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Low-lying coastal cities across the world are vulnerable to the combined impact of rainfall and storm tide. However, existing approaches lack the ability to model the combined effect of these flood mechanisms, especially under climate change and sea level rise (SLR). Thus, to increase flood resilience of coastal cities, modeling techniques to improve the understanding and prediction of the combined effect of these flood hazards are critical. To address this need, this study presents a modeling system for assessing the combined flood impact on coastal cities under selected future climate scenarios that leverages ocean modeling with land surface modeling capable of resolving urban drainage infrastructure within the city. The modeling approach is demonstrated in quantifying the impact of possible future climate scenarios on transportation infrastructure within Norfolk, Virginia, USA. A series of combined storm events are modeled for current (2020) and projected future (2070) climate scenarios. The results show that pluvial flooding causes a larger interruption to the transportation network compared to tidal flooding under current climate conditions. By 2070, however, tidal flooding will be the dominant flooding mechanism with even nuisance flooding expected to happen daily due to SLR. In 2070, nuisance flooding is expected to cause a 4.6% total link close time (TLC), which is more than two times that of a 50-year storm surge (1.8% TLC) in 2020. The coupled flood model was compared with a widely used but physically simplistic bathtub method to assess the difference resulting from the more complex modeling presented in this study. The results show that the bathtub method overestimated the flooded area near the shoreline by 9.5% and 3.1% for a 10-year storm surge event in 2020 and 2070, respectively, but underestimated the flooded area in the inland region by 9.0% and 4.0% for the same events. The findings demonstrate the benefit of sophisticated modeling methods compared to more simplistic bathtub approaches, in climate adaptive planning and policy in coastal communities. 

Climate change and sea-level rise are increasingly leading to higher and prolonged high tides, which, in combination with the growing intensity of rainfall and storm surges, and insufficient drainage infrastructure, result in frequent recurrent flooding in coastal cities. There is a pressing need to understand the occurrence of roadway flooding incidents in order to enact appropriate mitigation measures. Agency data for roadway flooding events are scarce and resource-intensive to collect. Crowdsourced data can provide a low-cost alternative for mapping roadway flood incidents in real time; however, the reliability is questionable. This research demonstrates a framework for asserting trustworthiness on crowdsourced flood incident data in a case study of Norfolk, Virginia. Publicly available (but spatially limited) flood incident data from the city in combination with different environmental and topographical factors are used to create a logistic regression model to predict the probability of roadway flooding at any location on the roadway network. The prediction accuracy of the model was found to be 90.5%. When applying this model to crowdsourced Waze flood incident data, 71.7% of the reports were predicted to be trustworthy. This study demonstrates the potential for using Waze incident report data for roadway flooding detection, providing a framework for cities to identify trustworthy reports in real time to enable rapid situation assessment and mitigation to reduce incident impact.