Community-Informed Urban Flood Modeling for Impact Mitigation

Ava, Spangler; Antonia, Hadjimichael

doi:10.57931/3011791

Title: Community-Informed Urban Flood Modeling for Impact Mitigation

Dataset
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Abstract

The intensification of the hydrologic cycle due to climate change poses a threat to aging and under-designed water infrastructure systems which cannot adequately manage intense storm events. Developing a comprehensive plan for managing rain-driven flooding events is challenging due to uncertainties in the magnitude and frequency of future storm events and conflicting stakeholder objectives. In the City of Baltimore, Maryland, stormwater infrastructure is struggling to keep up with rainfall-driven (pluvial) flooding events, which regularly damage housing and disrupt transportation for residents. In this study, a hybrid of community engagement, numerical modeling, and artificial intelligence techniques are employed to explore prospective urban flooding adaptations. Community engagement drives the development of an urban flooding model (EPA Storm Water Management Model) for the Baltimore Harbor watershed. The model integrates complex surface and subsurface stormwater infrastructure data from the City, high-resolution spatial data, insights from local public works experts, and the lived experiences of City residents. This co-developed model simulates adaptations of interest to stakeholders in the city, including green and grey infrastructure and operational management strategies. Stormwater management scenarios focused on inlet cleaning and spatially concentrated green infrastructure are found to be the most effective in reducing flood depths in community priority locations. Together, these adaptations can reduce the duration of intersection inundation by more thanmore »« less

Authors:

Ava, Spangler; Antonia, Hadjimichael

Pennsylvania State University

Publication Date:: Tue Dec 16 00:00:00 UTC 2025

DOE Contract Number:: AC05-76RL01830

Research Org.:: Pacific Northwest National Lab (United States)

Sponsoring Org.:: USDOE Office of Science (SC), Biological and Environmental Research (BER)

OSTI Identifier:: 3011791

DOI:: https://doi.org/10.57931/3011791

Citation Formats


                    Ava, Spangler, and Antonia, Hadjimichael. Community-Informed Urban Flood Modeling for Impact Mitigation.  United States: N. p., 2025. 
        Web.  doi:10.57931/3011791.

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                    Ava, Spangler, & Antonia, Hadjimichael. Community-Informed Urban Flood Modeling for Impact Mitigation.  United States.  doi:https://doi.org/10.57931/3011791

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                    Ava, Spangler, and Antonia, Hadjimichael. 2025.  
"Community-Informed Urban Flood Modeling for Impact Mitigation".  United States.  doi:https://doi.org/10.57931/3011791.  https://www.osti.gov/servlets/purl/3011791. Pub date:Tue Dec 16 00:00:00 UTC 2025

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@article{osti_3011791,

  title        = {Community-Informed Urban Flood Modeling for Impact Mitigation},

  author       = {Ava, Spangler and Antonia, Hadjimichael},

  abstractNote = {The intensification of the hydrologic cycle due to climate change poses a threat to aging and under-designed water infrastructure systems which cannot adequately manage intense storm events. Developing a comprehensive plan for managing rain-driven flooding events is challenging due to uncertainties in the magnitude and frequency of future storm events and conflicting stakeholder objectives. In the City of Baltimore, Maryland, stormwater infrastructure is struggling to keep up with rainfall-driven (pluvial) flooding events, which regularly damage housing and disrupt transportation for residents. In this study, a hybrid of community engagement, numerical modeling, and artificial intelligence techniques are employed to explore prospective urban flooding adaptations. Community engagement drives the development of an urban flooding model (EPA Storm Water Management Model) for the Baltimore Harbor watershed. The model integrates complex surface and subsurface stormwater infrastructure data from the City, high-resolution spatial data, insights from local public works experts, and the lived experiences of City residents. This co-developed model simulates adaptations of interest to stakeholders in the city, including green and grey infrastructure and operational management strategies. Stormwater management scenarios focused on inlet cleaning and spatially concentrated green infrastructure are found to be the most effective in reducing flood depths in community priority locations. Together, these adaptations can reduce the duration of intersection inundation by more than twenty minutes, allowing for quicker emergency response and restoration of typical transportation systems. Future work will combine this community engaged flooding model with the Deep Uncertainties Pathways framework to explore tradeoffs between adaptations and develop dynamic adaptations which align with community objectives, enhance climate resilience in Baltimore, and can be adjusted in response to changing future conditions.},

  doi          = {10.57931/3011791},

  journal      = {},

  number       = ,

  volume       = ,

  place        = {United States},

  year         = {Tue Dec 16 00:00:00 UTC 2025},

  month        = {Tue Dec 16 00:00:00 UTC 2025}

}

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DOI: https://doi.org/10.57931/3011791

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