Irrigation supply and demand
Basin models compare crop water requirements with available river flow, storage and groundwater by season, showing where irrigation will be curtailed in a dry year.
GCRI · Food Nexus
Hydrology for agriculture
Hydrology connects food production to the water that sustains it: rainfall, soil moisture, river flows, reservoirs and groundwater. In this field it quantifies irrigation supply and demand, the onset of agricultural drought and the flood exposure of farmland, and builds the water accounts on which allocation between farms, cities and ecosystems rests.
01Contents
Hydrology for agriculture in five parts.
02Application
What hydrology and water science does here.
Hydrology studies the movement, distribution and quality of water across the land surface, subsurface and atmosphere. It underpins the assessment of floods, droughts and water supply, which affect food, energy, health and infrastructure together. In food and agriculture, trade, resources and supply chains and climate and hazards, hydrological estimates set design standards for dams, levees and drainage and inform water allocation between competing users. They link climate hazards to downstream effects on power generation, crops and public health. The canonical description is on the STEM Commons.
Agricultural drought monitoring
Rainfall, soil moisture and streamflow indices are tracked together, so the onset of agricultural drought is identified when indicators agree rather than from one source alone.
Flood exposure of cropland
Flood hazard maps and satellite flood extents are intersected with cropland and stores to estimate exposed area and the stage of the crop when water arrives.
03Geography
Water now in the Black Sea region.
Root-zone soil moisture, rain, flood water and true-colour imagery over the Black Sea region, from NASA GIBS. Each layer carries its own date.
The interactive map needs JavaScript; the layer list and sources are below.
Layers 4
Water now, for this page
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 Black Sea region.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 HLS S30Sources 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
- 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
- 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
- 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
- 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
- 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.
- Observed (EO): NASA HLS S30 (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
- Root-zone soil moisture, 0 to 100 cm: SMAP Level 4, NASA NSIDC DAAC · 11 Oct 2026
- Precipitation rate, daily mean: GPM IMERG, NASA GES DISC · 12 Oct 2026
- Flood water, 3-day composite: VIIRS Global Flood Product, NASA LANCE · 14 Oct 2026
- Land cover type (IGBP), yearly: MODIS Terra and Aqua land cover (MCD12Q1), NASA LP DAAC · 2024
- Vegetation greenness (NDVI), rolling 8 days: MODIS Terra NDVI, NASA LANCE · 13 Oct 2026
- True colour: VIIRS NOAA-20 corrected reflectance, NASA LANCE · 13 Oct 2026
04Data
What the method consumes.
Datasets and records used by hydrology and water science on this platform, with the role each plays and the label its values carry. Sources are listed in the source register.
| Data | Role | Label |
|---|---|---|
| Stream gauge records | Water availability | OBSERVED |
| CHIRPS rainfall | Drought indices | ESTIMATED |
| Satellite soil moisture (SMAP, SMOS) | Agricultural drought | MODELLED |
| Groundwater observation wells | Aquifer supply to irrigation | OBSERVED |
| FAO AQUASTAT | Agricultural water withdrawal | Published statistics |
05Monitor
Forecast verification.
The screen a team would use to follow this method: its measures, states and decisions. Names are examples and values are illustrative.
Forecast verification
Food · Hydrology for agriculture · Run R-9576
Skill above referenceGCRI · Food Nexus
Forecast with range
Method card
- Method
- Hydrology and water science
- Reference
- climatology
- Lead times
- 1 to 6 steps
- Label
- MODELLED
Verification by lead time
held-out period
| T+2 | 0.36 | 0.24 | 0.28 | Marginal |
| T+4 | 0.22 | 0.30 | 0.10 | Skilful |
| T+6 | 0.18 | 0.13 | 0.38 | Marginal |
| T+8 | 0.24 | 0.16 | 0.11 | Skilful |
06Validation
How results are checked.
Models are calibrated on one period of observed flows and tested on an independent period or neighbouring catchment. Skill scores and comparison with surveyed flood extents are used to judge performance.
| Check | How it is done |
|---|---|
| Split-sample calibration | Models are calibrated on one period of record and tested on another |
| Water balance closure | Supply, consumption and return flows are reconciled, and residuals reported |
| Indicator agreement | Drought onset is checked across rainfall, soil moisture and flow indicators |
Assumptions of stationarity weaken as climate and land use change. Ungauged basins, karst aquifers and rapid urbanisation are poorly represented by parameters fitted elsewhere.
07Simulator
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 Black Sea region; 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 Food Nexus in Foundry
- Tested at Food Nexus in Universe
- Codified in Water and Food Systems in Standards
- Published in Food Nexus in Reports
- Taught in Faculty of Earth and Environmental Sciences in Academy
08Connections
Topics that rely on this method.
09Twin and cascade simulator
Model hydrology for agriculture as a system. Run the cascade.
Assembled from the Nexus Standards area profile Food and agriculture 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
Systems and couplings
- Food and agriculture risk standards
- Water risk standards Common driver
- Supply chain and trade risk Cascade
- Conflict and security risk standards Feedback
- Biological hazard standards Cascade
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-H60Hazard family: Supply-chain and flow disruption (NHS-1 sys.supply-chain)NRS-H81Hazard family: Malnutrition and micronutrient deficiency (NHS-1 health.nutrition)NRS-H43Hazard family: Livestock and aquaculture epizootics (NHS-1 health.animal-health)NRS-N3Food and agriculture risk and resilienceNRS-C7Early warning, triggers and anticipatory actionNRS-H44Hazard family: Plant disease and pests (NHS-1 health.plant-health)
Observation
- IPC acute food insecurity analyses (listed for reference)
- FEWS NET outlooks and data (listed for reference)
- CHIRPS quasi-global gridded precipitation (listed for reference)
- FAO GIEWS Food Price Monitoring and Analysis tool (listed for reference)
- ESA WorldCover 10 m land cover (listed for reference)
- Prevalence of undernourishment
- Prevalence of moderate or severe food insecurity (FIES)
- Indicator of food price anomalies
- Direct agricultural loss 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
- Night lights tonight, outages (VIIRS Day/Night Band), NASA GIBS
10Stewardship
Who maintains the method.
Water Nexus stewards hydrology and water science for the Consortium and reviews how it is applied across platforms.
11Sector signature
The sector signature of hydrology for agriculture.
The sectors hydrology for agriculture touches and how strongly they depend on each other: what a study has to observe and model together.
Select corners · hover lines6 / 15 relations
Signature
Water and Food
Water gives food irrigation (0.8): the strongest relations between the sectors this subject joins.
W · E · I · F · H · B
- Sectors
- 4 of 6
- Relations
- 6 of 15
- Coupling
- 0.46 share of total
Strongest relations in play
- Food → Biodiversity · land conversion0.6
- Water → Food · irrigation0.8
- Water → Biodiversity · environmental flows0.6
- Food → Health · nutrition0.7
12Connected
Where this page leads across the Consortium.
13Methods and evidence
How hydrology for agriculture 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 hydrology for agriculture: signs of influence, a reinforcing and a balancing loop, and cross-links where stress jumps.
FAIR guiding principles · GO FAIR and FORCE11W3C PROV · World Wide Web ConsortiumGuidance on consistent treatment of uncertainties · IPCCCEOS Analysis Ready Data · Committee on Earth Observation Satellites
14Questions
Questions and answers.
What are the limits of hydrology and water science in this field?
Assumptions of stationarity weaken as climate and land use change. Ungauged basins, karst aquifers and rapid urbanisation are poorly represented by parameters fitted elsewhere.
How are results checked?
Models are calibrated on one period of observed flows and tested on an independent period or neighbouring catchment. Skill scores and comparison with surveyed flood extents are used to judge performance.
Are the values on this page real?
The data names, roles and checks are real descriptions of practice. The canvas shows a design of the method's use, not a run; no result is reported here.
Where else is this method applied?
The canonical method page lists every platform that applies it.
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