Guest blog: Climate resilience via distributed data sharing and connected understanding, by Amit Bhave, CMCL

Digital Twin Hub > Articles & Publications > Guest blog: Climate resilience via distributed data sharing and connected understanding, by Amit Bhave, CMCL

Climate resilience via distributed data sharing and connected understanding Amit Bhave, CEO and Co-Founder of CMCL

Critical infrastructure and climate change resilience

Critical infrastructure networks, for example water energy, transport and telecommunications, are interdependent. The Joint Committee on the National Security Strategy report in 2022 highlighted the potential impact of climate change on cascading risks (failure or malfunctioning in one network causing a knock-on effect on other networks) affecting these sectors [1]. The Climate Change Committee, for instance, has warned that flooding is set to become more frequent and severe, affecting infrastructure including energy, water, transport, waste and digital communication. A lack of a formal mechanism for information sharing between critical infrastructure providers and the siloed regulation of each sector pose significant obstacles to climate adaptation and the development of climate resilience across the combined networks.

Substantial action is needed to develop anticipatory measures to help communities avoid climate disasters. Climate resilience is an intrinsically cross-sector challenge and a rapidly growing concern. The preparations to address it have started, but wider contributions are needed to match the severity of the challenge.

Cross-sector digitalisation and the CReDo approach

Consider a weather event such as a flood that has compromised assets from the energy network, this can in turn have a knock-on impact on other sectors. The other networks, however, may not be aware that they are vulnerable to this cascading risk (Figure 1).

Connecting digital twins to develop a shared understanding and actionable insights across sectors is a key means to address this cross-sector resilience challenge. The Climate Resilience Demonstrator (CReDo) is a climate change adaptation digital twin project that demonstrates how cross-sector data sharing can improve climate adaptation and resilience across networks. The underlying principle is a based on a connected system of systems approach, which a) offers more functionality, scale and insights than the sum of the constituent networks alone, and b) accounts for the interconnected nature of networks. This is essential to mimic the real-life cascading risks. This technical approach is powered by a knowledge graph-based connected digital twin approach, described elsewhere [2].

Figure 1: Network of interconnected critical infrastructure assets – failure cascading risk

Beyond the legal barriers, data silos and lack of interoperability represent major technical challenges to data sharing. Here, to understand the merits of developing a common language to enable data interoperability, I encourage the readers to read the blog posts by Jonathen Eyre [3] and Sarah Hayes [4], as well as the many previous insightful blogs and posts published on DT Hub. It is equally important to embrace the heterogeneity of data from different sources, and to cater for the varying levels of digital maturity across stakeholders. In CReDo, we have adopted a distributed data sharing architecture (Figure 2) that hosts asset owner data on separate servers, which could be hosted by asset owners in their own IT systems. This approach supports the extensibility of CReDo to enable the connection of data across more domains (sectors, expertise, know-how, etc.)

The distributed data sharing architecture enables the individual asset owners to retain control of their own data assets, with security and access controls safeguarding access to the data. It facilitates different views of the insights and data tailored to each individual user/asset owner, whilst retaining a connected understanding based on shared data.

Figure 2: Technical approach adopting a distributed shared data architecture

The internal data structure used by CReDo is based on a hierarchy of simple ontologies. The ontologies are used to represent the assets from the infrastructure networks, the connectivity and properties (such as its owner, location, operational state, etc.) of each asset, and flood data for different climate scenarios. The approach enables the straightforward mapping of data from asset owners to the CReDo data structure, ensuring outward compatibility. It is easily extensible, offering the possibility to broaden the scope of CReDo to include additional asset properties, new asset owners and new sectors. The assets and the connectivity between them result in a directed graph. This is shown using synthetic data in Figure 3. This is analogous to the knowledge graph that CReDo creates by using ontologies to representing the data and relationships between data items.

Figure 3: CReDo assets and connectivity – a directed information graph (synthetic data)

Use case and insights

CReDo supports scenarios comprising assets across infrastructure networks and different types of floods for different climate scenarios. The sensitivity of the combined network to cascading failures caused by different types of flooding (coastal, fluvial and pluvial) provides insights to support strategic planning decisions to improve the climate resilience of the combined infrastructure network.

Among the various use cases studied, just one case (also using synthetic data) is considered here for the sake of brevity. The primary substation that supplies power to two NHS hospitals in the region is depicted below in Figure 4. One of the hospitals (marked by a red circle) has directly failed and lost power and telephone service. The second hospital has not flooded but is experiencing indirect failure. The latter also shows the consequences of the flood cascading through the asset networks. An alternative scenario suggesting investments to improve the resilience of the power substation shows an option to avoid the loss of power for both hospitals.

Figure 4: NHS hospital infrastructure – direct and indirect asset failure (synthetic data)

CReDo also offers an analytics dashboard (Figure 5) that derives and summarises information across multiple scenarios, providing a simple view of complex information to support decision making. Examples include a list of assets with the highest vulnerability and economic information regarding potential interventions, etc.

A publicly accessible version of the CReDo demonstrator that uses synthetic data is available via the DT Hub, while a version that uses real asset data has been deployed in a secure environment hosted by the Science and Technology Facilities Council (STFC) within the Data and Analytics Facility for National Infrastructure (DAFNI).

Figure 5: CReDo analytics dashboard (synthetic data)


CReDo continues to be a rewarding journey for CMCL; being part of what is a pioneering collaborative project between industry, academia and government to deliver a connected climate adaptation digital twin. A technical showcase describing how the CReDo demonstrator works is now available via a video on the DT Hub.

While we embark on improving the capabilities of CReDo by considering additional weather events such as extreme heat; it would be great to leverage the extensibility of the technical approach and the distributed data sharing by engaging with other infrastructure asset owners, regulators and potential collaborators. You can help shape this engagement via the Digital Twin Hub:



2.      J. Akroyd, S. Mosbach, A. Bhave and M. Kraft (2021), Universal Digital Twin – A Dynamic Knowledge Graph, Data Centric Engineering, 2, e14




Amit Bhave is the CEO and Co-Founder of CMCL, a digital engineering company offering products and technical services to the industry. He is also a By-Fellow of Hughes Hall and an Affiliated Research Fellow at the CoMo Group, Department of Chemical Engineering and Biotechnology at the University of Cambridge. His research interests include cross-sector digitalisation, smart infrastructure, negative emissions technologies, carbon abatement, low-emission energy conversion and nanomaterials.


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