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.