FlowsDT: A Geospatial Digital Twin for Navigating Urban Flood Dynamics

2025-07-08

AI Summary

Climate change and rapid urbanization are intensifying urban flood risks, necessitating high-precision, real-time flood simulation and collaborative risk governance. To address this, we develop FlowsDT-Galveston, a geospatial digital twin system tailored for urban flooding. It innovatively integrates a coupled 1D–2D hydrodynamic model with social sensing data to enable four-dimensional dynamic flood propagation and risk identification at street-segment and subsurface infrastructure levels. Built upon the PCSWMM platform, the system incorporates LiDAR-derived topography, land cover, and stormwater network geometry to conduct high-resolution simulations of flood depth, extent, duration, and velocity under rainfall scenarios with return periods of 2–100 years. Results show that areas with building and road inundation depths ≥0.3 m expand by 5.7% and 6.7%, respectively, as return period increases. This framework provides an interpretable, interactive, and updatable digital foundation for resilient urban planning and multi-stakeholder disaster decision-making.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Computer Vision: Remote Sensing / Geospatial AI
• Humans and AI: Planning and Decision Support for Human-Machine Teams

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

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 modeling 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 (GDT) supported by 1D-2D coupled hydrodynamic models to strengthen urban resilience to pluvial and fluvial flooding. The objectives include: (1) developing a GDT (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 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 one foot increased by 5.7% from 2- to 100-year flood. Road inundations above 1 foot 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.

Problem

• Develop geospatial digital twin for urban flood resilience
• Validate flood models using historical and social data
• Identify at-risk zones under varying rainfall scenarios

Innovation

• 1D-2D coupled hydrodynamic models for flood simulation
• Geospatial digital twin integrating LiDAR and infrastructure data
• PCSWMM for dynamic urban landscape virtual replicas