A Campus-scale Digital Twin Framework for Urban Flood Monitoring, Simulation and Management

Urban flooding poses an increasing threat to lives and property in urbanized environments under climate change and growing human exposure. Digital Twin (DT) concept provides a potential framework for integrating real-time monitoring, numerical simulation, and decision support. However, DT implementations that exploit dense senor networks and high-resolution, physics-based hydrodynamic flood models to enable real-time, bi-directional information exchange between physical and virtual systems remain limited. In this study, we develop a campus-scale DT framework that couples real-time monitoring, high-resolution hydrodynamic modeling, 3D virtual representation within a unified data and computational environment supported by embedded data-analytics capabilities.

 

A high-resolution 3D digital campus model is reconstructed from ultra-high-resolution LiDAR point clouds to provide the geometric basis for spatial representation and data management. A dense IoT monitoring network, comprising rainfall gauges, water-level sensors, CCTV, and pipe flow meters, is deployed to acquire and transmit high-frequency hydrometeorological and hazard-related observations in real time. At the core of the framework is the dynamic coupling between real-time rainfall observations and the GPU-accelerated High-Performance Integrated hydrodynamic Modelling System (HiPIMS), which resolves surface water inundation processes at high spatial and temporal resolution. Static spatial data, real-time monitoring observations, and model outputs are ingested, harmonized, and managed within the unified data and computational environment, enabling automated model execution and coordinated system operation. Monitoring data and simulation outputs are mapped directly onto the 3D virtual environment to provide real-time visualization of spatiotemporal evolution of flooding and to support flood warning and emergency management.

 

The reliability of flood simulations is evaluated against historical flood records and further assessed through continuous comparison with in-situ water-level observations. The framework supports near-real-time flood forecasting and systematic identification of high-risk locations, providing information for early warning and emergency decision-making. Emergency interventions, such as deployment of temporal flood defenses and mobile pumping stations, can in turn influence flood dynamics and risk; these changes are subsequently captured by the monitoring-modelling system and reflected in updated DT outputs. This establishes a closed-loop, real-time monitoring-simulation-decision-feedback cycle, forming an operational DT framework for urban flood management.