GCRI · Data and Compute
Compute energy water
Data centres and supercomputers consume electricity for computing and cooling, and many use water for cooling directly or through power generation. As demand grows, they affect grids, water resources and emissions in the places they are built. This page sets out how computing uses energy and water, how efficiency is measured with standard metrics, and what evidence serves the operators, regulators and planners concerned.
Compute energy water on the record.
Compute energy water: how the risk develops.
Infrastructure impacts and sustainability. The dossier below sets out how the risk develops, what is measured, where the data come from, how it is modelled and who decides.
Electricity is the main input
Servers, storage and networking draw power continuously, and cooling adds overhead. Total demand depends on workload growth and efficiency gains.
Cooling choices trade water for energy
Evaporative cooling uses less electricity but consumes water; air and closed-loop liquid cooling use less water but may use more energy.
Siting concentrates impact
Clusters of data centres in one region can strain local grids and water supplies even when national totals look modest.
Heat can be reused
Waste heat from computing can supply district heating where networks exist nearby, turning a loss into a resource.
Water now in the contiguous United States.
Night lights, population density, flood water and true-colour imagery over the contiguous United States, 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 contiguous United States.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
- 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
- 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
- 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
- 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
- Night-time lights, daily: VIIRS Black Marble (VNP46A2), NASA LAADS DAAC · 12 Oct 2026
- Population density, 2020: Population: CIESIN, Columbia University, Gridded Population of the World v4.11 (2018), NASA SEDAC, CC BY 4.0 · static
- Flood water, 3-day composite: VIIRS Global Flood Product, NASA LANCE · 14 Oct 2026
- True colour: VIIRS NOAA-20 corrected reflectance, NASA LANCE · 13 Oct 2026
- Precipitation rate, daily mean: GPM IMERG, NASA GES DISC · 12 Oct 2026
- Land cover type (IGBP), yearly: MODIS Terra and Aqua land cover (MCD12Q1), NASA LP DAAC · 2024
Computing draws on energy and water.
Analyses are designed for the Foresight Outlook series. ISO/IEC 30134 defines data-centre indicators such as power usage effectiveness; the IEA's Energy and AI report (2025) gives demand scenarios.
- Measured facility energy and water data.
- Grid planning studies.
Compute facility console.
The screen a team would use to follow this subject: its measures, states and decisions. Names are examples and values are illustrative.
Compute facility console
Compute energy water · HPC facility · Month 12
- Utilisation, % of nodes76Active
- PUE1.24Normal
- WUE0.3l/kWhWatch
- Queue wait, p503.9h
Energy by use
- IT load
- Cooling
- Other
Allocations
| Climate ensemble | 724 | 985 | Near quota |
| Flood modelling | 437 | 587 | Within |
| Genomics pipeline | 282 | 224 | Near quota |
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 |
|---|---|---|---|---|
| Power usage effectiveness | ratio of total facility to IT energy | annual | Operator reporting under ISO/IEC 30134-2 | DECLARED |
| Water usage effectiveness | litres per kilowatt hour of IT energy | annual | Operator reporting | DECLARED |
| Data centre electricity consumption | terawatt hours | annual | Energy agency estimates | ESTIMATED |
| Renewable energy share | percent of electricity | annual | Operator reporting | DECLARED |
| Waste heat reused | megawatt hours | annual | Operator reporting | DECLARED |
| Data centre share of regional electricity demand | percent | annual | Grid operator data | ESTIMATED |
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 contiguous United States; 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
- 7%
- Assets still working
- 33%
- Amplification of the initial damage
- 9.0 times
- Steps to settle
- 3
Numbers, methods and sources
| Step | Data centres working (%) | Networks working (%) | Power working (%) |
|---|---|---|---|
| 0 | 89 | 100 | 89 |
| 1 | 56 | 89 | 44 |
| 2 | 33 | 61 | 22 |
| 3 | 33 | 44 | 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 Data and Compute in Foundry
- Tested at Data and Compute in Universe
- Codified in Data and Evidence in Standards
- Published in Data and Compute in Reports
- Taught in Faculty of Technology and Digital Systems in Academy
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 |
|---|---|---|
| EU data centre reporting under the Energy Efficiency Directive | Energy, water and efficiency data from EU data centres | Public summaries, EU terms (to check) |
| IEA data centre electricity estimates | Global and regional demand | Licence per product (to check) |
| Uptime Institute global data centre survey | Industry practices and efficiency trends | Public summaries, terms to check |
| Green500 list | Energy efficiency of supercomputers | Public, TOP500 terms |
| Grid operator load forecasts | Expected data centre load | Public records, terms per operator |
Model compute energy water as a system. Run the cascade.
Assembled from the Nexus Standards area profile Digital, telecom and platforms: 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
- Sea-ice loss
- Coastal erosion (set off downstream)
- Biodiversity loss (set off downstream)
- Arctic winter sea-ice collapse (set off downstream)
- Polar vortex disruption (set off downstream)
Systems and couplings
- Digital, telecom and platforms
- Energy utilities and grids Coupling
- Media and information Coupling
- Finance, insurance and capital markets Coupling
- Defence and civil protection Coupling
Controls
NS-0Constitution and principlesNS-9Contextual UNIs (deployments, instances, programme instances)NS-11Verification and monitoring (evidence, attestations, zero-trust conformance observation)NS-12Software, model and AI/ML artefacts (supply chain, model cards, dataset sheets)NRS-H86Hazard family: Telecom and data-centre failure (NHS-1 infra.digital)NRS-H54Hazard family: Signal and platform disruption (NHS-1 cyber.platform)NRS-H93Hazard family: Identity, trust and cryptographic failure (NHS-1 cyber.identity)NRS-H94Hazard family: Systemic cyber events (NHS-1 cyber.systemic-cyber)NRS-T8Software and supply-chain integrity riskNRS-X6Cyber and digital security risk
Observation
- ITU DataHub ICT indicators (listed for reference)
- World Development Indicators (listed for reference)
- WMO Alert Hub and Register of Alerting Authorities (listed for reference)
- Proportion of population covered by a mobile network, by technology
- Fixed broadband subscriptions per 100 inhabitants, by speed
- Individuals using the Internet (% of population)
- Number of people per 100,000 that are covered by early warning information through local governments or through national dissemination mechanisms
Example geography
- North America: 243 hexagonal cells
- Night lights tonight, outages (VIIRS Day/Night Band), NASA GIBS
- Vegetation disturbance alerts, 30 m (OPERA DIST-ALERT), NASA GIBS
Who decides, and what the evidence serves.
Data and Compute 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 |
|---|---|---|
| Energy regulators and grid operators | How to plan connections and supply for data centres | Regional impact analyses |
| Water authorities | Whether proposed cooling water use is sustainable | Water footprint evidence |
| Operators and public computing centres | Which cooling and heat reuse options to adopt | Footprint methods; heat reuse maps |
Outputs this topic produces
- Energy and water footprint methods for computing facilities
- Regional grid and water impact analyses
- Heat reuse opportunity maps
- Evidence notes on efficiency reporting
The sector signature of compute energy water.
The sectors compute energy water 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
What experts ask about compute footprints.
- How much additional grid capacity will new data centres need, where and when?
- How much water does cooling use, directly and through electricity generation, and in which catchments?
- Can computing loads be shifted in time or place to match renewable output or avoid grid peaks?
- Can waste heat be reused, for example in district heating?
How compute energy water 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.
Datasets and provenance
The data classes behind compute energy water and what is recorded about each before it is used.
| Data class | Provenance recorded | Resolution stated | Licence checked |
|---|---|---|---|
| Satellite imagery, optical and radar | sensor, processing level, acquisition date | pixel size and revisit | per scene |
| Reanalysis and climate model output | model, experiment, member, version | grid and time step | per release |
| In-situ sensors and gauges | station, instrument, calibration record | point and sampling rate | per network |
| Population and asset exposure | method, vintage, input sources | cell size and unit | per release |
| Boundaries and reference geography | producer, edition, date | scale | per edition |
- Lineage
- W3C PROV record from source to figure
- Principles
- FAIR: findable, accessible, interoperable, reusable
- Gaps
- shown as missing, never filled silently
How computing draws on energy and water.
Nearly all the electricity a computer uses becomes heat that must be removed. Cooling methods range from air cooling to liquid cooling and evaporative systems. Evaporative cooling uses water efficiently for energy but consumes water; dry cooling saves water but uses more electricity, especially in hot weather. The choice is a local trade-off between energy and water.
Large data centres are concentrated, steady loads that can require new transmission and generation. Their growth can compete with housing, industry and electrification for grid connections. Heatwaves stress both the grid and cooling systems at the same time, a common-cause risk for continuity. Some workloads, such as model training or batch analysis, can be scheduled flexibly; others, such as real-time services, cannot.
How footprints are measured and planned.
- 01
Efficiency metrics
Ratios of total facility energy to IT energy, and water use per unit of IT energy. Limits: they say nothing about whether the computing itself is efficient or useful.
- 02
Load forecasting
Projecting data centre demand for grid planning, with power systems engineering. Limits: announced projects often exceed what is built, making forecasts uncertain.
- 03
Life cycle assessment
Accounting for manufacturing, operation and disposal, drawing on energy technology. Limits: data on hardware manufacturing is limited.
- 04
Flexibility analysis
Assessing how much load could shift. Limits: depends on workload types and contracts.
How it is studied, and what the methods cannot say.
- 01Energy generation, storage and grid technologyCooling technology comparison · On-site energy options · Heat reuse feasibilityApplied here
- 02Power systems engineeringGrid connection studies · Power supply resilience · Demand flexibilityApplied here
- 03Optimisation and operations researchJob scheduling policies · Carbon-aware workload shifting · Capacity planningApplied here
- 04High-performance, cloud and edge computingWorkload characterisation · Porting codes to new architectures · Hybrid cloud and HPC workflowsApplied 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.
Questions and answers.
What is PUE?
Do data centres use a lot of water?
Can data centre heat be reused?
Is water use always a problem for data centres?
Can data centres help the grid?
Does the platform publish facility-level data?
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