The SWOT Sword of Science River Discharge Products dataset from the Surface Water and Ocean Topography (SWOT) mission and produced by the Discharge Algorithm Working Group (DAWG), provides estimates of river discharge derived from the SWOT Level 2 River Single-Pass Vector Data Product, and includes both unconstrained and gauge constrained estimates that leverage in-situ measurements. The SWOT mission is implemented jointly by NASA and Centre National D'Etudes Spatiales (CNES) to provide valuable data and information about the world's oceans and its terrestrial surface water such as lakes, rivers, and wetlands.Sword of Science data products are generated from the open-source SWOT Confluence program and contain river discharge parameter estimates as well as discharge time series for both river reaches and river nodes, following the SWOT River Database (SWORD) structure. Granules from both constrained and unconstrained branches are composed of prior information (e.g., mean annual flow predicted by global hydrological models) and the resulting discharge estimates. Priors and results files for both constrained and unconstrained branches are available in netCDF format. Users are encouraged to reference the SWOT Confluence documentation and notebook tutorials for full documentation of the data structure and variables available.Development of the SWOT Confluence program as well as the Sword of Science data products was funded by NASA’s Advanced Information Systems Technology (AIST) program.

Corresponding peer-reviewed publication

This dataset corresponds to all input and output files that were used in the study reported in:

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).

When making use of any of the files in this dataset, please cite both the aforementioned article and the dataset herein.

Summary

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.

MERIT-SWORD aims to:

Generate bidirectional, one-to-many links (i.e. translations) between river reaches in SWORD and MERIT-Basins (ms_translate files).

Provide a reach-specific evaluation of the quality of translations (ms_diagnostic files).

Data sources

The following sources were used to produce files in this dataset:

MERIT-Basins (version 1.0) derived from MERIT-Hydro (version 0.7) available under a CC BY-NC-SA 4.0 license. https://www.reachhydro.org/home/params/merit-basins

SWOT River Database (SWORD) (version 16) available under a CC BY 4.0. https://zenodo.org/records/10013982. DOI: 10.5281/zenodo.10013982

Mean Discharge Runoff and Storage (MeanDRS) dataset (version v0.4) available under a CC BY-NC-SA 4.0 license. https://zenodo.org/records/10013744. DOI: 10.5281/zenodo.10013744; 10.1038/s41561-024-01421-5

Software

The software that was used to produce files in this dataset are available at https://github.com/jswade/merit-sword.

Primary Data Products

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). For typical use of this dataset, download the 3 following zip folders listed below. The ms_translate.zip and ms_diagnostic.zip NetCDF files are best suited for scripting applications, while the ms_translate_shp.zip shapefiles are best suited for GIS applications.

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 COMID values of all MERIT-Basins reaches in region ii (as defined by MERIT-Basins) to corresponding SWORD reach_id values, which are ranked by their degree of overlap and stored in columns sword_1 – sword_40. The partial intersecting lengths (m) of SWORD reaches within related MERIT-Basins unit catchments are stored in columns part_len_1 – part_len_40 and can be used to weight data transfers from more than one SWORD reach. The sword_to_mb translations relate the reach_id values of all SWORD reaches in region ii (as defined by SWORD) to corresponding MERIT-Basins COMID values, which are ranked by their degree of overlap and stored in columns mb_1 – mb_40. The partial intersecting lengths (m) of SWORD reaches within related MERIT-Basins unit catchments are again stored in columns part_len_1 – part_len_40.

ms _translate.zip

mb_to_sword: mb_to_sword_pfaf_ii_translate.nc

sword_to_mb: sword_to_mb_pfaf_ii_translate.nc

The MERIT-SWORD diagnostic tables (.nc) contain evaluations of the quality of translations between MERIT-Basins and SWORD reaches, stored in column flag. 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:

0 = Valid translation.

1 = Translated reaches are not topologically connected to each other.

2 = Reach does not have a corresponding reach in the other dataset (absent translation).

21 = Reach does not have a corresponding reach in the other dataset due to flow accumulation mismatches.

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.

ms_diagnostic.zip

mb_to_sword: mb_to_sword_pfaf_ii_diagnostic.nc

sword_to_mb: sword_to_mb_pfaf_ii_diagnostic.nc

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.

ms _translate_shp.zip

mb: riv_pfaf_ii_MERIT_Hydro_v07_Basins_v01_translate.shp

sword: jj_sword_reaches_hbii_v16_translate.shp

Example Applications Data Products

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.

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; https://zenodo.org/records/8264511) 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 meanDRS_Q.

app_meandrs_to_sword.zip: xx_sword_reaches_hbii_v16_meandrs.shp

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 sword_wid.

app_sword_to_mb.zip: riv_pfaf_ii_MERIT_Hydro_v07_Basins_v01_sword.shp

Intermediate Data Products

The following files are intermediates used in generating the primary data. They are not required for typical use of the MERIT-SWORD dataset.

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.

ms_riv_trace.zip: meritsword_pfaf_ii_trace.shp

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.

ms_riv_network.zip: meritsword_pfaf_ii_network.shp

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.

ms _transpose.zip

mb_transposed: mb_to_sword_pfaf_ii_transpose.nc

sword_transposed: sword_to_mb_pfaf_ii_transpose.nc

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.

ms_translate_cat.zip

mb_to_sword: mb_to_sword_pfaf_ii_translate_cat.nc

sword_to_mb: sword_to_mb_pfaf_ii_translate.cat.nc

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.

ms_region_overlap.zip: sword_to_mb_reg_overlap.csv, sword_to_mb_reg_overlap.csv

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.

ms_riv_edit.zip: meritsword_edits.csv

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.

sword_edit.zip: xx_sword_reaches_hbii_v16.shp

Known bugs in this dataset or the associated manuscript

No bugs have been identified at this time.

References

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. Water Resources Research, 57(7), e2021WR030054. https://doi.org/10.1029/2021WR030054

Collins, E. L., David, C. H., Riggs, R., Allen, G. H., Pavelsky, T. M., Lin, P., Pan, M., Yamazaki,

** IMPORTANT UPDATE: **

Until now, the project and public versions of SWORD have been kept separate while algorithms were being developed in preparation for SWOT launch. Now that the SWOT mission is here, we have decided to publish the project version of SWORD 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. Everything else, reach definition, attribute values, etc. are the same between the two versions. For details on the filler variables please reference the Product Description Document provided with the downloads.

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. Water Resources Research. https://doi.org/10.1029/2021WR030054

You can also visit www.swordexplorer.com to explore the current version of SWORD before downloading.

1. Summary:

The upcoming Surface Water and Ocean Topography (SWOT) satellite mission, planned to launch in 2022, will vastly expand 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 will provide a range of relevant data products. One product the SWOT mission will provide are river vector products stored in shapefile format for each SWOT overpass (JPL Internal Document, 2020b). The SWOT vector data products will be most broadly useful if they allow multitemporal analysis of river nodes and reaches covering the same river areas. Doing so requires defining SWOT reaches and nodes a priori, so that SWOT data can be assigned to them. The SWOt River Database (SWORD) combines multiple global river- and satellite-related datasets to define the nodes and reaches that will 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.

2. Data Formats:

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.

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_v14.nc (i.e. na_sword_v14.nc).

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]_v14.shp (i.e. na_sword_nodes_hb74_v14.shp; na_sword_reaches_hb74_v14.shp).

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]_v14.gpkg (i.e. na_sword_nodes_v14.gpkg; na_sword_reaches_v14.gpkg).

3. Attribute Description:

This list contains the primary attributes contained in the SWORD netCDFs and shapefiles.

• x: Longitude of the node or reach ranging from 180°E to 180°W (units: decimal degrees).
• y: Latitude of the node or reach ranging from 90°S to 90°N (units: decimal degrees).
• node_id: 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).
• node_length (node files only): Node length measured along the GRWL centerline points (units: meters).
• reach_id: 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).
• reach_length (reach files only): Reach length measured along the GRWL centerline points (units: meters).
• wse: 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).
• wse_var: WSE variance along the GRWL centerline points used to calculate the average WSE for each node or reach (units: square meters).
• width: Average width for a node or reach (units: meters).
• width_var: Width variance along the GRWL centerline points used to calculate the average width for each node or reach (units: square meters).
• max_width: Maximum width value across the channel for each node or reach that includes island and bar areas (units: meters).
• facc: 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).
• n_chan_max: Maximum number of channels for each node or reach.
• n_chan_mod: Mode of the number of channels for each node or reach.
• obstr_type: 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.
• grod_id: The unique GROD ID for each node or reach with obstr_type values 1-4.
• hfalls_id: The unique HydroFALLS ID for each node or reach with obstr_type value 5.
• dist_out: Distance from the river outlet for each node or reach (units: meters).
• type: 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.
• lakeflag: GRWL water body identifier for each reach: 0 – river, 1 – lake/reservoir, 2 – canal, 3 – tidally influenced river.
• manual_add (node files only): 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.
• meand_len (node files only): 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).
• sinuosity (node files only): The total reach length the node belongs to divided by the Euclidean distance between the reach end points.
• slope (reach files only): Reach average slope calculated along the GRWL centerline points. Slopes are calculated using a linear regression (units: meters/kilometer).
• n_nodes (reach files only): Number of nodes associated with each reach.
• n_rch_up (reach files only): Number of upstream reaches for each reach.
• n_rch_down (reach files only): Number of downstream reaches for each reach.
• rch_id_up (reach files only): Reach IDs of the upstream neighboring reaches.
• rch_id_dn (reach files only): Reach IDs of the downstream neighboring reaches.
• swot_obs (reach files only):

Contact: Md Safat Sikder (mssikder@illinois.edu), Jida Wang (jidaw@illinois.edu)

Citation

Sikder, M. S., Wang, J., Allen, G. H., Sheng, Y., Yamazaki, D., Crétaux, J.-F., and Pavelsky, T. M., 2024. HarP: Harmonized Prior river-lake database. Zenodo, https://doi.org/10.5281/zenodo.14205131.

If you only use the PLD-TopoCat dataset, please cite the following paper:

Sikder, M. S., Wang, J., Allen, G. H., Sheng, Y., Yamazaki, D., Song, C., Ding, M., Crétaux, J.-F., and Pavelsky, T. M., 2023. Lake-TopoCat: A global lake drainage topology and catchment dataset. Earth System Science Data, 15, 3483-3511, https://doi.org/10.5194/essd-15-3483-2023.

Data description and components

The Harmonized Prior river-lake database (HarP) for SWOT integrated the SWOT River Database (SWORD) (Altenau et al., 2021) and the SWOT Prior Lake Database (PLD) (Wang et al., 2023) into a geometrically (lake/river) explicit but topologically harmonized vector database to allow for coupled fluvial-lacustrine applications, including a synergistic use of both river and lake products from SWOT.

In addition to the input river network (SWORD v16) and lake database (PLD v106), we used the MERIT Hydro v1.0.1 (Yamazaki et al., 2019), a high-resolution (~90 m) global hydrography dataset, to develop this database.

The SWORD-PLD harmonization process involves three major steps, with Step 3 being divided into three sub-steps. The processing chain is illustrated in the attached Figure "SWORD-PLD_harmonization_steps.jpg", as well as in Section 2 of the product description document. The HarP database consists of the outputs from each of the steps. For convenience, the global landmass (excluding Antarctica) was partitioned to 68 Pfafstetter Level-2 basins/regions, with their IDs shown in Figure "Pfaf2_basins.jpg" attached.

The HarP database consists of five datasets or components (outputs from each step), each with multiple features. The five datasets are described below, and more details are elaborated in the product description document.

1. Harmonized SWORD-PLD (file name "Harmonized_SWORD_PLD"): This is the fully harmonized SWORD-PLD dataset, the primary product of HarP (i.e., output of Step 3.3 in Figure "SWORD-PLD_harmonization_steps.jpg"). This dataset couples SWORD and PLD into a geometrically segmented but topologically integrated dataset at the node, reach, and catchment scales (stored by three feature layers, respectively):

(a) Harmonized feature nodes: Harmonized_feature_nodes_pfaf_xx
(b) Harmonized river network: Harmonized_river_network_pfaf_xx
(c) Harmonized feature catchments: Harmonized_feature_catchments_pfaf_xx
Note: ''pfaf_xx'' indicates the Pfafstetter Level-2 basin ID (shown in Fig. 'Pfaf2_basins.jpg').

Figure "HarP_example.jpg", attached to this database, is an example of the fully harmonized SWORD-PLD dataset for the Ohio River Basin. The example shows three main features of the dataset: feature nodes (i.e., reach downstream ends, lake inlets, and lake outlets; see Fig. 3 in the product description document for definitions), river reaches (i.e., reaches characterized by SWORD alone, characterized by TopoCat alone, and shared by both SWORD and TopoCat), and catchments segmented by each of the feature nodes.

2. Intersected SWORD-PLD drainage configuration (file name "Intersected_SWORD_PLD"): This dataset is the intersected SWORD-PLD (prior river-lake) features (i.e., output of Step 2 in Figure "SWORD-PLD_harmonization_steps.jpg"). This dataset was constructed independently from Step 1 and Step 3. In this dataset, the original geometries of SWORD and PLD are not altered, but instead, their geometric and drainage topological relationships are configured in the attribute tables. This dataset consists of three features:

(a) Intersected reaches: Intersected_SWORD_reaches_pfaf_xx
(b) Intersected nodes: Intersected_SWORD_nodes_pfaf_xx
(c) Intersected lakes: Intersected_PLD_lakes_pfaf_xx

3. PLD-TopoCat (file name "PLD_TopoCat"): This dataset is the lake drainage topology and catchments (TopoCat) for PLD lakes (i.e., output of Step 1 in Figure "SWORD-PLD_harmonization_steps.jpg"). PLD-TopoCat was developed to generate detailed lake drainage topology and connecting paths, which were later used to configure the off-SWORD-network PLD lakes into the tributaries that drain to SWORD. PLD-TopoCat was generated from PLD v106 and MERIT Hydro. Details of the developiong process and algorithm for TopoCat can be found at Sikder at al., (2023). PLD-TopoCat dataset contains six features:

(a) Lake original polygon: PLD_lakes_pfaf_xx
(b) Lake raster polygon: Lake_raster_polygons_pfaf_xx
(c) Lake outlets: Lake_outlets_pfaf_xx
(d) Lake catchments: Lake_catchments_pfaf_xx
(e) Inter-lake reaches: Inter_lake_reaches_pfaf_xx
(f) Lake-network basins: Lake_network_basins_pfaf_xx
Note: full version of the PLD-TopoCat is available here.

4. SWORD-mirror network (file name "SWORD_mirror"): The SWORD-mirror network was constructed to facilitate the SWORD-TopoCat network merging process (i.e., output of Step 3.1 in Figure "SWORD-PLD_harmonization_steps.jpg"). It is essentially a replica of SWORD except that the original SWORD reaches are geometrically modified to be aligned with the topological/hydrographic information depicted in MERIT Hydro. The SWORD-mirror network consists of four features:

(a) SWORD-original reaches: SWORD_original_reaches_pfaf_xx
(b) SWORD-mirror prelim. reaches: SWORD_mirror_prelim_reaches_pfaf_xx
(c) SWORD-mirror reaches: SWORD_mirror_reaches_pfaf_xx
(d) SWORD-mirror reach catchments: SWORD_mirror_reach_catchments_pfaf_xx

5. Merged SWORD-mirror – TopoCat network (file name "SWORD_TopoCat_merged"): This dataset is the output of Step 3.2 in Figure "SWORD-PLD_harmonization_steps.jpg". It is essentially the merged product of the inter-lake reaches (from Step 2) and SWORD-mirror reaches (from Step 3.1). The merged SWORD-mirror – TopoCat network consists of three features:

(a) Merged SWORD-TopoCat reaches: SWORD_TopoCat_merged_reaches_pfaf_xx
(b) SWORD nodes at SWORD-TopoCat confluence: SWORD_TopoCat_confluence_nodes_pfaf_xx
(c) Reach catchments for merged network: SWORD_TopoCat_reach_catchments_pfaf_xx

The attribute tables for each of the feature components are explained in Section 4 of the product description document. All files of HarP are available in both shapefile and geodatabase formats.

Disclaimer
Authors of this dataset claim no responsibility or liability for any consequences related to the use, citation, or dissemination of HarP. For any quesitons, please contact Safat Sikder and Jida Wang.

This resource includes a Jupyter notebook that offers a hands-on walkthrough for accessing, exploring, and analyzing global river discharge data derived from the SWOT (Surface Water and Ocean Topography) mission using the SWORD of Science (SoS) dataset. Using NASA's Earthdata earthaccess API and xarray, users learn how to search for and load SWOT SoS granules directly from the cloud without needing to download large files locally. The notebook guides users through identifying SWORD river reaches along a river of interest (e.g., the Rhine), visualizing reach centroid locations on an interactive map, and extracting reach-specific discharge time series from selected discharge algorithm outputs (e.g., HiVDI).

GROD v1.1 (filename: GROD_v1.1.csv), or Global River Obstruction Database version 1.1, contains 30549 manually identified human-made structures that obstructing river longitudinal flow. Obstructions have been identified on Google Earth Engine satellite map for all rivers mapped in the Global River Widths from Landsat (GRWL) database. Each obstruction has assigned one of the six types—Dam, Lock, Low head dam, Channel dam, Partial dam 1, Partial dam 2. Details of the mapping process and data quality can be found in the following publication:

Yang, X., Pavelsky, T.M., Ross, M.R.V., Januchowski-Hartley, S.R., Dolan,W., Altenau, E.H., Belanger, M., Byron, D.K., Durand, M.T., Dusen, I.V., Galit, H., Jorissen, M., Langhorst, T., Lawton, E., Lynch, R., Mcquillan, K.A., Pawar, S., Whittemore, A., in revision. Mapping ow-obstructing structures on global rivers. Water Resources Research.

The single csv file contain the version 1 of GROD that accompanying the above-mentioned publication. It contains 7 columns:

grod_id: unique identifier (character)

type: obstruction type (character)

lon: longitude in decimal degrees (float)

lat: latitude in decimal degrees (float)

sword_reach_id¹: nearest sword reach id (character)

distance_to_sword: distance to the nearest sword reach (float)

Note:

1. sword, or SWOT River Database (https://zenodo.org/record/3898570#.YU0urWZKhGo), is an improved version of GRWL with better topology. Majority of the GROD obstructions (N=30502) has been matched with the closest SWORD reach. The remaining 16 obstructions were further than 10km away from any SWORD reach and were not matched to SWORD.

2. GROD, along with many other large scale river obstructions databases, will be hosted on Global Dam Watch website (http://globaldamwatch.org/).

The River Sediment Database-Amazon (RivSed-Amazon) database contains surface suspended sediment concentrations (SSC) derived from Landsat 5, 7, and 8 Level 1 Collection 1 surface reflectance from all rivers in the Amazon River Basin that are ~60 meters wide or greater. SSC represent spatially integrated "reach" median concentrations over the footprint of SWOT River Database (SWORD, Altenau et al., 2021) centerlines (median reach length = 10 km) where high quality river water pixels were detected within each Landsat image from 1984-2018.

The methods used to produce this database were initially developed in the following publications:

• Gardner, J., Pavelsky, T. M., Topp, S., Yang, X., Ross, M. R., & Cohen, S. (2023). Human activities change suspended sediment concentration along rivers. Environmental Research Letters. https://iopscience.iop.org/article/10.1088/1748-9326/acd8d8 and
• Gardner et al. (2020). The color of rivers. Geophysical Research Letters. https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2020GL088946

The publication associated with RivSed-Amazon is in review.

Files:

1) Metadata (rivSed_Amazon_metadata_v1.01.pdf): Description key data files associated with this repository.

2) RiverSed (RiverSed_Amazon_v1.1.txt). Table of SSC and associated data that is joinable to SWORD based on the ""reach_id".

3) Shapefile of river centerlines over South America to which the reflectance data can be attached (SWORD_SA.shp).

4) Shapefile of the reach polygons associated with SWORD_SA over the Amazon Basin. (reach_polygons_amazon.shp).

5) SSC-Landsat matchup database with extended metadata on locations and in-situ data (train_full_v1.1.csv).

6) The final training data used to build the xgboost machine learning model (train_v1.1.csv).

7) The xgboost model that can make SSC predictions over inland waters in USA using Landsat bands/band combinations (tssAmazon_model_v1.1.rds and .rda). The model can only be loaded and used in R at this time.

8) The correction coefficients applied to Landsat 5 and 8 to harmonized surface reflectance across Landsat 5,7,8 and over all bands to enable time series analysis.

This resource contains river discharge data on the Noatak River in Alaska. This work is part of a NASA-funded project through the Terrestrial Hydrology Program focused on understanding seasonality in streamflow and sediment transport in an ungaged Arctic river. Data are correlated to river reaches as identified in SWORD v16 and represent average daily streamflow measurements taken on multiple reaches with unique reach IDs. Discharge data were collected with a Teledyne River Pro towed behind a kayak, and reported daily averages are from a minimum of two transects taken at the same cross-section. Discharge values are averaged from those reported in WinRiverII.

The CONtiguous United States (CONUS) “Flood Inundation Mapping Hydrofabric - ICESat-2 River Surface Slope” (FIM HF IRIS) dataset integrates river slopes from the global IRIS dataset for 117,357 spatially corresponding main-stream reaches within NOAA’s Office of Water Prediction operational FIM forecasting system, which utilizes the Height Above Nearest Drainage approach (OWP HAND-FIM) to help warn communities of floods. To achieve this, a spatial joining approach was developed to align FIM HF reaches with IRIS reaches, accounting for differences in reach flowline sources. When applied to OWP HAND-FIM, FIM HF IRIS improved flood map accuracy by an average of 31% (CSI) across eight flood events compared to the original FIM HF slopes. Using a common attribute, IRIS data were also transferred from FIM HF IRIS to the CONUS-scale Next Generation Water Resources Modeling Framework Hydrofabric (NextGen HF), creating the NextGen HF IRIS dataset. By referencing another common attribute, SWOT vector data (e.g., water surface elevation, slope, discharge) can be leveraged by OWP HAND-FIM and NextGen through the two resulting datasets. The spatial joining approach, which enables the integration of FIM HF with other hydrologic datasets via flowlines, is provided alongside the two resulting datasets.

If you use this dataset in your work, please cite:

Yixian Chen, Anupal Baruah, Dipsikha Devi, et al. Merging Remote Sensing Derived River Slope Datasets with High-Resolution Hydrofabrics for the United States. Authorea. May 15, 2025.
DOI: 10.22541/au.174733764.47269767/v1

1. Variable Description

1.1 FIM HF IRIS v1.0

HydroID:

Unique identifier for FIM HF stream reaches

From_Node:

Upstream node ID

To_Node:

Downstream node ID

reach_id:

The SWORD reach identifier

slope_iris_sword:

The IRIS slope joined to the corresponding FIM HF flowline, while the SWORD slopes were used for the flowlines where the IRIS slopes were unavailable [mm/mm]

slope_source_iris_sword:

Value indicating the slope of slope_iris_sword is from IRIS (1) or SWORD (2)

1.2 NextGen HF IRIS v1.0

id:

Unique identifier for NextGen HF flowline (flowpath)

to_id:

NextGen HF flowline id where water flows

reach_id:

The SWORD reach identifier

slope_iris_sword:

The IRIS slope joined to the corresponding NextGen HF flowline, while the SWORD slopes were used for the flowlines where the IRIS slopes were unavailable [mm/mm]

slope_source_iris_sword:

Value indicating the slope of slope_iris_sword is from IRIS (1) or SWORD (2)

2. Notes

The slope_iris_sword in FIM HF IRIS can be used with the Recalculate_Discharge_in_Hydrotable_useFIMHFIRIS.py script to regenerate the hydrotable for OWP HAND-FIM, where the discharge will be recalculated using slope_iris_sword. Consequently, the synthetic rating curves (SRCs) will be updated based on the new discharges (see more details here). The script can also be used to regenerate hydrotables using river slopes from other sources, such as NextGen HF, provided they are linked to the FIM HF flowlines.

The feature classes for FIMHF_IRIS and NextGenHF_IRIS are provided in formats of geopackage (.gpkg) and geodatabases (.gdb), which can be accessed using ArcGIS, QGIS, or relevant Python packages for inspection, visualization, or spatial analysis of slope_iris_sword.

This resource provides a Jupyter notebook designed to evaluate river discharge estimates from the Surface Water and Ocean Topography (SWOT) mission using the SWORD of Science (SoS) dataset. The SWOT SoS products, developed by the Discharge Algorithm Working Group (DAWG), include both unconstrained (satellite-only) and gauge-constrained (calibrated with in-situ data) estimates. In this notebook, we focus on validating the unconstrained discharge estimates using independent streamflow observations obtained through the Surface Water and Ocean Topography Hydrology Community Discharge repository (SWOT SHCQ). The SHCQ serves as a community-driven place for gathering in-situ hydrologic data and plays a critical role in constraining and validating SWOT-derived discharge parameters, particularly within the SWOT Confluence processing framework and in future product releases. By comparing unconstrained estimates to trusted ground-based observations, the notebook provides insights into the performance of SWOT algorithms and supports efforts to enhance the accuracy and usability of SWOT hydrology products across diverse river systems.

This notebook walks users through a hands-on workflow to access and explore SWOT discharge data produced by SoS. These datasets include both unconstrained estimates, which rely solely on SWOT observations and hydrologic modeling, and gauge-constrained estimates, which incorporate in-situ measurements for improved accuracy. Users will learn how to query and open SWOT SoS data in the cloud using the earthaccess API and xarray, identify river reaches of interest (e.g., the Rhine River), visualize those reaches on an interactive map, and extract discharge time series for individual reaches from selected algorithms. By linking satellite-derived estimates with ground-based observations, this workflow supports validation efforts and enhances the usability of SWOT products for local and global hydrologic research.

Corresponding manuscript

This dataset corresponds to all input and output files that were used in the study reported in:

• Collins, E.L., Altenau, E., Pavelsky, T., David, C.H., Getirana, A., and Johnson, E.. (Submitted), River Network Routing and Discharge Partitioning on a Multichannel River Network.

When making use of any of the files in this dataset, please cite both the aforementioned article and the dataset herein.

Files included in this version

All 3-hourly discharge simulations (netCDF) for the SWORD Mirror (traditional, single channel) and SWORD (multichannel) river networks for the Amazon (b62) and Mackenzie (b82) river basins. Note that there are three files each for the SWORD river network for each basin. These correspond to each of the discharge partitioning approaches tested in the study: (1) width only ('wid'), (2) width and length ('wid_len'), and (3) width and sinuosity ('wid_sin').

• Qout_SWORDMirror_b62_20150101_20240531_GLDASv21.nc.zip

• Qout_SWORDMirror_b82_20150101_20240531_GLDASv21.nc.zip

• Qout_SWORD_b62_20150101_20240531_GLDASv21_wid.nc.zip

• Qout_SWORD_b62_20150101_20240531_GLDASv21_wid_len.nc.zip

• Qout_SWORD_b62_20150101_20240531_GLDASv21_wid_sin.nc.zip

• Qout_SWORD_b82_20150101_20240531_GLDASv21_wid.nc.zip

• Qout_SWORD_b82_20150101_20240531_GLDASv21_wid_len.nc.zip

• Qout_SWORD_b82_20150101_20240531_GLDASv21_wid_sin.nc.zip

The SWORD Mirror and SWORD river network shapefiles for each basin with a column containing reach IDs so that discharge simulations from the netCDFs can be attached and visualized.

• SWORDMirror_b62.zip

• SWORDMirror_b82.zip

• SWORD_b62.zip

• SWORD_b82.zip

Here we present the database of water surface reflectance from 1985 to 2023 for global coastal river and estuaries. This database contains the Landsat Collection 2 Surface reflectance of TM, ETM+, OLI and OLI-2. To build the database, we used the estuary shapefile from Alder (2003) and river reach shapefile from the Surface Water and Ocean Topography (SWOT) Mission River Database (Altenau et al., 2021) within 250 km of the coastline. In total, we have pointed grid (234,868 points) within 1080 estuary boundary, and 113,080 coastal river reaches. Then we used the water masking algorithm, described in Prum et al. (2024) and Gardner et al. (2021) to pull the median surface reflectance of Landsat Collection 2 data. The harmonization of TM, ETM+, OLI and OLI-2 model is provided based on method from Prum et al. (under review) for harmonization coefficient model.

This dataset is currently under embargo and will available for public assess after peer review process of Prum et al. (under review) completed.

Reference: -Alder J (2003). Putting the coast in the “Sea Around Us”. The Sea Around Us Newsletter 15: 1-2. URL: http://seaaroundus.org/newsletter/Issue15.pdf; http://data.unep-wcmc.org/datasets/23 -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, 57, e2021WR030054. https://doi.org/10.1029/2021WR03005 -Gardner, J. R., Yang, X., Topp, S. N., Ross, M. R.. V., Altenau, E. H., & Pavelsky, T. M. (2021). The color of rivers. Geophysical Research Letters, 48, e2020GL088946. https://doi.org/10.1029/2020GL088946 -Prum, P., Harris, L. and Gardner, J. (2024), Widespread warming of Earth's estuaries. Limnol. Oceanogr. Lett, 9: 268-275. https://doi.org/10.1002/lol2.10389 -Prum et al. (under review) Harmonizing Landsat TM, ETM+, OLI, and OLI2 surface reflectance data for long-term surface water quality monitoring

ICESat-2 River Surface Slope (IRIS)

When using this data please cite Scherer D., Schwatke C., Dettmering D., Seitz F.: ICESat-2 river surface slope (IRIS): A global reach-scale water surface slope dataset. Scientific Data, 10(1), 359, 10.1038/s41597-023-02215-x, 2023.

A detailed description of the methodology and validation is published in Scherer D., Schwatke C., Dettmering D., Seitz F. : ICESat-2 Based River Surface Slope and Its Impact on Water Level Time Series From Satellite Altimetry. Water Resources Research, 10.1029/2022WR032842, 2022.

1. SummaryThe unique multibeam lidar altimeter of ICESat-2 is used to measure reach-scale water surface slope (WSS) every time the spacecraft’s orbit crosses a reach. The method of deriving WSS from simultaneous ICESat-2 ATL13 (Jasinski et al., 2021) observations is described in detail and validated in Scherer et al. (2022). In this ICESat-2 River Surface Slope (IRIS) dataset, we provide the minimum, average, and maximum slope derived with three different approaches (across, along, and combined) per reach. Additionally, we give the standard deviation and epochs of the derived WSS data. The reaches are defined by the SWOT River Database (SWORD, Altenau et al., 2021).

An interactive map is available at DAHITI.

1. Version History

IRIS v0: Only includes the reaches studied in Scherer et al. (2022).Based on ICESat-2 ATL13 v5, Cycle 1-13 (October 2018 to October 2021) and SWORD Version v1.

IRIS v1: Global coverage (limited by ICESat-2 data availability and cloud cover).Based on ICESat-2 ATL13 v5, Cycle 1-16 (October 2018 to August 2022) and SWORD Version v2.

IRIS v2: Global coverage with 6,083 additional reaches and 92,347 more observations compared to v1.Based on ICESat-2 ATL13 v6, Cycle 1-19 (October 2018 to April 2023) and SWORD Version v15.

IRIS v2.1: Based on ICESat-2 ATL13 v6, Cycle 1-19 (October 2018 to April 2023) and SWORD Version v16.

IRIS v2.2: 3,251 additional reaches and 58,862 new observations compared to v2.1.Based on ICESat-2 ATL13 v6, Cycle 1-20 (October 2018 to August 2023) and SWORD Version v16.

IRIS v2.3: 1,595 additional reaches and 32,590 new observations compared to v2.2.Based on ICESat-2 ATL13 v6, Cycle 1-21 (October 2018 to October 2023) and SWORD Version v16.

IRIS v2.6: 2,755 additional reaches and 362,136 new observations compared to v2.3.Based on ICESat-2 ATL13 v6, Cycle 1-23 (October 2018 to May 2024) and SWORD Version v16.

IRIS v2.9: 2,485 additional reaches and 184,549 new observations compared to v2.6.Based on ICESat-2 ATL13 v6, Cycle 1-24 (October 2018 to August 2024) and SWORD Version v16.Fixed some broken geometries in the gpkg data.

IRIS v3.0: Based on ICESat-2 ATL13 v6, Cycle 1-24 (October 2018 to August 2024) and SWORD Version v17.

1. Data Format and Variable Description

From Version 2.6, IRIS is also available as GeoPackage.The IRIS data is stored in a single NetCDF4 file which is structured in a single group containing the following variables:reach_id:The SWORD reach identifier [-]lon:Approx. centroid longitude of the SWORD reach [degrees east]lat:Approx. centroid latitude of the SWORD reach [degrees north]across_flag, along_flag, combined_flag:Flags indicating whether ICESat-2 [across/along/combined] slope is available (1) for the reach or not (0) [-]avg_across_slope, avg_along_slope, avg_combined_slope:Average (median) ICESat-2 [across/along/combined] slope for the reach [mm/km]min_across_slope, min_along_slope, min_combined_slope:Minimum ICESat-2 [across/along/combined] slope for the reach [mm/km]max_across_slope, max_along_slope, max_combined_slope:Maximum ICESat-2 [across/along/combined] slope for the reach [mm/km]std_across_slope, std_along_slope, std_combined_slope:ICESat-2 [across/along/combined] slope standard deviation for the reach [mm/km]n_across_slope, n_along_slope, n_combined_slope:Number of days with ICESat-2 [across/along/combined] slope observations for the reach [-]min_date_across_slope, min_date_along_slope:, min_date_combined_slope:First date of ICESat-2 [across/along/combined] slope observations for the reach [days since 2000-01-01]max_date_across_slope, max_date_along_slope:, max_date_combined_slope:Latest date of ICESat-2 [across/along/combined] slope observations for the reach [days since 2000-01-01]

1. References

Scherer D., Schwatke C., Dettmering D., Seitz F.: ICESat-2 river surface slope (IRIS): A global reach-scale water surface slope dataset. Scientific Data, 10(1), 359, 10.1038/s41597-023-02215-x, 2023Scherer D., Schwatke C., Dettmering D., Seitz F. (2022): ICESat-2 Based River Surface Slope and Its Impact on Water Level Time Series From Satellite Altimetry, Water Resources Research, https://doi.org/10.1029/2022WR032842Jasinski M., Stoll J., Hancock D., Robbins J., Nattala J., Morison J., Jones B., Ondrusek M., Pavelsky T.M., Parrish C. and the ICESat-2-Science-Team (2021). ATLAS/ICESat-2 L3A Inland Water Surface Height, Version 5. [Dataset]Altenau E.H., Pavelsky T.M., Durand, M.T., Yang X., Frasson, R.P.d.M., Bendezu, L. (2021): SWOT River Database (SWORD) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.3898569

Global River Obstruction

30549 Structures Mapped

By [source]

About this dataset

This incredible Global River Obstructions Database v1.1 (GROD v1.1) dataset contains detailed information about 30549 human-made obstructions that have been identified all over the world and are preventing the longitudinal flow of their respective rivers. Through extensive mapping on Google Earth Engine Satellite Maps, these obstructions have been classified into six different types - Dam, Lock, Low head dam, Channel dam, Partial dam 1 and Partial dam 2 - which provide a useful insight on identifying the cause of obstruction in a certain riverway. With this data set at hand it is possible to identify not only the location and type of each obstruction but also its distance from the nearest sword reach just with three simple columns: Type, Lon & Lat, Distance_to_Sword! Make sure to investigate further to maximize your understanding of global river obstructions and plan accordingly with this powerful tool of ours!

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How to use the dataset

The Global River Obstructions Database (GROD) v1.1 is a comprehensive dataset of 30549 human-made obstructions that hinder the longitudinal flow of global rivers. With this dataset, you can identify and analyze these river obstructions on an international scale. In this guide, we will show you how to properly access and use the GROD v1.1 database so that you can expand your research capabilities substantially!

Research Ideas

• To identify potential areas for sustainable hydropower generation and to develop new mitigation measures for existing obstacles that create unnecessary losses of river flow.
• To detect possible locations where removed obstructions could be relocated to so they can be used more efficiently and minimize their impact on the natural environment of the rivers, like maximizing fish migration or reducing sedimentation issues.
• To develop an automated system that can identify human-made obstructions from satellite images, using artificial intelligence (AI) and machine learning algorithms, in order to provide continuous monitoring of rivers worldwide

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: GROD_v1.1.csv | Column name | Description | |:----------------------|:-----------------------------------------------------------------------------------------------------| | type | The type of obstruction present in the river. (String) | | lon | The longitude coordinate of the obstruction. (Float) | | lat | The latitude coordinate of the obstruction. (Float) | | distance_to_sword | The estimated distance of an obstruction from its source of origin on a river's sword reach. (Float) |

File: grod_v1.0.csv | Column name | Description | |:----------------------|:-----------------------------------------------------------------------------------------------------| | type | The type of obstruction present in the river. (String) | | lon | The longitude coordinate of the obstruction. (Float) | | lat | The latitude coordinate of the obstruction. (Float) | | distance_to_sword | The estimated distance of an obstruction from its source of origin on a river's sword reach. (Float) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit .

This dataset contains a geospatial file that marks the locations of 561 river reaches across the continental USA. These river reaches have been examined and the proportion of the river with natural levees has been quantified at one of three ordinal levels: "abundant," "sparse," "absent." Each reach also has derived data from SWORD version 9 (Altenau et al. 2021), as well as other derived products from remote sensing datasets and the nearest USGS gage.

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. Water Resources Research, 57(7), e2021WR030054. https://doi.org/10.1029/2021WR030054

This is the location for Montana State University's Surface Water and Ocean Topography calibration data intended for the Surface Water and Ocean Topography Hydrology Community Discharge (SWOT SHCQ). The SWORD network version used here is v15. Discharge, depth, water surface elevation, velocity, cross-sectional area, width, measurements were taken with the Sontek M9 in conjunction with the Septentrio Polarx5 and processed with Qrev and RTKLIB respectively. Data was collected on the Yellowstone River, just north of Yellowstone National Park, and on the Rio San Pedro, the Rio Calle Calle, and the Rio Petrohue, in the south of Chile. Questions can be directed to trevor.wilkerson@student.montana.edu.

The repository contains supporting spatial data for the manuscript (under review). The Methods section detail how the data was processed and filtered.

Content:

1. AmazonMigrationRates.zip - River migration rates of 23,000 Amazon reaches, 1985-2020. This point layer includes the migration rates for each year (where the centerlines of subsequent years were valid in a reach), the long-term average migration rates for the entire period, the number and percentage of mapped years, and the SWORD¹ node ID and HydroSHEDS ² PFAF_ID.

2. AmazonCenterlines.zip - Amazonian river centerlines, 1984-2020. These are raster tiles of the centerlines, color-coded as in the manuscript figures.

3. AmazonRiverDeforestation.zip - raster tiles of 2000 forest areas removed by river migration or flooding during 2001-2020.

References

1. Altenau, E. H. et al. The Surface Water and Ocean Topography (SWOT) Mission River Database (SWORD): A Global River Network for Satellite Data Products. Water Resour Res 57, (2021).

2. Lehner, B. & Grill, G. Global river hydrography and network routing: Baseline data and new approaches to study the world’s large river systems. Hydrol Process 27, 2171–2186 (2013).

1. Summary:

This dataset contains water surface slopes (WSS) every kilometer along 11 Polish rivers derived from cross-calibrated multi-mission satellite altimetry (Schwatke et al. 2023a (in review). ). The approach to derive WSS is based on a weighted least-squares approach, which is described in detail in Schwatke et al. 2023b (in review).

2. Data Formats:

This dataset is provided in netCDF and shapefile formats. Each netCDF file contains the data of a single river and parameters such as river chainage, WSS, WSS error, location, and nearest centerline information from the SWORD database (v1.1, Altenau et al., 2021). The shapefile consists of five files (.cpg, .dbf, .prj, .shp, .shx) containing the data of the 11 Polish rivers. The attributes are identical to the netCDF, but the river name has been added.

3. Attribute Description:

The attributes of netCDFs and shapefiles are described in the following list:

• river_chainage: The river chainage describes the distance from the river mouth to the location of each bin along the river (units: km)

• wss: Water surface slopes (WSS) at each bin along the river. WSS are set to NaN/NULL for unprocessed lakes/reservoirs or short river segments (units: mm/km).

• wss_error: Errors of WSS at each bin along the river. WSS errors are set to NaN/NULL for unprocessed lakes/reservoirs or short river segments (units: mm/km).

• longitude: Longitude of the 1 km bins along the river (units: degree).

• latitude: Latitude of the 1 km bins along the river (units: degree).

• centerline_id: Nearest centerline id extracted from the SWORD database (v1.1, Altenau et al., 2021).

• node_id: Node id from the SWORD database (v1.1, Altenau et al., 2021) for the corresponding centerline id.

• reach_id: Reach id from the SWORD database (v1.1, Altenau et al., 2021) for the corresponding centerline id.

• river_name: The name of the river is only available in the Shapefile.

4. References:

Schwatke C., Dettmering D., Passaro M., Hart-Davis M., Scherer D., Müller F. L., Bosch W., Seitz F.: OpenADB: DGFI-TUM`s Open Altimeter Database. Geoscience Data Journal, 2023a (in Review)

Schwatke C., Halicki M., Scherer D.: Generation of high-resolution water surface slopes from multi-mission satellite altimetry. Water Resources Research, 2023b (in Review)

Altenau E.H., Pavelsky T.M., Durand M.T., Yang X., Frasson R.P.d.M., Bendezu L.: SWOT River Database (SWORD) (Version v1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.4917236, 2021

NOTE We are currently using SWORDv16 There is now a SWORD version field. Please update files accordingly. As we move forward, I will may users to request they update thier data to the correct reach and node IDs in newer versions of SWORD. This note will be updated to reflect the current version in Confluence processing. -

A gathering place for community hydrology data to be used in constraining and validating SWOT river discharge parameter estimates . How can you help? First find the .csv template in this collection and the associated readme file describing it (both 'TEST_Example Data' resources have it). Then construct your own resource with a csv file within. We request users submit CSVs following the template provided (SWOT_SHCQ_template.csv), and that they limit thier contributions to one such file that they update when needed. Leaving the resource unpublished allows users to update that file over time. Check manage access, and make sure your resources is set to public and sharable. Finally, use the SWOT_file_check_utility resource in this collection to verify that your file will be parsed correctly in confluence (instructions in that resources abstract). Then submit a link to Steve Coss via email coss.31@osu.edu. You may find that changes to your unpublished resource do not save after editing and closing the editor. This issue can be bypassed by clicking into the resource name field, entering any key stroke (even space) then deleting any new characters. Next click save changes. The resource contents should now be updated in the cache.

Contact: Md Safat Sikder (mssikder@illinois.edu), Jida Wang (jidaw@illinois.edu)

Citation

If you use Lake-TopoCat, please cite the following paper:

Sikder, M. S., Wang, J., Allen, G. H., Sheng, Y., Yamazaki, D., Song, C., Ding, M., Crétaux, J.-F., and Pavelsky, T. M., 2023. Lake-TopoCat: A global lake drainage topology and catchment dataset. Earth System Science Data, 15, 3483-3511, https://doi.org/10.5194/essd-15-3483-2023.

Data description and componentsThis version of Lake-TopoCat was constructed using the SWOT Prior Lake Database (PLD) v106 (Wang et al., 2023) lake mask and the 3-arc-second-resolution hydrography dataset MERIT Hydro v1.0.1 (Yamazaki et al., 2019). The drainage type of each PLD lake, such as isolated, inflow-headwater, headwater, flow-through, terminal, and coastal, was determined with assistance of MERIT Hydro-Vector (Lin et al., 2021), a high-resolution river network dataset with spatially-variable drainage densities.

For convenience, the global landmass (excluding Antarctica) was partitioned to 68 Pfafstetter Level-2 basins or regions, and the Lake-TopoCat data products were also organized based on these 68 regions, with their region or basin IDs shown in the Fig. 'Pfaf2_basins.jpg', attached to this database.

Lake-TopoCat consists of five feature components, each with multiple attributes depicting lake drainage relationships. The five features are:

1. Lake boundaries: polygons of 5,893,363 PLD lakes, larger than 1 ha.

   File name: Lakes_pfaf_xx where, 'pfaf_xx' indicates the Pfafstetter Level-2 basin ID (shown in Fig. 'Pfaf2_basins.jpg')

2. Lake outlets: points representing outlet or pour points of each individual lake. There are multiple outlets from a multifurcation lake. We identified 5,983,642 outlets for 5,893,363 lakes, where 83,819 lakes (~1.4% of the global lakes) show bi/multifurcation.

   File name: Outlets_pfaf_xx

3. Unit catchment: boundary polygons of catchment defining the drainage areas between cascading (i.e., immediately upstream and downstream) lake outlets. The count of unit catchments equal to the count of lake outlets, and bifurcation or multifurcation lakes have multiple local catchments. In total, the delineated catchments in Lake-TopoCat cover about 85.1 million km2, which is about 63% of the Earth’s land mass excluding the Antarctic.

   File name: Catchments_pfaf_xx

4. Inter-lake reaches: line features defining the drainage networks that connect the lake outlets to the inland sinks or the ocean. About 11 million connecting reaches were generated among ~6 million outlets. The total length of these inter-lake connecting reaches is ~19 million km, which is at least 8.75 times longer than the SWOT-visible river reaches as depicted in the SWOT River Database (SWORD) v16 (Altenau et al., 2021).

   File name: Reaches_pfaf_xx

5. Lake-network basins: boundary polygons of the entire drainage area containing each inter-lake network (i.e., a complete basin from the headwater to an inland sink or the ocean for all basins containing lakes). A total of 108,985 lake-network basins were identified. Among them, endorheic basins account for 2.75% by count and 19.5% by area of all lake-network basins. These endorheic basins cover ~17.5% of global surface excluding Antarctica.

   File name: Basins_pfaf_xx

The attribute tables for each of the feature components are explained in Section 4 of the product description document. For user convenience, we release the preliminary Lake-TopoCat lake outlets, unit catchments, and inter-lake reaches, with the affix '_prelim' in the file names (explained in the attached product description document). We also provide the polygon boundaries of the 68 Pfafstetter basins or regions in the file named 'Pfaf2_regions'. All files of Lake-TopoCat are available in both shapefile and geodatabase formats.

DisclaimerAuthors of this dataset claim no responsibility or liability for any consequences related to the use, citation, or dissemination of Lake-TopoCat.