used in this database were provided by Dr. Albert Kettner at INSTAAR, University of Colorado at Boulder.</p> <p><strong>References</strong></p> <p>Allen, G. H., and T. M. Pavelsky (2015), Patterns of river width and surface area revealed by the satellite-derived north american river width data set, <em>Geophysical Research Letters</em>, <em>42</em>(2), 395-402, doi: 10.1002/2014gl062764.</p> <p>Allen, G. H., and T. M. Pavelsky (2018), Global extent of rivers and streams, <em>Science</em>, doi: 10.1126/science.aat0636.</p> <p>Cohen, S., A. J. Kettner, and J. P. M. Syvitski (2014), Global suspended sediment and water discharge dynamics between 1960 and 2010: Continental trends and intra-basin sensitivity, <em>Glob. Planet. Change</em>, <em>115</em>, 44-58, doi: https://doi.org/10.1016/j.gloplacha.2014.01.011.</p> <p>LeFavour, G., and D. Alsdorf (2005), Water slope and discharge in the amazon river estimated using the shuttle radar topography mission digital elevation model, <em>Geophysical Research Letters</em>, <em>32</em>(17), doi: 10.1029/2005gl023836.</p> <p>Lehner, B., K. Verdin, and A. Jarvis (2008), New global hydrography derived from spaceborne elevation data, <em>EOS, TRANSACTIONS, AMERICAN GEOPHYSICAL UNION</em>, <em>89</em>(10), 93-94, doi: doi:10.1029/2008EO100001.</p> <p>Leopold, L. B., and M. G. Wolman (1960), River meanders, <em>Geological Society of America Bulletin</em>, <em>71</em>(6), 769-793, doi: 10.1130/0016-7606(1960)71[769:RM]2.0.CO;2.</p> <p> </p> <p> </p>
Global River Discharge, 1807-1991, V[ersion]. 1.1 (RivDIS)
The Global Monthly River Discharge Data Set (RivDIS) contains monthly averaged discharge measurements for 1,018 stations located throughout the world from 1807-1991. The period of record varies widely from station to station with a mean of 21.5 years. The data are derived from the published UNESCO archives for river discharge, and checked against information obtained from the Global Runoff Center in Koblenz, Germany through the U.S. National Geophysical Data Center in Boulder, Colorado.
Global River Discharge Reanalysis dataset (GRDR)
<p>This repository contains the Global River Discharge Reanalysis dataset (GRDR) generated from a work that is currently under review. GRDR is a global river discharge product that assimilated remotely sensed river discharge and hydrologic model simulations. It contains daily flows in ~2.9 million vectorized river reaches for 1984-2018.</p> <p>The data format is netCDF.</p> <p> </p>
ScienceDex guides
Understand access before you commit
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.