How do data centres affect local water supplies?
Data centres use little water nationally, but the same water matters far more where and when it is scarce.
Covers: How data centres use water for cooling, the volumes involved, the types of cooling systems and their water intensity, and what research and utility data show about effects on local water availability and quality. It does not cover energy use, carbon emissions or land-use impacts except where they directly relate to water.
Also answers: Do data centres use a lot of water? · Data centres and local water shortages · How much water do data centres consume? · Data centre water usage impact on communities
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The short answer
Evidence-backed AI-prepared starting mapData centres use water mainly for cooling, and the water question is really two questions: how much they take, and where and when they take it. In the United States, data centre water consumption of about 1.7 billion litres per day is small against total US water consumption of about 1218 billion litres per day, but in some cases 57% of the water used for cooling is potable water, and fewer than a third of operators measure their water consumption at all. Globally, one set of scenarios puts AI-related water withdrawal at roughly 4.2–6.6 billion cubic metres per year by 2027. The same volume of water can matter very differently depending on local and seasonal water stress, so national totals understate the pressure in water-scarce basins.123
- Evidence 25
- Interpretation 4
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Be the first to voteIn brief
Data centres use water mainly for cooling, and some of that water is potable: in one analysis, 57% of cooling water was sourced from potable water.1
Evidence-backedAt national scale the share is small: US data centre water consumption of about 1.7 billion litres/day compares with total US water consumption of about 1218 billion litres/day.1
Evidence-backedGlobal AI-related water withdrawal is projected at roughly 4.2–6.6 billion cubic metres per year by 2027 under the scenarios examined, a withdrawal range rather than a measured consumption figure.2
Evidence-backedThe same volume of water can matter very differently depending on when and where it is consumed, so local and seasonal water stress matters more than national averages.3
Evidence-backedTransparency is a core problem: fewer than a third of data centre operators measure water consumption, which limits any assessment of water efficiency.1
Evidence-backed
At a glance
The picture in numbers
Live · updated just now
0.14%
Data centres as a share of total US water use: about 1 in every 720 litres
- Data centres
- 1.7 billion litres/day
- Total US water use
- 1,218 billion litres/day
Data centres: ≈ 680 Olympic swimming pools a day
57%
57 in every 100
The evidence behind it
6 sources- Other studies and data5
- Background1
Published in 2021 and 2026
| Source | Kind | Year |
|---|---|---|
| The island where locals say data centres are taking their water | Background | 2026 |
| Artificial Intelligence and the Hidden Water Footprint of Data Centres: Cooling, Consumption and Environmental Sustainability | Other studies and data | 2026 |
| Assessing Water Consumption and Cooling Efficiency Challenges Associated with Large-Scale Data Centre Growth and Operations | Other studies and data | 2026 |
| Data centre water consumption | Other studies and data | 2021 |
| Balancing Bits and Drops: Stress-Adjusted Water Management for Data Centers | Other studies and data | 2026 |
| Governing AI Data Center Growth in Water-Stressed Regions | Other studies and data | 2026 |
The community around it
No one has added to this page yet. Firsthand experience, a newer study or a different reading of the numbers would show up here, credited to you.
Shares and multiples are worked out from the figures the page states. An Olympic swimming pool holds about 2.5 million litres.
What it means for you
Which fits you?
Pick the situation closest to yours. Each answer says what it rests on.
If you live near a proposed or operating data centre in a water-stressed area
the relevant questions are cumulative withdrawals, drought sensitivity, emergency curtailment, community participation and enforceable reporting, which is the set a proposed governance framework for AI data centre growth puts at the centre of place-based regulation.5
Evidence-backedIf you are comparing data centres or operators on water performance
check whether the figures are withdrawal or consumption, since treating the two as interchangeable can produce misleading conclusions, and note that fewer than a third of operators measure water consumption at all.21
Evidence-backedIf you are planning a facility in a region with seasonal water scarcity
stress-adjusted accounting that uses county-level monthly water availability and includes the off-site water footprint of electricity generation gives a different picture from total-water-use measures, and dry cooling, rainwater harvesting and water-and-carbon-aware workload scheduling are the options examined.3
Evidence-backedIf you want to cut freshwater dependence in an existing facility
the options assessed include direct-to-chip liquid cooling, closed-loop systems, dry cooling, immersion technologies, recycled-water utilisation and improved siting, weighed against thermal performance, electricity consumption, operational reliability and regional water availability.24
Evidence-backedIf you are reading a national statistic about data centre water use
it will not tell you about pressure in a particular basin or season, because the same unit of water has drastically different consequences depending on when and where it is consumed.3
Evidence-backedIf you are assessing whether AI growth is sustainable
energy and carbon metrics alone are not sufficient, because water availability must be considered in the planning, operation and governance of AI infrastructure.2
Evidence-backedThe full story · 5 chapters
01
Why data centres use water, and why withdrawal and consumption are not the same thing
AI summary:Cooling is the main direct water use, and withdrawal and consumption are different measures that should not be treated as interchangeable.
Evidence-backed: Cooling is essential to keep servers reliable and prevent equipment failure, and it is the main direct water use of a data centre. Water is also used indirectly, through the water requirements of the electricity generation that powers the facility. One analysis notes that cooling water is in some cases 57% sourced from potable water, meaning treated drinking water is being used for cooling.14
Evidence-backed: A central distinction is between water withdrawal (water taken from a source, some of which may be returned) and water consumption (water lost to evaporation or otherwise not returned to the same source). Treating these measures as interchangeable can produce misleading conclusions about how much a facility really removes from a local water system.2
Interpretation: The practical implication is that a headline number about how much water a data centre 'uses' can mean very different things depending on which measure is meant, and that comparisons between facilities or against other water users are only meaningful when the same measure is used.21
02
How large is data centre water use compared with other users?
AI summary:US data centre water use is a small national share, but global AI withdrawal is projected to grow and local stress is hidden by averages.
Evidence-backed: In the United States, data centre water consumption has been estimated at about 1.7 billion litres per day, against total US water consumption of about 1218 billion litres per day. On that comparison, data centres are a small share of national water use. The same analysis stresses that transparency is poor, with fewer than a third of data centre operators measuring water consumption, which makes water-efficiency comparisons difficult.1
Evidence-backed: Globally, existing research cited in one review indicates that AI-related water withdrawal could reach approximately 4.2–6.6 billion cubic metres annually by 2027 under the scenarios examined. This is a projected range for withdrawal, not a measured consumption figure, and it reflects the expansion of AI computing rather than data centres as a whole.2
Evidence-backed: National averages and global totals can hide the local picture. The same unit of water can have drastically different environmental consequences depending on when and where it is consumed, so a small national share can still coincide with real pressure in a water-stressed basin or during a dry season.3
03
Cooling systems and how much water they need
AI summary:Cooling designs range from water-intensive evaporative towers to dry, liquid and closed-loop systems, each with trade-offs.
Evidence-backed: Cooling approaches differ sharply in water intensity. Conventional cooling towers rely on evaporation and are the most water-intensive; air cooling and dry cooling avoid or greatly reduce water use; liquid cooling, direct-to-chip liquid cooling, immersion technologies, recycled-water utilisation and closed-loop systems are presented as ways to cut freshwater dependence. Facility capacity, climatic conditions, computing intensity and location all shape how much water a given design actually consumes.42
Evidence-backed: Dry cooling is examined as a water-free alternative to evaporative cooling, and rainwater harvesting is assessed as a supplemental water source. Workload scheduling that jointly optimises water and carbon efficiency is another lever, because shifting computing in time or space changes when and where water is consumed.3
Evidence-backed: There is a trade-off to weigh rather than a free win: cooling choices sit at the intersection of thermal performance, electricity consumption, operational reliability and regional water availability, so a design that saves water may cost energy or reliability, and the right balance depends on the local climate and water situation.4
04
Local effects, siting and governance
AI summary:In water-stressed areas, siting, cumulative withdrawals, drought timing and disclosure matter more than national percentages.
Evidence-backed: In water-stressed regions, siting decisions affect not only land and grid capacity but also basin resilience, transparency and justice. Cooling systems can raise local freshwater demand directly, while electricity procurement adds indirect water use across power systems.5
Evidence-backed: A governance framework proposed for AI data centre growth combines hydrological screening, public disclosure and water-justice safeguards, and argues that responsible expansion requires place-based regulation evaluating cumulative withdrawals, drought sensitivity, emergency curtailment, community participation and enforceable reporting.5
Evidence-backed: A stress-adjusted accounting approach uses county-level monthly variation in water availability and extends the analysis to the off-site water footprint of electricity generation, on the argument that prior research focused on reducing total water use while overlooking that the same unit of water has very different consequences depending on when and where it is consumed.3
Evidence-backed: One news report from an island community describes residents saying data centres are taking their water, with a local telling the BBC that no water had come through their taps for three months. This is a community account of a water-supply problem reported alongside data centre presence; it does not by itself establish that data centre abstraction caused the outage, and no hydrological measurement is given.6
05
Water quality and what the sources do not cover
AI summary:The sources cover water quantity and cooling design but do not quantify water quality or discharge impacts.
Interpretation: The sources here focus on water quantity, withdrawal, consumption and cooling design. Water quality effects are not quantified in this material: no source reports measured discharge, thermal or chemical impacts on receiving waters from data centre cooling.41
Evidence-backed: Recycled-water utilisation appears in the literature as a way to reduce freshwater dependence, which implies an interface with wastewater systems, but the sources do not report outcomes for local water quality from such arrangements.4
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Before you go
What to remember
Try to recall each hidden figure before you reveal it. Remembering, not rereading, is what makes it stick.
Data centres use water mainly for cooling, and some of that water is potable: in one analysis, of cooling water was sourced from potable water.
At national scale the share is small: US data centre water consumption of about billion litres/day compares with total US water consumption of about 1218 billion litres/day.
Global AI-related water withdrawal is projected at roughly billion cubic metres per year by 2027 under the scenarios examined, a withdrawal range rather than a measured consumption figure.
Your reading
0 of 5 chapters- Not read yet: 1. Why data centres use water, and why withdrawal and consumption are not the same thing
- Not read yet: 2. How large is data centre water use compared with other users?
- Not read yet: 3. Cooling systems and how much water they need
- Not read yet: 4. Local effects, siting and governance
- Not read yet: 5. Water quality and what the sources do not cover
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- 1Data centre water consumptionnpj Clean Water (Mytton)Published Feb 15, 2021Checked Oct 13, 2026
“To reliably support the online services used by these billions of users, data centres have been built around the world to provide the millions of servers they contain with access to power, cooling and internet connectivity. Whilst the energy consumption of these facilities regularly receives mainstream and academic coverage, analysis of their water consumption is scarce. Data centres consume water directly for cooling, in some cases 57% sourced from potable water, and indirectly through the water requirements of non-renewable electricity generation. Although in the USA, data centre water consumption (1.7 billion litres/day) is small compared to total water consumption (1218 billion litres/day), there are issues of transparency with less than a third of data centre operators measuring water consumption. This paper examines the water consumption of data centres, the measurement of that consumption, highlights the lack of data available to assess water efficiency, and discusses and where the industry is going in attempts to reduce future consumption.”
- 2Artificial Intelligence and the Hidden Water Footprint of Data Centres: Cooling, Consumption and Environmental SustainabilityZenodo (CERN European Organization for Nuclear Research) (Thakur)Published Sep 30, 2026Checked Oct 13, 2026
“This paper examines how AI data centres operate, why their cooling systems require water, and how the expansion of AI may affect freshwater resources. It adopts a secondary-research methodology based on peer-reviewed research, institutional reports, and publicly available environmental assessments. Particular attention is given to the distinction between water withdrawal and water consumption, because treating these measures as interchangeable can produce misleading conclusions. Existing research indicates that global AI-related water withdrawal could reach approximately 4.2–6.6 billion cubic metres annually by 2027 under the scenarios examined by Li et al. (2025). At the same time, technological developments such as direct-to-chip liquid cooling, closed-loop systems, dry cooling, improved data-centre siting, and water recycling provide opportunities to reduce freshwater dependence. The paper argues that the sustainability of AI cannot be assessed through energy and carbon metrics alone. Water availability must become an important consideration in the planning, operation, and governance of future AI infrastructure.”
- 3Balancing Bits and Drops: Stress-Adjusted Water Management for Data CentersarXiv (Cornell University) (Talukder et al.)Published Jun 15, 2026Checked Oct 13, 2026
“However, prior research has largely focused on reducing total water use, overlooking that the same unit of water can have drastically different environmental consequences depending on when and where it is consumed. In this paper, we introduce a stress-adjusted water framework that quantifies the true sustainability impact of data center water consumption by incorporating both spatial and temporal water stress. Using the AWARE-US model, we capture county-level monthly variations in water availability and extend this framework to account for the off-site water footprint of electricity generation. Based on this stress-aware accounting, we analyze stress-adjusted water-computing strategies spanning both the software and infrastructure layers. Specifically, we study workload scheduling policies that jointly optimize water and carbon efficiency, evaluate the potential of rainwater harvesting as a supplemental water source, and investigate the feasibility of dry cooling as a water-free alternative to evaporative cooling.”
- 4Assessing Water Consumption and Cooling Efficiency Challenges Associated with Large-Scale Data Centre Growth and OperationsInternational Journal of Science and Engineering Applications (Rawal)Published Oct 5, 2026Checked Oct 13, 2026
“ntensive cooling systems.Although cooling is essential for maintaining reliable computing environments and preventing equipment failure, growing facility capacity can substantially increase water withdrawals and consumption, particularly in regions experiencing water scarcity and climate-related resource pressures.This study assesses water consumption and cooling-efficiency challenges associated with large-scale data centre growth and operations.It examines cooling-system design, climatic conditions, computing intensity, facility location, water usage effectiveness, and the relationship between water and energy efficiency.The analysis evaluates conventional cooling towers, air cooling, liquid cooling, immersion technologies, recycled-water utilization, and closed-loop systems as alternative approaches for reducing freshwater dependence.Particular attention is given to balancing thermal performance, electricity consumption, operational reliability, and regional water availability.The study develops a sustainability-oriented perspective for improving cooling efficiency while minimizing water-related environmental impacts throughout expanding data centre infrastructure and operations.”
- 5Governing AI Data Center Growth in Water-Stressed RegionsAdvances in computational intelligence and robotics book series (Khaldy et al.)Published Jun 29, 2026Checked Oct 13, 2026
“AI data centers are becoming critical infrastructure, but their growth is also a water-governance issue. Cooling systems can raise local freshwater demand directly, while electricity procurement adds indirect water use across power systems. In water-stressed regions, siting decisions affect not only land and grid capacity, but also basin resilience, transparency, and justice. This chapter develops a governance framework for AI data center growth that combines hydrological screening, public disclosure, and water-justice safeguards. It argues that responsible expansion requires place-based regulation that evaluates cumulative withdrawals, drought sensitivity, emergency curtailment, community participation, and enforceable reporting. Drawing on research on data center sustainability, water disclosure, planning, and environmental justice, the chapter proposes policy instruments for governments, utilities, and operators seeking water-resilient digital infrastructure.”
- 6The island where locals say data centres are taking their waterBBC NewsPublished Oct 12, 2026Checked Oct 13, 2026
“"Not a single drop has come through our taps for the last three months," a local tells the BBC.”
How it changed
Published 1 time since Oct 13, 2026.
- Version 2Oct 13, 2026Live now
AI-prepared Starting Map from live research.
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Open questions
What do measured, facility-level withdrawal and consumption figures show for specific data centres in water-stressed basins, as opposed to national totals and scenario ranges?
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In cases where residents report losing tap water, how much of the shortfall is attributable to data centre abstraction versus drought, infrastructure or other users?
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What are the water-quality effects of data centre cooling, including discharge, thermal load and the use of recycled or non-potable water?
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What do stress-adjusted accounting methods find when applied to named locations, and how much do they change the ranking of sites compared with total-water-use measures?
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How do dry cooling, closed-loop and liquid cooling perform on electricity use and reliability when they are adopted to cut water use?
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