Exploring infiltration effects on coastal urban flooding: Insights from nuisance to extreme events using 2D/1D hydrodynamic modeling and crowdsourced flood reports

Coastal urban flooding presents significant challenges due to the complex interactions between surface runoff, storm tides, and drainage systems. In highly impervious urban areas, infiltration may seem negligible, particularly during extreme events when soil saturation is rapidly exceeded. However, for nuisance flooding-becoming more frequent due to sea level rise and shifting rainfall patterns-the role of infiltration warrants closer examination. This study aims to assess the impact of incorporating infiltration processes into an urban flood model by evaluating flood extent under 1-, 10-, 50-, and 100-years return periods. Specifically, we performed our test case over an urban coastal watershed in Norfolk, Virginia using a detailed 2D/1D hydrodynamic model. For the evaluation of the model, we incorporated community-sourced inputs from residents and drivers collected through data portal application and a mobile navigation platform. The results show that infiltration losses reduced the flooded areas by 33 % during the 1-year return period and by 12 %, 9 %, and 5 % during 10-, 50-, and 100-years return periods respectively. In terms of flood volume, infiltration led to a 29 % reduction for the 1-year event, while reductions were smaller for extreme events due to rapid soil saturation. We also found significant variability in water depth, with for example a nearly 20 cm difference between full and zero saturation conditions during the nuisance flooding event on a selected street in the Park Place neighborhood. While less intense nuisance storms did not achieve full soil saturation, thereby reducing flood impacts, extreme events rapidly overwhelmed infiltration capacity, leading to increased surface runoff and flood inundation. Finally, the study showed how crowdsourced flood reports can help to validate urban flood models. In our case, the model accurately predicted flood locations with 88 % of community reports and 89 % of driver reports of flooding aligning with simulated inundation maps.