Urban and coastal areas face escalating flood risk from compound drivers (storm surge, rainfall, riverine overflow, and tidal fluctuation) whose simultaneous interactions are poorly captured by traditional single-driver models. Digital twin (DT) frameworks, integrating real-time sensor networks, high-resolution hydrodynamic models, AI/ML emulators, and data assimilation, offer a transformative pathway for simulating, forecasting, and managing these dynamics across coastal and urban landscapes. This session invites contributions on: (i) DT architectures for compound flood modeling in coastal and urban systems; (ii) real-time data assimilation and uncertainty quantification; (iii) GPU-accelerated and surrogate model approaches; (iv) IoT and remote sensing integration; (v) decision-support applications for flood risk management and infrastructure resilience; and (vi) smart stormwater control and green infrastructure optimization. As climate change intensifies compound flood hazards in coastal and urban regions, this session addresses a scientifically urgent and societally critical frontier.

• IN - Informatics
• NH - Natural Hazards