GCRI · Systems Science and Modelling
Networks and cascades
Many systemic risks travel along networks: supply chains, power grids, financial exposures, transport and contact networks. Network models represent who is connected to whom and simulate how a failure or infection spreads. This page sets out how networks are built and analysed, how cascade models work and fail, and what evidence serves the institutions that use network analysis to manage interconnected risk.
How the work runs.
Coupled model runner
Networks and cascades · Coupled model A · Scenario set 3
Model coupling graph
nodes: models · edges: exchanged variables
Coupled components
| Hydrology | 1 day | runoff | Yes |
| Power system | 1 h | demand, outages | Yes |
| Economy | 1 quarter | output, prices | Partial |
| Population | 1 year | exposure | Yes |
Ensemble output
Fig.Coupled model runner: example names and illustrative values, not a record or a live system.
Networks and cascades: how the risk develops.
Network science for dependency, contagion and cascading failure. The dossier below sets out how the risk develops, what is measured, where the data come from, how it is modelled and who decides.
Structure shapes spread
Highly connected nodes, short paths and clusters determine how fast and far a cascade travels. The same shock spreads differently on different structures.
Thresholds and load matter
Nodes fail when stress exceeds a threshold, and failed nodes shift load to neighbours, which can trigger further failures.
Missing links distort results
Real networks are rarely fully observed. Missing links can make a system look more or less fragile than it is.
Networks are layered
Real systems form multilayer networks, such as power and communications, where failures jump between the layers.
Hazards and exposure in North America.
Population density, flood water and night lights over North America, from NASA GIBS. Each layer carries its own date.
The interactive map needs JavaScript; the layer list and sources are below.
Layers 3
Hazards and exposure, for this page
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 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
- 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
- Observed (clouds and overpass timing leave gaps)
- Source
- VIIRS NOAA-20 375 m (FIRMS) (NASA LANCE FIRMS)
- Provider
- NASA LANCE FIRMS
- Licence
- NASA open data
- Vintage
- daily; Satellite detection; clouds and overpass timing leave gaps
- Newest at build
- 2026-10-13
- Resolution
- about 500 m grid
- Uncertainty
- Satellite hot-spot detections for the day shown, including gas flares and industrial heat; not a confirmed fire, an incident report or a warning.
- 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.
Hazards and exposure, framed on North America.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 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
- 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
- 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
- NASA Fire Information for Resource Management System (FIRMS), VIIRS 375 m active fire product, NASA LANCE; vector tiles 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 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 (11 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
- 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
- Night-time lights, daily: VIIRS Black Marble (VNP46A2), NASA LAADS DAAC · 12 Oct 2026
- True colour: VIIRS NOAA-20 corrected reflectance, NASA LANCE · 13 Oct 2026
- Active fires and thermal anomalies: VIIRS NOAA-20 375 m (FIRMS), NASA LANCE FIRMS · 13 Oct 2026
- Land cover type (IGBP), yearly: MODIS Terra and Aqua land cover (MCD12Q1), NASA LP DAAC · 2024
Network models trace cascades across domains.
Network studies are designed for the Global Systems Risk and Resilience series. Buldyrev and colleagues (2010) showed abrupt collapse in coupled networks; real systems have operators and buffers the theory omits.
- Network datasets under terms.
- Cascade case studies.
Model networks and cascades as a system. Run the cascade.
Assembled from the Nexus Standards area profile Compound and cascading hazard standards: 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
- Natech event
- Chemical spill or leak (set off downstream)
- Emergency-response overload (set off downstream)
- Toxic gas release (set off downstream)
- Industrial fire (set off downstream)
Systems and couplings
Controls
NS-0Constitution and principlesNS-3Risk record UNIs (grammar, legacy crosswalk, allocation authority)NS-9Contextual UNIs (deployments, instances, programme instances)NS-11Verification and monitoring (evidence, attestations, zero-trust conformance observation)NRS-H61Hazard family: Cascading failure (NHS-1 sys.cascade)NRS-H62Hazard family: Compound and multi-hazard events (NHS-1 sys.compound)NRS-H60Hazard family: Supply-chain and flow disruption (NHS-1 sys.supply-chain)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-H7Hazard family: Large-scale circulation anomalies (NHS-1 atmo.climate-mode)
Observation
- Copernicus EMS rapid mapping activations (listed for reference)
- OpenStreetMap planet and replication (listed for reference)
- ERA5 reanalysis (Copernicus Climate Data Store) (listed for reference)
- Direct economic loss attributed to disasters in relation to global GDP
- Number of disruptions to basic services attributed to disasters
Example geography
- Bangladesh: 134 hexagonal cells
- Night lights tonight, outages (VIIRS Day/Night Band), NASA GIBS
- Carbon monoxide, smoke transport (AIRS), NASA GIBS
- Vegetation disturbance alerts, 30 m (OPERA DIST-ALERT), 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 |
|---|---|---|---|---|
| Network size | nodes and links | per dataset | Network data | OBSERVED |
| Degree distribution | distribution shape | per dataset | Network analysis | ESTIMATED |
| Centrality of key nodes | centrality score | per analysis | Network analysis | ESTIMATED |
| Cascade size in simulation | share of nodes failed | per scenario | Cascade models | MODELLED |
| Data completeness | estimated share of links observed | per dataset | Data assessment | ESTIMATED |
| Interlayer dependencies | count | per dataset | Multilayer network data | DECLARED |
How networks and cascades 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 networks and cascades, 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
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 |
|---|---|---|
| Published network datasets such as SNAP and Netzschleuder | Benchmark networks for methods testing | Open, licence per dataset |
| Input-output and trade networks (OECD ICIO, BACI) | Economic dependency networks | Public, OECD terms; open, Etalab licence |
| Power grid network models for research | Grid topology datasets | Licence per dataset (to check) |
| NetworkX, igraph and graph-tool | Open-source network analysis software | Open source, BSD and GPL licences |
| Network science literature on cascades | Models and validation studies | Licence per publication |
Who decides, and what the evidence serves.
Systems Science and Modelling 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 |
|---|---|---|
| Analysts across Nexus platforms | Which network methods suit their dependency data | Construction guides; model comparisons |
| Regulators and operators | How to read network-based risk findings | Sensitivity analyses |
| Data owners | Which links most need to be observed | Missing link sensitivity |
Outputs this topic produces
- Network construction guides for dependency data
- Cascade model comparisons
- Sensitivity analyses for missing links
- Method notes for platforms using network analysis
What experts ask about networks.
- Which nodes or links, if removed, would cause the largest disruption?
- How does network structure, such as clustering or hubs, affect the spread of failures?
- When do small shocks trigger large cascades?
- How do interdependent networks, such as power and communications, amplify failures?
- How can redundancy and modularity make networks more robust?
How cascades happen.
Network science has shown that many real networks have heterogeneous structures, with a few highly connected hubs. Such networks are robust to random failures but vulnerable to targeted loss of hubs. The same structure that makes them efficient makes them fragile in specific ways.
Cascades occur when a failure overloads neighbours, which then fail in turn. In power grids, a line failure shifts flow to other lines; in finance, a default imposes losses on creditors; in supply chains, a missing part halts downstream production. Whether a shock stays local or spreads depends on load, capacity and connectivity.
Interdependent networks add risk. Power systems depend on communications for control, which depend on power. Failures can bounce between networks, producing larger collapses than either network alone would suffer.
Methods and their limits.
- 01
Graph metrics
Centrality, connectivity and clustering measures, using network science. They describe structure but not dynamics.
- 02
Cascade models
Simulating failure propagation with load-redistribution or threshold rules. Results depend on assumed rules.
- 03
Interdependent network models
Coupling networks to study cross-system failure. Data on real interdependencies are scarce.
- 04
Robustness optimisation
Designing redundancy, using optimisation. Trade-offs with cost and efficiency.
Network data are often incomplete or confidential, and stylised models may not capture operational detail.
How it is studied, and what the methods cannot say.
- 01Network science and contagionCommon network representations · Contagion model comparison · Multilayer analysisApplied here
- 02Complex systems and agent-based modellingAgent-based model standards · Feedback and tipping point analysis · Early warning indicatorsApplied here
- 03Probability and statisticsBayesian calibration · Proper scoring of predictions · Ensemble statisticsApplied here
- 04High-performance, cloud and edge computingEnsemble design within budgets · Workflow orchestration · Reproducible environmentsApplied 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.
What is a cascade in network science?
What is a multilayer network?
Why do missing links matter?
Are hubs always a weakness?
Can cascades be prevented?
What data are needed?
Do financial networks behave like power grids?
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