HydroBASINS is a series of polygon layers that depict watershed boundaries and sub-basin delineations at a global scale. The goal of this product is to provide a seamless global coverage of consistently sized and hierarchically nested sub-basins at different scales (from tens to millions of square kilometers), supported by a coding scheme that allows for analysis of watershed topology such as up- and downstream connectivity

Water quality is largely reflective of processes occurring on the surrounding landscape. While national landcover data are widely available via remotely sensed products, they are usually not aggregated in a manner that is expeditiously merged with basin-level data. To facilitate national-scale analyses of basin-level landcover with co-located water quality data, we present aggregated land cover data for the Contiguous United States. Data are aggregated using the HydroBASINS basin shapefiles. HYBAS_ID is retained to enable merging with HydroBASINS parent datasets.

Water quality is largely reflective of processes occurring on the surrounding landscape. While terrestrial inputs often strongly influence water quality, atmospheric deposition can be a significant source of allochthonous constituents to aquatic ecosystems. In the Contiguous United States, the National Atmospheric Deposition Program (NADP) has been collecting in situ atmospheric deposition of several key ions for decades. However, merging these data with co-located aquatic data is challenging. To facilitate national-scale analyses of basin-level atmospheric deposition of sulfate, ammonium, nitrate, and hydrogen with co-located water quality data, we present aggregated atmospheric deposition data for the Contiguous United States. Data are aggregated using the HydroBASINS basin shapefiles. HYBAS_ID is retained to enable merging with HydroBASINS parent datasets.

This is a work in progress with data for accessing data for drought index (standard precipitation index) on the Nile.

WMS Resources for layers: Myanmar Hydrobasins

Shape files of global hexagons analyzed based on level-12 basins from HydroBASINS dataset, including ID (GRID_ID), width-to-length ratio (WLR), roughness in degree (Roughness), whole-basin slope in degree (Sb), detrended roughness in degree (DetrendRou), relative roughness (Rrel) and x, y coordinates of centroid.

This dataset was obtained by delineating drainage basin boundaries from hydrologically corrected elevation data (HydroSHEDS and Hydro1K). Input data resolution is 15 arc-seconds between 60 N and 60 S latitude (based on SRTM), and 30 arc-seconds for higher latitudes (based on GTOPO30). The dataset consists of the following information: numerical code (MAJ_BAS), name (MAJ_NAME) and area (MAJ_AREA) of the major basin in square km.

This is subset of the original dataset. Data is clipped to extent boundaries and filtered by field (DIS_AV_CMS >= 1). A link to the original dataset and its description is below.https://www.hydrosheds.org/hydroatlasHydroATLAS offers a global compendium of hydro-environmental characteristics for all sub-basins of HydroBASINS, all river reaches of HydroRIVERS, and all lake polygons of HydroLAKES.The HydroATLAS database is divided into three distinct sub-datasets: BasinATLAS, RiverATLAS, and LakeATLAS which represent sub-basin delineations (polygons), the river network (lines), and lake shorelines (polygons), respectively. In total, HydroATLAS contains 1.0 million sub-basins, 8.5 million river reaches, and 1.4 million lakes.HydroATLAS has been created by compiling and re-formatting a wide range of hydro-environmental attributes derived from existing global datasets in a consistent and organized manner. The resulting data compendium offers attributes grouped in seven categories: hydrology; physiography; climate; land cover & use; soils & geology; and anthropogenic influences. For each of the three sub-datasets, HydroATLAS contains 56 hydro-environmental variables, partitioned into 281 individual attributes.‍The HydroATLAS database is distributed in large file sizes due to the enriched attribute information. Users who only need geometric information and digital vector maps of sub-basin boundaries, river network lines, and lake shorelines may prefer to download the HydroBASINS, HydroRIVERS, or HydroLAKES products instead.

Awash river basin boundary is derived from the HydroBASINS product, which was obtained by delineating drainage basin boundaries from hydrologically corrected elevation data (WWF HydroSHEDS, Lehner et al. 2008; Lehner and Grill 2013) and supported by the topological concept of the Pfafstetter coding system (Verdin & Verdin 1999). Source: The HydroBASINS product has been developed on behalf of World Wildlife Fund US (WWF), with support and in collaboration with the EU BioFresh project, Berlin, Germany; the International Union for Conservation of Nature (IUCN), Cambridge, UK; and McGill University, Montreal, Canada. Major funding for this project was provided to WWF by Sealed Air Corporation; additional funding was provided by BioFresh and McGill University. Citations and acknowledgements of the HydroBASINS data should be made as follows: 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.

Data associated with manuscript: Historical Trends in Snowmelt Used for Irrigation by Kinnebrew et al., 2025, Environmental Research: Food Systems (https://doi.org/10.1088/2976-601X/adacec).These data represent the temporal mass center of runoff, or peak runoff timing, for each year (1985 to 2020) and within each watershed basin (HydroBASINS level 3; https://www.hydrosheds.org/products/hydrobasins). The data were summarized from snowmelt, rainfall and total runoff data from TerraClimate (https://doi.org/10.5061/dryad.vx0k6dk2h). Please see the manuscript methods for additional information.Column Names and Descriptions:1. watershedNum: the HUC12 identifier number from HydroBASINS level 32. year: data year, from 1985 to 20203. CT_Snow: the month in which the mass center of snowmelt runoff occurred. Values correspond to month and day (see note below)4. CT_Rain: the month in which the mass center of rainfall runoff occurred. Values correspond to month and day (see note below)5. CT_Total: the month in which the mass center of total (snowmelt and rainfall) runoff occurred. Values correspond to month and day (see note below)Converting values to month and day: The whole number corresponds to the month (1: January, 2: February, 3: March, etc.). The fraction can be converted to the day of the month by multiplying it by the number of days in the month. A value of 1.3 would correspond to January 9, or a value of 9.7 would correspond to September 21.

Google Earth KMZ files from a study of endorheic basins using the GIS program Global Mapper in combination with shapefiles from the HydroBASINS dataset (Lehner & Grill, 2013). The study created continental-scale models (DEM-derived basins, or DDNs) of the connections between endorheic and exorheic basins. The DDNs for endorheic basins associated with exorheic basins larger than 25,000 sq km are here presented in Google Earth KMZ format. […]

Publication

Please cite this publication if you use the dataset:

Sarrazin, F. J., Attinger, A., Kumar, R., Gridded dataset of nitrogen and phosphorus point sources from wastewater in Germany (1950-2019), submitted to Earth System Science Data.

Please also refer to the above publication for methodological details.

License

The "Gridded dataset of nitrogen and phosphorus point sources from wastewater in Germany (1950-2019)" is freely available under a Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0, https://creativecommons.org/licenses/by-nc-sa/4.0), in compliance with the terms of use of the Food and Agriculture Organization of the United Nations (FAO) data and German Cosmetic, Toiletry, Perfumery and Detergent Association (IKW) data that underlie the dataset.

Data description

The dataset includes estimates of nitrogen (N) and phosphorus (P) emissions from wastewater in Germany (1950-2019) at (1) grid level, and at different levels of aggregation, namely (2) at Nomenclature of Territorial units for statistics level 1 (NUTS-1), that correspond to the 16 German federal states (for this, we used the 2020 NUTS classification; BKG, 2020) and (3) at river basin level for 3778 river basins of the HydroBASINS v1.c of the HydroSHEDS database (HydroSHEDS, 2014; Lehner and Grill, 2013). It also includes the input and calibration data (at NUTS-1 and grid level) that were used to estimate the N and P emissions.

1. Input and calibration data at NUTS-1 level:
   spatial extent: Germany
   spatial resolution: NUTS-1
   time period: 1950-2019 (input data), 1987-2019 (calibration data)
   frequency: annual
   variables: input data, calibration data, parameter sample
   file format: CSV
   number of files: 3
   
   

2. Input data at grid level:
   spatial extent: Germany
   spatial resolution: 0.015625°
   time period: 1950-2019 (population data), 2020 (NUTS-1 map)
   frequency: annual
   variables: urban and rural population counts, NUTS-1 map
   file format: netCDF
   number of files: 2
   
   

3. Emission data at grid level:
   spatial extent: Germany
   spatial resolution: 0.015625°
   time period: 1950-2019
   frequency: annual
   variables:
   - N and P wastewater treatment plants (WWTPs) outgoing emissions (treated point sources)
   - N and P emissions collected in the public sewer system that are not treated in WWTPs (untreated point sources)
   unit: kg yr-1
   realisations: 200 realisations corresponding to 100 different parameter sets and 2 spatial disaggregation methods for the treated point sources
   file format: netCDF
   number of files: 100
   
4. Emission data aggregated at NUTS-1 (federal state) level:
   spatial extent: Germany
   spatial resolution: NUTS-1
   time period: 1950-2019
   frequency: annual
   variables:
   - N and P gross emissions
   - N and P total point sources (sum of treated and untreated components)
   - N and P treated point sources (WWTPs outgoing load)
   - N and P untreated point sources (emissions collected in the public sewer system that are not treated in WWTPs)
   - N and P emissions that are removed during treatment in WWTPs
   - N and P emissions lost during wastewater collection and transport
   - N and P emissions applied to agricultural soils in sewage farms
   - N and P emissions that are not collected in the sewer system nor treated in WWTPs
   - N and P incoming WWTPs load
   unit: kg yr-1
   realisations: 100 realisations corresponding to 100 different parameter sets
   file format: CSV
   number of files: 18
   
5. Emission data aggregated at river basin level:
   spatial extent: Germany
   spatial resolution: river basins from the HydroBASINS v1.c of the HydroSHEDS database (HydroSHEDS, 2014; Lehner and Grill, 2013).
   time period: 1950-2019
   frequency: annual
   variables: N and P WWTP outgoing emissions (treated point sources), N and P emissions collected in the public sewer system that are not treated in WWTPs (untreated point sources)
   unit: kg yr-1
   realisations: 200 realisations corresponding to 100 different parameter sets and 2 spatial disaggregation methods for the treated point sources
   file format: CSV
   number of files: 600

Acknowledgements and underlying datasets

Partial support for this work was provided by the Global Water Quality Analysis and Service Platform (GlobeWQ) project financed by the German Ministry for Education and Research (grant number 02WGR1527A). We thank Olaf Büttner for providing the WWTPs data that were collected from the authorities of the German federal states (Büttner et al., 2020). The dataset produced in this work builds on the NUTS map of the German Federal Agency for Cartography and Geodesy © GeoBasis-DE/BKG that is under a dl-de/by-2-0 license; the History Database of the Global Environment (HYDE) dataset available under a CC BY 4.0 license; protein data provided by the Food and Agriculture Organization of the United Nations © FAO provided under a CC BY-NC-SA 3.0 IGO license; detergent data from the German Cosmetic, Toiletry, Perfumery and Detergent Association © IKW (license here); data from the statistical offices of Germany and the federal states and the German and federal state authorities (details on data sources in the publication reported above: Sarrazin et al., submitted to Earth System Science Data); WWTP data available in the Waterbase dataset from the European Environment Agency © EEA under a CC BY 4.0 license. The river basins come from © HydroSHEDS (license here).

Contact

Fanny Sarrazin (fanny.sarrazin@inrae.fr)
Rohini Kumar (rohini.kumar@ufz.de)

References

BKG (Bundesamt für Kartographie und Geodäsie) (2020), NUTS regions 1 : 250 000, 31.12.2020, GeoBasis-DE [data set], Leipzig, Germany, https://gdz.bkg.bund.de/index.php/default/nuts-gebiete-1-250-000-stand-31-12-nuts250-31-12.html (last access: 1 November 2022).

Büttner, O. (2020), DE-WWTP - data collection of wastewater treatment plants of Germany (status 2015, metadata), HydroShare [data set],
https://doi.org/10.4211/hs.712c1df62aca4ef29688242eeab7940c.

HydroSHEDS (2014), HydroBASINS v1.c, https://www.hydrosheds.org/products/hydrobasins (last access: 23 October 2023).

Lehner, B. and 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, 2171–2186, https://doi.org/10.1002/hyp.9740.

Changes compared to v1.0 dataset version

Compared to the version 1.0 of the dataset, this version v1.1 contains the input and calibration data at NUTS-1 level and the input data at grid level used to calculate the emissions.

Amazon Geoglyphs Spatial Analysis Dataset

DATA & Tools

Data Overview and Sources

This dataset was constructed for the Phase 2 research described in the write-up document, analyzing the spatial relationships between geoglyphs (ancient earthwork structures) in the Amazon basin and hydrological environments to identify potential geoglyph locations.

Data sources

• HydroBASINS: www.hydrosheds.org - Global watershed boundaries
• GloRiC: www.hydrosheds.org - Global River Classification
• jqjacobs.net: Archaeogeodesy Placemarks (Amazon geoglyph category extracted from Google Earth KML)

File Structure

2_1_plan_research_area/
├── scripts/
│  └── kmz_point_extractor.py   # Data extraction script (Archaeogeodesy KMZ → geoglyph coordinates)
├── data/
│  ├── amazon_basin.gpkg     # Watershed boundaries (HydroBASINS Level 3 Amazon basin)
│  ├── amazon_gloric.gpkg     # River data (GloRiC clipped to basin extent)
│  ├── amazon_grid_gloric.gpkg  # Grid statistics (0.5° grid-based river environment statistics)
│  ├── sites_geoglyphs.gpkg    # Site locations (extracted geoglyph points)
│  ├── survey_area.gpkg      # Administrative areas (Brazil/Peru/Bolivia states of interest)
│  └── focus_area.gpkg      # Analysis area (potential geoglyph survey target region)
└── plan_research_area.qgz     # QGIS project (integrated layer management)

QGIS Processing Workflow

1. Watershed Boundary Extraction (amazon_basin.gpkg)

• (1) Vector → Research Tools → Select by Attribute: Select Amazon basin by attributes
• (2) Export → Save Selected Features As: Save selected features as new layer

2. River Data Clipping (amazon_gloric.gpkg)

• (1) Vector → Research Tools → Select by Location: Select GloRiC features intersecting with amazon_basin
• (2) Export → Save Selected Features As: Save selected river data
• (3) Vector → Data Management Tools → Add Geometry Attributes: Calculate river length

3. Grid-based Statistics Generation (amazon_grid_gloric.gpkg)

• (1) Vector → Research Tools → Create Grid: Create 0.5° interval grid
• (2) Vector → Research Tools → Select by Location: Select grids contained within amazon_basin
• (3) Vector → Analysis Tools → Join Attributes by Location (Summary): Calculate river characteristics statistics by grid
  • Aggregation functions: Mean, Standard Deviation
  • Target variables: Temp_min (minimum temperature), CMI_indx (climate moisture index), Log_elev (elevation)

4. Research Area Definition (survey_area.gpkg)

• (1) Vector → Research Tools → Select by Attribute: Select Amazon areas of interest from country-level state shapefiles
• (2) Export → Save Selected Features As: Save selected states as GPKG

5. Focus Research Area (focus_area.gpkg)

• (1) Layer → Create Layer → New Shapefile Layer: Create new polygon layer
• (2) Toggle Editing: Manually create rectangular polygon for potential geoglyph survey

This dataset serves as the foundation for Phase 2 research utilizing environmental filtering and Sentinel-2 multispectral analysis to identify potential geoglyph locations.

HydroSHEDS is a mapping product that provides hydrographic information for regional and global-scale applications in a consistent format. It offers a suite of geo-referenced datasets (vector and raster) at various scales, including river networks, watershed boundaries, drainage directions, and flow accumulations. HydroSHEDS is based on elevation data obtained in 2000 by NASA's Shuttle Radar Topography Mission (SRTM). This dataset provides polygons of nested, hierarchical watersheds, based on 15 arc-seconds (approx. 500 m at the equator) resolution raster data. The watersheds range from level 1 (coarse) to level 12 (detailed), using Pfastetter codes. Technical documentation: https://hydrosheds.org/images/inpages/HydroBASINS_TechDoc_v1c.pdf Note that the quality of the HydroSHEDS data is significantly lower for regions above 60 degrees northern latitude as there is no underlying SRTM elevation data available and thus a coarser-resolution DEM was (HYDRO1k provided by USGS). HydroSHEDS was developed by the World Wildlife Fund (WWF) Conservation Science Program in partnership with the U.S. Geological Survey, the International Centre for Tropical Agriculture, The Nature Conservancy, and the Center for Environmental Systems Research of the University of Kassel, Germany.

https://www.hydrosheds.org/products/hydrolakesHydroLAKES aims to provide the shoreline polygons of all global lakes with a surface area of at least 10 ha. HydroLAKES has been developed using a suite of auxiliary data sources of lake polygons and gridded lake surface areas. All lakes are co-registered to the global river network of the HydroSHEDS database via their lake pour points. The global coverage of HydroLAKES encompasses 1.4 million individual lakes or reservoirs representing a total surface area of 2.67 million km², a total shoreline length of 7.2 million km, and a total storage volume of 181,900 km³. HydroLAKES only includes a limited amount of (mostly geometric) attribute information, such as surface area, shoreline length, and estimates of average depth, water volume and residence time. Every lake is also co-registered to a river reach of the HydroRIVERS dataset and a sub-basin of the HydroBASINS database (via shared IDs).‍Note that the overarching HydroATLAS database fully contains all lakes of HydroLAKES, which have additionally been enhanced in HydroATLAS with a large number of hydro-environmental characteristics.

The dataset contains a sample of locations across Siberia and Africa, for which water-level time series were automatically derived from Sentinel-3 altimeters (methodology described in Machefer et al. 2022¹) from year 2016 to year 2021, together with the in-situ station records and the area covered by the altimetry measurements. The purpose of this dataset is validation and exemplification of the methodology.

The methodology described produces comprehensive water level records at a global scale based on altimetry satellite data. The validation against in-situ data was assessed in numerous environments in West Africa and complex locations such as Arctic rivers partially covered with ice.

This dataset offers a sample of the records at 3 locations in West Africa (Kemacina [Mali], Koulikouro [Mali], Lokoja [Niger]) and in the sub-arctic region (Yakutsk [Russia]). The data are organised by Level 1 of HydroBASINS² definition (ex: africa) in two folders, each containing: virtual stations (teroVIR) and insitu stations (insitu) as shapefiles with their associated metadata, the corresponding water level time series (teroWAT) in NetCDF, and the level 3 of HydroBASINS, corresponding to the largest river basins of each continent. Finally, a csv file (validation) presents the computed metrics assessing the accuracy of the processors.

N.B.: time series with less than two common date points between insitu and teroWAT have not been assessed.

[1] Machefer, M., Perpinyà-Vallès M., Escorihuela M.J., Gustafsson D., Romero L. (2022): Challenges and evolution of water level monitoring towards a comprehensive, world-scale coverage with remote sensing. Earth System Science Data (Under Reviewing)

[2] 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.

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

HydroSHEDS (Hydrological data and maps based on SHuttle Elevation Derivatives at multiple Scales) provides hydrographic information in a consistent and comprehensive format for regional and global-scale applications. HydroSHEDS offers a suite of geo-referenced data sets in raster and vector format, including stream networks, watershed boundaries, drainage directions, and ancillary data layers such as flow accumulations, distances, and river topology information. Recently available data derived from HydroSHEDS include comprehensive layers of major basins and smaller sub-basins (~100-2,500 km2) across the globe. These data layers are available to support watershed analyses, hydrological modeling, and freshwater conservation planning at a quality, resolution, and extent that had previously been unachievable in many parts of the world. Data includes Void-Filled elevation, Hydrologically conditioned elevation, drainage directions, flow accumulation, river network, basin outlines, HydroBASINS License information: https://www.hydrosheds.org/page/license

Context

I made this dataset while performing Integrated Valuation of Ecosystem Services and Tradeoffss (InVEST) models of wetlands in India.

Content

This dataset is a collection of Geographic Information System (GIS) data sourced from various public domains. It includes shapefiles, image raster files, etc which can are primarily developed with the aim of using with GIS software such as ArcGIS Pro, QGIS, etc. Most of the datasets are global in nature with some, like the OpenStreetMap data pertaining to India only. The data is as described below:

"One belt, one road" delineation of the key Asian regional watershed boundaries is based on the following principles: Principle 1: along the Silk Road Principle 2: located in arid and semi-arid areas Principle 3: high water risk Principle 4: watershed integrity 1. Division basis of arid area Food and Agriculture Organization of the United Nations. FAO GEONETWORK. Global map of aridity - 10 arc minutes (GeoLayer). (Latest update: 04 Jun 2015) Accessed (6 Mar 2018). URI: http://data.fao.org/ref/221072ae-2090-48a1-be6f-5a88f061431a.html?version=1.0 2. Water resources risk data: Gassert, F., M. Landis, M. Luck, P. Reig, and T. Shiao. 2014. Aqueduct Global Maps 2.1. Working Paper. Washington, DC: World Resources Institute. 3. Poverty index data: Elvidge C D, Sutton P C, Ghosh T, et al. A global poverty map derived from satellite data. Computers & Geosciences, 2009, 35(8): 1652-1660. https://www.ngdc.noaa.gov/eog/dmsp/download_ poverty.html 4. Basic basin boundary data: (1) Watershed boundaries were derived from HydroSHEDS drainage basins data (Lehner and Grill 2013) based on a grid resolution of 15 arc-seconds (approximately 500 m at the equator), which can be free download via https://hydrosheds.cr.usgs.gov/hydro.php (2) AQUASTAT Hydrological basins: This dataset is developed as part of a GIS-based information system on water resources. It has been published in the framework of the AQUASTAT - programme of the Land and Water Division of the Food and Agriculture Organization of the United Nations. The map is also available in the SOLAW Report 15: “Sustainable options for addressing land and water problems – A problem tree and case studies”. Data can be free download via http://www.fao.org/nr/water/aquamaps/ (3) HydroBASINS: https://www.hydrosheds.org/downloads 5. The GloRiC provides a database of river types and sub-classifications for all river reaches globally. https://www.hydrosheds.org/page/gloric 6. HydroATLAS offers a global compendium of hydro-environmental sub-basin and river reach characteristics at 15 arc-second resolution. https://www.hydrosheds.org/page/hydroatlas It covers an area of 1469400 square kilometers, including the following areas: Nujiang River Basin, Dead Sea basin, Sistan River Basin, Yellow River Basin, Jordan Syria eastern basin, Indus River Basin, Iran inland flow area, urmiya Lake Basin, Shiyang River Basin, hallelud mulgarb River Basin, Lianghe River Basin, Shule River Basin, Heihe River Basin, issekkor Lake Basin, Tata River Basin Limu River Basin, Turpan Hami basin, Ebinur Lake Basin, Junggar basin, Amu Darya River Basin, Manas River Basin, ulungu River Basin, Emin River Basin, Chu River Talas River Basin, Xil River Basin, Ili River Basin, Caspian Sea basin, Lancang River Basin, Yangtze River Basin, Qinghai lake water system, Eastern Qaidam Basin, western Qaidam Basin and Qiangtang plateau District, Yarlung Zangbo River Basin