Interconnected Digital Twins for Water Contamination Management

Emergencies such as storms, wildfires, floods, or earthquakes can affect various interconnected environmental and critical infrastructure systems, potentially causing failures that may cascade from one system to another. For instance, such events can contaminate water sources, disrupt the operation of water treatment, supply, disinfection, and distribution, cause overflows in sewerage and drainage systems, and disrupt services in power grids, telecommunication networks, and transportation networks. Especially in cases of contamination or disruption of disinfection, such events can result in severe risks to public health, the economy, and the environment.

Addressing these challenges requires a unified Cyber-Physical-Socio-Environmental System (CPSES) approach that models the interactions and dependencies among the various components. We propose an Integrated Digital Twin architecture as a holistic framework that incorporates and coordinates different Digital Twins modelling the different Cyber, Physical, Social and Environmental systems, to capture the propagation of contaminants and estimate their impact.

The CPSES framework incorporates real-time sensor data, geographical information systems (GIS), computational models, and state-estimation algorithms to dynamically model events and enable proactive planning and real-time decision support for local authorities, first responders, utility operators, and public health officials.

For example, a sudden storm can increase water levels in a reservoir, causing an overflow that significantly raises the water level in a downstream river. This, in turn, can lead to sewage overflow from a nearby manhole, potentially affecting first responder operations, and flooding a power substation, which disrupts its operation and, in turn, disconnects a pump supplying water to a central tank.

A core technology for implementing this framework are the Data Spaces, which serve as secure, standardized environments for ingesting and sharing data among multiple stakeholders and infrastructure operators. Moreover, State Estimation is critical for producing realistic assessments of the current and near-future states of the system. State Estimation can be extended by combining physics-based models with machine learning, to estimate unobserved system states and continuously update parameter values. As a result, data spaces, integrated with GIS, computational models, state estimation, and machine learning, provide a Digital Twin that serves as a single point of reference. This allows risk analysts to assess vulnerabilities, estimate the spread of events, and model cascading effects on other systems.

This integration, facilitates rapid and precise interventions, such as rerouting water supplies, isolating at-risk sewer lines, or reconfiguring power distribution. The HPC-based urgent-computing paradigm can also be considered to ensure stakeholders receive risk assessments, contamination maps, and infrastructure failure forecasts within the strict timeframes required for crisis response.

To demonstrate real-world applicability, we discuss the Cyprus Digital Twin, an innovative platform where a simulated emergency triggered a contamination/overflow event in the Yermasogia Reservoir. This event threatened the aquifer and the extraction of potable water from boreholes. By integrating contamination propagation models, public health models, flood hazard models, geospatial data, power network fragility curves, and real-time sensor measurements, the Digital Twin and its tools were able to provide comprehensive situational awareness, assess the potential impact of the event, and support the rapid decision-making process.