GCRI · Water Nexus
Water-energy interdependence
Water systems need energy and energy systems need water. Pumping, treatment and desalination draw power; hydropower, thermal cooling and fuel production draw water. Drought and heat stress both at once, and a failure in one propagates into the other within hours. This page sets out the couplings, how they are measured, and what the coupled evidence serves.
Water-energy interdependence on the record.
Water-energy interdependence: how the risk develops.
Pumping, cooling, hydropower and demand. The dossier below sets out how the risk develops, what is measured, where the data come from, how it is modelled and who decides.
Water for energy
Hydropower depends on inflow and head; thermal plants on cooling water below a temperature limit; some fuels on process water. Low rivers and warm water curtail generation when demand peaks.
Energy for water
Groundwater pumping, long-distance conveyance, treatment and desalination are energy-intensive. An outage stops supply unless storage and backup are in place.
Shared peaks
Heat raises cooling demand, irrigation demand and electricity demand together while lowering supply on both sides. The coupling turns a hazard into a compound event.
Shared decisions
Reservoir releases serve generation, irrigation, flood control and ecosystems at once. A rule curve is a cross-sector decision whether or not it is governed as one.
Water now in the Nile Basin.
Night lights, flood water and rain 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
- Source
- VIIRS Black Marble (VNP46A2) (NASA LAADS DAAC)
- Provider
- NASA LAADS DAAC
- Licence
- NASA open data
- Vintage
- daily; Satellite radiance, corrected for moonlight and atmosphere
- Newest at build
- 2026-10-12
- Resolution
- about 500 m grid
- Uncertainty
- Satellite night-time radiance for the date shown; lights are a proxy for electricity use and outages, not a measurement of supply or access.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
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
- 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
- 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
- Estimated
- Source
- Gridded Population of the World v4.11 (NASA SEDAC (CIESIN, Columbia University))
- Provider
- NASA SEDAC (CIESIN, Columbia University)
- Licence
- CC BY 4.0
- Vintage
- null; Census-based estimate, static (2020)
- Resolution
- about 1 km grid
- Uncertainty
- Census-based estimate of people per square kilometre in 2020, shown as exposure context; not a count of people affected by any hazard.
- 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 SMAP · 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 Black Marble night-time lights, NASA Goddard Space Flight Center; distributed by the NASA Level-1 and Atmosphere Archive and Distribution System (LAADS DAAC); 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
- 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
- Center for International Earth Science Information Network (CIESIN), Columbia University. 2018. Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11. NASA Socioeconomic Data and Applications Center (SEDAC). Licensed under CC BY 4.0. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). doi:10.7927/H49C6VHW. CC BY 4.0
- 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 SMAP. 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
- Night-time lights, daily: VIIRS Black Marble (VNP46A2), NASA LAADS DAAC · 12 Oct 2026
- 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
- True colour: VIIRS NOAA-20 corrected reflectance, NASA LANCE · 13 Oct 2026
- Root-zone soil moisture, 0 to 100 cm: SMAP Level 4, NASA NSIDC DAAC · 11 Oct 2026
- Population density, 2020: Population: CIESIN, Columbia University, Gridded Population of the World v4.11 (2018), NASA SEDAC, CC BY 4.0 · static
Water for energy and energy for water, in measurable terms.
| Direction | Coefficient | Unit and source |
|---|---|---|
| Cooling water withdrawal by thermal plants | Varies by cooling type: once-through withdraws far more than recirculating or dry cooling | Cubic metres per megawatt-hour; Macknick and others, 2012 |
| Cooling water consumption | Higher for recirculating towers than once-through | Cubic metres per megawatt-hour |
| Energy for water supply and treatment | Rises with lift, distance, treatment level and desalination | Kilowatt-hours per cubic metre |
| Hydropower output | Proportional to flow and head | Megawatt-hours per cubic metre released at a given head |
Van Vliet and colleagues showed in 2016 how warmer, lower rivers reduce usable capacity at thermal and hydropower plants. The links continue into food (food, water and energy) and health (healthcare infrastructure), where a drought that curtails power also stops pumps and cold chains.
Shared basin data exchange.
The screen a team would use to follow this subject: its measures, states and decisions. Names are examples and values are illustrative.
Shared basin data exchange
Water-energy interdependence · Shared basin · Riparian states A, B, C
Joint fact-finding
- Data protocolagreed
- Gauge harmonisationdone
- Joint modelrunning
- Scenario setdraft
- Commission review
- Publication
Data exchange status
per agreement annex
| Border gauge 1 | A | Daily | On time |
| Border gauge 2 | B | Daily | Late |
| Dam release log | C | Weekly | On time |
| Water quality set | B | Monthly | Missing |
Flow at border points
- State A
- State B
- State C
Notifications under the agreement
- N-1Planned release change, reservoir C2Prior notice period running5 d
- N-2New abstraction scheme, State AConsultation open21 d
Where water-energy findings go.
Joint water-energy analyses are designed for the Scenario and Stress-Test series, where a dry, hot season is tested across both systems at once. Plant-level coefficients are often confidential; studies use published technology averages unless an operator provides data under terms. Models would carry NS-12 cards under the proposed Nexus Standards.
- Plant curtailment records in low-flow or high-temperature periods.
- Utility energy-use data by process.
- Basin cases where power and water planning were done together, or failed to be.
A cascade through interdependent networks.
A hazard footprint takes out co-located assets in every layer; fragmentation, dependency between layers and overload of the survivors then spread the failure. The 3D view separates the layers by depth. 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
- Assets per layer
- 18
- Hazard footprint
- 8% of area
- Assets that depend on another layer
- 60%
- Spare capacity (tolerance)
- 30%
Readouts
- Assets hit by the hazard
- 9%
- Assets still working
- 19%
- Amplification of the initial damage
- 8.8 times
- Steps to settle
- 4
Numbers, methods and sources
| Step | Water working (%) | Power working (%) | Telecoms working (%) |
|---|---|---|---|
| 0 | 89 | 94 | 89 |
| 1 | 50 | 67 | 56 |
| 2 | 28 | 28 | 44 |
| 3 | 22 | 17 | 39 |
| 4 | 22 | 11 | 22 |
Methods
- Interdependent networks Buldyrev and others (2010), Catastrophic cascade of failures in interdependent networks, Nature 464
- Overload cascades Motter and Lai (2002), Physical Review E 66
- Betweenness load Brandes (2001), Journal of Mathematical Sociology 25
- Multilayer networks Kivela and others (2014), Journal of Complex Networks 2
- Infrastructure interdependencies Rinaldi, Peerenboom and Kelly (2001), IEEE Control Systems 21(6)
- 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:
- Major roads Natural Earth
- Railways Natural Earth
- Seaports Natural Earth
- Airports Natural Earth
- Urban extents Natural Earth
- Night lights tonight, outages (VIIRS Day/Night Band) NASA/NOAA VIIRS (Suomi NPP) · register nasa.gibs.context-live-layers
- Natural events (NASA EONET) NASA EONET · register nasa.eonet.events
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 |
|---|---|---|---|---|
| Hydropower generation against inflow | gigawatt hours; cubic metres per second | daily | Operator records, gauges | Published statistics |
| Cooling-water temperature margin | degrees Celsius below the permit limit | daily | Station sensors | OBSERVED |
| Energy intensity of water supply | kilowatt hours per cubic metre | monthly | Utility metering | OBSERVED |
| Water intensity of generation | litres per megawatt hour | annual | Plant reporting | Published statistics |
| Coincident peak index | dimensionless | daily | Demand records across sectors | ESTIMATED |
| Reserve margin at evening peak | percent | daily | Grid operator | Published statistics |
Model water-energy interdependence 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 |
|---|---|---|
| Grid and hydropower operators | Generation, inflow, reserve margin and curtailment records | By agreement |
| Global Runoff Data Centre and national gauges | River flow at intakes and dams | Data policy; by agreement |
| ERA5 and CHIRPS | Temperature and rainfall forcing | Free and open; public domain |
| Water utility metering | Energy per cubic metre by works | By agreement |
| Global Power Plant Database (WRI) | Plant locations, fuel and capacity | Free and open, attribution |
The sector signature of water-energy interdependence.
The sectors water-energy interdependence touches and how strongly they depend on each other: what a study has to observe and model together.
Signature
Water and Energy
Water gives energy cooling water and hydropower (0.6): 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
- Energy → Infrastructure · power for transport and telecoms0.8
- Water → Energy · cooling water and hydropower0.6
- Water → Food · irrigation0.8
- Infrastructure → Water · pipes, dams and treatment works0.6
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 |
|---|---|---|
| Grid operators and energy ministries | How much replacement capacity to secure before a dry season and at what cost | Inflow forecasts, hydropower curtailment scenarios, reserve margins at peak |
| Reservoir operators and basin authorities | How to set releases when generation, irrigation and ecosystems compete | Coupled scenarios with each use’s loss stated separately |
| Utilities and civil protection | Which works lose supply first in an outage and how long storage lasts | Dependency map with endurance hours; outage scenarios by feeder |
Outputs this topic produces
- Coupled water-energy model of the basin and grid
- Compound heat-drought stress tests
- Release schedules examined across all uses
- Cascade traces with evidence labels per step
How water-energy interdependence 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 water-energy interdependence: signs of influence, a reinforcing and a balancing loop, and cross-links where stress jumps.
What researchers ask about the water and energy link.
- How much water does each type of power generation withdraw and consume?
- How do drought and heat reduce generating capacity, and when does that coincide with peak demand?
- How much energy does each water service use, from abstraction to wastewater treatment?
- How do decarbonisation choices change water demand?
Shared stress in drought and heat.
During heatwaves and droughts, rivers run low and warm. Thermal plants may have to reduce output when cooling water is scarce or too warm to discharge within environmental limits, just as electricity demand for cooling rises. Hydropower output falls with reservoir levels. At the same time, water utilities pump harder and further, and desalination plants draw heavily on the grid.
Power outages stop pumps and treatment, so electricity failure becomes water failure within hours. Choices about the energy transition matter: some low-carbon options, such as wind and solar photovoltaics, use little water in operation, while others, such as some forms of hydrogen production, bioenergy and carbon capture, can add water demand.
Modelling coupled systems.
- 01
Coupled models
Joint water and power system models test drought scenarios, using hydrology and water science and forecasting and time-series analysis. Coupling different model types introduces its own uncertainties.
- 02
Water and energy accounting
Inventories of water use per unit of energy and energy use per unit of water. Plant-level data are often unpublished.
- 03
Climate scenarios
Scenarios of flow and temperature translated into capacity constraints, with uncertainty quantification. Results are scenarios, not forecasts.
How it is studied, and what the methods cannot say.
- 01Hydrology and water scienceRainfall-runoff modelling of a basin · Water balance accountingApplied here
- 02Forecasting and time-series analysisFlood forecasting by reach · Seasonal drought outlooks · Inflow forecasts for reservoirsApplied here
- 03Sensors, IoT and observation networksGauge and well telemetry · Utility network monitoring · Low-cost sensing with stated uncertaintyApplied here
- 04Uncertainty quantification and model validationEnsemble and parameter uncertainty · Propagation through the cascade · Evidence labellingApplied here
- 05Extreme value theory and tail riskReturn-period flows and rainfall · Regional frequency analysisApplied 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.
- World Energy Outlook 2016, special report on water-energy nexus (International Energy Agency, 2016)
- Macknick and others, Operational water consumption and withdrawal factors for electricity generating technologies, Environmental Research Letters 7 (2012)
- van Vliet and others, Power-generation system vulnerability and adaptation to changes in climate and water resources, Nature Climate Change 6 (2016)
- NS-12, Software, model and AI/ML artefacts, a proposed standard
Where this page leads across the Consortium.
Questions and answers.
Why does heat threaten both systems at once?
Can storage decouple them?
Who governs the coupling?
What is the difference between withdrawal and consumption?
Does renewable energy remove the water constraint?
How does desalination change the picture?
Can Water Nexus model a specific power plant?
Contribute evidence from this page.
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