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SWOT River Database (SWORD)
<p><strong>VERSION NOTES:</strong></p> <p><strong>v17 versus v17b</strong></p> <ul> <li>"Type" change for 1662 reaches and associated nodes globally. Please reference the Product Description Document for the "Type" identifier definition. </li> <li>Updates to reach and node lengths and distance-from-outlet variable to correct a bug in the node length calculation in select reaches (<2% of reaches were impacted globally).</li> <li>SWORD v17b is the official version for SWOT <strong>Version D</strong> <a href="https://podaac.jpl.nasa.gov/SWOT?tab=datasets-information&sections=about"><strong>RiverSP Vector Products</strong></a>.</li> </ul> <p>The project and public versions of SWORD were kept separate while algorithms were being developed in preparation for SWOT's launch in 2022. Now that the SWOT mission is here, the project version of SWORD is published as the public version which is why the version numbers jump after v2. The primary difference between the project and public versions of SWORD are extra "filler" variables in the NetCDF format that will be used for calculating discharge. For details on the filler variables please reference the Product Description Document provided with the downloads. </p> <p>If you use the SWORD Database in your work, please cite: Altenau et al., (2021) The Surface Water and Ocean Topography (SWOT) Mission River Database (SWORD): A Global River Network for Satellite Data Products. <em>Water Resources Research</em>. <a href="https://doi.org/10.1029/2021WR030054">https://doi.org/10.1029/2021WR030054</a></p> <p>You can also visit <a href="http://www.swordexplorer.com"><strong>www.swordexplorer.com</strong></a> to explore the current version of SWORD before downloading. </p> <p><strong>1. Summary:</strong></p> <p>The Surface Water and Ocean Topography (SWOT) satellite mission vastly expands observations of river water surface elevation (WSE), width, and slope. In order to facilitate a wide range of new analyses with flexibility, the SWOT mission provides a range of relevant data products. One product the SWOT mission provides are river vector products stored in shapefile format for each SWOT overpass (JPL Internal Document, 2020b). The <strong>SWO</strong>t <strong>R</strong>iver <strong>D</strong>atabase (<strong>SWORD</strong>) combines multiple global river- and satellite-related datasets to define the nodes and reaches that constitute SWOT river vector data products. SWORD provides high-resolution river nodes (200 m) and reaches (~10 km) in shapefile and netCDF formats with attached hydrologic variables (WSE, width, slope, etc.) as well as a consistent topological system for global rivers 30 m wide and greater.</p> <p><strong>2. Data Formats:</strong></p> <p>The SWORD database is provided in netCDF, geopackage, and shapefile formats. All files start with a two-digit continent identifier ("af" – Africa, "as" – Asia / Siberia, "eu" – Europe / Middle East, "na" – North America, "oc" – Oceania, "sa" – South America). File syntax denotes the regional information for each file and varies slightly between netCDF and shapefile formats.</p> <p>NetCDF files are structured in 3 groups: centerlines, nodes, and reaches. The centerline group contains location information and associated reach and node ids along the original GRWL 30 m centerlines (Allen and Pavelsky, 2018). Node and reach groups contain hydrologic attributes at the ~200 m node and ~10 km reach locations (see description of attributes below). NetCDFs are distributed at continental scales with a filename convention as follows: [continent]_sword_v17.nc (<em>i.e. na_sword_v17.nc</em>).</p> <p>SWORD shapefiles consist of four main files (.dbf, .prj, .shp, .shx). There are separate shapefiles for nodes and reaches, where nodes are represented as ~200 m spaced points and reaches are represented as polylines. All shapefiles are in geographic (latitude/longitude) projection, referenced to datum WGS84. Shapefiles are split into HydroBASINS (Lehner and Grill, 2013) Pfafstetter level 2 basins (hbXX) for each continent with a naming convention as follows: [continent]_sword_[nodes/reaches]_hb[XX]_v17.shp (<em>i.e. na_sword_nodes_hb74_v17.shp; na_sword_reaches_hb74_v17.shp</em>).</p> <p>SWORD geopackage files are split into two files for nodes and reaches per continental region, where nodes are represented as 200 m spaced points and reaches are represented as polylines. All geopackage files are in geographic (latitude/longitude) projection, referenced to datum WGS84. Geopackage file names are distributed at continental scales and are defined by a two-digit identifier (Table 2): [continent]_sword_[nodes/reaches]_v17.gpkg (i.e. na_sword_nodes_v17.gpkg; na_sword_reaches_v17.gpkg).</p> <p><strong>3. Attribute Description:</strong></p> <p>This list contains the primary attributes contained in the SWORD database.</p> <ul> <li><strong>x:</strong> Longitude of the node or reach ranging from 180°E to 180°W (units: decimal degrees).</li> <li><strong>y:</strong> Latitude of the node or reach ranging from 90°S to 90°N (units: decimal degrees).</li> <li><strong>node_id:</strong> ID of each node. The format of the id is as follows: CBBBBBRRRRNNNT where C = Continent (the first number of the Pfafstetter basin code), B = Remaining Pfafstetter basin code up to level 6, R = Reach number (assigned sequentially within a level 6 basin starting at the downstream end working upstream), N = Node number (assigned sequentially within a reach starting at the downstream end working upstream), T = Type (1 – river, 3 – lake on river, 4 – dam or waterfall, 5 – unreliable topology, 6 – ghost node).</li> <li><strong>node_length </strong><em>(node files only</em>): Node length measured along the GRWL centerline points (units: meters).</li> <li><strong>reach_id:</strong> ID of each reach. The format of the id is as follows: CBBBBBRRRRT where C = Continent (the first number of the Pfafstetter basin code), B = Remaining Pfafstetter basin codes up to level 6, R = Reach number (assigned sequentially within a level 6 basin starting at the downstream end working upstream, T = Type (1 – river, 3 – lake on river, 4 – dam or waterfall, 5 – unreliable topology, 6 – ghost reach).</li> <li><strong>reach_length </strong>(<em>reach files only</em>): Reach length measured along the GRWL centerline points (units: meters).</li> <li><strong>wse:</strong> Average water surface elevation (WSE) value for a node or reach. WSEs are extracted from the MERIT Hydro dataset (Yamazaki et al., 2019) and referenced to the EGM96 geoid (units: meters).</li> <li><strong>wse_var:</strong> WSE variance along the GRWL centerline points used to calculate the average WSE for each node or reach (units: square meters).</li> <li><strong>width:</strong> Average width for a node or reach (units: meters).</li> <li><strong>width_var:</strong> Width variance along the GRWL centerline points used to calculate the average width for each node or reach (units: square meters).</li> <li><strong>max_width: </strong>Maximum width value across the channel for each node or reach that includes island and bar areas (units: meters).</li> <li><strong>facc: </strong>Maximum flow accumulation value for a node or reach. Flow accumulation values are extracted from the MERIT Hydro dataset (Yamazaki et al., 2019) (units: square kilometers).</li> <li><strong>n_chan_max:</strong> Maximum number of channels for each node or reach.</li> <li><strong>n_chan_mod:</strong> Mode of the number of channels for each node or reach.</li> <li><strong>obstr_type: </strong>Type of obstruction for each node or reach based on the Globale Obstruction Database (GROD, Whittemore et al., 2020) and HydroFALLS data (http://wp.geog.mcgill.ca/hydrolab/hydrofalls). Obstr_type values: 0 - No Dam, 1 - Dam, 2 - Channel Dam, 3 - Lock, 4 - Low Permeable Dam, 5 - Waterfall.</li> <li><strong>grod_id:</strong> The unique GROD ID for each node or reach with obstr_type values 1-4.</li> <li><strong>hfalls_id:</strong> The unique HydroFALLS ID for each node or reach with obstr_type value 5.</li> <li><strong>dist_out:</strong> Distance from the river outlet for each node or reach (units: meters).</li> <li><strong>type:</strong> Type identifier for a node or reach: 1 – river, 2 – lake off river, 3 – lake on river, 4 – dam or waterfall, 5 – unreliable topology, 6 – ghost reach/node.</li> <li><strong>lakeflag</strong>: GRWL water body identifier for each reach: 0 – river, 1 – lake/reservoir, 2 – canal, 3 – tidally influenced river.</li> <li><strong>manual_add </strong>(<em>node files only</em>): Binary flag indicating whether the node was manually added to the public GRWL centerlines (Allen and Pavelsky, 2018). These nodes were originally given a width = 1, but have since been updated to have the reach width values.</li> <li><strong>meand_len </strong>(<em>node files only</em>): Length of the meander that a node belongs to, measured from beginning of the meander to its end in meters. For nodes longer than one meander, the meander length will represent the average length of all meanders belonging to the node (units: meters).</li> <li><strong>sinuosity </strong>(<em>node files only</em>): The total reach length the node belongs to divided by the Euclidean distance between the reach end points.</li> <li><strong>slope </strong>(<em>reach files only</em>): Reach average slope calculated along the GRWL centerline points. Slopes are calculated using a linear regression (units: meters/kilometer).</li> <li><strong>n_nodes</strong> (<em>reach files only</em>): Number of nodes associated with each reach.</li> <li><strong>n_rch_up</strong> (<em>reach files only</em>): Number of upstream reaches for each reach.</li> <li><strong>n_rch_down</strong> (<em>reach files only</em>): Number of downstream reaches for each reach.</li> <li><strong>rch_id_up</strong> (<em>reach files only</em>): Reach IDs of the upstream neighboring reaches.</li> <li><strong>rch_id_dn</strong> (<em>reach files only</em>): Reach IDs of the downstream neighboring reaches.</li> <li><strong>swot_obs </strong>(<em>reach files only</em>): The maximum number of SWOT passes to intersect each reach during the 21 day orbit cycle.</li> <li><strong>swot_orbits </strong>(<em>reach files only</em>): A list of the SWOT orbit tracks that intersect each reach during the 21 day orbit cycle.</li> <li><strong>river_name:</strong> All river names associated with a node or reach. If there are multiple names for a node or reach they are listed in alphabetical order and separated by a semicolon.</li> <li><strong>edit_flag:</strong> Numerical flag indicating the type of update applied to SWORD nodes or reaches from the previous version. Flag descriptions are listed in the Product Description Documentation included with the file downloads.</li> <li><strong>trib_flag: </strong>Binary flag indicating if a large tributary not represented in SWORD is entering a node or reach. 0 - no tributary, 1 - tributary.</li> </ul> <p><strong>4. References:</strong></p> <p>Allen, G. H., & Pavelsky, T. M. (2018). Global extent of rivers and streams. <em>Science</em>, 361(6402), 585-588.</p> <p>Altenau, E. H., Pavelsky, T. M., Durand, M. T., Yang X., Frasson, R. P. d. M., & Bendezu, L. (2021). The Surface Water and Ocean Topography (SWOT) Mission River Database (SWORD): A global river network for satellite data products". Water Resources Research.</p> <p>Biancamaria, S., Lettenmaier, D. P., & Pavelsky, T. M. (2016). The SWOT mission and its capabilities for land hydrology. In Remote Sensing and Water Resources (pp. 117-147). Springer, Cham.</p> <p>JPL Internal Document (2020b). Surface Water and Ocean Topography Mission Level 2 KaRIn high rate river single pass vector product, JPL D-56413, Rev. A, https://podaac-tools.jpl.nasa.gov/drive/files/misc/web/misc/swot_mission_docs/pdd/D-56413_SWOT_Product_Description_L2_HR_RiverSP_20200825a.pdf</p> <p>Lehner, B., Grill G. (2013): Global river hydrography and network routing: baseline data and new approaches to study the world's large river systems. Hydrological Processes, 27(15): 2171–2186. Data is available at www.hydrosheds.org.</p> <p>Tessler, Z. D., Vörösmarty, C. J., Grossberg, M., Gladkova, I., Aizenman, H., Syvitski, J. P. M., & Foufoula-Georgiou, E. (2015). Profiling risk and sustainability in coastal deltas of the world. Science, 349(6248), 638-643.</p> <p>Whittemore, A., Ross, M. R., Dolan, W., Langhorst, T., Yang, X., Pawar, S., Jorissen, M., Lawton, E., Januchowski-Hartley, S., & Pavelsky, T. (2020). A Participatory Science Approach to Expanding Instream Infrastructure Inventories. <em>Earth's Future</em>, <em>8</em>(11), e2020EF001558.</p> <p>Yamazaki, D., Ikeshima, D., Sosa, J., Bates, P. D., Allen, G., & Pavelsky, T. (2019). MERIT Hydro: A high-resolution global hydrography map based on latest topography datasets. Water Resources Research. <a href="https://doi.org/10.1029/2019WR024873">https://doi.org/10.1029/2019WR024873</a>.</p> <p>Yang, X., Pavelsky, T. M., Allen, G. H. (2019). The past and future of global river ice. Nature.</p> <p>SWOT Orbits: https://www.aviso.altimetry.fr/en/missions/future-missions/swot/orbit.html</p> <p>HydroFALLS: <a href="http://wp.geog.mcgill.ca/hydrolab/hydrofalls/">http://wp.geog.mcgill.ca/hydrolab/hydrofalls/</a></p>
Dataset generated to evaluate in situ sampling strategies to reconstruct fine-scale ocean currents in the context of SWOT satellite mission (H2020 EuroSea project)
<p><strong>Dataset generated in Subtask 2.3.1 of the H2020 EuroSea project.</strong></p> <ul> <li> <p><em>H2020 EuroSea project:</em><br> The H2020 EuroSea project aims at improving and integrating the European Ocean Observing and Forecasting System (see official website: <a href="https://eurosea.eu/">https://eurosea.eu/</a>). It has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 862626).</p> </li> <li> <p><em>Task 2.3:</em><br> Task 2.3 has the objective to improve the design of multi-platform experiments aimed to validate the Surface Water and Ocean Topography (SWOT) satellite observations with the goal to optimize the utility of these observing platforms. Observing System Simulation Experiments (OSSEs) have been conducted to evaluate different configurations of the in situ observing system, including rosette and underway CTD, gliders, conventional satellite nadir altimetry and velocities from drifters. High-resolution models have been used to simulate the observations and to represent the “ocean truth”. Several methods of reconstruction have been tested: spatio-temporal optimal interpolation, machine-learning techniques, model data assimilation and the MIOST tool. The planned OSSEs are detailed in this public report <a href="https://doi.org/10.3289/eurosea_d2.1">Barceló-Llull et al. (2020)</a> and the complete analysis is available here <a href="https://doi.org/10.3289/eurosea_d2.3">Barceló-Llull et al. (2022)</a>. Contributors to Task 2.3 are CSIC (Spain), CLS (France), SOCIB (Spain), IMT-Atlantique (France) and Ocean-Next (France).</p> </li> <li> <p><em>Subtask 2.3.1:</em><br> Subtask 2.3.1 aims to evaluate different in situ sampling strategies to reconstruct fine-scale ocean currents (~20 km) in the context of SWOT. An advanced version of the classic optimal interpolation used in field experiments, which considers the spatial and temporal variability of the observations, has been applied to reconstruct different configurations with the objective to evaluate the best sampling strategy to validate SWOT.</p> </li> <li> <p><em>Where?</em><br> The analysis focuses on two regions of interest: (i) the western Mediterranean Sea and (ii) the Subpolar North West Atlantic. In the western Mediterranean Sea, the target area is located within a swath of SWOT, while in the North West Atlantic the region of study includes a crossover of SWOT during the fast-sampling phase.</p> </li> </ul> <p><strong>Report with the full analysis</strong></p> <p>The complete analysis can be found in this report: <a href="https://doi.org/10.3289/eurosea_d2.3">Barceló-Llull et al. (2022)</a>.</p> <p><strong>Codes for the analysis</strong></p> <p>The codes generated to develop Subtask 2.3.1 can be found on GitHub: <a href="https://github.com/bbarcelollull/EuroSea_subTask_2.3.1">https://github.com/bbarcelollull/EuroSea_subTask_2.3.1</a></p> <p><strong>The dataset</strong></p> <p>The dataset includes:</p> <p>1) Model outputs used to simulate the observations in different configurations in both regions of study. The folder "2D_model_outputs" contains 2D data used to simulate SSH observations for the analysis of the temporal correlation scale (<a href="https://doi.org/10.3289/eurosea_d2.3">Barceló-Llull et al., 2022</a>, p. 28-42). The folder "3D_model_outputs" contains 3D model outputs used to simulate observations of temperature and salinity. Note that eNATL60 outputs have been interpolated onto a new regular grid. </p> <p>2) Simulated configurations (or sampling strategies) in each region (PKL file format).</p> <p>3) Observations simulated in each configuration in both regions of study. The observations simulated are temperature and salinity. ADCP horizontal velocities are also simulated, however for eNATL60 they will be corrected in the future to account for the rotated original axes. File format: region_configuration_period_model.nc. The folder "SSH" includes the simulated SSH observations for the analysis of the temporal correlation scale (<a href="https://doi.org/10.3289/eurosea_d2.3">Barceló-Llull et al., 2022</a>, p. 28-42).</p> <p>4) Reconstructed fields with the spatio-temporal optimal interpolation. File format: region_configuration_period_model_stOI_Lx_Lt_cd_YYYYMMDDhhmm_var.nc (stOI = spatio-temporal optimal interpolation, Lx = spatial correlation scale, Lt = temporal correlation scale, cd = map on the central date of the sampling, YYYYMMDDhhmm = date and time of the map, var = variable interpolated (temperature and salinity) or the derived variables (dynamic height, geostrophic velocities and the Rossby number)).</p> <p>5) Compared fields (ocean truth from model outputs vs. reconstructed fields) for each region and model (PKL file format).</p> <p> </p>
Coefficients for a Proxy Long-Wavelength Correction for the SWOT 1-Day Repeat Mission
<p>This archive contains NetCDF-formatted files containing the weighting coefficients which define the proxy long-wavelength correction (LWC) described in a manuscript submitted to AGU Earth and Space Science, "The Significance of the Long-Wavelength Correction for Studies of Baroclinic Tides with SWOT".</p>
Solar Chemicals and Fuels - SWOT ANALYSIS
<p>This dataset includes a comprehensive collection of materials and analyses used in the publication "Exploring the SWOTs (Strengths, Weaknesses, Opportunities, and Threats) of Solar Chemicals and Fuels: Findings from a Literature Review and a Workshop Exercise."</p> <p>The dataset consists of a PDF file, with the full-text NVivo coding results, and an Excel file, which contains:</p> <ol> <li>A complete list of documents analyzed, along with their bibliographic information.</li> <li>A compilation table of the SWOT analysis developed after the text-mining exercise and literature review.</li> <li>Word trees retrieved from NVivo.</li> <li>Full quantitative analysis of the NVivo codes and their references.</li> <li>Citation analysis data.</li> <li>Matrix query results on "Policy versus Workshop."</li> </ol>
SWOT_FoundationSeamounts
<p>east_new.grd, north_new.grd:</p> <p>the east and north slope constructed using the traditional radar altimetry and ship measurements.</p> <p> </p> <p>mss_sio_32.1.nc</p> <p>mean sea surface (geoid) constructed at Scripps Institution of Oceaography.</p> <p> </p> <p>dot.cls21.grd</p> <p>The CLS2021 mean dynamics topography model. </p>
MERIT-SWORD: Bidirectional Translations Between MERIT-Basins and the SWOT River Database (SWORD)
<p><strong>Corresponding peer-reviewed publication</strong></p> <p>This dataset corresponds to all input and output files that were used in the study reported in:</p> <ul> <li>Wade, J., David, C.H., Collins, E.L., Altenau, E.H., Coss, S., Cerbelaud, A., Tom, M., Durand, M., Pavelsky T.M. (In Review), Bidirectional Translations Between Observational and Topography-based Hydrographic Datasets: MERIT-Basins and the SWOT River Database (SWORD).</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> </p> <p><strong>Summary</strong></p> <p>The MERIT-SWORD data product reconciles critical differences between the SWOT River Database (SWORD; Altenau et al., 2021), the hydrography dataset used to aggregate observations from the Surface Water and Ocean Topography (SWOT) Mission, and MERIT-Basins (MB; Lin et al., 2019; Yang et al., 2021), an elevation-derived vector hydrography dataset commonly used by global river routing models (Collins et al., 2024). The SWORD and MERIT-Basins river networks differ considerably in their representation of the location and extent of global river reaches, complicating potential synergistic data transfer between SWOT observations and existing hydrologic models.</p> <p>MERIT-SWORD aims to:</p> <ol> <li>Generate bidirectional, one-to-many links (i.e. translations) between river reaches in SWORD and MERIT-Basins (ms_translate files).</li> <li>Provide a reach-specific evaluation of the quality of translations (ms_diagnostic files).</li> </ol> <p> </p> <p><strong>Data sources</strong></p> <p>The following sources were used to produce files in this dataset:</p> <ul> <li>MERIT-Basins (version 1.0) derived from MERIT-Hydro (version 0.7) available under a CC BY-NC-SA 4.0 license. <a href="https://www.reachhydro.org/home/params/merit-basins">https://www.reachhydro.org/home/params/merit-basins</a></li> <li>SWOT River Database (SWORD) (version 16) available under a CC BY 4.0. https://zenodo.org/records/10013982. DOI: 10.5281/zenodo.10013982</li> <li>Mean Discharge Runoff and Storage (MeanDRS) dataset (version v0.4) available under a CC BY-NC-SA 4.0 license. <a href="../records/10013744">https://zenodo.org/records/10013744</a>. DOI: 10.5281/zenodo.10013744; 10.1038/s41561-024-01421-5</li> </ul> <p> </p> <p><strong>Software</strong></p> <p>The software that was used to produce files in this dataset are available at <a href="https://github.com/jswade/merit-sword">https://github.com/jswade/merit-sword</a>.</p> <p> </p> <p><strong>Primary Data Products</strong></p> <p>The following files represent the primary data products of the MERIT-SWORD dataset. Each file class generally has 61 files, corresponding to the 61 global hydrologic regions (region ii).<strong> For typical use of this dataset, download the 3 following zip folders listed below. </strong>The <strong>ms_translate.zip</strong> and <strong>ms_diagnostic.zip</strong> NetCDF files are best suited for scripting applications, while the <strong>ms_translate_shp.zip</strong> shapefiles are best suited for GIS applications.</p> <p>The MERIT-SWORD translation tables (.nc) establish links between corresponding river reaches in MERIT-Basins and SWORD in both directions. The mb_to_sword translations relate the <em>COMID </em>values of all MERIT-Basins reaches in region ii (as defined by MERIT-Basins) to corresponding SWORD <em>reach_id</em> values, which are ranked by their degree of overlap and stored in columns <em>sword_1</em> – <em>sword_40</em>. The partial intersecting lengths (m) of SWORD reaches within related MERIT-Basins unit catchments are stored in columns <em>part_len_1 </em>– <em>part_len_40 </em>and can be used to weight data transfers from more than one SWORD reach. The sword_to_mb translations relate the <em>reach_id</em> values of all SWORD reaches in region ii (as defined by SWORD) to corresponding MERIT-Basins <em>COMID</em> values, which are ranked by their degree of overlap and stored in columns <em>mb_1­ </em>– <em>mb_40</em>. The partial intersecting lengths (m) of SWORD reaches within related MERIT-Basins unit catchments are again stored in columns <em>part_len_1 </em>– <em>part_len_40</em>.</p> <ul> <li><strong>ms _translate.zip</strong> <ul> <li><strong>mb_to_sword: </strong>mb_to_sword_pfaf_ii_translate.nc</li> <li><strong>sword_to_mb: </strong>sword_to_mb_pfaf_ii_translate.nc</li> </ul> </li> </ul> <p> </p> <p>The MERIT-SWORD diagnostic tables (.nc) contain evaluations of the quality of translations between MERIT-Basins and SWORD reaches, stored in column <em>flag</em>. The mb_to_sword diagnostic files contain integer quality flags for each MERIT-Basins reach translation in region ii. The sword_to_mb diagnostic files contain integer quality flags for each SWORD reach translation in region ii. The quality flags are as follows:</p> <ul> <li>0 = Valid translation.</li> <li>1 = Translated reaches are not topologically connected to each other.</li> <li>2 = Reach does not have a corresponding reach in the other dataset (absent translation).</li> <li>21 = Reach does not have a corresponding reach in the other dataset due to flow accumulation mismatches.</li> <li>22 = Reach does not have a corresponding reach in the other dataset because it is located in what the other dataset defines as the ocean.</li> </ul> <ul> <li><strong>ms_diagnostic.zip</strong> <ul> <li><strong>mb_to_sword: </strong>mb_to_sword_pfaf_ii_diagnostic.nc</li> <li><strong>sword_to_mb: </strong>sword_to_mb_pfaf_ii_diagnostic.nc</li> </ul> </li> </ul> <p> </p> <p>For GIS applications, the translations and diagnostic tables are also available in shapefile format, joined to their respective MERIT-Basins and SWORD river vector shapefiles. The MERIT-Basins and SWORD shapefiles retain their original attribute tables, in additional to the added translation and diagnostic columns.</p> <ul> <li><strong>ms _translate_shp.zip</strong> <ul> <li><strong>mb: </strong>riv_pfaf_ii_MERIT_Hydro_v07_Basins_v01_translate.shp</li> <li><strong>sword: </strong>jj_sword_reaches_hbii_v16_translate.shp</li> </ul> </li> </ul> <p> </p> <p> </p> <p><strong>Example Applications Data Products</strong></p> <p>The following files are example use cases of transferring data between MERIT-Basins and SWORD. They are not required for typical use of the MERIT-SWORD dataset.</p> <p>The MeanDRS-to-SWORD application example files demonstrate how the MERIT-SWORD translation tables can be used to transfer discharge simulations along MERIT-Basins reaches (i.e. MeanDRS; <a href="../records/8264511">https://zenodo.org/records/8264511</a>) to corresponding SWORD reaches in region ii and continent xx. MeanDRS discharge simulations (m3 s-1) are transferred to SWORD reaches based on a weighted average translation of corresponding reaches and stored in the column <em>meanDRS_Q</em>.</p> <ul> <li><strong>app_meandrs_to_sword.zip: </strong>xx_sword_reaches_hbii_v16_meandrs.shp</li> </ul> <p><strong> </strong></p> <p>The SWORD-to-MERIT-Basins application example files demonstrate how the MERIT-SWORD translation tables can be used to transfer variables of interest (in this case, river width) from SWORD reaches to corresponding MERIT-Basins reaches in region ii. SWORD width estimates (m) are transferred to MERIT-Basins reaches based on a weighted average translation of corresponding reaches and stored in the column <em>sword_wid</em>.</p> <ul> <li><strong>app_sword_to_mb.zip: </strong>riv_pfaf_ii_MERIT_Hydro_v07_Basins_v01_sword.shp</li> </ul> <p><strong> </strong></p> <p><strong>Intermediate Data Products</strong></p> <p>The following files are intermediates used in generating the primary data. They are not required for typical use of the MERIT-SWORD dataset.</p> <p>The MERIT-SWORD river trace files represent our first approximation of MERIT-Basins reaches that correspond to SWORD reaches in region ii, prior to the manual removal of mistakenly included reaches. The river trace files are only used to generate the final river network files and are not used elsewhere in the dataset.</p> <ul> <li><strong>ms_riv_trace.zip: </strong>meritsword_pfaf_ii_trace.shp</li> </ul> <p> </p> <p>The MERIT-SWORD river network shapefiles contain the MERIT-Basins reaches that in aggregate best correspond to the location and extent of the SWORD river network for each of the Pfafstetter level 2 regions as defined by SWORD v16 (i.e. the 61 values of ii). The MERIT-SWORD river networks serve as an intermediary data product to enable reliable translations.</p> <ul> <li><strong>ms_riv_network.zip: </strong>meritsword_pfaf_ii_network.shp<strong> </strong></li> </ul> <p> </p> <p>The MERIT-SWORD transpose files are used to confirm that the translation tables in one direction can recreated in their entirety using only data from the translation tables in the other direction, ensuring ~3,500 less data transfer. These files are exact copies of the files contained in ms_translate.zip.</p> <ul> <li><strong>ms _transpose.zip</strong> <ul> <li><strong>mb_transposed: </strong>mb_to_sword_pfaf_ii_transpose.nc</li> <li><strong>sword_transposed: </strong>sword_to_mb_pfaf_ii_transpose.nc</li> </ul> </li> </ul> <p> </p> <p>The MERIT-SWORD translation catchment files contain the MERIT-Basins unit catchments corresponding to each reach used in generating the mb_to_sword and sword_to_mb translations for each region ii. The files are used internally during the translation process and not required for typical dataset use.</p> <ul> <li><strong>ms_translate_cat.zip</strong> <ul> <li><strong>mb_to_sword: </strong>mb_to_sword_pfaf_ii_translate_cat.nc</li> <li><strong>sword_to_mb: </strong>sword_to_mb_pfaf_ii_translate.cat.nc</li> </ul> </li> </ul> <p> </p> <p>The hydrologic regions as defined by MERIT-Basins and SWORD are not identical and overlap in many cases, complicating translations. The region overlap files provide bidirectional mappings between region identifiers in both datasets. The files are used in most dataset scripts to determine the regional files from each dataset that need to be loaded.</p> <ul> <li><strong>ms_region_overlap.zip: </strong>sword_to_mb_reg_overlap.csv, sword_to_mb_reg_overlap.csv</li> </ul> <p><strong> </strong></p> <p>The MERIT-SWORD river edit files contain ~3,500 MERIT-Basins river reaches that were mistakenly included during river network generation and do not correspond to any SWORD reaches. These reaches are removed from the river trace files to generate the final MERIT-SWORD river network data product.</p> <ul> <li><strong>ms_riv_edit.zip: </strong>meritsword_edits.csv</li> </ul> <p><strong> </strong></p> <p>Near the antimeridian, MERIT-Basins and SWORD shapefiles differ in their longitude convention. Additionally, the SWORD dataset lacks a shapefile for region 54, which does not have any SWORD reaches. The SWORD edit files contain copies of SWORD files, altered to match the longitude convention of MERIT-Basins and including a dummy shapefile for region 54.</p> <ul> <li><strong>sword_edit.zip: </strong>xx_sword_reaches_hbii_v16.shp</li> </ul> <p><strong> </strong></p> <p><strong>Known bugs in this dataset or the associated manuscript</strong></p> <p>No bugs have been identified at this time.</p> <p> </p> <p><strong>References</strong></p> <p>Altenau, E. H., Pavelsky, T. M., Durand, M. T., Yang, X., Frasson, R. P. de M., & Bendezu, L. (2021). The Surface Water and Ocean Topography (SWOT) Mission River Database (SWORD): A Global River Network for Satellite Data Products. <em>Water Resources Research</em>, <em>57</em>(7), e2021WR030054. https://doi.org/10.1029/2021WR030054</p> <p>Collins, E. L., David, C. H., Riggs, R., Allen, G. H., Pavelsky, T. M., Lin, P., Pan, M., Yamazaki, D., Meentemeyer, R. K., & Sanchez, G. M. (2024). Global patterns in river water storage dependent on residence time. <em>Nature Geoscience</em>, 1–7. https://doi.org/10.1038/s41561-024-01421-5</p> <p>Lin, P., Pan, M., Beck, H. E., Yang, Y., Yamazaki, D., Frasson, R., David, C. H., Durand, M., Pavelsky, T. M., Allen, G. H., Gleason, C. J., & Wood, E. F. (2019). Global Reconstruction of Naturalized River Flows at 2.94 Million Reaches. <em>Water Resources Research</em>, <em>55</em>(8), 6499–6516. https://doi.org/10.1029/2019WR025287</p> <p>Yang, Y., Pan, M., Lin, P., Beck, H. E., Zeng, Z., Yamazaki, D., David, C. H., Lu, H., Yang, K., Hong, Y., & Wood, E. F. (2021). Global Reach-Level 3-Hourly River Flood Reanalysis (1980–2019). <em>Bulletin of the American Meteorological Society</em>, <em>102</em>(11), E2086–E2105. https://doi.org/10.1175/BAMS-D-20-0057.1</p> <p><strong> </strong></p>
PDF4 - Semantics and Vocabularies Hackathon SWOT Visualization
<p>This SWOT visualization outlines the community's feedback regarding the overall polar RDM strengths, weaknesses, opportunities, and threats. This visualization is valuable to convey the overarching themes/ topics of interest, and allows for stakeholders to determine where resources should be allocated. The content for this visualization was compiled through community input during the 'Semantics and Vocabularies' hackathon at the 4th Polar Data Forum in September 2021. </p>
SWOT calibration dataset
<p>Dataset and saved model weights used for the "Scale aware neural calibration of wide swath altimetry data" publication.</p> <p>Contains:</p> <p>OSSE setting for SSH mapping:</p> <p>Gridded data 1/20°:</p> <p>- 1 year SSH data from NATL60</p> <p>- pseudo-observations of nadir altimeters</p> <p>Pseudo-observations in sensor geometry sampled using the SWOT simulator :</p> <p>- 5 nadirs</p> <p>- Wide swath SWOT SSH data</p> <p>- SWOT error signals</p>
Perturbed Synthetic SWOT Datasets for Testing and Development of a Kalman Filter Approach to Estimate Daily Discharge
<p><strong>1. Introduction</strong></p> <p>Datasets are used to evaluate the performance of a Kalman filter approach to estimate daily discharge. This is a perturbed version of synthetic SWOT datasets consisting of 15 river sections, which are commonly agreed datasets for evaluating the performance of SWOT discharge algorithms (Frasson et al., 2020, 2021). The benchmarking manuscript entitled “A Kalman Filter Approach for Estimating Daily Discharge Using Space-based Discharge Estimates” is currently under review at Water Resources Research. Once the manuscript is accepted, its DOI will be included here.</p> <p> </p> <p><strong>2. </strong><strong>File description</strong></p> <p>The datasets are generally divided into two categories: river information (River_Info) and time series data (Timeseries_Data). River information provides fundamental and general river characteristics, whereas time series data offers daily reach-averaged data for each reach. In time series data, the data mainly contains three components: true data, perturbed measurements, and true and perturbed flow law parameters (A0, an, and b). For each reach, there are 10000 realizations of perturbed measurements per time step and there are 100 realizations of time-invariant perturbed flow law parameters through a Monte Carlo simulation (Frasson et al., 2023). Moreover, to support our proposed Kalman filter approach to estimate daily discharge, the datasets provide the median of the perturbed discharge, river width, water surface slope, and change in the cross-sectional area, as well as the uncertainty of the perturbed discharge and change in the cross-sectional area based on the interquartile range (Fox, 2015).</p> <p>To support reproducibility and facilitate example usage, we now include a MATLAB code package (<code>KalmanFilter_Code.zip</code>) that demonstrates how to run the Kalman filter approach using the Missouri Downstream case as an example. </p> <p>Datasets are contained in a .mat file per river. The detailed groups and variables are in the following:</p> <p><strong>River_Info</strong></p> <p>Name: River name, data type: char</p> <p>QWBM: Mean annual discharge from the water balance model WBMsed (Cohen et al., 2014)</p> <p>rch_bnd: Reach boundaries measured in meters from the upstream end of the model</p> <p>gdrch: Good reaches in the study. They were used to exclude small reaches defined around low-head dams and other obstacles where Manning’s equation should not be applied.</p> <p><strong>Timeseries_Data</strong></p> <p>t: Time measured in days since the first day or “0-January-0000” for cases when specific dates were available. Dimension: 1, time step.</p> <p>A: Reach-averaged cross-sectional area of flow in m<sup>2</sup>. Dimension: Reach, time step.</p> <p>Q_true: True reach-averaged discharge (m<sup>3</sup>/s). Dimension: Reach, time step.</p> <p>Q_ptb: Perturbed discharge (m<sup>3</sup>/s), including 10000 realizations for each measurement. Dimension: Good reach, time step, 10000.</p> <p>med_Q_ptb: Median perturbed discharge (m<sup>3</sup>/s) across the 10000 realizations. Dimension: Good reach, time step.</p> <p>sigma_Q_ptb: Uncertainty of the perturbed discharge (m<sup>3</sup>/s), calculated based on the interquartile range. Dimension: Good reach, time step.</p> <p>W_true: True reach-averaged river width (m). Dimension: Reach, time step.</p> <p>W_ptb: Perturbed river width (m), including 10000 realizations for each measurement. Dimension: Good reach, time step, 10000.</p> <p>med_W_ptb: Median perturbed river width (m) across the 10000 realizations. Dimension: Good reach, time step.</p> <p>H_true: True reach-averaged water surface elevation (m). Dimension: Reach, time step.</p> <p>H_ptb: Perturbed water surface elevation (m), including 10000 realizations for each measurement. Dimension: Good reach, time step, 10000.</p> <p>S_true: True reach-averaged water surface slope (m/m). Dimension: Reach, time step.</p> <p>S_ptb: Perturbed water surface slope (m/m), including 10000 realizations for each measurement. Dimension: Good reach, time step, 10000.</p> <p>med_S_ptb: Median perturbed water surface slope (m/m) across the 10000 realizations. Dimension: Good reach, time step.</p> <p>dA_true: True reach-averaged change in the cross-sectional area (m<sup>2</sup>). Dimension: Good reach, time step.</p> <p>dA_ptb: Perturbed change in the cross-sectional area (m<sup>2</sup>), including 10000 realizations for each measurement. Dimension: Good reach, time step, 10000.</p> <p>med_dA_ptb: Median perturbed change in the cross-sectional area (m<sup>2</sup>) across the 10000 realizations. Dimension: Good reach, time step.</p> <p>sigma_dA_ptb: Uncertainty of the perturbed change in the cross-sectional area (m<sup>2</sup>), calculated based on the interquartile range. Dimension: Good reach, time step.</p> <p>A0_true: True baseline cross-sectional area (m<sup>2</sup>). Dimension: Good reach, 1.</p> <p>A0: Perturbed baseline cross-sectional area (m<sup>2</sup>), including 100 realizations for each parameter. Dimension: Good reach, 100.</p> <p>na_true: True friction coefficient. Dimension: Good reach, 1.</p> <p>na: Perturbed friction coefficient, including 100 realizations for each parameter. Dimension: Good reach, 100.</p> <p>b_true: True exponent coefficient. Dimension: Good reach, 1.</p> <p>b: Perturbed exponent coefficient, including 100 realizations for each parameter. Dimension: Good reach, 100.</p>
MAB-SWOT CPIES Level 2 Processed Data
<div> <p>These are the hourly (P#.mat) and lowpass filtered (P#lp.mat) files from the current- and pressure- sensor equipped inverted echo sounders (CPIESs) deployed as part of the MAB-SWOT program. Version 2 of this dataset (1) includes data from P3 and (2) uses corrected clock drifts to determine each instruments' time base. A description of the processing and details about the experiment can be found at: Wang, Y. D., & Andres, M. (2025). SWOT Adopt a Crossover Field Campaign—Cape Hatteras (MAB-SWOT) Current and Pressure Sensor Equipped Inverted Echo Sounder (CPIES) Technical Report (Version 2). Zenodo. <a href="https://doi.org/10.5281/zenodo.12583849" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.16851887</a></p> <p> </p> <p> </p> </div>
Supporting Datasets for 'SWOT Satellite Reveals Devastating Flood Impact in Rio Grande do Sul, Brazil'
<p>This dataset contains multiple sources of geospatial and environmental data used for flood extension and volume analysis in the study of the May 2023 extreme flood that hit south Brazil. The data integrates satellite remote sensing, atmospheric reanalysis, hydrological measurements, terrain data, and socioeconomic indicators to evaluate flooding impacts and their connection to climate variability.</p> <p>The dataset include:</p> <ol> <li> <p><strong>Sentinel-2 Satellite Data</strong></p> </li> <li> <p><strong>SWOT Mission High-Density Pixel Cloud Data</strong></p> </li> <li> <p><strong>HIDROWEB Water Level Data</strong></p> </li> <li> <p><strong>Forest And Buildings Removed Copernicus Digital Elevation Model (FABDEM)</strong></p> </li> <li> <p><strong>Modern-Era Retrospective Analysis for Research and Applications 2 (MERRA-2)</strong></p> </li> <li> <p><strong>Integrated Multi-satellite Retrievals for Global Precipitation Measurements (IMERG)</strong></p> </li> <li> <p><strong>Brazilian Meteorological Database (BDMEP)</strong></p> </li> <li> <p><strong>Institute of Hydraulic Research (IPH) Flood Extent Map</strong></p> </li> <li> <p><strong>Socioeconomic Data from the Social Vulnerability Atlas</strong></p> </li> </ol> <p> </p> <p><strong>Disclaimer:</strong></p> <p>This dataset is a compilation of various data sources, each of which may be subject to its own specific licensing terms and conditions. Users of this dataset are strongly advised to review the license associated with each individual sub-dataset before using, modifying, or redistributing the data. While we have provided the dataset under a Creative Commons Attribution 4.0 International (CC BY 4.0) license, certain sub-datasets may have additional restrictions or requirements, such as attribution, non-commercial use, or limitations on derivative works. It is the responsibility of the user to ensure compliance with all applicable licenses. Please refer to the original data sources and their respective licenses for detailed information.</p>
Remotely Sensing River Greenhouse Gas Exchange Velocity Using the SWOT Satellite
<p>Scripts and results for our "Remotely Sensing River Greenhouse Gas Exchange Velocity Using the SWOT Satellite" manuscript.</p> <p>Consult the README file for a more detailed description of the data, results, and scripts.</p>
Data files for SWOT correlated error analysis
Open the record for dataset details and reuse information.
SWOT-compliant GRDC river flow dataset for the estimation of Flow-Duration Curves
<p>This repository hosts the data used in the manuscript "Potential legacy of SWOT mission for the estimation of Flow-Duration Curves” by Alessio Domeneghetti, Serena Ceola, Alessio Pugliese, Simone Persiano, Irene Palazzoli, Attilio Castellarin, Alberto Marinelli, Armando Brath</p> <p><span>The study investigates the potential of the Surface Water and Ocean Topography (SWOT) mission for the estimation of Flow-Duration Curves (FDCs) globally. SWOT-like river flow data is derived from the Global Runoff Data Centre (GRDC) dataset by selecting river gauging stations with a cross section wider than 100 m and with more than 10-year long daily river flow time series. Overall, 1200 river gauging stations are considered, from which 24 alternative SWOT-like river flow datasets can be derived by assuming different satellite revisiting times, biases and random errors, as detailed in the manuscript.</span></p> <p><span>The dataset includes detailed features on the selected 1200 river gauging stations. GRDC river flow daily time series for each station is provided as .csv. A .shp file provides the geographical location of each GRDC gauging station, including GRDC number, river, gauging station municipality, country, latitude and longitude, contributing area, altitude and Köppen-Geiger climate classification.</span></p>
Preliminary Results of Marine Gravity Anomaly and Bathymetry from SWOT Wide-Swath altimeter data
<p><span>We explore the potential of Ka-band radar interferometry (KaRIn) data from the SWOT mission for marine gravity and bathymetry applications. Our evaluation includes determining the deflection of vertical (DOV) using the least squares collocation (LSC) method, recovering gravity anomalies with the inverse Vening-Meinesz (IVM) approach, and predicting bathymetry through the gravity-geological method (GGM). SWOT-derived gravity anomalies in the study area, located between 20ºN - 30ºN and 135ºE - 145ºE, lies at the convergence of the Pacific and Eurasian tectonic plates. KaRIn data (only seven months) recovered gravity anomalies with an accuracy of 2.37 mGal, which is better than current models.</span></p> <p><span>The details of the method are discussed in a manuscript submitted to GRL.</span></p> <p> </p>
IS Development Strategy in the Leather Bag Craft Industry: A SWOT Analysis Perspective
<p>This material has presented on 2nd International Conference on Advance Research in Social and Economic Science in October 25, 2023.</p>
Datasets for Abyssal Marine Tectonics from the SWOT Mission
<p> </p> <p>swot_vgg.grd:</p> <p>vertical gravity gradient from SWOT ocean data spanning April 2023 to July 2024.</p> <p>VGG SWOT.kmz:</p> <p>google earth overlay showing details of SWOT VGG.</p> <p>The GEBCO grid is from:</p> <p>https://www.gebco.net/data_and_products/gridded_bathymetry_data/#global</p> <p>Nadir models (east_32.1.nc, north_32.1.nc, curve_32.1.nc) are from: <a href="https://topex.ucsd.edu/pub/global_grav_1min/" rel="nofollow">https://topex.ucsd.edu/pub/global_grav_1min/</a></p>
SWOT_VGG
<p>vgg_swot: SWOT vertical gravity gradients on 1 min by 1 min grids.</p> <p>Nadir models (east_32.1.nc, north_32.1.nc, curve_32.1.nc) are from:<br>https://topex.ucsd.edu/pub/global_grav_1min/</p> <p>vgg_swot.kml: Google earth overlay of the SWOT VGG:</p> <p>All codes to process SWOT L2 low-rate ocean data and make plots are available at Github: <a href="https://github.com/YaoYu9404/SWOT_VGG">https://github.com/YaoYu9404/SWOT_VGG</a></p> <p> </p>
Factors associated with the gender gap in the STEM sector: Comparison of theoretical and empirical concept maps and qualitative SWOT analysis
<p>Word tree STEM</p>
Factors associated with the gender gap in the STEM sector: Comparison of theoretical and empirical concept maps and qualitative SWOT analysis
<p>Word tree gender</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.