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 Macrophytes Database (RMD) is a Microsoft Access database constructed to house data on the plant communities of rivers in Great Britain and Northern Ireland. It includes data from over 7000 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 data held in the RMD are the result of collaborative work across all four statutory nature conservation bodies: Scottish Natural Heritage (SNH), Natural England (NE), Natural Resources Wales (NRW, formerly CCW) and the Northern Ireland Environment Agency (NIEA). The River Macrophytes Database can be downloaded from the JNCC website: https://hub.jncc.gov.uk/assets/0a26368d-400c-44e1-beaf-d4b89b7badcd#extent-detail

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,

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).

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 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 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_v2.nc (i.e. na_sword_v2.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]_v2.shp (i.e. na_sword_nodes_hb74_v2.shp; na_sword_reaches_hb74_v2.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, 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: Maxim...

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 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.

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.

The River Sediment Database (RivSed) 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 contiguous USA that are ~60 meters wide or greater. SSC represent spatially integrated "reach" median concentrations over the footprint of NHDPlusV2 centerlines where high quality river water pixels were detected within each Landsat image from 1984-2018. This is built in the River Surface Reflectance database (RiverSR) also in Zenodo (Gardner et al,. 2020 Geophysical Research Letters).

The paper associated with RivSed: 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

Files:

1) Metadata (riverSed_v1.0_metadata.pdf): Description of all data files associated with this repository.

2) RiverSed (RiverSed_USA_v1.1.txt). Table of SSC and associated data that is joinable to nhdplusv2_modified_v1.0.shp based on the "ID" column and to the original NHDplusV2 flowlines with the "COMID" column.

3) Shapefile of river centerlines to which the reflectance data can be attached (nhdplusv2_modified_v1.0.shp).

4) Shapefile of the reach polygons associated with each nhdplusv2_modified reach. (nhdplusv2_polygons_v1.0.shp).

5) The look up table for reach IDs of original (COMID) and modified (ID) NHDplusV2 centerlines. (COMID_ID.csv). Short reaches were joined together to optimize for remote sensing data collection and make more consistent reach lengths.

6) SSC-Landsat matchup database with extended metadata on locations and in-situ data derived from Aquasat (Ross et al., 2019) (Aquasat_TSS_v1.1.csv)

7) The final training data used to build the xgboost machine learning model (train_clean_xgb_v1.1.csv)

8) The xgboost model that can make SSC predictions over inland waters in USA using Landsat bands/band combinations (finalmodel_xgb_v1.1.rds and .RData). The model can only be loaded in R for now.

A major problem related to large-scale water quality modeling has been the lack of available observation data with a good spatiotemporal coverage. This has affected the reproducibility of previous studies and the potential improvement of existing models. In addition to the observation data itself, insufficient or poor quality metadata has also discouraged researchers to integrate the already available datasets. Therefore, improving both the availability and quality of open water quality data woould increase the potential to implement predictive modeling on a global scale. We aim to address the aforementioned issues by presenting the new Global River Water Quality Archive (GRQA) by integrating data from five existing global and regional sources: Canadian Environmental Sustainability Indicators program (CESI), Global Freshwater Quality Database (GEMStat), GLObal RIver Chemistry database (GLORICH), European Environment Agency (Waterbase) and USGS Water Quality Portal (WQP). The resulting dataset covering the timeframe 1898 - 2020 contains a total of over 17 million observations for 42 different forms of some of the most important water quality parameters, focusing on nutrients, carbon, oxygen and sediments. Supplementary metadata and statistics are provided with the observation time series to improve the usability of the dataset.

Last update: 2022-03-11

GRQA_v1.2 contains three updated files compared to GRQA_v1.1:

• NH4N_GRQA.csv
• NO2N_GRQA.csv
• NO3N_GRQA.csv

The files were updated, because the assumed conversion constants used for the corresponding GLORICH observations were found to be incorrect. The corresponding files in GRQA_figures.zip and GRQA_meta.zip are yet to be updated, but will be in GRQA_v1.3.

The explanation for the updated conversion constants is given in this notebook:
https://nbviewer.org/github/LandscapeGeoinformatics/GRQA_src/blob/main/testing/glorich_conversion_test.ipynb

An overview of all the files in the dataset can be found in README_v1.2.txt.

Statistical overview of all 42 parameters is given in the data catalog file GRQA_data_catalog.pdf.

For more information about the development of this dataset look for Virro, H., Amatulli, G., Kmoch, A., Shen, L., and Uuemaa, E.: GRQA: Global River Water Quality Archive, Earth Syst. Sci. Data, 13, 5483–5507, https://doi.org/10.5194/essd-13-5483-2021, 2021.

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.

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.

The Global River Topology (GRIT) is a vector-based, global river network that not only represents the tributary components of the global drainage network but also the distributary ones, including multi-thread rivers, canals and delta distributaries. It is also the first global hydrography (excl. Antarctica and Greenland) produced at 30m raster resolution. It is created by merging Landsat-based river mask (GRWL) with elevation-generated streams to ensure a homogeneous drainage density outside of the river mask (rivers narrower than approx. 30m). Crucially, it uses a new 30m digital terrain model (FABDEM, based on TanDEM-X) that shows greater accuracy over the traditionally used SRTM derivatives. After vectorisation and pruning, directionality is assigned by a combination of elevation, flow angle, heuristic and continuity approaches (based on RivGraph). The network topology (lines and nodes, upstream/downstream IDs) is available as layers and attribute information in the GeoPackage files (readable by QGIS/ArcMap/GDAL).

Regions

Vector files are provided in 6 continental regions with the following codes:

• AF - Africa
• AS - Asia
• EU - Europe
• NA - North America
• SA - South America
• SP - South Pacific/Australia

The domain polygons (GRITv04_domain_GLOBAL.gpkg.zip) provide 60 subcontinental catchment groups that are available as vector attributes. They allow for more fine-grained subsetting of data (e.g. with ogr2ogr --where).

Network segments

Lines between inlet, outlet, confluence and bifurcation nodes. Files have lines and nodes layers.

Attribute description of lines layer

Name	Data type	Description
cat	integer	domain internal feature ID
global_id	integer	global river segment ID, same as FID
catchment_id	integer	global catchment ID
upstream_node_id	integer	global segment node ID at upstream end of line
downstream_node_id	integer	global segment node ID at downstream end of line
upstream_line_ids	text	comma-separated list of global river segment IDs connecting at upstream end of line
downstream_line_ids	text	comma-separated list of global river segment IDs connecting at downstream end of line
direction_algorithm	float	code of RivGraph method used to set the direction of line
width_adjusted	float	median river width in m without accounting for width of segments connecting upstream/downstream
length_adjusted	float	segment length in m without accounting for width of segments connecting upstream/downstream in m
is_mainstem	integer	1 if widest segment of bifurcated flow or no bifurcation upstream, otherwise 0
cycle	integer	>0 if segment is part of an unresolved cycle, 0 otherwise
length	float	segment length in m
azimuth	float	direction of line connecting upstream-downstream nodes in degrees from North
sinuous	float	ratio of line length and Euclidean distance between upstream-downstream nodes, i.e. 1 meaning a perfectly straight line
domain	text	catchment group ID, see domain index file

Attribute description of nodes layer

Name	Data type	Description
cat	integer	domain internal feature ID
global_id	integer	global river node ID, same as FID
catchment_id	integer	global catchment ID
upstream_line_ids	text	comma-separated list of global river segment IDs flowing into node
downstream_line_ids	text	comma-separated list of global river segment IDs flowing out of node
node_type	text	description of node, one of bifurcation, confluence, inlet, coastal_outlet, sink_outlet, grwl_change
grwl_value	integer	GRWL code at node
grwl_transition	text	GRWL codes of change at grwl_change nodes
cycle	integer	>0 if segment is part of an unresolved cycle, 0 otherwise
continuity_violated	integer	1 if flow continuity is violated, otherwise 0
domain	text	catchment group, see domain index file

Network reaches

Segment lines split to not exceed 1km in length, i.e. these lines will be shorter than 1km and longer than 500m unless the segment is shorter. A simplified version with no vertices between nodes is also provided. Files have lines and nodes layers.

Attribute description of lines layer

Name	Data type	Description
cat	integer	domain internal feature ID
segment_id	integer	global segment ID of reach
global_id	integer	global river reach ID, same as FID
catchment_id	integer	global catchment ID
upstream_node_id	integer	global reach node ID at upstream end of line
downstream_node_id	integer	global reach node ID at downstream end of line
upstream_line_ids	text	comma-separated list of global river reach IDs connecting at upstream end of line
downstream_line_ids	text	comma-separated list of global river reach IDs connecting at downstream end of line
length	float	length of reach in m
sinuousity	float	ratio of line length and Euclidian distance between upstream-downstream nodes, i.e. 1 meaning a perfectly straight line
azimuth	float	direction of line connecting upstream-downstream nodes in degrees from North
domain	text	catchment group, see domain index file

Attribute description of nodes layer

Name	Data type	Description
cat	integer	domain internal feature ID
segment_node_id	integer	global ID of segment node at segment intersections, otherwise blank
n_segments	integer	number of segments attached to node
global_id	integer	global river reach node ID, same as FID
upstream_line_ids	text	comma-separated list of global river reach IDs flowing into node
downstream_line_ids	text	comma-separated list of global river reach IDs flowing out of node
domain	text	catchment group, see domain index file

Catchments

Catchment outlines for entire river basins (network components, including coastal drainage areas), segments (aka. subbasins) and reaches.

Attribute description

Name	Data type	Description
cat	integer	domain internal feature ID
global_id	integer	global catchment ID, same as global_id of segment/reach ID if is_coastal == 0 for respective catchments or the catchment_id for component_catchments, same as FID
area	float	catchment area in km2
is_coastal	integer	1 for coastal drainage areas, 0 otherwise
domain	text	catchment group, see domain index file

Raster

Upstream drainage area, flow direction and other raster-based products are also available upon request.

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

Global prevalence of non-perennial rivers and streamsJune 2021prepared by Mathis L. Messager (mathis.messager@mail.mcgill.ca)Bernhard Lehner (bernhard.lehner@mcgill.ca)1. Overview and background 2. Repository content3. Data format and projection4. License and citations4.1 License agreement4.2 Citations and acknowledgements1. Overview and backgroundThis documentation describes the data produced for the research article: Messager, M. L., Lehner, B., Cockburn, C., Lamouroux, N., Pella, H., Snelder, T., Tockner, K., Trautmann, T., Watt, C. & Datry, T. (2021). Global prevalence of non-perennial rivers and streams. Nature. https://doi.org/10.1038/s41586-021-03565-5In this study, we developed a statistical Random Forest model to produce the first reach-scale estimate of the global distribution of non-perennial rivers and streams. For this purpose, we linked quality-checked observed streamflow data from 5,615 gauging stations (on 4,428 perennial and 1,187 non-perennial reaches) with 113 candidate environmental predictors available globally. Predictors included variables describing climate, physiography, land cover, soil, geology, and groundwater as well as estimates of long-term naturalised (i.e., without anthropogenic water use in the form of abstractions or impoundments) mean monthly and mean annual flow (MAF), derived from a global hydrological model (WaterGAP 2.2; Müller Schmied et al. 2014). Following model training and validation, we predicted the probability of flow intermittence for all river reaches in the RiverATLAS database (Linke et al. 2019), a digital representation of the global river network at high spatial resolution.The data repository includes two datasets resulting from this study:1. a geometric network of the global river system where each river segment is associated with:i. 113 hydro-environmental predictors used in model development and predictions, andii. the probability and class of flow intermittence predicted by the model.2. point locations of the 5,516 gauging stations used in model training/testing, where each station is associated with a line segment representing a reach in the river network, and a set of metadata.These datasets have been generated with source code located at messamat.github.io/globalirmap/.Note that, although several attributes initially included in RiverATLAS version 1.0 have been updated for this study, the dataset provided here is not an established new version of RiverATLAS. 2. Repository contentThe data repository has the following structure (for usage, see section 3. Data Format and Projection; GIRES stands for Global Intermittent Rivers and Ephemeral Streams):— GIRES_v10_gdb.zip/ : file geodatabase in ESRI® geodatabase format containing two feature classes (zipped) |——— GIRES_v10_rivers : river network lines |——— GIRES_v10_stations : points with streamflow summary statistics and metadata— GIRES_v10_shp.zip/ : directory containing ten shapefiles (zipped) Same content as GIRES_v10_gdb.zip for users that cannot read ESRI geodatabases (tiled by region due to size limitations). |——— GIRES_v10_rivers_af.shp : Africa |——— GIRES_v10_rivers_ar.shp : North American Arctic |——— GIRES_v10_rivers_as.shp : Asia |——— GIRES_v10_rivers_au.shp : Australasia|——— GIRES_v10_rivers_eu.shp : Europe|——— GIRES_v10_rivers_gr.shp : Greenland|——— GIRES_v10_rivers_na.shp : North America|——— GIRES_v10_rivers_sa.shp : South America|——— GIRES_v10_rivers_si.shp : Siberia|——— GIRES_v10_stations.shp : points with streamflow summary statistics and metadata— Other_technical_documentations.zip/ : directory containing three documentation files (zipped)|——— HydroATLAS_TechDoc_v10.pdf : documentation for river network framework|——— RiverATLAS_Catalog_v10.pdf : documentation for river network hydro-environmental attributes|——— Readme_GSIM_part1.txt : documentation for gauging stations from the Global Streamflow Indices and Metadata (GSIM) archive— README_Technical_documentation_GIRES_v10.pdf : full documentation for this repository3. Data format and projectionThe geometric network (lines) and gauging stations (points) datasets are distributed both in ESRI® file geodatabase and shapefile formats. The file geodatabase contains all data and is the prime, recommended format. Shapefiles are provided as a copy for users that cannot read the geodatabase. Each shapefile consists of five main files (.dbf, .sbn, .sbx, .shp, .shx), and projection information is provided in an ASCII text file (.prj). The attribute table can be accessed as a stand-alone file in dBASE format (.dbf) which is included in the Shapefile format. These datasets are available electronically in compressed zip file format. To use the data files, the zip files must first be decompressed.All data layers are provided in geographic (latitude/longitude) projection, referenced to datum WGS84. In ESRI® software this projection is defined by the geographic coordinate system GCS_WGS_1984 and datum D_WGS_1984 (EPSG: 4326).4. License and citations4.1 License agreement This documentation and datasets are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License (CC-BY-4.0 License). For all regulations regarding license grants, copyright, redistribution restrictions, required attributions, disclaimer of warranty, indemnification, liability, waiver of damages, and a precise definition of licensed materials, please refer to the License Agreement (https://creativecommons.org/licenses/by/4.0/legalcode). For a human-readable summary of the license, please see https://creativecommons.org/licenses/by/4.0/.4.2 Citations and acknowledgements.Citations and acknowledgements of this dataset should be made as follows:Messager, M. L., Lehner, B., Cockburn, C., Lamouroux, N., Pella, H., Snelder, T., Tockner, K., Trautmann, T., Watt, C. & Datry, T. (2021). Global prevalence of non-perennial rivers and streams. Nature. https://doi.org/10.1038/s41586-021-03565-5 We kindly ask users to cite this study in any published material produced using it. If possible, online links to this repository (https://doi.org/10.6084/m9.figshare.14633022) should also be provided.

Quality characteristics for 21586 river flow time series from 13 datasets worldwide. The 13 datasets are: the Global Runoff Database from the Global Runoff Data Center (GRDC), the Global River Discharge Data (RIVDIS; Vörösmarty et al., 1998), Surface-Water Data from the United States Geological Survey (USGS), HYDAT from the Water Survey of Canada (WSC), WISKI from the Swedish Meteorological and Hydrological Institute (SMHI), Hidroweb from the Brazilian National Water Agency (ANA), National data from the Australian Bureau of Meteorology (BOM), Spanish river flow data from the Ecological Transition Ministry (Spain), R-ArcticNet v. 4.0 from the Pan-Arctic Project Consortium (R-ArcticNet), Russian River data (NCAR-UCAR; Bodo, 2000), Chinese river flow data from the China Hydrology Data Project (CHDP; Henck et al., 2010, 2011), the European Water Archive from GRDC - EURO-FRIEND-Water (EWA), and the GEWEX Asian Monsoon Experiment (GAME) – Tropics dataset provided by the Royal Irrigation Department of Thailand. Quality characteristics are based on availability, outliers, homogeneity and trends: overall availability (%), longest availability (%), continuity (%), monthly availability (%), outliers ratio (%), homogeneity of annual flows (number of statistical tests agreeing), trend in annual flows, trend in one month of the year.

Bodo, B. (2000) Russian River Flow Data by Bodo. Boulder CO: Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory. Retrieved from http://rda.ucar.edu/datasets/ds553.1/

Henck, A. C., Huntington, K. W., Stone, J. O., Montgomery, D. R. & Hallet, B. (2011) Spatial controls on erosion in the Three Rivers Region, southeastern Tibet and southwestern China. Earth and Planetary Science Letters 303(1–2), 71–83. doi:10.1016/j.epsl.2010.12.038

Henck, A. C., Montgomery, David R., Huntington, K. W. & Liang, C. (2010) Monsoon control of effective discharge, Yunnan and Tibet. Geology 38(11), 975–978. doi:10.1130/G31444.1

Vörösmarty, C. J., Fekete, B. M. & Tucker, B. A. (1998) Global River Discharge, 1807-1991, V[ersion]. 1.1 (RivDIS). doi:10.3334/ornldaac/199

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 ...

This theme offers detailed information on lakes and waterways throughout Quebec. All the descriptors available in this layer come directly from the Lakes and Rivers (LCE) database. The data includes lake centroids and stream junctions and includes information on lake morphology such as length, width, depth, volume, and elevation, as well as the area of watersheds. This data is intended for researchers, engineers, government agencies, government agencies, environmental professionals, as well as students and industries, for applications in the environment, hydrology, and hydraulics.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

These macro-invertebrate data incorporate the results from the national river water quality network (NRWQN) from 66 sites throughout New Zealand for the purpose of monitoring long-term trends. Data included: 1990 to 2008. The NRWQN was funded by the Foundation for Research, Science, & Technology through NIWA's Nationally Significant Database: Water Resources & Climate programme. Current funding (from July 2011) comes from the NIWA Environmental Information/Monitoring programme core funding. The data are collected annually in summer, and data collection was initiated in January 1989.