2024-W19 Environmental Digital Twins for Sustainable Water Management

Overview

The development of digital twins for the natural environment represents a crucial frontier in modern environmental science and sustainability efforts. These digital replicas, mirroring complex ecosystems, landscapes, and natural resources, offer transformative potential for understanding, managing, and preserving our planet’s delicate ecosystems. Digital twins enable a comprehensive, real-time monitoring and modelling of natural systems, providing invaluable insights into dynamic processes like climate change, hydrology, and biodiversity fluctuations. By integrating data from various sources, such as satellite imagery, remote sensing, computational models, and environmental sensors, digital twins facilitate a holistic view of ecosystems, offering the ability to make informed decisions for conservation, pollution control, and water resource management. Digital twins empower proactive and efficient management by enabling scenario testing and forecasting, facilitating simulating the impact of various environmental stressors, human interventions, or climate change scenarios on ecosystems, which is vital for devising effective mitigation strategies and adapting to future challenges. Moreover, these digital tools can also enhance public engagement and awareness by visualizing complex environmental phenomena in an accessible manner, fostering a sense of shared responsibility for safeguarding our natural environments. 

However, there exists a significant research gap in the field of digital twins for the natural environment. Development of high-fidelity digital twins for entire ecosystems remains a formidable task, as it demands a delicate balance between data accuracy and computational complexity. Current models often struggle to capture the full complexity of natural systems, including the intricate interactions between species, landforms, and environmental variables. Bridging this gap requires breakthroughs in data collection, machine learning, and computational modeling to create more accurate and responsive digital twins. 

This project will develop digital twins for an integrated constructed wetlands to enhance its design and performance efficiency and facilitate informed decisions for conservation and resource management. The proposed DTs will be piloted for the UK flagship constructed wetland site located in Norfolk, where we have already established a living lab and collecting environmental, pollution, and climatic data. This project will integrate the data from our living lab, remote sensors, and numerical models to provide detailed information on the performance of CWs and inform optimal design, operation, and maintenance protocols for CWs. This project will provide crucial digital tools and data to help the UK Water Industry to transitioning towards Net Zero by facilitating wider and more reliable adoption of nature-based solutions.