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9 results for “global water levels”
Global reanalysis of riverine water levels at the river mouth
<p>Dataset prepared for manuscript "The effect of surge on riverine flood hazard and impact in deltas globally" (Eilander <em>et al </em>2020)</p> <p>This dataset includes water level data and discharge at 3433 river mouth locations globally, including several components of nearshore still water levels based on a model framework for global compound flood simulations. We usedof runoff from tier 2 of the EartH2Observe (E2O) project (Dutra <em>et al</em> 2017, Schellekens <em>et al</em> 2017) with meteorological forcing from ERA-Interim (Dee <em>et al</em> 2011) and MSWEP v1.2 (Beck <em>et al</em> 2017), surge levels from the Global Tide and Surge Reanalysis (GTSR) based on the GTSM model (Muis <em>et al</em> 2016), and tide levels from the FES2012 model (Carrere <em>et al</em> 2012). These runoff and dynamic sea level (surge and tide) data were used to force the global river routing model CaMa-Flood (Yamazaki <em>et al</em> 2011) to simulate riverine water levels.</p> <p>The accompanying excel file provides an table explaining the data dimensions, variables and metadata.</p>
Global Water Body Levels Derived from ICESat-2
<p>This dataset contains water level records derived from ICESat-2 for 227,386 lakes spanning Oct 14, 2018 to July 16, 2020. These records have been updated to reflect an error in the calculation of lake area in the previous version which led to an overestimation of lake area at high latitudes. For details on how this correction was performed, please see the 2023 Addenda to Cooley et al (2021).</p> <p>A complete description of the method used to derive water level from ICESat-2 can be found in Cooley et al (2021), but is briefly summarized below:</p> <ol> <li>We create a conservative water mask modified from the Global Surface Water Occurrence (GSWO) product (Pekel et al., 2016);</li> <li>We intersect ATL08 mean terrain height returns with this water mask, requiring water bodies to receive at least three ICESat-2 point observations on the same day to be included in the analysis;</li> <li>We filter observations based on the mean standard deviation of returns, among other factors;</li> <li>We aggregate observations to monthly timesteps;</li> <li>We calculate seasonal variability in water level as the maximum minus the minimum monthly water level over the 22-month period.</li> </ol> <p>This dataset contains:</p> <ol> <li><strong>ICESat2_lake_variability_v2_updated.shp</strong>: A shapefile containing lake points and summary statistics (i.e. height variability, storage variability, etc) <em>*updated to include corrected lake area and storage values</em></li> <li><strong>ICESat2_lake_height_time_series_v2_updated.csv</strong>: A csv file of the monthly water height time series used to calculate the global lake level variability <em>*updated to include corrected lake area values</em></li> <li><strong>265 water mask GeoTiffs</strong>: Water masks created from GSWO which we intersect with ICESat-2 data to produce the water level time series <em>*unchanged from previous version</em></li> <li><strong>ICESat2_mask_reference_v2_updated.csv</strong> – A csv file which lists the corresponding water mask for each water body in the dataset <em>*updated to include corrected lake area values</em></li> <li><strong>USGS_height_validation_v2_updated.csv </strong>– A csv file containing the height comparison between ICESat-2 and USGS gauges used for validation and uncertainty analyses <em>*updated to include corrected lake area values</em></li> <li><strong>USGS_range_validation_ v2_updated</strong>.<strong>csv </strong>– A csv file containing the range comparison between ICESat-2 and USGS gauges used for validation and uncertainty analyses <em>*updated to include corrected lake area values</em></li> <li><strong>California_storage_validation_v2_updated.csv </strong>– A csv file containing the storage comparison between ICESat-2 and California Department of Water Resource gauges used for validation and uncertainty analyses <em>*updated to include corrected lake area and storage values</em></li> </ol> <p>See the README file for a more detailed description of this dataset. Anyone wishing to use this dataset should cite Cooley et al. 2021) and contact Sarah Cooley at <a href="mailto:scooley2@uoregon.edu">scooley2@uoregon.edu</a> with a description of the work and any questions so that we may offer guidance in regards to the best usage of our dataset. </p> <p>Cooley, S.W., Ryan, J.C., and Smith, L.C., (2021), Human alteration of global surface water storage variability, <em>Nature, </em>https://doi.org/10.1038/s41586-021-03262-3 </p>
Supplementary dataset for "Global water gaps under future warming levels"
<p>The folder contains water gap data relative to the paper:<br>Rosa, L., Sangiorgio, M. Global water gaps under future warming levels. Nat Commun 16, 1192 (2025). https://doi.org/10.1038/s41467-025-56517-2</p> <p>All water gaps data are in km3/yr.</p> <p><br>Gridded data(NetCDF at 0.5°)<br> - baseline<br> water_gap_baseline.nc: Baseline water gap in the period 2001-2010<br> - 1.5°C warming (5 models + average)<br> water_gap_15C_average.nc: Water gap under 1.5°C warming (multi-model average)<br> water_gap_15C_h08_ipsl-cm6a-lr.nc: Water gap under 1.5°C warming (h08 + ipsl-cm6a-lr)<br> water_gap_15C_h08_mri-esm2-0.nc: Water gap under 1.5°C warming (h08 + mri-esm2-0)<br> water_gap_15C_h08_ukesm1-0-ll.nc: Water gap under 1.5°C warming (h08 + ukesm1-0-ll)<br> water_gap_15C_h08_mpi-esm1-2-hr.nc: Water gap under 1.5°C warming (h08 + mpi-esm1-2-hr)<br> water_gap_15C_h08_gfdl-esm4.nc: Water gap under 1.5°C warming (h08 + gfdl-esm4)<br> - 3°C warming (5 models + average)<br> water_gap_3C_average.nc: Water gap under 3°C warming (multi-model average)<br> water_gap_3C_h08_ipsl-cm6a-lr.nc: Water gap under 3°C warming (h08 + ipsl-cm6a-lr)<br> water_gap_3C_h08_mri-esm2-0.nc: Water gap under 3°C warming (h08 + mri-esm2-0)<br> water_gap_3C_h08_ukesm1-0-ll.nc: Water gap under 3°C warming (h08 + ukesm1-0-ll)<br> water_gap_3C_h08_mpi-esm1-2-hr.nc: Water gap under 3°C warming (h08 + mpi-esm1-2-hr)<br> water_gap_3C_h08_gfdl-esm4.nc: Water gap under 3°C warming (h08 + gfdl-esm4)</p> <p><br>Aggregated data (.xlsx)<br> - source_data.xlsx: Water gap aggregated by country and basin for all the considered scenarios (including multi-model average and agreement analysis)</p> <p>Note: the global water gap obtained by summing all the countries/basins is not completely equivalent to the sum of all the pixels because some pixels' center is outside the polygon of the corresponding country/basin (differences in the order of 1km/yr3, <0.3%). See sheet "Figure 3" A249:N251 (countries) and "Figure 5" A235:N237 (basins) for further details.</p>
Interagency report: Global and Regional Sea Level Rise Scenarios for the United States: Updated Mean Projections and Extreme Water Level Probabilities Along U.S. Coastlines
<p><strong>Code and data for Section 2 of the Interagency report: Global and Regional Sea Level Rise Scenarios for the United States: Updated Mean Projections and Extreme Water Level Probabilities Along U.S. Coastlines</strong></p> <p><strong>Versions:</strong></p> <p>Version 1.1 This one:</p> <ul> <li>updated region names</li> </ul> <p>Version 1.0 <a href="https://doi.org/10.5281/zenodo.5951626">https://doi.org/10.5281/zenodo.5951626</a></p> <p>This repository contains the code and data needed to produce the trajectories, projections, and observations for the Interagency report: Global and Regional Sea Level Rise Scenarios for the United States: Updated Mean Projections and Extreme Water Level Probabilities Along U.S. Coastlines.</p> <p>The report can be found on <a href="https://oceanservice.noaa.gov/hazards/sealevelrise/sealevelrise-tech-report-sections.html">https://oceanservice.noaa.gov/hazards/sealevelrise/sealevelrise-tech-report-sections.html</a></p> <p>An interactive tool to study the observations, trajectories, and scenarios can be accessed from <a href="https://sealevel.nasa.gov/task-force-scenario-tool">https://sealevel.nasa.gov/task-force-scenario-tool</a></p> <p>Frequently-asked questions: <a href="https://sealevel.nasa.gov/faq/16/">https://sealevel.nasa.gov/faq/16/</a></p> <p><strong>Authors</strong></p> <ul> <li>William V. Sweet, NOAA National Ocean Service</li> <li>Benjamin D. Hamlington, NASA Jet Propulsion Laboratory</li> <li>Robert E. Kopp, Rutgers University</li> <li>Christopher P. Weaver, U.S. Environmental Protection Agency</li> <li>Patrick L. Barnard, U.S. Geological Survey</li> <li>Michael Craghan, U.S. Environmental Protection Agency</li> <li>Gregory Dusek, NOAA National Ocean Service</li> <li>Thomas Frederikse, NASA Jet Propulsion Laboratory</li> <li>Gregory Garner, Rutgers University</li> <li>Ayesha S. Genz, University of Hawai‘i at Mānoa, Cooperative Institute for Marine and Atmospheric Research</li> <li>John P. Krasting, NOAA Geophysical Fluid Dynamics Laboratory</li> <li>Eric Larour, NASA Jet Propulsion Laboratory</li> <li>Doug Marcy, NOAA National Ocean Service</li> <li>John J. Marra, NOAA National Centers for Environmental Information</li> <li>Jayantha Obeysekera, Florida International University</li> <li>Mark Osler, NOAA National Ocean Service</li> <li>Matthew Pendleton, Lynker</li> <li>Daniel Roman, NOAA National Ocean Service</li> <li>Lauren Schmied, FEMA Risk Management Directorate</li> <li>William C. Veatch, U.S. Army Corps of Engineers</li> <li>Kathleen D. White, U.S. Department of Defense</li> <li>Casey Zuzak, FEMA Risk Management Directorate</li> </ul> <p><strong>Contents</strong></p> <p>This data and code set contains the following directories:</p> <p><em>Results</em></p> <p>The <code>Results</code> folder contains the resulting projections, trajectories and observations from the report.</p> <ul> <li><code>TR_global_projections.nc</code>: GMSL projections, trajectory, and observations</li> <li><code>TR_regional_projections.nc</code>: Regional observations, projections and trajectories</li> <li><code>TR_local_projections.nc</code>: Local observations, projections and trajectories</li> <li><code>TR_gridded_projections.nc</code>: Gridded projections</li> </ul> <p>These files are in the NetCDF forrmat. To read the NetCDF files, many free software packages are available, including <a href="http://meteora.ucsd.edu/~pierce/ncview_home_page.html">ncview</a> and <a href="https://www.giss.nasa.gov/tools/panoply/">Panoply</a>. Free NetCDF packages are available to directly import the data into <a href="https://github.com/Alexander-Barth/NCDatasets.jl">Julia</a> and <a href="https://unidata.github.io/netcdf4-python/">Python</a> code.</p> <p><em>Code</em></p> <p>The <code>Code</code> folder contains all the computer code used to read and analyze the observations and the projections, and to generate the trajectories.</p> <p>To run this code, you need <a href="https://julialang.org/">Julia</a>. The code requires the Julia packages <code>CSV</code>, <code>Interpolations</code>, <code>JSON</code>, <code>LoopVectorization</code>, <code>MAT</code>, <code>NCDatasets</code>, <code>NetCDF</code>, <code>Plots</code>, <code>XLSX</code>, <code>LinearAlgebra</code>, and <code>Statistics</code>. They can be installed by pressing <code>]</code> at the Julia REPL and typing:</p> <pre><code>add CSV Interpolations JSON LoopVectorization MAT NCDatasets NetCDF Plots XLSX LinearAlgebra Statistics </code></pre> <p>This program also requires <a href="http://segal.ubi.pt/hector/">Hector</a>. Hector needs to be installed or compiled. In the file <code>Hector.jl</code> update the path to the Hector executable on lines 30 and 104.</p> <p>Run <code>Run_TR.jl</code> in the REPL or run <code>julia Run_TR.jl</code> from the command line to run the projections. The projections are then written to the <code>.\Data</code> directory.</p> <p>The folder contains the following files:</p> <ul> <li><code>Run_TR.jl</code>: This is the main routine that (eventually) calls all the functions to compute the projections.</li> <li><code>ConvertNCA5ToGrid.jl</code>: Converts the original NCA5 projections to a set of netCDF files that's used throughout this code</li> <li><code>ProcessObservations.jl</code>: Reads and processes the tide-gauge and altimetry observations</li> <li><code>GlobalProjections.jl</code>: Reads and processes the GMSL observations and projections, and computes the trajectory</li> <li><code>RegionalProjections.jl</code>: Reads and processes the regional projections and computes the trajectories</li> <li><code>LocalProjections.jl</code>: Reads and processes the local projections at the tide-gauge locations and computes the trajectories</li> <li><code>GriddedProjections.jl</code>: Reads the gridded NCA5 projections and add a GMSL baseline correction for the 2005 vs 2000 baseline</li> <li><code>SaveFigureData.jl</code>: Reads the results and writes text files for GMT</li> <li><code>Hector.jl</code>: Wrapper for <a href="http://segal.ubi.pt/hector/">Hector</a>, used to compute trends and uncertainties.</li> <li><code>Masks.jl</code>: Defines the region masks for each region.</li> </ul> <p><em>Data</em></p> <p>The <code>Data</code> directory contains the input data sets used during the computations. Please appropriately cite the input data if you use it. It contains the following:</p> <p>Directories:</p> <ul> <li><code>ClimIdx</code>: Map with climate indices (NAO, PDO, MEI) used to remove internal variability. All the indices come from NOAA <a href="https://psl.noaa.gov/data/climateindices/">Physical Sciences Laboratory (PSL)</a> and <a href="https://www.cpc.ncep.noaa.gov/data/teledoc/telecontents.shtml">NOAA Climate Prediction Centre (CPC)</a></li> <li><code>NCA5_projections</code> Contains the NCA5 projections for each scenario (Low, IntLow, Int, IntHigh, and High). For each scenario, the GMSL projections, projections at tide-gauge locations and on a 1-degree grid are provided.</li> </ul> <p>Files:</p> <ul> <li><code>basin_codes.nc</code>: Map with basin codes. from Eric Leuliette/NOAA. Data provided by the NOAA Laboratory for Satellite Altimetry.</li> <li><code>CDS_monthly_1993_2020.nc</code>: Monthly-mean sea level (1993-2020) from gridded altimetry. Obtained from <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-sea-level-global">Copernicus Climate Data Store</a>. This dataset contains modified Copernicus Climate Change Service information [2020]</li> <li><code>enso_correction.mat</code>: GMSL correction for ENSO/PDO from Hamlington, B. D., Frederikse, T., Nerem, R. S., Fasullo, J. T., & Adhikari, S. (2020). Investigating the Acceleration of Regional Sea‐level Rise During the Satellite Altimeter Era. Geophysical Research Letters. <a href="https://doi.org/10.1029/2019GL086528">https://doi.org/10.1029/2019GL086528</a></li> <li><code>filelist_psmsl.txt</code>: List with PSMSL file names and PSMSL IDs. Obtained from the Permanent Service for Mean Sea Level (<a href="http://www.psmsl.org/">PSMSL</a>), 2021, Retrieved 29 Nov 2021. Simon J. Holgate, Andrew Matthews, Philip L. Woodworth, Lesley J. Rickards, Mark E. Tamisiea, Elizabeth Bradshaw, Peter R. Foden, Kathleen M. Gordon, Svetlana Jevrejeva, and Jeff Pugh (2013) New Data Systems and Products at the Permanent Service for Mean Sea Level. Journal of Coastal Research: Volume 29, Issue 3: pp. 493 – 504. <a href="https://doi.org/:10.2112/JCOASTRES-D-12-00175.1">https://doi.org/:10.2112/JCOASTRES-D-12-00175.1</a>.</li> <li><code>GEBCO_bathymetry_05.nc</code>: Bathymetry map of the global oceans from the General Bathymetric Chart of the Oceans (<a href="https://www.gebco.net/">GEBCO</a>). Source: GEBCO Compilation Group (2021) GEBCO 2021 Grid (<code>doi:10.5285/c6612cbe-50b3-0cff-e053-6c86abc09f8f</code>) The source data have been re-gridded onto a 0.5 degree grid.</li> <li><code>GIA_Caron_stats_05.nc</code>: Glacial Isostatic Adjustment estimates from Caron, L., Ivins, E. R., Larour, E., Adhikari, S., Nilsson, J., & Blewitt, G. (2018). GIA Model Statistics for GRACE Hydrology, Cryosphere, and Ocean Science. Geophysical Research Letters, 45(5), 2203–2212. <a href="https://doi.org/10.1002/2017GL076644">https://doi.org/10.1002/2017GL076644</a>. The source data have been re-gridded onto a 0.5 degree grid.</li> <li><code>global_timeseries_measures.nc</code>: Time series of estimated 20th-century GMSL and its components, based on Frederikse, T., Landerer, F., Caron, L., Adhikari, S., Parkes, D., Humphrey, V. W., Dangendorf, S., Hogarth, P., Zanna, L., Cheng, L., & Wu, Y.-H. (2020). The causes of sea-level rise since 1900. Nature, 584(7821), 393–397. <a href="https://doi.org/10.1038/s41586-020-2591-3">https://doi.org/10.1038/s41586-020-2591-3</a></li> <li><code>GMSL_ensembles.nc</code>: Ensemble GMSL reconstruction from tide-gauges based on Frederikse, T., Landerer, F., Caron, L., Adhikari, S., Parkes, D., Humphrey, V. W., Dangendorf, S., Hogarth, P., Zanna, L., Cheng, L., & Wu, Y.-H. (2020). The causes of sea-level rise since 1900. Nature, 584(7821), 393–397. <a href="https://doi.org/10.1038/s41586-020-2591-3">https://doi.org/10.1038/s41586-020-2591-3</a></li> <li><code>GMSL_TPJAOS_5.0_199209_202106.txt</code>: Global Mean Sea Level Trend from Integrated Multi-Mission Ocean Altimeters TOPEX/Poseidon, Jason-1, OSTM/Jason-2, and Jason-3 Version 5.1 [Data set]. NASA Physical Oceanography DAAC. <a href="https://doi.org/10.5067/GMSLM-TJ151">https://doi.org/10.5067/GMSLM-TJ151</a>. This altimetry dataset uses the methods as described in Beckley, B. D., Callahan, P. S., Hancock, D. W., Mitchum, G. T., & Ray, R. D. (2017). On the “Cal-Mode” Correction to TOPEX Satellite Altimetry and Its Effect on the Global Mean Sea Level Time Series. Journal of Geophysical Research: Oceans, 122(11), 8371–8384. <a href="https://doi.org/10.1002/2017JC013090">https://doi.org/10.1002/2017JC013090</a></li> <li><code>grd_1992_2020.nc</code>: Seafloor deformation due to contemporary GRD effects based on Frederikse, T., Landerer, F., Caron, L., Adhikari, S., Parkes, D., Humphrey, V. W., Dangendorf, S., Hogarth, P., Zanna, L., Cheng, L., & Wu, Y.-H. (2020). The causes of sea-level rise since 1900. Nature, 584(7821), 393–397. <a href="https://doi.org/10.1038/s41586-020-2591-3">https://doi.org/10.1038/s41586-020-2591-3</a></li> <li><code>region_mask.nc</code>: Mask with the definition of all regions.</li> <li><code>US_tg_monthly.xlsx</code>: Tide gauge observations from the NOAA tide gauge network</li> </ul> <p><em>GMT</em></p> <p>This directory contains the <a href="https://www.generic-mapping-tools.org/">GMT</a> scripts to make Figures 1.2, 2.1, 2.2, 2.6, and A.1.2 from the report. To generate the figures, make sure GMT is installed and run the Shell script in each directory.</p>
Data Supporting Dissipation Scaled Internal Wave Drag in a Global Heterogeneously Coupled Internal/External Mode Total Water Level Model
Open the record for dataset details and reuse information.
Data for "Food demand displaced by global refugee migration1 has unequal effects on country-level water stress"
<p>Dataset associated with the paper "Food demand displaced by global refugee migration1<br> has unequal effects on country-level water stress" to appear in Nature Communications in 2023.</p>
CALIPSO IIR Lidar Level 3 Global Energy and Water Cycle Experiment (GEWEX) Cloud, Standard V1-00
CAL_IIR_L3_GEWEX_Cloud-Standard-V1-00 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) IIR Level 3 Global Energy and Water Cycle Experiment (GEWEX) Cloud, Standard Version 1-00 data product. Data for this product was collected using the CALIPSO Imaging Infrared Radiometer (IIR) instrument.This product reports global distributions of IIR cloud effective radius, water path averages, and histograms on a uniform 2-dimensional (2D) spatial grid. This product is designed to follow the general guidance of the GEWEX Cloud Assessment. Cloud amount, radiative temperature, effective emissivity, and optical depth characterize the cloud samples for which IIR microphysical retrievals are reported. Cloud properties are reported for ice clouds, liquid water clouds, and high ice clouds of layer pressure lower than 440 hPa. All level 3 parameters are derived from the IIR version 4 level 2 track products, with the temporal extent averaging one month. CALIPSO was launched on April 28, 2006, to study the impact of clouds and aerosols on the Earth's radiation budget and climate. It flies in the international A-Train constellation for coincident Earth observations. The CALIPSO satellite comprises three instruments: The Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), Imaging Infrared Radiometer (IIR), and Wide Field Camera (WFC). CALIPSO is a joint satellite mission between NASA and the French Agency, Centre National d'Etudes Spatiales (CNES).
CALIPSO Lidar Level 3 Global Energy and Water Cycle Experiment (GEWEX) Cloud, Standard V1-00
CAL_LID_L3_GEWEX_Cloud-Standard-V1-00 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) Lidar Level 3 Global Energy and Water Cycle Experiment (GEWEX) Cloud, Standard Version 1-00 data product. Data for this product was collected using the CALIPSO Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument. Data collection for this product is complete.This product is a reformatted version of the CALIPSO contribution to the GEWEX cloud assessment of global cloud datasets from satellites. The data submitted by the CALIPSO team for this project had to conform to a specific format: yearly netCDF files organized by parameter. To be compatible with another publicly orderable lidar level 3 CALIPSO aerosol and cloud products reported as monthly HDF files, this new lidar level 3 CALIPSO GEWEX cloud product was created. These files report global distributions of cloud amount and cloud top as averages and histograms on a uniform 2-dimensional (2D) spatial grid. All level 3 parameters are derived from the CALIPSO version 4. x Level 2, 5 km cloud merged layer products, with a temporal averaging of one month.CALIPSO was launched on April 28, 2006, to study the impact of clouds and aerosols on the Earth's radiation budget and climate. It flies in the international A-Train constellation for coincident Earth observations. The CALIPSO satellite comprises three instruments: CALIOP, Imaging Infrared Radiometer (IIR), and Wide Field Camera (WFC). CALIPSO is a joint satellite mission between NASA and the French Agency CNES (Centre National D’Etudes Spatiales).
Numerical modeling on global-scale mantle water cycle and its impact on the sea-level change
<p>This zipped tar file includes scripts and data for submitting manuscript to EPSL.</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.