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Digital twins can give emergency teams a shared, data-linked setting for exploring how a hazard might affect infrastructure and services, then rehearsing decisions against those possible conditions. Their value depends on the decision being tested, the quality and limits of the underlying data and models, and whether agencies evaluate the exercise and act on what they learn. A simulation is not proof that a response plan—or the twin itself—is reliable.
What a digital twin adds to a crisis rehearsal
A digital twin is more than a 3D model or a dashboard. NIST describes digital twins as electronic representations of real-world physical or non-physical entities, supported by models that can predict future states, behaviors, or outcomes and assist with simulation, monitoring, optimization, or decisions. Its report examines components, modeling and simulation, cybersecurity, and trust. See NIST IR 8356 and the NIST digital-twins overview.
For a crisis exercise, the useful capability is connecting a scenario to relevant infrastructure, hazards, and operational information. A team might explore which assets or routes could be affected by flooding, how interruptions might affect other services, and what choices are available. The twin supplies a scenario environment and decision-support context; people still need to interpret the results, coordinate, and make decisions.
What makes a twin suitable for decisions?
The UK Government’s 2025 definition sets out a practical test: a twin is tied to a real-world entity, has assumptions and a defined validation envelope, works at a timeframe appropriate to the decisions, and allows two-way data flow. That means a detailed visual representation is not, by itself, evidence that a system is suitable for crisis decisions. Read the UK Government’s official digital-twin definition.
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- Decision scope: Specify the choice the rehearsal should inform—such as which facilities, routes, or services need attention under a defined hazard.
- Data fitness: Identify where each input comes from, how current it is, who maintains it, and which gaps or incompatibilities remain.
- Model limits: Record the assumptions and conditions for which the twin has been validated. A result outside that envelope needs reassessment, not an assumption of reliability.
- Operational safeguards: Set cybersecurity and access controls for connected systems and sensitive operational information. NIST treats security and trust as part of digital-twin design, not an add-on.
How to run a digital-twin-supported rehearsal
Use the twin within a conventional exercise process, with explicit objectives and an evaluation plan. FEMA describes exercises as a low-risk, cost-effective way to test and validate plans, policies, procedures, and capabilities and to identify resource requirements, gaps, strengths, and improvements. Its exercise guidance and HSEEP approach cover program management, design, conduct, evaluation, and improvement planning.
- Choose a bounded readiness question. Define the hazard, geographic area, and decision to rehearse. “How should we respond if this flood scenario cuts access to these facilities?” is more actionable than “simulate the whole city.”
- Assemble the relevant information. Bring together spatial and asset data with hazard information and operational sources needed for the question. Make provenance, freshness, ownership, and known gaps visible to participants.
- Set the scenario and assumptions. Decide what conditions participants will face, including plausible effects across connected systems. State what the twin models and what it does not.
- Rehearse decisions and coordination. Use the visualization or simulation to examine possible conditions, then have agencies practice roles, communications, escalation, and choices. Do not make interface interaction the exercise’s sole activity.
- Evaluate and improve. Compare actions with exercise objectives, record capability gaps and resource needs, and turn findings into updates to plans, procedures, or models.
What documented projects and uses demonstrate
Public examples show a range of visualization, planning, and development work—not a common level of operational maturity or measured improvement in crisis outcomes.
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| Example | What the source describes | Evidence status |
|---|---|---|
| Isle of Wight, UK | Tools for assessing infrastructure vulnerability and visualizing event impacts, intended to support resilience planning and incident response. | National Digital Twin Programme demonstrator; its tools were to be tested during a resilience exercise day. |
| Tokyo | Combining river levels, estimated inundation areas, camera feeds, and evacuation-site information to help users identify evacuation locations; publishing damage-assessment data after the 1 January 2024 Noto Peninsula Earthquake. | Municipal information and visualization uses described by the Tokyo Metropolitan Government. |
| Dublin | Exploration of campus-twin use cases for pre-incident planning and emergency response with Dublin City University and Dublin Fire Brigade. | Dublin City Council describes a pilot action project. |
| PANTHEON | A community-based smart-city twin for disaster-risk assessment and resilience, with described functions including simulation, training, evaluation, and use of multiple data sources. | Project aims and capabilities in a European Commission CORDIS fact sheet. |
| RESCUE-MATE, Hamburg | Development of a storm-surge situation picture combining rescue radio, environmental sensors, traffic reports, social media, and drone-reconnaissance data. | University of Hamburg describes a 2023–2027 research project. |
Sources: Isle of Wight demonstrator, Tokyo Metropolitan Government, Dublin City Council, CORDIS PANTHEON project page, and the University of Hamburg RESCUE-MATE project page.
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How to judge whether an approach fits your exercise
Compare proposed approaches against the actual readiness question, not the visual detail of a demonstration. Useful criteria include:
- Hazard, decision, geographic area, and asset coverage.
- Data freshness, provenance, ownership, and interoperability.
- Model assumptions, validation envelope, and ability to represent cascading effects.
- Which agencies and affected communities participate in scenario design and rehearsal.
- Cybersecurity, access controls, and usability under exercise conditions.
- Whether evaluation findings lead to concrete plan, capability, or model updates.
The cited public examples do not provide comparable performance results or costs, so they cannot support a product ranking or a claim that one approach is more effective than another.
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Where false confidence can enter
- Visual authority: A realistic display can make incomplete inputs or uncertain model outputs appear settled. Keep assumptions and uncertainty visible to participants.
- Stale or fragmented feeds: A combined view is only as useful as its component data. A missing, delayed, or incompatible source can change the picture teams act on.
- Unclear trust boundaries: Connected data and operational information require safeguards, defined access, and attention to cybersecurity.
- Confusing project status with effectiveness: A demonstrator, pilot, or project description establishes that work is being explored or developed; it does not establish a mature operational capability or measured reduction in harm.
- Neglecting human performance: A technically plausible scenario can still expose problems in communication, role clarity, escalation, or coordination. Those are exercise outcomes to evaluate, not interface features.
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