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.

Using the HydroSHEDS database at 15 arc-second resolution, watersheds were delineated in a consistent manner at different scales, and a hierarchical sub-basin breakdown was created following the topological concept of the Pfafstetter coding system. The resulting polygon layers are termed HydroBASINS and represent a subset of the HydroSHEDS database.

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.

HydroBASINS is covered by the same License Agreement as the HydroSHEDS database.

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.

Hydro-basins provide hydrographic data layers that allow for the derivation of watershed boundaries for any given location based on the near-global, high-resolution SRTM digital elevation model. Watersheds were delineated in a consistent manner at different scales, and a hierarchical sub-basin breakdown was created following the topological concept of the Pfafstetter coding system (Verdin & Verdin 1999). The resulting polygon layers are termed HydroBASINS and represent a subset of the HydroSHEDS database. There are 12 levels. Level 6 represents major river systems from headwaters to coast. This version is sourced from FAO.

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.

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.

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 dataset represents the Level 5 delineation of the Shabelle River Basin in Ethiopia, as defined by the HydroBASINS framework. The Level 5 (L5) subdivision provides a mid-scale segmentation of the Shabelle Basin, capturing key hydrological structures while maintaining computational efficiency.

More information at Hydrosheds - Hydrobasins

WMS Resources for layers: Myanmar Hydrobasins

Boundary of the Nile River Basin and sub-basins obtained from HydroBASINS (Lerner and Grill, 2013).

Data publication: 2019-07-01

Contact points:

Metadata Contact: Solomon Seyoum

Data lineage:

Contact WWF Hydrosheds for more information at https://www.hydrosheds.org/page/hydrobasins

Resource constraints:

HydroSHEDS License Agreement at https://www.hydrosheds.org/page/license

Online resources:

WWF Hydrosheds official website

In times of a changing hydroclimate and growing human population, there is a need to assess how various climatic and demand conditions influence water availability on the landscape. Tendency for drought conditions is a prime example of a key hydroclimatic metric that is useful for understanding water retention in the surrounding landscape. However, merging drought climatological data with co-located aquatic data is challenging. To facilitate national-scale analyses of basin-level drought conditions (i.e., Palmer Drought Severity Index; PDSI) with co-located water quality data, we present aggregated PDSI 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.

HydroBASINS represents a series of vectorized polygon layers that depict sub-basin boundaries 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 catchment topology such as up- and downstream connectivity. HydroBASINS has been extracted from the gridded HydroSHEDS core layers at 15 arc-second resolution.

More info: HydroBASINS

a hydro-geomorphic unit hydrograph dataset that characterizes the rainfall and runoff response relationship in 18440 catchments from the HydroBASINS dataset across the Tibetan Plateau has been produced based on the WFIUH extraction framework. Additionally, this dataset includes 18 attributes pertaining to the basic shape and geomorphic characteristics of 18440 HydroBASINS catchments.

USGS Hydrobasin boundaries

All HydroBASINS layers were derived from World Wildlife Fund’s HydroSHEDS data based on a grid resolution of 15 arc-seconds (approximately 500 m at the equator). Watersheds were delineated in a consistent manner at different scales, and a hierarchical sub-basin breakdown was created following the topological concept of the Pfafstetter coding system. The resulting polygon layers are termed HydroBASINS and represent a subset of the HydroSHEDS database. This shapefile contains (sub-)basin polygons for Siberia at Pfafstetter level 4.

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

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.

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) point sources 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):

1. 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
   
2. 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
   
3. 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@ufz.de)
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.

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

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.

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.