Digital twins of water networks: transforming leak, pollution and health risk management
Digital twins of water networks are rapidly becoming a cornerstone of modern water utility management. By creating a virtual replica of pipes, pumps, reservoirs and treatment plants, utilities can anticipate leaks, detect pollution pathways and reduce sanitary risks before they affect consumers. This combination of real-time data, advanced modelling and predictive analytics is redefining how cities and industries think about water security.
In this article, we explore how digital twins help operators monitor complex water systems, identify anomalies faster and support investment decisions. We also look at the technologies behind these virtual models, the practical benefits for utilities and the key challenges to implementation.
What is a digital twin of a water network?
A digital twin of a water network is a dynamic, virtual model that mirrors the behaviour and state of a physical water system. Unlike a static hydraulic model updated once a year, a digital twin connects continuously to data sources and updates itself constantly.
Typically, a water network digital twin integrates:
- Hydraulic models that simulate flows, pressures and velocities in distribution and transmission mains
- Water quality models tracking residual disinfectant, temperature, turbidity or contaminants
- Real-time data from sensors, SCADA, smart meters and laboratory analyses
- Asset information such as pipe age, material, repair history and break records
- Topographical and GIS data about the network and surrounding environment
- Operational rules, pump schedules and valve settings
Because this digital twin is continuously fed by operational data, it becomes a living representation of the network. It does not just display information. It predicts how the system will react to changes, disruptions or long-term trends.
How digital twins help anticipate leaks in water networks
Non-revenue water and hidden leakage are major issues for utilities worldwide. Traditional leak detection methods rely on periodic acoustic campaigns, pressure logging or field inspections, which can be time-consuming and reactive. Digital twins offer a more proactive, data-driven approach.
By combining hydraulic models with real-time sensor data, a digital twin can show where the network is behaving abnormally. Pressure drops, unexplained flow increases or changing night flows can be turned into actionable leak alerts.
Key capabilities for leak anticipation include:
- Real-time hydraulic balance – Comparing expected flows and pressures from the model with measured values to highlight suspicious zones.
- Pattern recognition and anomaly detection – Using machine learning to identify deviations from normal operating patterns that may indicate small or emerging leaks.
- District metered area (DMA) optimisation – Simulating the impact of valve operations and DMA boundaries to improve leak localisation and reduce pressure-related stress on pipes.
- Pressure management scenarios – Testing different pressure reduction strategies in the digital twin to minimise burst frequency without compromising service levels.
The result is a move from reactive leak repair to predictive leak management. Instead of discovering failures only when customers complain or bursts occur, operators can schedule targeted inspections where the digital twin suggests the highest probability of leakage.
Predicting and managing pollution events with water network digital twins
Beyond leak detection, digital twins are powerful tools for modelling water quality and pollution transport in distribution systems. A contamination event can spread quickly through a complex network. Understanding its movement, dilution and potential impact is critical for public health protection.
Digital twins of water networks support pollution management in several ways:
- Real-time water quality tracking – Integrating online sensors (for chlorine, turbidity, pH, conductivity, organic carbon) with the hydraulic model to follow changes in water quality across the network.
- Contamination source identification – Running backtracking simulations from detection points to estimate where a contaminant might have entered the system and at what time.
- Scenario-based emergency response – Testing contamination scenarios in the virtual model to define optimal flushing strategies, hydrant operations and valve closures.
- Disinfection optimisation – Simulating chlorine decay and by-product formation to fine-tune dosing strategies and maintain residuals without over-chlorination.
Because the digital twin can simulate multiple “what if” pollution scenarios in seconds, utilities can pre-define emergency procedures and test them without any risk to customers. This preparedness reduces response time and helps limit the spatial extent and duration of contamination incidents.
Reducing sanitary risks and protecting public health
Sanitary risks in drinking water networks emerge from a combination of ageing assets, infrastructure failures, extreme weather events and operational errors. Digital twins contribute to health risk reduction by providing early warning capabilities and robust decision support.
Some of the most relevant applications for sanitary risk management include:
- Modelling low-pressure and backflow events – Identifying areas with frequent low pressure or negative pressure events that could allow contaminants to enter through leaks or cross-connections.
- Assessing stagnation and water age – Estimating water age to highlight dead-ends and storage zones where microbial regrowth or disinfectant loss is more likely.
- Integrating microbial risk assessment – Coupling hydraulic and water quality models with quantitative microbial risk assessment (QMRA) frameworks.
- Supporting regulatory compliance – Providing a transparent, data-rich environment for demonstrating compliance with drinking water standards and risk-based regulations.
In practice, this means that a digital twin can help utilities decide where to invest in pipe renewal, where to improve disinfection strategies and where to focus monitoring efforts. It becomes a tool not only for engineers, but also for health authorities and regulators.
Key technologies powering smart water digital twins
The performance of a water network digital twin depends on the integration of several technologies. Each layer brings specific capabilities and constraints.
- Hydraulic and water quality modelling software – Classic network modelling platforms (e.g. EPANET-based tools or commercial suites) form the core of many twins, extended with real-time solvers and calibration engines.
- SCADA systems and IoT sensors – Supervisory control systems, pressure loggers, flow meters, water quality probes and smart meters feed continuous data into the platform.
- Data integration and middleware – APIs, OPC servers and data lakes provide the glue between legacy systems and cloud-based digital twin environments.
- Advanced analytics and AI – Machine learning models help detect patterns, recognise anomalies and predict failures or demand variations.
- GIS and 3D visualisation – Geospatial tools allow operators to visualise hydraulic results and risk zones in an intuitive, map-based interface.
Many vendors now offer turnkey “smart water” or “digital water” platforms that package these components into integrated solutions for utilities. However, each network is unique. Successful deployment often requires custom integration work, data cleaning and model calibration.
Operational benefits for utilities and cities
Investing in a digital twin for water networks is not just a technological choice; it is a strategic decision that influences performance, costs and resilience. Water utilities that adopt these solutions typically seek a combination of operational and long-term benefits.
Notable benefits include:
- Improved leak detection and reduced non-revenue water – Better localisation of hidden leaks, faster repair times and smarter pressure management.
- Enhanced water quality management – Early detection of anomalies, better control of disinfectant residuals and reduced risk of consumer complaints or health incidents.
- Optimised energy usage – Pump scheduling and operational optimisation to reduce energy consumption and peak demand charges.
- Informed asset management – Prioritised renewal of critical pipes and equipment based on simulated risk and performance, not just age.
- Greater resilience to climate and demand variability – Scenario planning for droughts, floods, population growth or industrial demand changes.
- Better communication and transparency – Visual tools for explaining decisions to stakeholders, regulators, local authorities and customers.
For many cities, these benefits align with broader “smart city” strategies and sustainability goals, including climate adaptation and circular water management.
Challenges and limitations of water network digital twins
Despite the clear potential of digital twins, deploying them at scale in water networks is not trivial. Utilities encounter several recurring obstacles, both technical and organisational.
- Data quality and availability – Incomplete maps, outdated asset registers, missing sensor data or inconsistent naming conventions can degrade model reliability.
- Model calibration and maintenance – Keeping hydraulic and water quality models aligned with reality requires time, expertise and governance.
- Integration with legacy systems – Older SCADA platforms and proprietary protocols can complicate real-time data flows.
- Cybersecurity and data governance – As more systems become connected, utilities must reinforce cybersecurity, access control and data protection policies.
- Change management and skills – Engineers and operators need training to trust and use digital twins in daily operations, not just as occasional planning tools.
- Cost and return on investment – Initial investments in sensors, software and integration can be significant, and benefits must be clearly demonstrated to decision-makers.
Vendors and utilities are increasingly addressing these challenges through modular platforms, phased implementations and capacity-building programmes. Many start with pilot zones or specific use cases, such as leak detection in a limited area, before expanding the digital twin across the entire network.
How to start a digital twin project for a water network
For utilities or industrial water managers interested in adopting digital twin technology, the most effective approach is incremental and use-case driven. Rather than aiming for a perfect, system-wide twin from day one, projects usually focus on delivering tangible value quickly.
Typical steps include:
- Define clear objectives – For example, reducing leak-related losses by a given percentage, improving water quality monitoring or optimising pump energy costs.
- Assess existing data and infrastructure – Identify current hydraulic models, SCADA coverage, available sensors and gaps in asset information.
- Select priority pilot areas – Choose districts with known problems, good data coverage or high strategic value.
- Choose appropriate technologies and partners – Evaluate digital twin software, analytics tools and integration partners based on compatibility, scalability and support.
- Build, calibrate and test the twin – Develop the virtual model, connect it to live data sources and validate its behaviour against real events.
- Integrate into operational processes – Embed digital twin outputs into daily control room practices, maintenance planning and emergency response.
- Measure performance and expand – Track key indicators, refine models and progressively scale to other areas or functions.
This iterative approach helps teams gain confidence in the technology, adjust workflows and refine data governance before full-scale deployment.
Digital twins and the future of smart water management
As climate pressures intensify and infrastructure ages, the ability to anticipate problems rather than react to them will become essential for water utilities. Digital twins of water networks offer a way to combine physical infrastructure, data and intelligence into a single decision-support environment.
From leak detection and pressure management to pollution modelling and sanitary risk assessment, these virtual replicas are enabling a new kind of proactive, risk-based water management. They make invisible phenomena visible. They translate complex hydraulic and quality processes into intuitive maps and dashboards.
For readers considering investments in smart water technologies or digital twin platforms, the key is to align projects with concrete operational needs and realistic data capabilities. When designed with clear objectives and grounded in high-quality data, digital twins can significantly enhance the reliability, safety and sustainability of drinking water services.