GCRI · Labs
Future of energy and water labs
Energy and water transitions depend on technologies moving from the laboratory to the grid, the treatment works and the farm: storage, electrolysers, heat pumps, desalination, water reuse, sensing and control. Many stall somewhere between a working prototype and a reliable system at scale. This lab studies that passage: how readiness is assessed, how pilots are designed and read, and what regulators, utilities and funders need before a technology is adopted.
Future of energy and water labs on the record.
Future of energy and water labs: how the risk develops.
Water, energy and technical transitions. The dossier below sets out how the risk develops, what is measured, where the data come from, how it is modelled and who decides.
The gap between pilot and market
Public research funds early stages and private capital funds demonstrated ones. Between them, demonstration at scale is costly, slow and risky, and technologies can stall there regardless of technical merit.
Integration risk exceeds component risk
A component demonstrated alone can fail when connected to a grid, a network or a treatment train. Control interactions, fouling, intermittency and maintenance burdens appear only at system level.
Pilots are chosen to succeed
Demonstration sites are often selected for favourable conditions, committed staff and close supervision. Results then overstate what a typical operator will achieve, unless the pilot design accounts for that selection.
Lock-in and rebound
Infrastructure lasts decades, so early choices fix later options. Efficiency gains can raise use, and shifting load between systems can move stress rather than remove it, as when desalination raises electricity demand.
Water now worldwide.
Night lights, population density and flood water across the world, 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
- 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
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
- 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
- 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
- Modelled (latest year GIBS publishes)
- Source
- MODIS Terra and Aqua land cover (MCD12Q1) (NASA LP DAAC)
- Provider
- NASA LP DAAC
- Licence
- NASA open data
- Vintage
- yearly; Satellite classification; latest year GIBS publishes
- Newest at build
- 2024-01-01
- Resolution
- about 500 m grid
- Uncertainty
- Satellite land cover classes for the year shown, including croplands; a classification with known errors, not a farm register.
- 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 world.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 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
- 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 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
- 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
The energy and water labs test the coupling directly.
- WaterIn practiceEnergy-efficient pumping and treatment.What a reader can checkMeasured energy per volume
- EnergyIn practiceRenewable power for water systems.What a reader can checkReliability measures
- FoodIn practiceSolar irrigation and its groundwater effects.What a reader can checkWater table monitoring
Findings feed the Labs Evidence series. Results are reported with site conditions.
- Pilot sites.
- Monitoring data.
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.
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
- 28%
- Amplification of the initial damage
- 7.8 times
- Steps to settle
- 3
Numbers, methods and sources
| Step | Water working (%) | Power working (%) | Telecoms working (%) |
|---|---|---|---|
| 0 | 78 | 100 | 94 |
| 1 | 50 | 94 | 72 |
| 2 | 33 | 44 | 28 |
| 3 | 33 | 28 | 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 in Labs
- Built in Labs in Foundry
- Tested at Labs in Universe
- Codified in Data and Evidence in Standards
- Published in Labs in Reports
- Taught in Faculty of Risk Science and Methods 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 |
|---|---|---|---|---|
| Technology readiness level | level on a named TRL scale | per technology | Independent readiness assessment | ESTIMATED |
| Commercial readiness | level on a named commercial readiness scale | per technology | Readiness assessment | ESTIMATED |
| Pilot availability | percent of scheduled operating hours | monthly | Pilot operating logs | OBSERVED |
| Specific energy consumption of treatment | kilowatt hours per cubic metre | monthly | Metered pilot data | OBSERVED |
| Public energy RD&D budget | national currency at constant prices | annual | IEA RD&D budget statistics | Published statistics |
| Gap between pilot and rated performance | percent of rated output | per pilot | Pilot data against manufacturer specification | OBSERVED |
| Pilot results published, including shortfalls | percent of funded pilots | annual | Programme records and repositories | REPORTED |
Model future of energy and water labs as a system. Run the cascade.
Assembled from the Nexus Standards area profile Global and systemic risk: 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
- Dam failure
- Financial contagion
- River flood (set off downstream)
- Flash flood (set off downstream)
- Mass displacement (set off downstream)
- Widespread power outage (set off downstream)
- Water supply shortfall (set off downstream)
- Sovereign debt crisis (set off downstream)
- Currency crisis (set off downstream)
Systems and couplings
- Global and systemic risk
- Compound and cascading hazard standards Mechanism
- Financial stability and prudential risk Mechanism
- Supply chain and trade risk Cascade
- Future generations and long-term risk Common driver
Controls
NS-0Constitution and principlesNS-3Risk record UNIs (grammar, legacy crosswalk, allocation authority)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)NS-14Conformance and crosswalk to external standardsNRS-H120Hazard family: Structural fragility of interconnected systems (NHS-1 sys.fragility)NRS-C6Systemic and cascading riskNRS-H61Hazard family: Cascading failure (NHS-1 sys.cascade)NRS-H62Hazard family: Compound and multi-hazard events (NHS-1 sys.compound)NRS-H94Hazard family: Systemic cyber events (NHS-1 cyber.systemic-cyber)NRS-H60Hazard family: Supply-chain and flow disruption (NHS-1 sys.supply-chain)
Observation
- INFORM Risk Index (listed for reference)
- World Bank Open Data and World Development Indicators API (listed for reference)
- IMF data services (WEO, IFS, FSI via SDMX) (listed for reference)
- Direct economic loss attributed to disasters in relation to global GDP
- Macroeconomic Dashboard
Example geography
- Bangladesh: 134 hexagonal cells
- Night lights tonight, outages (VIIRS Day/Night Band), NASA GIBS
- 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
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 |
|---|---|---|
| IEA Energy Technology RD&D Budgets | Public spending on energy research, development and demonstration by technology | IEA terms, attribution |
| IEA Clean Energy Technology Guide | Readiness levels of clean energy technologies along value chains | IEA terms, attribution |
| IRENA statistics (International Renewable Energy Agency) | Renewable capacity, generation and costs by country | Free, attribution |
| CORDIS (European Commission) | Energy and water demonstration projects and their deliverables | Free and open, CC BY |
| OSTI.gov (US Department of Energy) | Technical reports from national laboratories and funded projects | Public access, terms per report |
| Utility and pilot operating records | Metered performance, downtime and maintenance logs | By agreement with each operator |
The sector signature of future of energy and water labs.
The sectors future of energy and water labs 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.
Labs 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 and water regulators | Whether a new technology may be connected, approved or admitted to the regulated asset base | Integration test results, safety and quality compliance, operating record |
| Utilities and grid operators | Whether to move from pilot to procurement, and at what scale | Pilot performance against baseline, availability, maintenance burden, cost |
| Public funders and innovation agencies | Which technologies to fund through demonstration, and when to stop | Independent readiness evidence, stage-gate criteria, published pilot results |
| Development finance institutions and investors | Whether technology risk is low enough for project finance | Operating history, readiness assessment, performance gap, warranty terms |
Outputs this topic produces
- Readiness assessments with evidence per level and the operating environment stated
- Pilot designs that account for site selection and compare against a baseline
- System-integration test plans covering control, fouling, intermittency and maintenance
- Published pilot records, including those that fell short
How future of energy and water labs 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.
Model, equations and assumptions
The model structure used for future of energy and water labs, the assumptions it makes and how each is tested.
Ri = f(Hi, Ei, Vi, Ci)hazard, exposure, vulnerability and coping capacityEAL = ∑k pk · Lkover events k with annual probability pkxt+1 = A xt + B ut + εtA carries the couplings, u the shock, ε the error| Assumption | Why it is made | How it is tested |
|---|---|---|
| Hazard and exposure are independent within an event | Keeps the loss integral tractable | Compare with a copula fit on joint records |
| Couplings are linear near the operating point | Lets shocks propagate through A | Perturb beyond the linear range and compare |
| Vulnerability curves transfer between places | Local curves are rarely available | Validate against observed losses where they exist |
- Inputs
- versioned datasets with provenance
- Outputs
- distributions, never single numbers
- Code
- released with the model card and tests
- Reproduction
- same data, code, environment and seed
What experts ask at the energy and water interface.
- How much water does each energy pathway need, where and in which season?
- How much energy does each water service need, and how flexible is that demand?
- How do droughts, heatwaves and floods affect both systems at once?
- Which technologies or operating rules reduce joint vulnerability, and at what cost to other goals?
- How do storage, in reservoirs, batteries or aquifers, change the coupling?
How the two systems are coupled.
Thermal power stations withdraw large volumes of water for cooling, and their output can be limited when river temperatures or flows exceed environmental limits during heatwaves. Hydropower depends directly on river flow and reservoir levels. Bioenergy competes with food and ecosystems for water. Wind and solar photovoltaic generation use little water in operation, which changes the coupling as energy systems shift.
In the other direction, pumping, treating and distributing water and treating wastewater are significant electricity loads. Desalination and long-distance transfers are energy-intensive. When power fails, water and sanitation services can fail with it, and the reverse is true where water shortages curtail generation.
Compound events, such as a heatwave during drought, stress both systems together. These are the conditions under which cascading failures are most likely, and they are becoming more relevant as the climate changes.
How the labs work.
- 01
Coupled system models
Linked models of river basins and power systems, developed with Systems Science and Modelling. Results depend on how the coupling is represented.
- 02
Pilot and testbed trials
Field tests of operating rules, sensors, storage or treatment technologies under testbed rules.
- 03
Observed data
River gauges, reservoir records, plant operating data and meter data. Records are uneven and operational data are often confidential.
- 04
Scenario analysis
Exploration of climate and demand futures. Scenarios are not forecasts, and the choice of scenarios shapes conclusions.
Lab-scale or pilot results rarely translate directly to a whole system. Findings state the scale and conditions tested.
How it is studied, and what the methods cannot say.
- 01Systems engineeringRequirements to verification traceability · Stage-gate readiness reviewsApplied here
- 02Uncertainty quantification and model validationMeasurement uncertainty budgets · Sensitivity analysis of simulation modelsApplied here
- 03Causal inference and impact evaluationRandomised pilots and stepped rollouts · Quasi-experimental programme evaluationApplied here
- 04Probability and statisticsSample size and power planning · Meta-analysis with bias assessmentApplied here
- 05Artificial intelligence and machine learningAssisted screening for systematic reviews · Surrogate models to guide experimentsApplied 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.
Why do technologies stall after successful pilots?
Is a higher readiness level always stronger evidence?
Why publish pilots that fell short?
Do the labs recommend particular technologies?
Why combine energy and water in one lab?
Can a utility host a pilot?
Contribute evidence from this page.
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