GCRI · Critical Infrastructure
Interdependency and cascade
Infrastructure systems depend on each other: power runs water pumps and telecommunications, telecommunications control the grid, fuel needs both, and all need transport and staff. A failure in one can cascade into others, and restoration must follow the same dependencies. This page sets out how interdependencies arise and cascade, how they are mapped and modelled, and what evidence serves those who plan for multi-system failure.
Interdependency and cascade on the record.
Interdependency and cascade: how the risk develops.
Shared failure points and restoration sequence. The dossier below sets out how the risk develops, what is measured, where the data come from, how it is modelled and who decides.
Physical dependency
One system needs a physical output of another, as pumps need electricity and generators need fuel. When the supply stops, the dependent system stops once its buffer runs out.
Cyber dependency
Control systems, billing and coordination depend on data networks. Communications failure can stop operations even when physical equipment is undamaged.
Geographic dependency
Systems share corridors, bridges, tunnels and sites. One flood, fire or earthquake damages several at once, removing the redundancy that separate systems seemed to offer.
Restoration interlocks
Repairing power needs communications and road access; restoring communications needs power. Restoration plans that ignore these loops stall.
Infrastructure exposure in Western Europe.
Night lights, population density, flood water and true-colour imagery over Western Europe, from NASA GIBS. Each layer carries its own date.
The interactive map needs JavaScript; the layer list and sources are below.
Layers 4
Infrastructure exposure, for this page
Keyboard, with the map focused
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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 Western Europe.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 · U.S. Geological Survey · 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
- 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.
- Live: U.S. Geological Survey 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.
- 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
Interdependency is the coupling of the domains, made physical.
Cascade models are designed for the Scenario and Stress-Test series. Buldyrev and colleagues (2010) showed how coupled networks can collapse abruptly; models would carry NS-12 cards under the proposed Nexus Standards.
- Dependency data under operator terms.
- Cascade case studies with sources.
Model interdependency and cascade 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
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 |
|---|---|---|---|---|
| Mapped interdependencies between sectors | count of documented dependencies | annual | Dependency inventories | DECLARED |
| Co-located critical assets | count of sites hosting several sectors | per assessment | Geospatial analysis | ESTIMATED |
| Buffer time by dependency | hours | per asset class | Operator continuity data | DECLARED |
| Services affected per initiating failure | count of dependent services | per scenario | Interdependency models | MODELLED |
| Joint restoration exercises held | count | annual | Exercise records | REPORTED |
| Multi-sector incidents | count of incidents affecting more than one sector | annual | Incident databases | REPORTED |
How interdependency and cascade 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.
System model
The couplings between the systems in interdependency and cascade: signs of influence, a reinforcing and a balancing loop, and cross-links where stress jumps.
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 |
|---|---|---|
| UNDRR Principles for Resilient Infrastructure and stress test methodology | Approach for national infrastructure stress testing | Public, UN terms of use |
| CDRI Global Infrastructure Resilience report | Cross-sector infrastructure risk analysis | Public, terms to check |
| National critical infrastructure strategies and sector plans | Sector definitions and dependency analyses | Public records, licence per government |
| Post-event reports on multi-system failures | Documented cascades across lifelines | Public records, terms per publisher |
| OpenStreetMap infrastructure layers | Locations of power, water, telecom and transport assets | Open, ODbL |
Who decides, and what the evidence serves.
Critical Infrastructure 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 |
|---|---|---|
| National critical infrastructure coordinators | Which interdependencies need joint planning and oversight | Dependency maps; cascade scenarios |
| Sector operators | Which upstream dependencies to secure with backup or contracts | Buffer times; co-location analyses |
| Emergency management agencies | How to coordinate restoration across sectors | Joint restoration studies; exercise results |
Outputs this topic produces
- Cross-sector dependency maps at national or city scale
- Co-location analyses of shared corridors and sites
- Cascade scenarios with buffer timing
- Joint restoration sequencing studies
What experts ask.
- Where are the shared failure points that many services depend on without knowing it?
- How do cascades grow, and what stops them?
- How do restoration dependencies create deadlocks, where each system waits for another?
- How much does the timing of failures matter compared with their scale?
- What evidence from past events can calibrate cascade models?
Drivers and failure pathways.
Research on interdependent networks has shown that coupling can make systems more fragile than either would be alone: a small failure in one network can trigger disproportionate failure in another. Real cascades combine physical, cyber, geographic and logical dependencies, described on dependencies and consequences. Time is critical: many dependencies are buffered by storage or backup, so cascades often unfold in waves as each buffer is exhausted.
Restoration has its own dependency structure. Power restoration needs communications and access; communications restoration needs power; road clearance needs fuel. Without coordination, operators can each wait for the others. Restoration science therefore treats recovery as a scheduling problem across systems, with limited crews, parts and fuel.
Systemic risk in a broader sense is studied at Systemic Risk Management.
How it is studied.
- 01
Network models
Coupled network models from network science and contagion explore how failures propagate between systems.
- 02
System-of-systems simulation
Agent-based and input-output models from systems engineering represent operators, buffers and timing.
- 03
Restoration optimisation
Scheduling methods from optimisation and operations research explore restoration orders under resource constraints.
- 04
Limits
Cascade models are sensitive to dependency data that is often missing or confidential. Abstract network models can overstate or understate fragility, and real behaviour depends on human decisions in the moment.
How it is studied, and what the methods cannot say.
- 01Network science and contagionCritical node and link analysis · Interdependent network models · Redundancy assessmentApplied here
- 02Systems engineeringResilience requirements · Interface mapping · Verification across the life cycleApplied here
- 03Reliability and resilience engineeringResilience metrics for lifelines · Reliability analysis of networks · Restoration planningApplied here
- 04Optimisation and operations researchResilience investment portfolios · Restoration scheduling · Network design under uncertaintyApplied here
- 05Sensors, IoT and observation networksStructural health monitoring · Leak and pressure monitoring · Grid sensingApplied 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.
- Buldyrev and others, Catastrophic cascade of failures in interdependent networks, Nature 464 (2010)
- Rinaldi, Peerenboom and Kelly, Identifying, understanding and analysing critical infrastructure interdependencies, IEEE Control Systems Magazine 21 (2001)
- NS-12, Software, model and AI/ML artefacts, a proposed standard
Where this page leads across the Consortium.
Questions and answers.
What is infrastructure interdependency?
Why is co-location a risk?
How do you stress test infrastructure?
Can cascades be predicted?
What is a shared failure point?
Is this topic tested in exercises?
Who reviews coverage of this topic?
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