The Global Centre for Risk and Innovation (GCRI)
Water, energy, food, health, the economy and infrastructure depend on each other, so models of one system miss effects that arrive through the others. Coupled modelling links models so that drought can reduce hydropower, raise food prices and strain health services in one simulation. This page sets out how coupled models are built, where coupling introduces error, and what evidence serves the institutions that use integrated assessments.
GCRI · In development · service level R0
Coupled systems modelling is the linking of models of different systems, such as water, energy, food, health and the economy, so their interactions appear in one simulation. Feedbacks cross boundaries, scales and units must match, interfaces carry assumptions and uncertainty compounds along the chain.
For coupled systems modelling, in GCRI's spatial-intelligence reading, the map opens on night lights, population density, true-colour imagery and land cover with relief in 3D, framed on North America; every other NASA layer, live feed and forecast waits in the layer list.
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For coupled systems modelling, in GCRI's spatial-intelligence reading, the map opens on night lights, population density, true-colour imagery and land cover with relief in 3D, framed on North America; every other NASA layer, live feed and forecast waits in the layer list.Satellite and model products as NASA publishes them, each with its own date; indicators, not assessments of any place; claim ceiling INTEGRATED.
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Linking water, energy, food, health and infrastructure models without hiding assumptions. The dossier below sets out how the risk develops, what is measured, where the data come from, how it is modelled and who decides.
Effects travel between systems and sometimes return, as when water scarcity raises energy demand for pumping, which raises water demand for cooling.
Coupled models must reconcile different time steps, spatial resolutions and units. Mismatches create artefacts that look like results.
What passes between models, and how often, is a modelling choice. Loose coupling is simpler; tight coupling captures feedback but is harder to test.
Uncertainty in each component propagates through the chain, so coupled results can be more uncertain than any single model.
Each term as this page uses it. Where a standard-setting body defines the term, the wording follows that body.
| Term | Definition |
|---|---|
| Loose coupling | Linking models by passing outputs between them at intervals without tight feedback. |
| Tight coupling | Linking models so they exchange information frequently and influence each other during a run. |
| Integrated assessment model | A model that combines economic, energy, land and climate components to explore policy scenarios. |
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 |
|---|---|---|---|---|
| Components coupled | count of models in a coupled system | per configuration | Model documentation | DECLARED |
| Coupling frequency | exchanges per simulated period | per configuration | Model documentation | DECLARED |
| Conservation error at interfaces | imbalance of mass or energy | per run | Model diagnostics | MODELLED |
| Coupled uncertainty range | range of outputs | per experiment | Ensemble runs | MODELLED |
| Feedbacks represented | count by type | per configuration | Model documentation | DECLARED |
| Validation of coupled outputs | error against observations | per evaluation | Validation studies | ESTIMATED |
The topic as a computational graph: named sources feed the methods, an evidence review gates the outputs, and the decisions stay with the authorities that hold them.
Fig. 02Nodes carry their type, two metric rows and a state; connectors show what feeds what; dashed frames name the owners; the timeline is the cadence. Design of the pipeline, not a record of a run.
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 |
|---|---|---|
| Integrated assessment model documentation (IAMC wiki) | Structure and assumptions of integrated assessment models | Public, terms to check |
| ISIMIP cross-sectoral simulations | Linked impact model results | Open, licence per dataset (to check) |
| Coupling frameworks such as OASIS, BMI and preCICE documentation | Technical standards for linking models | Open source, licence per project |
| Water-energy-food nexus modelling literature | Approaches and case studies | Licence per publication |
| National integrated planning models where published | Applied coupled models | Licence per publisher |
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 |
|---|---|---|
| Planning ministries and integrated assessment teams | Which interactions to represent in national scenarios | Coupling designs; comparisons |
| Modelling groups | How to link models soundly | Interface checks |
| Users of integrated scenarios | How much to trust coupled results | Uncertainty analyses |
Each sector model is built for its own purpose. A hydrological model may run daily on river basins; a power system model may run hourly on grid nodes; an economic model may run annually on national sectors. Coupling them requires translating between scales, which involves aggregation and disaggregation choices that can change results.
Feedbacks matter. In a two-way coupling, reduced river flow lowers power output, which changes electricity prices, which changes water pumping demand. One-way couplings miss such loops; two-way couplings are harder to build and to stabilise numerically.
Assumptions travel silently. A demand projection embedded in one model becomes a hidden constraint on another. Without documentation, users of the coupled system may not know which assumptions drive the result.
Exchanging outputs between separate models, or integrating them in one framework. Hard coupling is more consistent but less flexible.
Aggregation, downscaling and interpolation between resolutions. Each step adds error.
Shared data formats and variable definitions, drawing on systems engineering.
Propagating uncertainty through the chain, using uncertainty quantification. Computationally demanding.
Coupled models can show interactions that single models miss, but their complexity makes errors harder to detect. Results state which couplings were represented and which were not.
The current service level is R0: information and enquiries. An enquiry receives a reference and names the next step; applications, payments, compute allocations and registrations open only when their team, terms and review capacity exist.