GCRI · Water Nexus
Catchment hydrology
A catchment is the area that drains to one point: every drop of rain that falls inside it either evaporates, soaks in or runs to that outlet. Catchment hydrology is the accounting of that water through time, and it is the base on which every water decision in the basin rests.
Catchment hydrology on the record.
Catchment hydrology: how the risk develops.
Catchment hydrology and the complete water balance. The dossier below sets out how the risk develops, what is measured, where the data come from, how it is modelled and who decides.
The water balance
Precipitation equals evapotranspiration plus runoff plus the change in storage in soils, aquifers, snow and reservoirs. The balance closes only when every term is measured or estimated with a stated error.
Runoff generation
Water reaches rivers by overland flow when rain exceeds infiltration, by shallow subsurface flow, and by groundwater discharge that sustains dry-season flow. Land cover and soil decide the split.
Routing and travel time
A flood wave attenuates and lags as it moves downstream; channels, floodplains and reservoirs each change its shape. Travel time is what gives a warning its lead.
Human alteration
Dams, abstraction, drainage, urban surfaces and irrigation returns rewrite the natural balance. A model calibrated on the natural regime misreads a regulated one.
Water now in the Nile Basin.
Flood water, rain and root-zone soil moisture over the Nile Basin, from NASA GIBS. Each layer carries its own date.
The interactive map needs JavaScript; the layer list and sources are below.
Layers 3
Water now, for this page
About this layer
- Evidence
- Observed (clouds leave gaps)
- Source
- VIIRS Global Flood Product (NASA LANCE)
- Provider
- NASA LANCE
- Licence
- NASA open data
- Vintage
- daily; Satellite detection; clouds leave gaps
- Newest at build
- 2026-10-14
- Resolution
- about 250 m grid
- Uncertainty
- Satellite detection; clouds leave gaps. Satellite or model raster as the provider publishes it. Clouds, swath gaps and retrieval error leave gaps and noise; values are indicators, not measurements at a place.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
- Evidence
- Estimated
- Source
- GPM IMERG (NASA GES DISC)
- Provider
- NASA GES DISC
- Licence
- NASA open data
- Vintage
- daily; Satellite estimate, near real time
- Newest at build
- 2026-10-12
- Resolution
- about 2 km grid
- Uncertainty
- Satellite estimate, near real time. Satellite or model raster as the provider publishes it. Clouds, swath gaps and retrieval error leave gaps and noise; values are indicators, not measurements at a place.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
- Evidence
- Modelled
- Source
- SMAP Level 4 (NASA NSIDC DAAC)
- Provider
- NASA NSIDC DAAC
- Licence
- NASA open data
- Vintage
- daily; Model with satellite data assimilated
- Newest at build
- 2026-10-11
- Resolution
- about 2 km grid
- Uncertainty
- Model with satellite data assimilated. Satellite or model raster as the provider publishes it. Clouds, swath gaps and retrieval error leave gaps and noise; values are indicators, not measurements at a place.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
- Evidence
- Observed
- Source
- VIIRS NOAA-20 corrected reflectance (NASA LANCE)
- Provider
- NASA LANCE
- Licence
- NASA open data
- Vintage
- daily; Satellite image
- Newest at build
- 2026-10-14
- Resolution
- about 250 m grid
- Uncertainty
- Satellite image. Satellite or model raster as the provider publishes it. Clouds, swath gaps and retrieval error leave gaps and noise; values are indicators, not measurements at a place.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
- Evidence
- Modelled (latest year GIBS publishes)
- Source
- MODIS Terra and Aqua land cover (MCD12Q1) (NASA LP DAAC)
- Provider
- NASA LP DAAC
- Licence
- NASA open data
- Vintage
- yearly; Satellite classification; latest year GIBS publishes
- Newest at build
- 2024-01-01
- Resolution
- about 500 m grid
- Uncertainty
- Satellite land cover classes for the year shown, including croplands; a classification with known errors, not a farm register.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
- Evidence
- Observed (latest month GIBS publishes)
- Source
- GRACE-FO JPL mascons (NASA JPL PO.DAAC)
- Provider
- NASA JPL PO.DAAC
- Licence
- NASA open data
- Vintage
- monthly; Satellite gravimetry; latest month GIBS publishes
- Newest at build
- 2022-07-01
- Resolution
- about 2 km grid
- Uncertainty
- Satellite gravimetry; latest month GIBS publishes. Satellite or model raster as the provider publishes it. Clouds, swath gaps and retrieval error leave gaps and noise; values are indicators, not measurements at a place.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
Keyboard, with the map focused
Hold Ctrl or ⌘ and scroll to zoom
Latest available
NASA GIBS / EOSDIS · ASTER GDEM (METI, NASA) · Blue Marble (NASA) · © OpenStreetMap contributors (ODbL) · Natural Earth
Loading the latest NASA imagery.
Drag to move. Ctrl or ⌘ and scroll, pinch or the buttons to zoom; hover for the values under the pointer; reset returns to the region.
Water now, framed on the Nile Basin.Satellite and model products as NASA publishes them, each with its own date. They are indicators, not assessments of any place.
Data: NASA GIBS · ASTER GDEM · Blue Marble · © OpenStreetMap contributors · Natural Earth · NASA EONET · NASA GPM IMERG Early · NASA JPL OPERA DIST-ALERTSources and licences
- We acknowledge the use of imagery provided by services from NASA’s Global Imagery Browse Services (GIBS), part of NASA’s Earth Science Data and Information System (ESDIS). Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- We acknowledge the use of imagery provided by services from NASA’s Global Imagery Browse Services (GIBS), part of NASA’s Earth Science Data and Information System (ESDIS). Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- VIIRS near-real-time products (flood, land surface temperature, snow cover, corrected reflectance), NASA LANCE; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- GPM IMERG precipitation, NASA Precipitation Processing System; distributed by the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC); imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- SMAP Level 4 soil moisture and snow mass, NASA Global Modeling and Assimilation Office; distributed by the NASA National Snow and Ice Data Center DAAC; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- MODIS land products (vegetation indices, gross primary production, land cover), NASA LP DAAC and LANCE; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- GRACE and GRACE-FO JPL mascon solution, NASA Jet Propulsion Laboratory; distributed by the NASA Physical Oceanography DAAC (PO.DAAC); imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- ASTER GDEM is a product of METI and NASA (ASTER Global Digital Elevation Model V003, NASA LP DAAC); imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). Cite as: NASA/METI/AIST/Japan Spacesystems and U.S./Japan ASTER Science Team (2019). ASTER Global Digital Elevation Model V003. doi:10.5067/ASTER/ASTGTM.003. NASA open data
- Made with Natural Earth. Free vector and raster map data @ naturalearthdata.com. Public domain
- © OpenStreetMap contributors (ODbL), via NASA GIBS (coastlines, boundaries, roads and place names). Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). ODbL
- Blue Marble, NASA Earth Observatory; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- Context on the globe and flat map: coastlines, boundaries, roads and place names from NASA GIBS reference layers (OpenStreetMap data, ODbL, © OpenStreetMap contributors); Blue Marble imagery as an optional base; 3D terrain and the elevation read-out from the ASTER GDEM elevation colour index (5 m to 200 m classes, exaggerated for legibility). Boundaries are drawn as published and imply no position on any territory.
- Live events: NASA Earth Observatory Natural Event Tracker (EONET); events are indicative and keep their original source links.
- Live: NASA EONET Events are indicative, not authority warnings.
- Live: NASA GPM IMERG Early. We acknowledge the use of imagery provided by services from NASA’s Global Imagery Browse Services (GIBS), part of NASA’s Earth Science Data and Information System (ESDIS).
- Observed (EO): NASA JPL OPERA DIST-ALERT (contains modified Copernicus Sentinel data 2026). We acknowledge the use of imagery provided by services from NASA’s Global Imagery Browse Services (GIBS), part of NASA’s Earth Science Data and Information System (ESDIS).
- NASA GIBS: We acknowledge the use of imagery provided by services from NASA’s Global Imagery Browse Services (GIBS), part of NASA’s Earth Science Data and Information System (ESDIS).
- River discharge forecast, 30 days with range (GloFAS via Open-Meteo): Contains modified Copernicus Emergency Management Service information 2026; not a flood warning.
- Past year, daily temperature (ERA5 via Open-Meteo): Contains modified Copernicus Climate Change Service information 2026.
- Past year, daily temperature and sunshine (NASA POWER): These data were obtained from the NASA Langley Research Center (LaRC) POWER Project funded through the NASA Earth Science/Applied Science Program..
- Weather data by Open-Meteo.com (https://open-meteo.com/), licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/).
- Optional layers (14 sources in the layer list: live, forecast, observed and context) print their provider credit on the map and here when switched on; forecasts are model output and derived indices are estimates.
Layers and dates
- Flood water, 3-day composite: VIIRS Global Flood Product, NASA LANCE · 14 Oct 2026
- Precipitation rate, daily mean: GPM IMERG, NASA GES DISC · 12 Oct 2026
- Root-zone soil moisture, 0 to 100 cm: SMAP Level 4, NASA NSIDC DAAC · 11 Oct 2026
- True colour: VIIRS NOAA-20 corrected reflectance, NASA LANCE · 13 Oct 2026
- Land cover type (IGBP), yearly: MODIS Terra and Aqua land cover (MCD12Q1), NASA LP DAAC · 2024
- Water storage anomaly, monthly: GRACE-FO JPL mascons, NASA JPL PO.DAAC · July 2022
The catchment water balance as the shared input to every other domain.
| Domain | How it couples here | What to watch |
|---|---|---|
| Energy | Run-of-river and reservoir hydropower are planned on the flow duration curve the catchment produces. See hydropower and water. | Shifts in seasonal flow timing as snow and glaciers change |
| Food | Irrigation abstraction and rain-fed yields both depend on soil water and runoff. See food, water and energy. | Abstraction share of renewable water in dry months |
| Health | Source water quality depends on catchment land use, sediment and pathogens carried in runoff. | Turbidity peaks after storms at treatment works intakes |
| Biodiversity | Forests, soils and wetlands shape infiltration and baseflow; land-cover change alters the water balance. See forests and landscapes. | Land-cover change in headwaters |
Basin stress monitor.
The screen a team would use to follow this subject: its measures, states and decisions. Names are examples and values are illustrative.
Catchment evidence in reports, and the data that would most improve it.
Catchment models and water balances are designed for the Methods and Technical series and, where a national team commissions them, the Country Systems Risk and Resilience series. Large-sample datasets such as CAMELS and machine-learning rainfall-runoff models (Kratzert and others, 2019) have improved prediction in ungauged basins; each model would carry a model card under NS-12 of the proposed Nexus Standards stating where it was trained and where it should not be used.
- Gauge records shared with the Global Runoff Data Centre or offered under national terms.
- Rating curve updates after channel change.
- Catchment attribute data (soils, geology, land use) for basins missing from large-sample sets.
Route a storm through a river network.
Rain becomes runoff by the curve number method, drains each sub-catchment through a linear reservoir and travels down the main stem by Muskingum routing; play it to watch the flood wave move and flatten. Teaching model with stated illustrative values, not a prediction. The map on this page shows The Nile Basin; the model itself is not tied to a place.
Controls
- Storm rainfall depth
- 120 mm
- Storm duration
- 12 h
- Curve number (soil and land cover)
- 78
- Catchment area
- 800 km²
- Reaches on the main stem
- 5
- Travel time per reach (Muskingum K)
- 6.0 h
- Muskingum X (storage weighting)
- 0.20
- Channel capacity per unit area
- 300 L/s per km²
Readouts
- Peak discharge at the outlet
- 452 m³/s
- Time to peak
- 21.6 h
- Attenuation by the channel
- 61%
- Hours over capacity
- 26.4 h
- Runoff coefficient
- 52%
Numbers, methods and sources
| Hour | Outlet (m³/s) | Unrouted (m³/s) | Upper reach (m³/s) |
|---|---|---|---|
| 2.4 | 0.0 | 678.9 | 0.0 |
| 12.0 | 308.3 | 1,151.4 | 181.8 |
| 21.6 | 452.4 | 35.1 | 73.7 |
| 31.2 | 363.3 | 1.1 | 11.1 |
| 40.8 | 198.8 | 0.0 | 1.5 |
| 50.4 | 79.3 | 0.0 | 0.2 |
| 60.0 | 25.4 | 0.0 | 0.0 |
| 69.6 | 6.9 | 0.0 | 0.0 |
| 79.2 | 1.7 | 0.0 | 0.0 |
| 88.8 | 0.4 | 0.0 | 0.0 |
| 98.4 | 0.1 | 0.0 | 0.0 |
| 108.0 | 0.0 | 0.0 | 0.0 |
| 117.6 | 0.0 | 0.0 | 0.0 |
| 127.2 | 0.0 | 0.0 | 0.0 |
| 136.8 | 0.0 | 0.0 | 0.0 |
Methods
- Curve number runoff USDA Soil Conservation Service (1972), National Engineering Handbook, Section 4: Hydrology
- Linear reservoir Nash (1957), The form of the instantaneous unit hydrograph, IAHS 45
- Muskingum channel routing McCarthy (1938); Cunge (1969), Journal of Hydraulic Research 7
- Uncertainty fan (parameter Monte Carlo) Spiegelhalter, Pearson and Short (2011), Visualising uncertainty about the future, Science 333
- One-at-a-time sensitivity (tornado) Saltelli and others (2008), Global Sensitivity Analysis: The Primer, Wiley
Data
The model runs on the teaching values set by the controls. To calibrate it for a place, read it against these admitted layers and series, which this page’s map can draw:
- Rivers Natural Earth
- Lakes and reservoirs Natural Earth
- Cities Natural Earth
- Rain rate, last half hour (IMERG) NASA GPM IMERG Early · register nasa.gibs.context-live-layers
- River discharge forecast, 30 days with range (GloFAS via Open-Meteo) Open-Meteo.com (CC BY 4.0), Copernicus GloFAS · register open-meteo.public-pages
- Surface soil moisture (SMAP) NASA SMAP · register nasa.gibs.context-live-layers
- Surface water, 30 m (OPERA DSWx-HLS) NASA JPL OPERA DSWx-HLS (contains modified Copernicus Sentinel data 2026) · register nasa.gibs.context-live-layers
- Surface water through cloud, 30 m (OPERA DSWx-S1 radar) NASA JPL OPERA DSWx-S1 (contains modified Copernicus Sentinel-1 data 2026) · register nasa.gibs.context-live-layers
Across the six pillars, led by Nexus Consortium
- Experiments Labs
- Built in Water Nexus in Foundry
- Tested at Water Nexus in Universe
- Codified in Water and Food Systems in Standards
- Published in Water Nexus in Reports
- Taught in Faculty of Earth and Environmental Sciences in Academy
What is measured, and how well.
Each indicator carries its unit, its cadence, the kind of source behind it and the evidence label a value would carry. No values are shown: a programme supplies them under its own authority.
| Indicator | Unit | Cadence | Source type | Label |
|---|---|---|---|---|
| Catchment precipitation | millimetres per month | monthly | Gauge and satellite rainfall | ESTIMATED |
| Actual evapotranspiration | millimetres per month | monthly | Satellite energy balance, land-surface model | MODELLED |
| Runoff coefficient | dimensionless | seasonal | Gauged flow against rainfall | ESTIMATED |
| Baseflow index | dimensionless | annual | Hydrograph separation | ESTIMATED |
| Flow duration curve percentiles | cubic metres per second | period of record | Stream gauges | OBSERVED |
| Water yield | millimetres per year | annual | Water balance | MODELLED |
Model catchment hydrology as a system. Run the cascade.
Assembled from the Nexus Standards area profile Water risk standards: the hazards it names, the systems and sectors that carry the loss, the controls that dampen it and the data that observe it. Choose the components, pick an initiating hazard and step through the cascade. The Map view places the run on an example geography with open data. Every coupling weight is a stated assumption you can change. A teaching model, not a forecast, an assessment or a certification. To model a place of your own choosing, open the full System Builder.
Hazards
- Flash flood
- Meteorological drought
- Groundwater depletion
- Dam failure
- Debris flow (set off downstream)
- Agricultural drought (set off downstream)
- Hydrological drought (set off downstream)
- Wildfire (set off downstream)
- Peat and underground fire (set off downstream)
- Forest dieback (set off downstream)
- Water supply shortfall (set off downstream)
- Acute food crisis (set off downstream)
- Dust storm (set off downstream)
- Land subsidence (set off downstream)
- Saltwater intrusion (set off downstream)
- River flood (set off downstream)
- Mass displacement (set off downstream)
- Amazon rainforest dieback (set off downstream)
Systems and couplings
- Water risk standards
- Energy risk standards Common driver and cascade
- Food and agriculture risk standards Cascade
- Health risk standards Cascade
- Hydrometeorological hazard standards Shared hazard records
Controls
NS-0Constitution and principlesNS-1The UNI and the artefact catalogue (grammar v0.2: domain, sector, region and environment segments)NS-3Risk record UNIs (grammar, legacy crosswalk, allocation authority)NS-3bDataset and product metadata profile (STAC, DCAT, PROV-O)NS-9Contextual UNIs (deployments, instances, programme instances)NS-10Policies and clauses (machine-readable obligations: definition, binding, evaluation and enforcement)NS-11Verification and monitoring (evidence, attestations, zero-trust conformance observation)NRS-U28Universe platform profile: Water NexusNRS-H61Hazard family: Cascading failure (NHS-1 sys.cascade)NRS-K42Domain pack profile: Cross border basins (DOMAIN-042)NRS-N1Water risk and resilienceNRS-N7Water-energy-food-health-biodiversity-infrastructure nexus couplingNRS-H62Hazard family: Compound and multi-hazard events (NHS-1 sys.compound)
Observation
- USGS Water Data APIs (listed for reference)
- GloFAS global flood forecasts and reanalysis (listed for reference)
- GRACE/GRACE-FO terrestrial water storage mascons (listed for reference)
- Aqueduct 4.0 Water Risk Atlas (listed for reference)
- CHIRPS quasi-global gridded precipitation (listed for reference)
- Level of water stress: freshwater withdrawal as a proportion of available freshwater resources
- Proportion of transboundary basin area with an operational arrangement for water cooperation
- Proportion of population using safely managed drinking water services
- Number of disruptions to basic services attributed to disasters
Example geography
- The Ganges and Brahmaputra Basin: 178 hexagonal cells
- Surface water through cloud, 30 m (OPERA DSWx-S1 radar), NASA GIBS
- Rain rate, last half hour (IMERG), NASA GIBS
- Surface soil moisture (SMAP), NASA GIBS
- Night lights tonight, outages (VIIRS Day/Night Band), NASA GIBS
Where the evidence comes from.
Named public datasets and institutional records, each with its licence class. Sources by agreement are used only under their terms and never republished.
| Source | What it gives | Licence class |
|---|---|---|
| HydroSHEDS and HydroBASINS | Catchment boundaries and river networks derived from elevation | Free, attribution and terms |
| Copernicus DEM and SRTM | Elevation for delineation and slope | Free and open, attribution |
| CHIRPS and ERA5 | Rainfall and atmospheric forcing | Public domain; free and open |
| MODIS evapotranspiration | Satellite evapotranspiration at five hundred metres | Public domain |
| Global Runoff Data Centre | Gauged discharge for calibration | Data policy |
| Copernicus land cover and ESA WorldCover | Land cover for runoff parameters | Free and open, attribution |
The sector signature of catchment hydrology.
The sectors catchment hydrology touches and how strongly they depend on each other: what a study has to observe and model together.
Signature
Water and its partners
Water gives irrigation to food (0.8), cooling water and hydropower to energy (0.6) and environmental flows to biodiversity (0.6).
W · E · I · F · H · B
- Sectors
- 4 of 6
- Relations
- 6 of 15
- Coupling
- 0.43 share of total
Strongest relations in play
- Food → Biodiversity · land conversion0.6
- Water → Energy · cooling water and hydropower0.6
- Water → Food · irrigation0.8
- Water → Biodiversity · environmental flows0.6
Who decides, and what the evidence serves.
Water Nexus prepares evidence. The decisions below are taken by the authorities named, under their own rules, and never by the platform.
| Who decides | The question | Evidence this page serves |
|---|---|---|
| Basin authorities | How much water the basin yields in a dry year, and how that yield is shared | Water balance with stated error; flow duration curves at the points of allocation |
| Dam and irrigation operators | How inflows respond to a wet or dry season and to land-use change upstream | Calibrated rainfall-runoff model with its test-period skill |
| Urban drainage authorities | How development changes peak runoff and travel time | Runoff coefficients before and after, routing with the new surfaces |
Outputs this topic produces
- Delineated catchments with gauged and ungauged status
- Closed water balances with error terms
- Calibrated rainfall-runoff models with test-period skill
- Regionalised parameters for ungauged basins
How catchment hydrology is modelled, measured and tested.
The model, its assumptions, its data and its uncertainty, in the form a scientist or engineer would check. Structures follow published methods. The values drawn are illustrative, not findings.
System model
The couplings between the systems in catchment hydrology: signs of influence, a reinforcing and a balancing loop, and cross-links where stress jumps.
What experts ask about a catchment.
- How much precipitation falls, and how much returns to the atmosphere through evaporation and transpiration?
- How much is stored in snowpack, soil, wetlands, reservoirs and aquifers, and for how long?
- How quickly does rainfall become streamflow, and what controls peak flows and low flows?
- How do land use, abstraction and climate change alter each term of the balance?
The processes behind the water balance.
Over any period, precipitation equals evapotranspiration plus runoff plus the change in storage, with groundwater flows across the catchment boundary added where they matter. Each term is hard to measure directly. Evapotranspiration depends on energy, vegetation and soil moisture; runoff depends on soil infiltration capacity, slope and antecedent wetness; storage changes slowly in aquifers and quickly in soils.
Climate warming shifts the balance by increasing atmospheric demand for water, changing the timing of snowmelt and altering rainfall intensity. Land change, such as urban paving or deforestation, alters how much water infiltrates and how fast it runs off.
Observation, models and their limits.
- 01
Observation
Rain gauges, streamflow gauges, soil moisture probes and groundwater wells, complemented by Earth observation for precipitation, snow, evaporation and storage anomalies. Gauge networks are sparse in many regions and have declined in some.
- 02
Models
Conceptual and physically based hydrologic models simulate the balance, analysed with hydrology and water science and geospatial information systems. Models calibrated to past conditions may not hold under changed land or climate.
- 03
Uncertainty
Different models and parameter sets can fit the same record equally well, so results are reported as ranges using uncertainty quantification. Ungauged catchments rely on regionalisation, which adds uncertainty.
How it is studied, and what the methods cannot say.
- 01Hydrology and water scienceRainfall-runoff modelling of a basin · Water balance accountingApplied here
- 02Geospatial information systemsExposure mapping · Catchment and basin delineation · Public-safe basin mapsApplied here
- 03Earth observation and remote sensingFlood extent from radar · Surface water and reservoir monitoringApplied here
- 04Spatial statistics and geostatisticsRainfall and groundwater interpolation · Regionalisation to ungauged basinsApplied here
- 05Uncertainty quantification and model validationEnsemble and parameter uncertainty · Propagation through the cascade · Evidence labellingApplied here
Where this page’s references lead.
The bodies and documents this page is read against. Status and editions change; each link goes to the issuing body.
- Guide to Hydrological Practices, WMO-No. 168, volume I (WMO, 2020 edition)
- Global Runoff Data Centre (operating under the auspices of WMO)
- Lehner, Verdin and Jarvis, New global hydrography derived from spaceborne elevation data (HydroSHEDS), Eos 89 (2008)
- Addor and others, The CAMELS data set: catchment attributes and meteorology for large-sample studies, Hydrology and Earth System Sciences 21 (2017)
- Kratzert and others, Towards learning universal, regional and local hydrological behaviours via machine learning applied to large-sample datasets, Hydrology and Earth System Sciences 23 (2019)
- NS-12, Software, model and AI/ML artefacts, a proposed standard
Questions and answers.
What is an ungauged basin?
Why does the water balance not close?
Can a model calibrated elsewhere be used here?
Why does the water balance rarely close exactly?
Can satellites replace gauges?
How does Water Nexus treat ungauged catchments?
Does Water Nexus publish catchment models?
Contribute evidence from this page.
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