U.S. Department of EnergyOffice of Scientific and Technical Information
Mitigating Impact Through Community-Engaged Flood Modeling
Abstract
Urban pluvial flooding poses a growing threat to the city of Baltimore, driven by heavy rainfall, increased impervious area, and aging infrastructure. Adapting to the risks posed by pluvial flooding is critical for building greater climate resiliency in Baltimore's Inner Harbor Watershed. This study addresses these challenges through community-informed decision analysis, which uses hydrologic modeling and optimization tools to identify robust flooding adaptation pathways. We will collaborate with community partners to identify key concerns and objectives regarding flooding. These concerns have been purposefully built in to a combined surface-subsurface dynamic flow simulation model. Model outputs are used to identify flooding locations within the Inner Harbor, and to test adaptation methods. Machine learning will be used search for solutions which meet diverse environmental, financial, and social goals, and solution performance will be examined under a wide range of potential future climatic conditions and integrated with an adaptive planning approach. This novel set of adaptation pathways will enhance the City's capacity to respond to evolving pluvial flood risk.
- Publication Date:
- Research Org.:
- Pacific Northwest National Lab (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC); Biological and Environmental Research (BER); Awarding Entity, Inc.
- Subject:
- Flood Risk; Machine Learning; Urban; Water; climate resilience; pluvial
- OSTI Identifier:
- 2566821
- DOI:
- https://doi.org/10.57931/2566821
Citation Formats
Spangler, Ava, and Hadjimichael, Antonia. Mitigating Impact Through Community-Engaged Flood Modeling. United States: N. p., 2025.
Web. doi:10.57931/2566821.
Spangler, Ava, & Hadjimichael, Antonia. Mitigating Impact Through Community-Engaged Flood Modeling. United States. doi:https://doi.org/10.57931/2566821
Spangler, Ava, and Hadjimichael, Antonia. 2025.
"Mitigating Impact Through Community-Engaged Flood Modeling". United States. doi:https://doi.org/10.57931/2566821. https://www.osti.gov/servlets/purl/2566821. Pub date:Mon May 19 04:00:00 UTC 2025
@article{osti_2566821,
title = {Mitigating Impact Through Community-Engaged Flood Modeling},
author = {Spangler, Ava and Hadjimichael, Antonia},
abstractNote = {Urban pluvial flooding poses a growing threat to the city of Baltimore, driven by heavy rainfall, increased impervious area, and aging infrastructure. Adapting to the risks posed by pluvial flooding is critical for building greater climate resiliency in Baltimore's Inner Harbor Watershed. This study addresses these challenges through community-informed decision analysis, which uses hydrologic modeling and optimization tools to identify robust flooding adaptation pathways. We will collaborate with community partners to identify key concerns and objectives regarding flooding. These concerns have been purposefully built in to a combined surface-subsurface dynamic flow simulation model. Model outputs are used to identify flooding locations within the Inner Harbor, and to test adaptation methods. Machine learning will be used search for solutions which meet diverse environmental, financial, and social goals, and solution performance will be examined under a wide range of potential future climatic conditions and integrated with an adaptive planning approach. This novel set of adaptation pathways will enhance the City's capacity to respond to evolving pluvial flood risk.},
doi = {10.57931/2566821},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Mon May 19 04:00:00 UTC 2025},
month = {Mon May 19 04:00:00 UTC 2025}
}