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.

This dataset is public for a manuscript under review in Water Resources Research (WRR).

2. Data Formats:

The SWORD database is provided in netCDF 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_v1.nc (i.e. na_sword_v1.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]_v1.shp (i.e. na_sword_nodes_hb74_v1.shp; na_sword_reaches_hb74_v1.shp).

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, 2 – lake off 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, 2 – lake off 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).
• 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 (reach files only): GRWL water body identifier for each reach: 0 – river, 1 – lake/reservoir, 2 – tidally influenced river, 3 – canal.
• 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): The maximum number of SWOT passes to intersect each reach during the 21 day orbit cycle.
• swot_orbits (reach files only): A list of the SWOT orbit tracks that intersect each reach during the 21 day orbit cycle.

4. References:

Allen, G. H., & Pavelsky, T. M. (2018). Global extent of rivers and streams. Science, 361(6402), 585-588.

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

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.

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. Earth's Future, 8(11), e2020EF001558.

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. https://doi.org/10.1029/2019WR024873.

HydroFALLS: http://wp.geog.mcgill.ca/hydrolab/hydrofalls/

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/).

American Rivers’ Dam Removal Database includes all dam removals in the United States (of which we have been made aware) in which a significant portion of the dam has been removed for the full height of the dam, such that ecological function, natural river flow and fish passage can be restored at the site. This database is revised and updated annually with information provided by contributors across the country. The database may be used by anyone provided that citation is given to American Rivers and the DOI link is included.

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,

Supporting datasets for Allen et al. (2018) - Global Estimates of River Flow Wave Travel Times and Implications for Low-Latency Satellite Data, Geophysical Research Letters, https://doi.org/10.1002/2018GL077914

The code used to produce these data is available as a Github repository, permanently hosted on Zenodo: https://doi.org/10.5281/zenodo.1219784

Abstract

Earth-orbiting satellites provide valuable observations of upstream river conditions worldwide. These observations can be used in real-time applications like early flood warning systems and reservoir operations, provided they are made available to users with sufficient lead time. Yet, the temporal requirements for access to satellite-based river data remain uncharacterized for time-sensitive applications. Here we present a global approximation of flow wave travel time to assess the utility of existing and future low-latency/near-real-time satellite products, with an emphasis on the forthcoming SWOT satellite. We apply a kinematic wave model to a global hydrography dataset and find that global flow waves traveling at their maximum speed take a median travel time of 6, 4 and 3 days to reach their basin terminus, the next downstream city and the next downstream dam respectively. Our findings suggest that a recently-proposed ≤2-day latency for a low-latency SWOT product is potentially useful for real-time river applications.

Description of repository datasets:

1. riverPolylines.zip contains ESRI shapefile polylines of river networks with outputs from main analysis. These continental-scale shapefiles contain the following attributes for each river segment:

"ARCID" : unique identifier for each river segment line, defined as the river reach between river junctions/heads/mouths. The first 10 attributes are taken from Andreadis et al. (2013): https://doi.org/10.5281/zenodo.61758

"UP_CELLS" : number of upstream cells (pixels)

"AREA" : upstream drainage area (km2)

"DISCHARGE" : discharge (m3/s)

"WIDTH" : mean bankfull river width (m)

"WIDTH5" : 5th percentile confidence interval bankfull river width (m)

"WIDTH95" : 95th percentile confidence interval bankfull river width (m)

"DEPTH" : mean bankfull river depth (m)

"DEPTH5" : 5th percentile bankfull river depth (m)

"DEPTH95" : 95th percentile confidence bankfull river depth (m)

"LENGTH_KM" : segment length (km)

"ORIG_FID" : original ID of segment

"ELEV_M" : lowest elevation of segment (m). Derived from HydroSHEDS 15 sec hydrologically conditioned DEM: https://hydrosheds.cr.usgs.gov/datadownload.php?reqdata=15demg

"POINT_X" : longitude of lowest point of segment (WGS84, decimal degrees)

"POINT_Y" : latitude of lowest point of segment (WGS84, decimal degrees)

"SLOPE" : average slope of segment (m/m)

"CITY_JOINS" : an index associated with how likely a city/population center is located on the segment. Population center data from: http://web.ornl.gov/sci/landscan/ and http://www.naturalearthdata.com/downloads/10m-cultural-vectors/10m-populated-places/

"CITY_POP_M" : population of joined city (max N inhabitants)

"DAM_JOINSC" : an index associated with how likely a dam is located on the segment. Dam data from Global Reservoir and Dam (GRanD) Database: http://www.gwsp.org/products/grand-database.html

"DAM_AREA_S" : surface area of joined dam (m2)

"DAM_CAP_MC" : volumetric capacity of joined dam (m3)

"CELER_MPS" : modeled river flow wave celerity (m/s)

"PROPTIME_D" : travel time of flow wave along segment (days)

"hBASIN" : main basin UID for the hydroBASINS dataset: http://www.hydrosheds.org/page/hydrobasins

"GLCC" : Global Land Cover Characterization at segment centroid: https://lta.cr.usgs.gov/glcc/globdoc2_0

"FLOODHAZAR" : flood hazard composite index from the DFO (via NASA Sedac): http://sedac.ciesin.columbia.edu/data/set/ndh-flood-hazard-frequency-distribution

"SWOT_TRAC_" : SWOT track density (N overpasses per orbit cycle @ segment centroid). Created using SWOTtrack SWOTtracks_sciOrbit_sept15 polygon shapefile, uploaded here.

"UPSTR_DIST" : upstream distance to the basin outlet (km)

"UPSTR_TIME" : upstream flow wave travel time to the basin outlet (days)

"CITY_UPSTR" : upstream flow wave travel time to the next downstream city (days)

"DAM_UPSTR_" : upstream flow wave travel time to the next downstream dam (days)

"MC_WIDTH" : mean of Monte Carlo simulated bankfull widths (m)

"MC_DEPTH" : mean of Monte Carlo simulated bankfull depths (m)

"MC_LENCOR" : mean of Monte Carlo simulated river length correction (km)

"MC_LENGTH" : mean of Monte Carlo simulated river length (m)

"MC_SLOPE" : mean of Monte Carlo simulated river slope (-)

"MC_ZSLOPE" : mean of Monte Carlo simulated minimum slope threshold (m)

"MC_N" : mean of Monte Carlo simulated Manning’s n (s/m^(1/3))

"CONTINENT" : integer indicating the HydroSHEDS region of shapefile

1. hydrosheds_connectivity.zip contains network connectivity CSVs for river polyline shapefiles. The tables do not contain headers:

Col1: segment unique identifier (UID) corresponding to the ARCID column of the riverPolylines shapefiles

Col2: Downstream UID

Col3: Number of upstream UIDs

Col4 – Col12: Upstream UIDs

1. SWOTtracks_sciOrbit_sept15_density.zip contains a polygon shapefile derived from SWOTtracks_sciOrbit_sept15_completeOrbit containing the sampling frequency of SWOT (number of observations per complete orbit cycle). Polygon attributes correspond to each unique shape formed from overlapping swaths:

FID : unique identifier of each polygon

CENTROID_X : polygon centroid longitude (WGS84 - decimal degrees)

CENTROID_Y : polygon centroid latitude (WGS84 - decimal degrees)

COUNT_count: SWOT sampling frequency (N observations per complete orbit cycle)

1. USGS_gauge_site_information.csv : table containing the list of USGS sites analyzed in the validation and obtained from http://nwis.waterdata.usgs.gov/nwis/dv Header descriptions contained within table.

2. validation_gaugeBasedCelerity.zip contains polyline ESRI shapefiles covering North and Central America, where USGS gauges provided gauge-based celerity estimates. These files have FIDs and attributes corresponding to riverPolylines shapefiles described above and also contrain the folllowing fields:

GAUGE_JOIN : an index associated with how likely a gauge is located on the segment. Gauge location information is contained in USGS_gauge_site_information.csv

GAUGE_SITE: USGS gauge site number of joined gauge

GAUGE_HUC8: which hydrological unit code the gauge is located in

OBS_CEL_R: gauge-based correlation score (R). Upstream and downstream gauges were compared via lagged cross correlation analysis. The calculated celerity between the paired gauges were assigned to each segment between the two gauges. If there were multiple pairs of upstream and downstream gauges, the the mean celerity value was assigned, weighted by the quality of the correlation, R. Same weighted mean was applied in assigning R.

OBS_CEL_MPS: gauge-based celerity estimate (m/s).

1. tab1_latencies.csv contains data shown in Table 1 of the manuscript.

2. figS3S4_monteCarloSim_global_runMeans.csv contains the mean of the Monte Carlo simulation inputs and outputs shown in Figure S3 and Figure S4. Column headers descriptions are given in riverPolylines (dataset #1 above). Some columns have rows with all the same value because these variables did not vary between ensemble runs.

3. figS5_travelTimeEnsembleHistograms.zip contains data shown in Figure S5. Each csv corresponds to a figure component:

tabdTT_b.csv : basin outlet travel times for all rivers

tabdTT_b_swot.csv : basin outlet travel times for SWOT

tabdTT_c.csv : next downstream city travel times for all rivers

tabdTT_c_swot.csv : next downstream city travel times for SWOT

tabdTT_d.csv : next downstream dam travel times for all rivers

tabdTT_d_swot.csv : next downstream dam travel times for SWOT

The Water Level is defined as the height, in meters above the geoid, of the reflecting surface of continental water bodies. It is observed by space radar altimeters that measure the time it takes for radar pulses to reach the ground targets, directly below the spacecraft (nadir position), and return. Hence, only water bodies located along the satellite's ground tracks can be monitored, with a quality of measurement that not only depends of the size of the water body, but also on the reflecting targets in its surroundings such as topography or vegetation. Water Level is computed as time series: over lakes ; over rivers, at the intersections of the river network with the satellite ground tracks, so-called Virtual Stations. The Water Level of lakes is recognized as an Essential Climate Variable (ECV) by the Global Climate Observing System (GCOS).

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.

1. 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

2. 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.

3. 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

4. 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.

DisclaimerAuthors 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.

Hydrographic and Impairment Statistics (HIS) is a National Park Service (NPS) Water Resources Division (WRD) project established to track certain goals created in response to the Government Performance and Results Act of 1993 (GPRA). One water resources management goal established by the Department of the Interior under GRPA requires NPS to track the percent of its managed surface waters that are meeting Clean Water Act (CWA) water quality standards. This goal requires an accurate inventory that spatially quantifies the surface water hydrography that each bureau manages and a procedure to determine and track which waterbodies are or are not meeting water quality standards as outlined by Section 303(d) of the CWA. This project helps meet this DOI GRPA goal by inventorying and monitoring in a geographic information system for the NPS: (1) CWA 303(d) quality impaired waters and causes; and (2) hydrographic statistics based on the United States Geological Survey (USGS) National Hydrography Dataset (NHD). Hydrographic and 303(d) impairment statistics were evaluated based on a combination of 1:24,000 (NHD) and finer scale data (frequently provided by state GIS layers).

This data set contains small-scale base GIS data layers compiled by the National Park Service Servicewide Inventory and Monitoring Program and Water Resources Division for use in a Baseline Water Quality Data Inventory and Analysis Report that was prepared for the park. The report presents the results of surface water quality data retrievals for the park from six of the United States Environmental Protection Agency's (EPA) national databases: (1) Storage and Retrieval (STORET) water quality database management system; (2) River Reach File (RF3) Hydrography; (3) Industrial Facilities Discharges; (4) Drinking Water Supplies; (5) Water Gages; and (6) Water Impoundments. The small-scale GIS data layers were used to prepare the maps included in the report that depict the locations of water quality monitoring stations, industrial discharges, drinking intakes, water gages, and water impoundments. The data layers included in the maps (and this dataset) vary depending on availability, but generally include roads, hydrography, political boundaries, USGS 7.5' minute quadrangle outlines, hydrologic units, trails, and others as appropriate. The scales of each layer vary depending on data source but are generally 1:100,000.

The description for this record is not currently available.

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

The JNCC River Macrophytes Database (RMD) is a Microsoft Access database constructed to house data on the plant communities of rivers in Great Britain and Northern Ireland. An extract in Excel format is also provided. The RMD includes data from over 7,000 survey sites and is the most comprehensive database of its kind. Data have been collected from all over the UK between 1977 and the present day, following the methods of Holmes et al. (1999). The macrophyte survey method records aquatic and marginal plants in a 500 m-long survey section of river. Species from the river channel and the margins/base of the bank are recorded separately on a three-point scale of relative abundance and percentage cover. A standard check-list of species is used to aid recording. The field data can be used to classify the plant community as described by Holmes et al. (1999), and the database has a facility that allows keying-out of the community to sub-type level. This plant community classification has been used as the basis of river SSSI selection (see chapter 6 of Guidelines for Selection of Biological SSSIs). The database also holds a small amount of fish data. The RMD is an ‘active’ database (i.e. survey records can still be added). However, with a new standard method of river plant survey now being adopted by the UK conservation agencies (i.e. the LEAFPACS method), it is likely that less surveys will be added in future, and it may develop into more of a 'legacy' database. In 2011 the RMD was made available through the JNCC website with some restrictions on re-use. In June 2018, JNCC re-published the RMD as open data under the Open Government Licence. In November 2019 the invertebrate survey records were removed whilst some of these records underwent further validity checks. Background information on the classification system: Holmes, N.T, Boon, P.J., & Rowell, T.A. Vegetation communities of British rivers - a revised classification (1999) Link to the Vegetation communities of British rivers on the JNCC Resource hub: https://hub.jncc.gov.uk/assets/a974944a-3cd4-4574-9c1a-c977d482c0ed

The dataset presents 687 rivers associated to 405 Major River Basins.Data was collected within the framework of the BGR-UNESCO "World-wide Hydrogeological Mapping and Assessment Programme" (WHYMAP): www.whymap.org

The SWOT Level 2 River Single-Pass Vector Reach Data Product from the Surface Water Ocean Topography (SWOT) mission provides water surface elevation, slope, width, and discharge derived from the high rate (HR) data stream from the Ka-band Radar Interferometer (KaRIn). SWOT launched on December 16, 2022 from Vandenberg Air Force Base in California into a 1-day repeat orbit for the "calibration" or "fast-sampling" phase of the mission, which completed in early July 2023. After the calibration phase, SWOT entered a 21-day repeat orbit in August 2023 to start the "science" phase of the mission, which is expected to continue through 2025. Water surface elevation, slope, width, and discharge are provided for river reaches (approximately 10 km long) and nodes (approximately 200 m spacing) identified in the prior river database, and distributed as feature datasets covering the full swath for each continent-pass. These data are generally produced for inland and coastal hydrology surfaces, as controlled by the reloadable KaRIn HR mask. The dataset is distributed in ESRI Shapefile format. This collection is a sub-collection of its parent: https://podaac.jpl.nasa.gov/dataset/SWOT_L2_HR_RiverSP_2.0 It contains only river reaches.

This resource references a large temperature database developed for the western U.S. that contains records for >23,000 unique stream and river sites and consists of data contributed by hundreds of professionals working for dozens of natural resource agencies. All the records have been QA/QC’d and linked to the National Hydrography Dataset and fully documented with metadata for easy use. The website describing the NorWeST project and serving the data is here (https://www.fs.fed.us/rm/boise/AWAE/projects/NorWeST.html). The publication, The NorWeST Summer Stream Temperature Model and Scenarios for the Western U.S.: A Crowd‐Sourced Database and New Geospatial Tools Foster a User Community and Predict Broad Climate Warming of Rivers and Streams can be found here: https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2017WR020969

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 repository contains supplementary data and technical documentation for the research article by Grill, G., Lehner, B., Thieme, M., Geenen, B., Tickner, D., Antonelli, F., Babu, S., Borrelli, P., Cheng, L., Crochetiere, H., Ehalt Macedo, H., Filgueiras, R., Goichot, M., Higgins, J., Hogan, Z., Lip, B., McClain, M., Meng, J., Mulligan, M., Nilsson, C., Olden, J.D., Opperman, J., Petry, P., Reidy Liermann, C., Saenz, L., Salinas-Rodríguez, S., Schelle, P., Schmitt, R.J.P., Snider, J., Tan, F., Tockner, K., Valdujo, P.H., van Soesbergen, A., Zarfl, C. (2019) Mapping the world's free-flowing rivers. Nature. https://doi.org/10.1038/s41586-019-1111-9.

This database was developed for the Water Availability Tool for Environmental Resources (WATER) for the Delaware River Basin (DRB), a decision support tool that provides a consistent and objective method of simulating streamflow under historical, forecasted, and managed conditions (Williamson and others, 2015). This database provides historical spatial and climatic data for simulating streamflow for 2001–11, in addition to land-cover forecasts and general circulation model (global climate model; GCM) projections that focus on 2030 and 2060. The database provides for geospatial sampling, at a 10-30 m resolution, of landscape characteristics, including topographic and soil properties, land cover and impervious surface, water use, and GCM change factors for precipitation, temperature, and a radiation-based potential evapotranspiration. These data are available as a cohesive unit, that provides the file structure required by the hydrologic tool, in addition to some layers being provi ...

The GEMS-GLORI register, circulated by UNEP for review in 1996, lists 555 world major rivers discharging to oceans (Q > 10 km**3/year, or A > 10 000 km**2, or sediment discharge > 5Mt/year, or basin population >5M people). Up to 48 river attributes are listed, including major ions and nutrients (C, N, P) in both dissolved, particulate, organic and inorganic forms. For many rivers, two or three sets of data are provided with relevant periods of records and references. Although half of the selected rivers are not yet documented for water quality, most of the first 40 rivers are well described (Irrawady, Zambezi, Ogooue, Magdalena, are noted exceptions). Altogether about 10 000 individual data from 500 references are listed. The global coverage in terms of river discharge and/or drainage area ranges from 40 to 67% for most major water quality attributes but drops to 25% for some organic and/or particulate forms of N and P. Planned development of the register includes collection of information on particulate chemistry and data on endorheic rivers and selected tributaries.

Species Groups recorded: liverwort, insect - true bug (Hemiptera), bony fish (Actinopterygii), insect - beetle (Coleoptera), insect - caddis fly (Trichopte, terrestrial mammal, insect - butterfly, insect - mayfly (Ephemeroptera, crustacean, annelid, mollusc, flowering plant, insect - dragonfly (Odonata), moss, insect - stonefly (Plecoptera), amphibian Dataset Status: Contains all species level data collected as part of the survey. Data is unpublished Additional Information: Background on the river monitoring programme is provided on the EPA's websites http://www.epa.ie/ .hidden { display: none }