The idea of a “digital twin” was born at NASA in the 1960s as a “living model” of the Apollo mission. In response to Apollo 13’s oxygen tank explosion and subsequent damage to the main engine, NASA employed multiple simulators to evaluate the failure and extended a physical model of the vehicle to include digital components (Allen 2021). Figure 1 shows Apollo Simulators at Mission Control in Houston.

Why has the “digital twin” become popular in various fields after so many years? With the development of technologies like the Internet of things (IoT), artificial intelligence (AI), space-based satellites, etc., building digital twins has become more practical. A digital twin is built as a digital replica of an object, being, or system (Wikipedia 2024a). It not only contains real-world processes, but also includes their states, and allows comprehensive supervision (Attaran and Celik 2023). It facilitates stakeholders in getting a clear picture of the real-world situation, enabling intelligent decision-making and control. 

How do digital twins simulate real-world water systems? 

In water systems, digital twins generally contain the following key procedures:

• Collecting multisource data. 
• processing information. 
• modeling or prediction. 
• platform visualization. 
• data sharing; and 
• providing comprehensive analysis to support decision making. 

Space-based satellites or other devices provide meteorological, hydrologic, environmental or water quality monitoring data for the virtual DT water system. The watershed or water infrastructure condition is provided by geographic information and building information system. Then, artificial intelligence (AI) algorithms are used for information extraction or assimilation (Li et al. 2023). Technologies, such as internet of things, are used to connect and exchange data in different devices or modules in DT (Manocha, Sood, and Bhatia 2024). Simulators (including hydrological/hydraulic model/ computational fluid dynamics) process input data collected to simulate the real-world processes like flooding, ice melting, etc. (Huang et al. 2022; Gourmelen 2022) And then the simulated processes are visualized on the platform to support decision making. This is a basic workflow of DT in water systems. For different purposes, DTs may contain other functions or modules. Below you find short explanations of technologies commonly utilized in digital twins, especially in those mirroring water systems:

• Hydrological/Hydraulic modeling/Computational fluid dynamics (CFD)

These models are the core of water systems, generally based on hydrology principles and hydrodynamics. For example, the Telemac2D hydrodynamic model is used in FloodDAM-DT (SCO 2020). They can simulate the river flow or flood extent by inputs of precipitation or evaporation from multiple sensors in DTs.

• Space-based technologies and remote sensing

Space-based technologies and remote sensing produce satellite images, radar data, and other Earth observations and measurements that contribute high-dimensional spatio-temporal meteorological datasets (i.e. precipitation) and geospatial information (i.e. land cover) in digital twins, especially for simulating Earth systems (Moigne and Smith 2022; NASA 2023).

• Artificial intelligence (AI)

AI (incl. machine learning algorithms) enables learning agents to perform tasks that mimics human intelligence and human cognitive functions. AI provides an advanced analytical tool by processing multiform and multi-source data. An example would be extracting land cover change from satellite images (Mangkhaseum et al. 2024). Machine learning is also used in Earth System Digital Twins (ESDT) to predict flood (Moigne and Smith 2022).

• Internet of things (IoT) and crowdsourced data

IoT allows devices to connect and exchange of data with each other through embedded sensors and rapid transmission (i.e. 5G) to obtain real-time data about objects in physical environment (Wang et al. 2024). Crowdsourced data are information collected from a large number of people. For example, local meteorological data can be obtained from smart phones of participants over a region (NASA). IoT and crowdsourcing are utilized for obtaining data about current state as input data to DTs, respectively from devices and people.

• Geographic information system (GIS)

GIS connects data to a map, integrating location data with environmental, hydrological, meteorological, or other descriptive information (ESRI 2024). GIS can be used to create digital twins of the natural and built environment. Furthermore, it can be used to gain information on the spatial context and to integrate many different digital representations of the real world (Andrews 2021).

• Building information modeling (BIM)

BIM is the virtual provision of the entire lifecycle of a building, comprised not only of a 3D model of the building, geometric information and design, but also the progress of construction and state information of the building for management (AlBawaba 2019). BIM is a useful tool for the construction and operation of water conservancy facilities (Ye et al. 2021).

Through these technologies in information collecting, processing, exchange and association, a virtual replica, a water system digital twin, is built for monitoring, predicting or researching the real-world water process. 

Applications of digital twins in water systems

Water systems are complex. From global scale to regional scale, water systems are dominated by different factors or processes. In this section, application scenarios of DT from global scale to regional scale are introduced as follows, and the representative DTs are also shown as examples.

1. Climate and Ocean

Currently, DT in development include such simulate the global-scale water cycle process (ESA 2021; Li et al. 2023; NASA 2023), predicting the Antarctic ice sheet (Gourmelen 2022), ocean-atmosphere dynamical systems, and climate change (Bauer, Stevens, and Hazeleger 2021), etc. Compared to traditional physical models, DTs utilize satellite-based Earth observation, real-time data fusion, or AI, etc. to simulate the global water cycle more realistically. Figure 2 shows the scheme of a representative DT of the Earth system, named Earth Systems Digital Twins (ESDT). It utilizes multisource observations to help scientists to understand the complexities of the water cycle (NASA 2023).

2. Regional water resources management and flood control

Water resources management is crucial for agriculture production, flood or drought disasters reduction in a region (Zhou 2024). The digital twins, such as FloodDAM-DT (SCO 2020) shown in Figure 3, are under development for flood forecasting and early warning at regional scale or even global scale. Taking advantage of satellite data and visualization techniques, digital twins will facilitate transboundary river management by equitable information sharing of hydrological, meteorological, water quality monitoring data, forecasts, and early warning information for better coordination, shared responsibility, and also maintaining a balance of interests in water resource utilization among riparian countries. (Silva 2024).

3. Water supply, water treatment and water quality

In urban areas, a digital twin can act as a smart system for water supply, distribution, water treatment, and water quality monitoring (Ramos et al. 2023; Wang et al. 2024; ESRI 2021). The urban water cycle entails devices and technologies engineers use to monitor and operate the urban water cycle including water sources to people’s houses, and further to wastewater treatment plants, as can be seen in Figure 4. Smart devices, like acoustic devices or video cameras connected with the Internet, are utilized to integrate real-time data for detecting leaks of pipelines. Therefore, DTs can increase the efficiency of water utilities and support decisions.

Key challenges and opportunities of DT

• The accuracy of DT:

A major goal of DT is prediction accuracy (Dihan et al. 2024; Saren et al. 2024). The most challenging part is how to build a DT so that it can ‘mimic’ real-world process realistically (Saren et al. 2024). Moreover, valid input data are important. Nowadays, technological breakthroughs in AI enhance the accuracy in extracting information from satellite images (Mangkhaseum et al. 2024).

• Interoperability and collaboration:

The standards for interoperative digital twins are considered as “bridges” between DTs, which facilitate the integration of multiple digital twins into a system, or a better exchange between users of different DT (Klar and Angelakis 2023; Santhanavanich et al. 2022). For example, standards by the Open Geospatial Consortium (OGC) define interoperable approaches to data encoding, access, processing, and visualization, as well as to metadata and catalogue services. Open interfaces and protocols are built, that enable the interoperability between different brands or kinds of spatial processing systems (OGC 2024). Interoperability can also enhance collaboration between stakeholders to support decision making.

• Data security in DT:

In terms of data sharing and storage in a digital twin, data security should be taken into considerations (Jeremiah et al. 2024; Dihan et al. 2024). Since hydrological data are confidential in some regions and countries, data security needs to be considered when storing data. Compliance with standards and regulations for information security management helps ensure data security in DT.
Conclusions and perspectives
DT is considered as a powerful tool with numerous applications in water management currently under development. Compared to traditional methods, the advantage of digital twins is that they can simulate hydrologic processes or model water-related affairs more efficiently and serviceably by collecting real-time information, extracting information form multiform data, and responding rapidly. Additionally, a DT presents a relatively transparent platform that allows researchers, the decision-maker, and other stakeholders get a clear picture of situation based on results simulated by the DT. It helps in data-sharing, and consequently allows making the decision based on more comprehensive analysis.