FlowsDT: A geospatial digital twin for navigating urban flood dynamics

Communities worldwide increasingly confront flood hazards intensified by climate change, urban expansion, and environmental degradation. Addressing these challenges requires real-time flood analysis, precise flood forecasting, and robust risk communications with stakeholders to implement efficient mitigation strategies. Recent advances in hydrodynamic modeling and digital twins afford new opportunities for high-resolution flood simulation and visualization at the street and basement levels. Focusing on Galveston City, a barrier island in Texas, U.S., this study created a geospatial digital twin supported by 1D-2D coupled hydrodynamic models to strengthen urban resilience to pluvial and fluvial flooding. The objectives include: (1) developing a Geospatial Digital Twin (FlowsDT-Galveston) incorporating topography, hydrography, and infrastructure; (2) validating the twin using historical flood events and social sensing; (3) modeling hyperlocal flood conditions under 2-, 10-, 25-, 50-, and 100-year return period rainfall scenarios; and (4) identifying at-risk zones under different scenarios. This study employs the PCSWMM to create dynamic virtual replicas of urban landscapes and accurate flood modeling. By integrating high-resolution LiDAR data, land cover, and storm sewer geometries, the model can simulate flood depth, extent, duration, and velocity in a 4-D environment across different historical and design storms. Results show buildings inundated over 0.3 m (1 ft) increased by 5.7% from 2- to 100-year flood. Road inundations above 0.3 m (1 ft) increased by 6.7% from 2- to 100-year floods. The proposed model can support proactive flood management and urban planning in Galveston; and inform disaster resilience efforts and guide sustainable infrastructure development. The framework can be extended to other communities facing similar challenges.