operator stringclasses 4
values | location stringclasses 10
values | W_d_ML_d float64 0.09 14.6 | r float64 0.58 0.83 | PF float64 2.21 30 | K_ML_d float64 7.6 52 | stated_WCI float64 0.18 1.86 | grid_region stringclasses 10
values | reservoir stringclasses 2
values | dam stringclasses 2
values | double_coupled_candidate stringclasses 2
values | source_tag stringclasses 8
values | motive_tier stringclasses 3
values | cooling stringclasses 2
values | WUE_L_per_kWh float64 0.2 1.8 | EWIF_lo float64 0.8 5 | EWIF_hi float64 1.5 15 | W_d_2025_ML_d float64 2.73 18.1 ⌀ | r_2025 float64 0.6 0.84 ⌀ | vintage_note stringclasses 6
values | range_flag stringclasses 1
value |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Meta | Lebanon, IN | 4.81 | 0.77 | 6.3 | 30.3 | 0.77 | MISO-Indiana | null | null | null | ren_seed_NOT_operational_closedloop | planning_not_measured | closed_loop | 0.2 | 1.8 | 3 | null | null | null | null |
Google | Council Bluffs, IA | 14.62 | 0.716 | 6.5 | 36.6 | 1.86 | MISO-Iowa | null | null | null | google_fy2024_verified | primary | evaporative | 1.8 | 1.5 | 3 | 18.112 | 0.7707 | Google 2026 Env Report, FY2025: 1,746.4 M gal withdrawn / 1,346.0 consumed | null |
Google | Mayes Co., OK | 11.49 | 0.752 | 2.5 | 52 | 0.44 | SPP-Oklahoma | null | null | null | utility_openrecords_2024_verified | primary | evaporative | 1.8 | 0.8 | 1.5 | 14.485 | 0.7743 | Google 2026 Env Report, FY2025: 1,396.7 / 1,081.4 | null |
Google | The Dalles, OR | 4.78 | 0.784 | 2.21 | 45.4 | 0.18 | Columbia-River | Columbia-River | The-Dalles-Dam | Y_primary | google_fy2024_verified | primary | evaporative | 1.8 | 5 | 15 | 6.786 | 0.7168 | Google 2026 Env Report, FY2025: 654.3 / 469.0 | null |
Google | Douglas Co., GA | 4.6 | 0.826 | 2.5 | 22.7 | 0.41 | Southern-Co-GA | null | null | null | google_fy2024_reclaimed_verified | primary | evaporative | 1.8 | 1.8 | 3.5 | 4.569 | 0.8332 | Google 2026 Env Report, FY2025: 440.6 / 367.1 | null |
Microsoft | Wisconsin | 0.087 | 0.77 | 30 | 7.6 | 0.34 | MISO-Wisconsin | null | null | null | ren_table6 | planning_not_measured | closed_loop | 0.2 | 1.8 | 3 | null | null | null | null |
Google | Botetourt Co., VA | 7.57 | 0.77 | 2.5 | 10.2 | 1.45 | PJM-Dominion-VA | null | null | null | contracted_2MGD_NOT_operational | planning_not_measured | evaporative | 1.8 | 1.8 | 3 | null | null | null | null |
xAI | Memphis, TN | 3.79 | 0.77 | 4.5 | 22.7 | 0.57 | TVA-Tennessee | null | null | null | ren_seed_W_in_primary_range_multisite | framework_secondary | evaporative | 1.8 | 2 | 3.5 | null | null | null | 0.81-7.0 MGD across Colossus 1 and 2; the static row is Ren's single-site value and is indicative only |
Google | Midlothian, TX | 2.29 | 0.825 | 2.5 | 15.1 | 0.31 | ERCOT-Texas | null | null | null | google_fy2024_verified | primary | evaporative | 1.8 | 0.8 | 1.5 | 2.734 | 0.8354 | Google 2026 Env Report, FY2025: 263.6 / 220.2 | null |
Google | Henderson, NV | 3.73 | 0.576 | 2.5 | 15.2 | 0.36 | Colorado-River | Lake-Mead | Hoover-Dam | Y_source_confirmed | henderson_city_2024_Wverified_r_unconf | primary | evaporative | 1.8 | 2 | 6 | 4.166 | 0.596 | Google 2026 Env Report, FY2025: 401.7 / 239.4 | null |
AI Data-Centre Water Tracker
An open, reproducible referee for the water burden of AI data centres. It rebuilds the Water Consumption Impact index for ten sites from public data, self-checks each value against its source, and adds the two channels the original framework leaves open: the hydropower coupling, and the off-site relocation of water that closed-loop cooling produces. Figures are bands and decompositions, every input named and motive-tagged.
- Author: NM AI Research (independent analyst)
- ORCID: 0009-0003-4213-7769
- DOI: https://doi.org/10.5281/zenodo.21318960
- Source and code: https://github.com/NMAIResearch/ai-water-tracker
Files
sites.csv(10 rows): the per-site inputs and rebuilt index. Columns includeoperator,location,W_d_ML_d(daily water, ML/day),r,PF,K_ML_d,stated_WCI,grid_region,reservoir,dam,double_coupled_candidate,source_tag,motive_tier,cooling,WUE_L_per_kWh,EWIF_lo,EWIF_hi.reproduce.py: standard-library reproducer that rebuilds the index fromsites.csv.SOURCES.md: per-input provenance and motive tags.LICENSE: Creative Commons Attribution 4.0 International.
Method
Each site's Water Consumption Impact value is rebuilt from public data and checked against its source, then decomposed into on-site use and the off-site relocation that closed-loop cooling moves to the grid (roughly 92 to 95 per cent of the footprint). Every input is named and tagged by the incentive of its source. Drafting is AI-assisted; the judgement is not.
Citation
NM AI Research. AI Data-Centre Water Tracker. Zenodo. https://doi.org/10.5281/zenodo.21318960 . Licensed CC BY 4.0.
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