High-Resolution Urban Flooding

Evaluating low impact development practices as a mitigation strategy for compound flooding in urban-coastal watersheds

Jiseon Song, Sergio A. Barbosa, Jonathan L. Goodall

https://doi.org/10.1016/j.jenvman.2026.130085

Abstract

Flooding along the U.S. coastline has become increasingly frequent due to the combined effects of intense rainfall and tide. In fully developed urban-coastal areas, constructing large-scale detention facilities or expanding stormwater networks is often infeasible. In this context, distributed, small-scale measures such as low impact development (LID) practices represent a complementary, community-driven approach to supplement conventional drainage infrastructure by capturing runoff on both public and private properties to enhance flood resilience. This study evaluates the potential of parcel-scale LID in mitigating compound flooding under different rainfall intensities and tidal conditions in an urban-coastal watershed. A hydrodynamic model was developed for a watershed in Norfolk, Virginia to simulate interactions between overland flow and drainage networks across a range of LID adoption scenarios. Results show the extent to which LID practices can reduce flood extent from a 1-year compound flooding event, particularly for inland areas primarily affected by pluvial compound flooding. For example, under the 1-year event, flood extent in the case study inland area was reduced by an average of 43% across scenarios, which is 3.57 times the reduction observed for the entire study area. Overall, parcel-scale LID in urban-coastal watersheds may provide a complementary mitigation strategy, especially for low return period nuisance flooding events in systems with similar hydrologic and infrastructure characteristics.

Evaluating low impact development practices as a mitigation strategy for compound flooding in urban-coastal watersheds

Jiwoo Jeong, Jonathan L. Goodall, Julianne D. Quinn

https://doi.org/10.1016/j.ejrh.2026.103405

Abstract

Study region

Norfolk, Virginia, United States

Study focus

For real-time urban flood prediction at the street-scale, both speed and accuracy are critical. Among deep learning algorithms, Long Short-Term Memory (LSTM) networks are effective for time series prediction. However, the optimal approach for training LSTM models for accurate prediction remains debated in the hydrology literature. Using a dataset of 40 flood-prone streets, this study explores strategies for training LSTM models for street-scale flood prediction. Three experiments were designed to (1) compare different data-grouping approaches across global, clustered, and street models, (2) assess how the availability of water-depth information influences prediction accuracy, and (3) test the scalability of the models for newly added streets.

New hydrologic insights for the region

Grouping streets into hydrologically similar clusters enhanced prediction accuracy over the global model for the test events, while street models achieved the lowest errors in most cases. This suggests that the uniqueness of street-scale flooding dynamics in urban environments requires hyper-focused model training. When testing model performance with varying water-depth inputs, LSTM models trained on streets experiencing a wide range of flood depths performed well for streets with shallow water depths. The cluster models outperformed the global model in predicting flooding on newly added streets. This suggests that flooding behavior is better captured when the training dataset consists of hydrologically similar streets rather than a diverse set.

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

Sergio A. Barbosa, Yidi Wang, Jonathan L. Goodall

https://doi.org/10.1016/j.scitotenv.2025.178908

Abstract

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.