GCRI · Global challenges
Infrastructure dependencies
Power, water, transport, ports, telecommunications, data centres and payments are lifelines. When one fails, the others feel it within hours. Most run on electricity and digital control, and many share corridors, suppliers and operators. The Global Centre for Risk and Innovation (GCRI) maps these dependencies, the single points of failure and the bottlenecks that slow recovery. Part of global challenges.
01Register
Infrastructure dependencies on the record.
The Global Centre for Risk and Innovation
- 01InstitutionThe Global Centre for Risk and Innovation (GCRI)
- 02The challengeLifelines depend on each other more than their owners can see
- 03Dependency typesFour kinds of link between lifelines, and the measures that describe them
- 04Methods and limitsHow dependency and restoration are modelled, and what the models miss
- 05GCRI’s partDependency evidence for operators and authorities, with restricted detail kept restricted
- 06How GCRI approaches itFrom dependency maps to tested options
- 07BoundariesRestricted detail stays restricted
02The challenge
Lifelines depend on each other more than their owners can see.
Interdependence
Everything needs power
Water treatment, hospitals, cold chains, communications and payments all stop when the grid fails, and the grid needs communications and water to restart.
Concentration
Hidden single points
A shared substation, cable landing, fuel terminal or cloud region can sit beneath services that appear independent.
Recovery
Bottlenecks after the event
Spare transformers, specialist crews, fuel logistics and access routes often decide how long an outage lasts.
03Geography
Infrastructure exposure 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
Infrastructure exposure, 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
- 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
About this layer
- Evidence
- Observed (clouds leave gaps)
- Source
- MODIS Terra EVI (NASA LANCE)
- Provider
- NASA LANCE
- Licence
- NASA open data
- Vintage
- daily; Satellite index; clouds leave gaps
- Newest at build
- 2026-10-13
- Resolution
- about 250 m grid
- Uncertainty
- Satellite vegetation index for the 8 days to the date shown, a habitat condition indicator; not a measure of species or biodiversity.
- 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.
Infrastructure exposure, 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/NOAA VIIRS · 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
- 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/NOAA VIIRS (Suomi NPP). 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.
- 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 (12 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
- Land cover type (IGBP), yearly: MODIS Terra and Aqua land cover (MCD12Q1), NASA LP DAAC · 2024
- Enhanced vegetation index, rolling 8 days: MODIS Terra EVI, NASA LANCE · 13 Oct 2026
04Dependency types
Four kinds of link between lifelines, and the measures that describe them.
Rinaldi, Peerenboom and Kelly set out in 2001 the classification still used in infrastructure studies. A dependency runs one way; an interdependency runs both ways, which is why restarting a grid can need working telecommunications that themselves need power.
| Type | Example | Measure |
|---|---|---|
| Physical | A water treatment works needs electricity; a power station needs cooling water | Input dependency share; backup duration in hours of on-site fuel or storage |
| Cyber | Grid control, payment clearing and port logistics rely on shared networks and software | Count of services relying on one platform, region or provider class |
| Geographic | Cables, pipelines and roads share a corridor, bridge or landing site | Co-located assets within a hazard footprint |
| Logical | Markets, regulation or staff shared across systems | Shared crews or spares per region; restoration priority rules |
Service performance is expressed in standard reliability terms. Availability is the share of time a service is up. Mean time to repair is in hours. For power systems, SAIDI and SAIFI measure customer-minutes, as defined in IEEE Standard 1366.
05Twin and cascade simulator
Model infrastructure dependencies as a system. Run the cascade.
Assembled from the Nexus Standards area profile Critical infrastructure and critical entities: 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
- 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)
Systems and couplings
- Critical infrastructure and critical entities
- Energy risk standards Dependency
- Space weather, spectrum and timing risk standards Common-mode dependency
- Compound and cascading hazard standards Cascade
- Cyber resilience and digital operational resilience 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)NRS-H61Hazard family: Cascading failure (NHS-1 sys.cascade)NRS-H94Hazard family: Systemic cyber events (NHS-1 cyber.systemic-cyber)NRS-H107Hazard family: Digital infrastructure concentration and monoculture (NHS-1 tech.digital-concentration)NRS-K31Domain pack profile: Cyber national resilience (DOMAIN-031)NRS-K36Domain pack profile: Critical infrastructure (DOMAIN-036)NRS-K48Domain pack profile: Financial system resilience (DOMAIN-048)
Observation
- OpenStreetMap planet and replication (listed for reference)
- Global Power Plant Database (listed for reference)
- NASA Black Marble night-time lights (VNP46) (listed for reference)
- Global Human Settlement Layer (listed for reference)
- Damage to critical infrastructure attributed to disasters
- Number of disruptions to basic services attributed to disasters
- Proportion of the rural population who live within 2 km of an all-season road
Example geography
- Europe: 113 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
06Methods and limits
How dependency and restoration are modelled, and what the models miss.
| Method | What it answers | Limit |
|---|---|---|
| Network criticality analysis | Which nodes or links, if lost, disconnect the most users | Topology alone ignores capacity, operator response and repair priorities |
| Interdependent network simulation | How a failure crosses from power to water, telecoms and transport | Coupling data are usually restricted; public studies use permitted or synthetic networks |
| Fragility curves and restoration functions | Probability of damage at a hazard intensity, and how fast service returns | Curves often borrowed from other countries’ asset stock; restoration depends on crews and spares |
| Economic impact of outages (input-output, surveys of firms) | Value of lost production and household welfare | Customer damage functions vary by country and season; estimates carry wide ranges |
The World Bank’s Lifelines study of 2019 and the UNDRR Principles for Resilient Infrastructure of 2022 both make the same point. The cost of disruption to users, not damage to assets, drives the case for resilience. That cost is the part least often measured.
07Risk and response
Coupled risk, applied response.
The sectors urban flood touches and how strongly they depend on each other: what a study has to observe and model together.
Choose a context
ACoupled risk across six sectors6 / 15 relations
Sectors · Urban floodStress
applied to
BApplied response across six pillars11 / 18 handoffs
Pillars · Urban floodKPI
In this contextDependence or handoff, heavier is strongerOutside this context
Programme yearAll phases
Context
Urban flood
Flooded streets take out substations, pumping stations and clinic access together; drinking water fails when power does.
W · E · I · F · H · B
Risk
- Sectors
- 4 of 6
- Relations
- 6 of 15
- Coupling
- 0.46 share of total
Hover a corner, a line or a row to trace it across both sides.
Route
Applied to urban flood
L · F · U · S · R · A
Response
- Pillars
- 5 of 6
- Handoffs
- 11 of 18
- Loops
- 8 closed
Expected value
- What is at stake
- $390Ba year at least, in infrastructure disruption costs to households and firms in low- and middle-income countriesWorld Bank, Lifelines, 2019
- Return on resilience
- 4 : 1benefit to costWorld Bank, Lifelines, 2019
- Avoided loss
- 30%less damage with 24 hours’ warning of a storm or heatwaveGlobal Commission on Adaptation, Adapt Now, 2019
Hover a pillar, a line or a row to read how it is applied.
A: a teaching cascade (shock 0.6, gain 0.35, four rounds) over teaching values for a typical city-region, not measurements. B: illustrative applications and the KPI each pillar answers for; values are published benchmarks, cited, not forecasts. Open events: NASA EONET, live.
08GCRI’s part
Dependency evidence for operators and authorities, with restricted detail kept restricted.
GCRI develops dependency and restoration methods, runs studies with operators under agreed terms and publishes what review and security allow. It does not inspect, regulate or operate infrastructure, and it holds no role in national designation of critical entities, which in the European Union follows Directive 2022/2557. Field work sits with the Critical Infrastructure platform.
Public findings are designed for the Strategic Systems series and the Scenario and Stress-Test series; dependency records would carry identifiers under the proposed Nexus Standards. Contributions that help: restoration timelines from past outages, fragility data for local asset types, and cases where a hidden shared dependency surfaced.
09How GCRI approaches it
From dependency maps to tested options.
- 01
Map dependencies
Which services depend on which assets, suppliers and operators, using network science and systems engineering.
- 02
Find common-mode failure
Where one hazard, failure or attack can disable several systems at once.
- 03
Test under stress
Controlled scenarios using reliability and resilience engineering, with results labelled as modelled.
- 04
Hand off to the operator
Options and their limits go to the operator or authority that decides. GCRI never operates infrastructure.
10Boundaries
Restricted detail stays restricted.
Infrastructure studies often involve sensitive topology. Public pages describe mechanisms and published findings only; operator data is used under agreed terms and never appears in public examples. Membership of a programme never grants access to production systems.
GCRI does not regulate, inspect or certify infrastructure.
11Where to go next
Fields, platforms and counterparts.
Field work is organised through the Critical Infrastructure platform, and the critical infrastructure field gateway. Operators can start at infrastructure and industry. The Global Risks Alliance (GRA) examines the finance questions under infrastructure preparedness. See also digital and cyber dependencies.
12Sources
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.
- Rinaldi, Peerenboom and Kelly, Identifying, understanding and analysing critical infrastructure interdependencies, IEEE Control Systems Magazine 21 (2001)
- Buldyrev and others, Catastrophic cascade of failures in interdependent networks, Nature 464 (2010)
- Hallegatte, Rentschler and Rozenberg, Lifelines: the resilient infrastructure opportunity (World Bank, 2019)
- UNDRR Principles for Resilient Infrastructure (UNDRR, 2022)
- Directive (EU) 2022/2557 on the resilience of critical entities
- ISO 22301:2019 Security and resilience, business continuity management systems (ISO)
- Sendai Framework for Disaster Risk Reduction 2015–2030 (UNDRR, 2015)
- NS-3, NXS-GRX (canonical data, provenance and risk) record UNIs, a proposed standard
13Questions
Questions and answers.
Does GCRI assess the security of specific facilities?
No. GCRI studies dependencies and resilience with operators under agreed terms, and the operator remains responsible for its assets. Security reports about GCRI’s own services go to the security address.
Can a utility take part without sharing sensitive data?
Yes. Studies can be designed around data the operator is able to share, and restricted topology never appears in public previews or examples.
What is a recovery bottleneck?
A constraint on how fast service returns after a failure: scarce spare equipment, specialist crews, fuel, access, or the order in which interdependent systems must restart.
Is this the same as cybersecurity?
Cyber threats are one cause of infrastructure failure. Digital dependencies are covered on their own challenge page, and this page looks at all causes, physical and digital.
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