As the world struggles with water scarcity, drought, flood and impacts of climate change, a smarter water management system is needed. Recently, a revolutionized technology emerged—the digital twins.
As the world struggles with water scarcity, drought, flood and impacts of climate change, a smarter water management system is needed. Recently, a revolutionized technology emerged—the digital twins. The digital twins represent a virtual environment that reflects the real-world physical system, called the physical twins, and its processes by continuous synchronization with that real-world system over time, throughout its lifecycle (Semanjski 2023). For this, the digital twins depend on continuous and automated updating over time status from various available sources of information regarding diverse matters. Water resources management is a complex topic that combines with other sectors from the point of extraction to the final distribution to users , and introducing digital twins in the modeling and representation of water resources management represents an innovative methodology for a sustainable future. The management and decision-making approach is defined as experience-based, post-event summary, and manpower-intensive tactics that are not only time- and energy-consuming but also demanding to achieve satisfactory results, causing the water conservancy engineering system and management system to struggle to exert the synergistic effect of "1+1>2" in practical work (Li et al., 2024).
Table 1. Applications of digital twins technology in water resources management
Application | Description |
Water distribution and supply management |
Flood prediction and management | Simulates watersheds for early flood warnings and mitigation strategies. |
Water and wastewater treatment management | Improves treatment plant efficiency, reduces costs, and monitors equipment health. |
Groundwater management and recharge | Real-time Monitoring aquifers and recharge rates, ensuring sustainable extraction and usage. |
Water quality management | Tracks real-time water quality, detecting pollutants. |
Nowadays, having a system for water resources management to combine every sector with their effects is not difficult with digital twins technology. Digital twins are here to rectify manpower and intensify problems. This article delves into the 5 applications of digital twins technology in water resources management.
Water distribution systems are vital for providing water for different users and sectors, especially in populated cities with high demand. Digital twins technology can revolutionize water distribution and supply as follows (Cavalieri and Gambadoro 2024):
Helping the operators to select the optimal decisions in real-time by simulating the effect on any operation prior to taking the action in the real system.
Employ energy-saving strategies, such as using a fewer number of pumps when demand falls below a dynamically chosen limit.
Identifying anomalies to establish an enhanced maintenance system, providing diminishing maintenance costs and downtime to limit disruptions to end-users.
Optimizing the operation of the system in order to improve the quality of the service and the water quality.
Developing emergency response plans and modeling the behavior of the system under emergency conditions for detecting an early warning system against possible contamination into the network.
Pipes and pumps are intricate systems with complex internal mechanisms and exact control requirements, necessitating careful safety measures. They play a crucial role in maintaining the flow, pressure, and control of fluids in various processes (Baena-Miret et al., 2024). Indeed, they are components of more extensive systems, such as industrial plants or pipeline networks, and can be expensive assets to buy, manage, and maintain. They can also become one of the major sources of water losses, through leakage.
One of the common issues in water distribution systems is water losses , which is waste of treated fresh water and energy resources. The issue of water losses in municipal water networks can reach up to 80% of the conveyed water or higher in some urban networks (Hommes and Boelenz 2017). Digital twins technology, in conjunction with machine learning (ML) and the internet of things (IoT), can control and reduce the water losses, including flow management, water, and energy monitoring, along with water grid control, to work together to boost the efficiency of water distribution systems. Using a detection and communication system, water losses can be reduced through intelligent supervision of sensors, telemetry, and actuators that control water pressure and flow at critical network points (Ramos et al., 2023). In the management hub, data collected by monitoring systems can be processed, enabling continuous monitoring of water and energy consumption. The integration of a remote-control platform, powered by big data analytics, allows water-energy network managers to optimize system performance through real-time control and data-driven decisions, progressively improving the overall efficiency of the network.
One of the successful pioneer countries in adopting digital twins technology in the water distribution system is singapore. Singapore's digital twins for its water distribution system gathers data from sensors in pipes, pumps, and valves, facilitating simulations that predict water flow, pressure, and quality. Early detection of leaks and blockages can be facilitated by this technology, enhancing repair response times and reducing water loss. It supports predictive maintenance, allowing the Public Utilities Board (PUB) to preempt equipment failures, thus minimizing disruptions. The digital twins optimizes water flow and pressure, lowering energy consumption while improving efficiency (Singapore's National Water Agency). By analyzing weather data and consumption patterns, it adjusts to demand fluctuations. Additionally, it helps in developing smart water grids, ensuring better resource allocation. This innovative system aligns with Singapore's goals for water sustainability, serving as a good example of using digital twins technology for water distribution and supply management.
Due to the impacts of climate change in certain parts of the world floods are becoming more prevalent, causing significant human and financial loss. Digital twins can help to prevent those damages. One of the challenges associated in flood modeling is spatial variability of flood events, due to the factors such as local topography, land use, and geographical features. Therefore, forecasting must account for these site-specific conditions, demanding meticulous and often site-specific modelling, making predictions, as what holds true for one area may not apply to another, even within the same watershed (Shafiee Roudbari et al., 2024). Digital twins can overcome these challenges by creating a dynamic and detailed virtual replica of the real-world environment. Different scenarios can reveal the sensitivity of the system to specific variables. For instance, evaluating how the placement of concrete barriers or the density of tree trunks influences water flow and how it impacts floods. This sensitivity analysis aids in identifying critical factors and their associated variability. Moreover, visual representations of potential flood scenarios are critical in engaging and educating local communities about flood risks and the importance of being prepared, raising awareness and supporting flood mitigation efforts.
Internet of Things (IoT) is a paramount component of digital twins for flood forecasting, providing dynamical information about weather and environmental observation data. However, the raw data from these sensors often present spatial and temporal gaps. Scientific models are essential for merging this disparate data into a continuous spatiotemporal context. Different fields necessitate varied scientific models to address multiple variables and for effective integration. A complete digital twins simulation requires incorporating multiple cross-disciplinary scientific models alongside real-time sensor data (Chen et al., 2024). This interaction among models enhances simulation accuracy and application value. As highlighted, successful digital twins establishment relies on integrating diverse IoT time-series data for real-time monitoring and predicting changes in the physical world, especially in the context of increasing extreme precipitation events.
Digital twins technology has a vital role in Netherlands flood management strategy for increasing water resilience. The Delta Program uses digital twins of the Dutch water system to simulate flood events, assess risks, and develop strategies for flood prevention and mitigation, involving multiple big and local stakeholders and communities. Delta Program facilitates real-time tracking and predictive assessment of the water system's functions, allowing officials to implement preventive actions ahead of potential flood incidents (Delta Program) . The model can simulate floods across different climate scenarios, tidal conditions, and river currents to help stakeholders in making informed choices about infrastructure spending, flood protection, and emergency preparedness planning. Additionally, the MAGES system in Paris integrates 2,000 sensors with real-time data, a hydraulic model consisting of nearly 23,000 computational elements to detect and control combined sewer overflows in the city’s sewer system (Tabuchi et al., 2020). Additionally, The CSOnet system (Indiana, US) uses 120 sensors and 12 actuators along with an online hydraulic model to increase the storage and transmission of the sewer network (Montestruque and Lemmon, 2015). Also, Louisville MSD (Kentucky, US) utilizes an optimization system that incorporates monitoring data, weather forecasting, and data analytics to maximize the wastewater system's capacity (Tao et al., 2020).
The emerging new technology, water and wastewater treatment sector, is embracing new technologies to boost its efficiency and reduce the costs in chemicals, energy, residuals, and labor. The water and wastewater treatment process is complex and dynamic, which undergoes daily variations influenced by weather conditions (dry, rainy, and stormy), presenting a significant challenge in establishing mechanism models and effectively monitoring processes (Liu et al., 2023). Therefore, digital twins are a useful tool in this domain. For instance, digital twins can help water and wastewater treatment plants optimize their operations by providing real-time insights and data analysis. Also, this technology can monitor the equipment's health and either provide early alert for the issues or predict when maintenance or repairs will be required, helping in minimizing downtime and prolonging the lifespan of machinery.
Additionally, the forecasting abilities of digital twins allow for the simulation of different operational scenarios derived from weather predictions, incoming water quality, or load demands, enabling plants to adapt their operations in real time according to shifting conditions, maximizing both efficiency and resource utilization. For instance, in a storm event, the flow of water could rise considerably, leading to changes in water quality (Liu et al., 2023). Digital twin technology can model how these variations will influence the treatment process and assist in modifying chemical treatments or operational settings accordingly to ensure optimal performance.
The concept of digital twin use is relatively new in water and wastewater treatment and studies about it continue to grow. For example, in a study presented by Johnson et al. (2022) a digital twin of the sewage network of Gothenburg city, using a model of the sewer system, data on flows and water levels in the network, as well as at treatment plants presented. This model, provided weather forecasts for the next two days, implements a network management strategy to optimize the pumping system to maintain a constant flow into treatment plants for the next 12 h. The results showed a reduction of up to 50% in untreated water discharges into the environment, totaling 1.5 billion liters . It also improved process efficiency by maintaining more stable flows, reduced the risk of critical situations through predictive algorithms, and increased the capacity to address problems with pumps or at the entrance to water treatment plants through predictive control.
Groundwater is one of the important parts of the hydrological cycle and is used frequently for agricultural sources , especially in arid and semi-arid regions where surface water is limited. Groundwater management is a major challenge for cities, especially cities in the delta area. The fluctuations in the groundwater are due to climatic events such as droughts and heavy rains, which have become more frequent and intense as a result of climate change (Klimaateffectatlas, 2022). These fluctuations pose various problems for urban areas, including waterlogging and building flooding. Digital twins for groundwater monitoring can monitor real time data from sensors distributed across the aquifers, aiding in decision making by identifying areas that require automated water pump drainage and sending signals through the internet to activate the water pumps based on the groundwater table value, maintaining the table value levels under the surface (Morales Ortega 2023 ). The model also includes features such as historical data visualisation based on previous sensor readings and a predictive machine learning model to predict ground water level based on historical precipitation data.
With the advent of the IoT the scope of automation and smart irrigation has expanded, enabling the connection of physical devices and online services. Equipped with sensors and connectivity capabilities, IoT devices can communicate with web applications and services, enabling automated actions based on predefined conditions to better monitor groundwater level.
Digital twins are an emerging concept in groundwater and they are replacing the traditional model for better future sustainability. In the literature, there are a few studies that concentrated on the application of digital twins in groundwater . One of Then there is the study by Morales Ortega in 2023 that created digital twins by combining real-time data, prediction models, and IoT connectivity in a 3D geospatial model for groundwater control across the city of Enschede in the Netherlands. The result showed effective visualization and prediction, aiding decision-making for pump activation and water retention strategies. Further enhancements in usability and predictive functionalities are suggested.
Water quality is directly impacting public health, ecosystem stability, and resource sustainability. Efficient water quality management is crucial for avoiding pollution, reducing the effects of human actions, and protecting biodiversity. In the context of water quality, there is a growing demand for digital twins that can monitor, predict, and communicate water quality dynamics by combining contaminant fate and transport models, online sensor data assimilation, and real-time visualization and response capabilities. In the context of watershed-scale water quality, digital twins provide innovative features for real-time response, enabling real-time monitoring, prediction, and management of water quality hazards such as algal blooms, chemical spills, and combined sewer overflows (Kim and Bartos 2024). This can enhance the forecasting skill of eutrophication models and enable pro-active mitigation of algal blooms through improved reservoir management (Cho et al., 2020).
Early warning systems for chemical spills are a significant issue for water quality management, and digital twins can enhance emergency response by enabling real-time detection and tracking of contaminant plumes (Quansah et al., 2010). Digital twin models help mitigate water quality issues by active control of hydraulic infrastructure because real-time control of urban drainage systems can significantly reduce combined sewer overflows (Mollerup et al., 2017). This is effective, especially in Smart Cities , where real-time monitoring and early warning systems play a crucial role. For instance, emploting actuated valves to limit outflows from detention basins and therefore improve sedimentation and reduce downstream erosion (Sharior et al., 2019). Digital twins can implement these control strategies at the watershed scale with online models that can monitor water quality and determine appropriate control actions in real-time.
One of the emerging tools in the realm of real time water quality monitoring is unmanned aerial vehicles, or drones. Other than water quality drones have many applications in water resources. Real time and high-resolution data gathered by drones can be used in digital twins for detecting contamination. By employing multispectral and hyperspectral sensors drones can monitor factors like water temperature, turbidity, pH levels, dissolved oxygen ,chlorophyll levels, suspended solids, nutrient concentrations and contaminants in aquatic environments. (Mabhaudhi et al., 2022). These features allow digital twins for rapid evaluation of water quality across large areas, enabling more informed and proactive management of water bodies with spatial data, which is particularly useful for identifying localized contamination sources, tracking changes over time. Drones have various different models, which can be used in water quality monitoring.
Digital twins have shown their potential in various applications in the water industry, including water quality management. There are examples of using digital twins for different aspects of water quality management both locally and nationwide. Norway is a pioneer to use digital twins in water quality monitoring and management. Water Quality Digital Twin of the Ocean (DTO) in Norway collects in-situ data by stationary sensor platforms in surface (buoys) and underwater (benthic). Moreover, drones collect on-demand data in the water column and DTO continuously offers real-time or near real-time data on various water quality parameters like temperature, salinity, ocean currents, turbidity, pollutants (Ocean-twin). Analytic tools and ocean and transport models process data to assess current situation or status, by analyzing the past and predicting future events. Near real-time raw or processed data is presented in the form of time-series graphs, 2D maps or in 3D space. In another case, The K-HIT system in Korea, uses sensor data and online models to optimize reservoir operations and mitigate water quality contaminations and algal blooms (Kim et al., 2020).
Digital twin technology has revolutionized different sectors from space science to food industry and water resources is not an exception. The integration of digital twins into water resource management offers revolutionary possibilities in multiple areas, enhancing the efficiency and sustainability of water distribution and supply management, flood management and predictions, water and wastewater treatment processes, groundwater monitoring and recharge by delivering real time simulations and predictive insights. Digital twins also allow for proactive management of water quality, guaranteeing adherence to regulations and identifying possible contaminants. With climate change, rising population levels, and limited resources putting more pressure on water systems, the use of digital twins emerges as a promising technology for enhancing resilience, optimizing resource management, and helping informed decision-making. Adopting digital twin technology can promote more sustainable, flexible, and economical water management practices, securing a sustainable and safe water for future both nation wide and locally.
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