FloodOps Twin: A Role-Based Spatial Intelligence Digital Twin for Reducing Cognitive Overload in Urban Flood Emergency Operations

S M Samiul Islam , Ibrahim Demir

Rapid-onset urban flooding generates significant operational challenges for Emergency Operation Centers (EOCs), where decision-makers must interpret large volumes of heterogeneous hydrological, infrastructural, and transportation data under severe time constraints. Existing flood dashboards frequently rely on centralized visualization paradigms that expose all users to the same high-density information environment regardless of operational role, potentially increasing cognitive burden and decision latency. This study presents FloodOps Twin, a web-based urban flood digital twin framework designed to support adaptive operational intelligence through a Role-Based Spatial Intelligence (RBSI) architecture. The framework integrates real-time hydrological telemetry, National Weather Service flood thresholds, transportation network conditions, and parcel-level exposure datasets within a synchronized spatial decision-support environment. Iowa City, Iowa and conditions associated with the 2008 Iowa River flood were used as the operational case study to evaluate system behavior during escalating flood scenarios. The framework dynamically transformed shared telemetry into differentiated operational products for emergency managers, transportation teams, public works personnel, planners, and public users. Results demonstrated the capability of the system to support contextual situational awareness, roadway passability analysis, automated detour generation, infrastructure-oriented operational filtering, and dynamic economic exposure estimation within a unified operational environment. The study highlights the potential of cognitively adaptive digital twin architectures to improve urban flood coordination, operational scalability, and role-specific emergency intelligence. Future work should incorporate formal usability evaluation, predictive flood modeling, and AI-assisted operational analytics to further strengthen adaptive flood response capabilities.