FlowsDT: A geospatial digital twin for navigating urban flood dynamics.

Area of Science:

• Environmental Science
• Urban Planning
• Geospatial Technology

Background:

• Climate change, urbanization, and environmental degradation exacerbate global flood hazards.
• Effective flood risk management necessitates real-time analysis, accurate forecasting, and stakeholder communication.
• Advancements in hydrodynamic modeling and digital twins offer enhanced flood simulation capabilities.

Purpose of the Study:

• To develop a geospatial digital twin (FlowsDT-Galveston) for high-resolution flood simulation in Galveston City.
• To incorporate topography, hydrography, and infrastructure data into the digital twin.
• To identify at-risk zones and support urban resilience against pluvial and fluvial flooding.

Main Methods:

• Utilized 1D-2D coupled hydrodynamic models and PCSWMM software for flood modeling.
• Integrated high-resolution LiDAR, land cover, and storm sewer data into a 4-D digital twin.
• Validated the model using historical flood data and social sensing, then simulated various rainfall scenarios.

Main Results:

• Modeled hyperlocal flood conditions for 2-, 10-, 25-, 50-, and 100-year return period rainfall events.
• Observed a 5.7% increase in building inundation (>0.3m) and a 6.7% increase in road inundation (>0.3m) from 2- to 100-year floods.
• Identified specific at-risk zones under different flood scenarios.

Conclusions:

• The FlowsDT-Galveston model supports proactive flood management and urban planning in Galveston.
• Findings inform disaster resilience strategies and guide sustainable infrastructure development.
• The developed framework is adaptable for other coastal communities facing similar flood challenges.