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Digital twins can give crisis-readiness exercises a more connected picture of hazards, infrastructure and operations, letting teams explore possible impacts and rehearse decisions against defined scenarios. They are decision-support tools, not proof that a plan will work: exercise design, model validation, human judgment and evaluation still determine what a team learns.
What a digital twin adds to a crisis rehearsal
A digital twin is an electronic representation of a real-world entity or process. Depending on its purpose, it may combine models and current data to simulate possible conditions, monitor a system, or support decisions. NIST’s 2025 report covers twin components, modeling and simulation, cybersecurity and trust; its digital-twins overview describes models that can predict future states, behaviors or outcomes.
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For crisis readiness, the useful shift is from rehearsing against a static map or isolated report to exploring how a particular hazard could affect connected assets and services. A flood scenario, for instance, might bring together inundation areas, roads, facilities and evacuation information. The twin can help participants examine what may be affected and where decisions or coordination are needed; it cannot establish that the scenario is accurate simply by displaying it convincingly.
The UK Government’s 2025 definition adds practical tests: a twin is tied to a real-world entity, has stated assumptions and a defined validation envelope, works at a timeframe appropriate for the decisions, and supports two-way data flow. A 3D model or dashboard alone does not necessarily meet those criteria or provide a trustworthy basis for crisis decisions. UK Government, Digital Twin (official)
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How to use one in an exercise
Start with the readiness question, not with the technology. A useful exercise asks what a team needs to decide, coordinate or test under specified conditions. The twin then provides context for the rehearsal rather than becoming the exercise objective.
- Set a bounded decision. Choose a concrete question, such as which critical assets or routes may be threatened by a flood or storm surge, or how agencies would coordinate if a service were disrupted.
- Assemble the relevant information. Bring together spatial data, infrastructure and asset information, hazard inputs and operational sources. Make data ownership, provenance, freshness and known gaps visible to participants.
- Define scenarios and assumptions. Specify the hazard conditions and how they may affect connected systems. Record what the model represents, what it excludes and the conditions under which its outputs have been validated.
- Explore consequences and rehearse decisions. Use the twin to visualize or simulate possible conditions, then have participants test roles, communication, escalation and choices against the scenario.
- Evaluate and improve. Compare actions with the exercise objectives, document capability gaps and resource needs, and feed findings into plans and model updates.
FEMA describes exercises as a low-risk, cost-effective way to test and validate plans, policies, procedures and capabilities and identify gaps, strengths, resource requirements and improvements. Its exercise guidance includes the Homeland Security Exercise and Evaluation Program (HSEEP), a common approach to exercise-program management, design, conduct, evaluation and improvement planning. A digital twin can support that work; a simulation by itself is not an exercise evaluation.
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What documented projects show—and what they do not
Public examples show different ways to connect spatial, hazard and operational information. Their stated purposes and development stages vary, so they should not be treated as equivalent deployments or evidence of measured reductions in harm.
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The UK National Digital Twin Programme case study describes a demonstrator intended to support asset security and resilience, planning and incident response. It included a tool for assessing infrastructure vulnerability and a visualization tool for understanding event impacts, with tools to be tested during a resilience exercise day. This documents intended capabilities and planned evaluation—not a mature, validated island-wide operational system. UK NDTP case study
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Tokyo: evacuation and post-disaster information
The Tokyo Metropolitan Government describes combining river levels, estimated inundation areas, camera feeds and evacuation-site information to help users identify evacuation locations. It also describes publishing damage-assessment data following the 1 January 2024 Noto Peninsula Earthquake to support recovery and inform preparedness. These are information and visualization uses; the description does not establish forecast accuracy or prove that the system caused a reduction in harm. Tokyo Metropolitan Government
Dublin: pre-incident planning pilot
Dublin City Council says a partnership with Bentley Systems explored campus digital twins with Dublin City University and Dublin Fire Brigade for pre-incident planning and emergency-response use cases. It is presented as a pilot, not as evidence that a citywide emergency-response capability has been validated. Dublin City Council
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PANTHEON: community-based disaster-risk project
The European Commission’s CORDIS project page describes PANTHEON as a community-based smart-city digital-twin project for disaster-risk assessment and resilience. Its described capabilities include simulation, training and evaluation using multiple data sources, including earth observation and drone sensing. These are project aims and capabilities described in the fact sheet, not independently established outcomes. CORDIS project page
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RESCUE-MATE: storm-surge situation mapping
The University of Hamburg describes RESCUE-MATE as a 2023–2027 research project developing a digital twin for storm-surge situation mapping. The project aims to combine rescue-radio communications, environmental sensors, traffic reports, social media and drone-reconnaissance data to support an operational situation picture. Its project description should not be read as proof of a completed or validated operational deployment. University of Hamburg
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Smart firefighting: a capability framework
ITU-T Recommendation Y.4601 sets out a capability framework for firefighting digital twins. It includes a digital representation of a fire scene and capabilities such as tracking personnel and hazards, analyzing scene dynamics and optimizing rescue strategy. A standards framework describes capabilities to consider; it does not show that every system marketed or used as a twin implements them. ITU-T Y.4601
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What to validate before trusting a scenario
A twin can create false confidence if its visual detail outpaces the quality of its data or models. Participants should be able to distinguish observed information from modeled estimates and see the assumptions behind scenario outputs.
- Validation envelope: identify the conditions and uses for which the model has been checked. If a scenario falls outside that envelope, reassess its outputs rather than treating them as established.
- Data freshness and provenance: note when information was last updated, where it came from, who owns it and what is missing. Stale or incompatible feeds can undermine an otherwise useful scenario.
- Connected-system effects: establish which dependencies and cascading impacts are represented. A model that covers a facility but not the services it depends on may leave important consequences out.
- Cybersecurity and access: protect connected systems and operational information with appropriate security and access controls. NIST treats security and trust as part of the digital-twin problem, not as optional add-ons. NIST IR 8356
- Human performance: use the rehearsal to test communication, roles, escalation and decision-making as well as whether people can operate the interface.
How to judge whether the approach fits
Before selecting or expanding a digital-twin approach, compare options against the decisions the exercise must support. Useful criteria include hazard and geographic scope, asset coverage, data freshness and provenance, interoperability, the validation envelope, scenario and cascading-impact capability, participating agencies and community involvement, security and access controls, and whether findings lead to updates to plans and models.
The public project descriptions above cover different scopes and stages; they do not provide comparable performance results or costs, so they cannot support a defensible product ranking. For readiness, the more meaningful test is whether the chosen system helps participants work through a relevant scenario, exposes its own limits and produces findings the organization can act on.
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