Advancing real-time flood prediction in large estuaries: iFLOOD a fully coupled surge-wave automated web-based guidance system. (September 2020) - Khalid, Arslaan; Ferreira, Celso M.
Abstract: Real-time flood forecasting computational frameworks that can dynamically integrate oceanic, coastal and estuarine processes are becoming essential to provide accurate and timely information for emergency response and planning in largely populated estuaries during extreme events. This study presents a newly developed real-time total water flood guidance system that is fully automated based on the coupled surge-wave (ADCIRC + SWAN) model and provides water level forecasts in the Chesapeake Bay for a lead-time of 84 h twice a day displayed on a web-based public interface. This system improved the current total water level predictions in the Bay (RMSE < 0.12 m) when compared to the existing operational forecasting systems over the period of 6 months (Jan'19-Jun'19). Furthermore, we demonstrated that a bias correction scheme and a multi-member ensemble forecast improve the overall flood prediction. Results suggests that this framework can improve our current capacity to predict total water levels in large estuaries. Highlights: iFLOOD is a prototype surge-wave operational guidance system for the Chesapeake Bay. iFLOOD provides a Web-based public interface for data visualization and situation awareness. Water level guidance from iFLOOD showed less bias than two official flood forecast systems. Ensemble-based forecast with iFLOOD out-performs all current official flood forecasts.