Abstract

Under Climate change, especially Global Warming, the increased intensity and frequency of extreme precipitation events in more local areas have illustrated the importance of having a building-scaled flood forecasting system for urban risk management strategies. However, a building-scaled hydrodynamic model is rarely employed in operational forecasting, and the expositions of buildings' contribution to the flood dynamics in the urban environment are unsatisfactory. The present study aims to propose a framework for an operational flood forecasting system for the urban environment. We construct a parameter selection module to capture the time-varying nature of parameters in an operational hydrologic model. This framework was further applied with a focus on riverain flooding induced by Hurricane Ida. We find that the model would return similar or even better results by considering the time-varying nature of parameters. Besides, the prior hydro-conditions dominate the optimal parameter selection for a hydrologic model. The simulation results illustrate that excluding buildings from the computational mesh predicts shallower and slower flooding. We also find that adjusting manning's roughness would return comparable floodwater depth and duration but will cause significant bias in the simulated velocity and further impact the accuracy of advanced flood risk assessments.