Climate Resilience Demonstrator, CReDo, is a climate change adaptation digital twin demonstrator project to improve resilience across infrastructure networks
CReDo will develop a digital twin across energy, water and telecoms networks to improve climate resilience across infrastructure.
will develop a digital twin of infrastructure comprising Anglian Water’s water and sewerage network, BT’s communication network and UKPN’s power network in a specific location. The digital twin maps a weather scenario from a present or future climate to consequences across the system-of-systems, through a series of components as in the following diagram, for support of operational and capital planning decisions:
Specific use case
The initial use case will explore the impact of flooding on the networks in question. A key challenge with this is the need to develop hydrology modelling that maps weather to hazards directly affecting assets. Other types of weather will also be considered in the demonstrator.
One or more geographical areas will be identified in consultation with the asset owners, based on how prone they are to flooding.
pieces together a picture of the interconnected infrastructure system and how an extreme weather event, such as flooding, impacts that system-of-systems. A model of the system, the digital twin, will be built to test out the impact of alternative interventions. The insights derived from sharing data across the asset owners will be used to minimise the disruption caused by the flooding, enabling benefits for both the asset owners, their customers and wider society.
The diagram to the right shows how data is used to make decisions that reduce disruption resulting from flooding.
The infrastructure digital twin
The digital twin structure consists of a series of mappings. The ‘minimum viable product’ (MVP) in the project will consist of:
- Mapping of flood depth (or other weather hazard) to an individual asset’s availability status, based on combination of available data, with expert knowledge of the asset owners on consequence of given combination of conditions and mitigation strategies
- Mapping from individual asset status to system- level outcomes
- The network model, based on dependence relationships derived from the networks’ structures (.e. A requires B, A required B and C, A requires B or C, etc.), including dependence both within and between the three networks. This allows the identification of ‘cascade effects’ arising from the mutual dependence of water, electricity and communication systems.
As well as providing practical insights, this MVP will demonstrate how the Information Management Framework (IMF) of the National Digital Twin programme supports interoperability of data between organisations. The ambition of the project extends beyond this, however, and plans extend also to consider network restoration and resilience, as well as considering whether a more detailed engineering model of the three networks is required. will demonstrate how cross-organisational information sharing across a system-of-systems will improve outcomes for asset owners and citizens, as shown in the diagram below.
Weather and climate
The infrastructure digital twin will directly map a weather scenario to system-of-systems-level consequences. This can be used directly for studying system performance in given weather conditions, and one contribution of will be to demonstrate how this can also be used in capital planning through scenario-based decision support.
In principle, a full decision analysis would require consideration of all possible future climates and weather events, which is not possible due to the vast number of permutations and the time and computing power required to model them. Therefore, will demonstrate how a logical framework can be developed for climate resilience investment decisions, based on consideration of a finite number of scenarios that can feasibly be analysed.
The data will be securely hosted, and a focus on security throughout the project will help to identify the priority measures to consider in developing the IMF, and in connecting data sets and digital twins.
Asset owners will need to be able to use the digital twin model to ensure that they are prepared for various flooding scenarios and able to minimise the resulting cost and disruption. The outputs of the digital twin model will therefore need to be available to the asset owners in the form of a decision support tool that relates scenarios, uncertainty and outcomes to their own planning and response processes. will develop a prototype of such a tool for testing and subsequent development.