City-scale digital twin framework for flood impact analysis: Integrating urban infrastructure and real-time data analytics

Urban areas are increasingly vulnerable to flooding due to climate change and rapid urbanization. Traditional mapping and decision-support tools lack the capability to integrate real-time data or analyze cascading disruptions across interconnected urban systems. Digital twins offer a promising solution by enabling real-time monitoring, simulation, and optimization of urban environments. This study presents a comprehensive city-scale digital twin framework that integrates flood forecasting, transportation networks, and critical infrastructure systems into a unified, real-time cyberinfrastructure. By leveraging data from sensors, hydrological models, and geographic information systems (GIS), the framework enables interactive, three-dimensional simulations to assess flood impacts and their cascading effects on urban mobility and infrastructure. Using Waterloo, Iowa, as a case study, we demonstrate the framework's ability to simulate flood scenarios, assess transportation disruptions, and generate actionable insights for disaster preparedness. The results highlight the framework's potential to enhance urban resilience by providing a holistic understanding of interdependent urban systems, supporting data-driven decision-making, and advancing flood risk management strategies.