Assessing Stormwater Flooding in Richmond, VA to Improve Water Quality Monitoring and Management
Discipline: Ecology Environmental and Earth Sciences
Subcategory: Geosciences and Earth Sciences
Session: 2
Room: Chinatown
Trinity Johnson - Louisiana State University
Co-Author(s): Kaylee Tanner, Brigham Young University, Provo, UTBrodie Thiele, North Carolina State University Raleigh, NCMaggie Lincoln, University of Pittsburgh, Pittsburgh, PA
Pluvial flooding disproportionately impacts low-income, disadvantaged urban neighborhoods, where large areas of impervious surfaces and inadequate drainage systems contribute to surface ponding during intense precipitation. This study identified high-priority census tracts with significant flood risk and social vulnerability index scores. We hypothesized that low-income, disadvantaged communities (LIDAC) would be more vulnerable to severe flood risk and have fewer flood mitigation efforts and infrastructure in their neighborhoods. We used data from NASA’s Global Precipitation Measurement satellites, the InVEST Urban Flood Risk Mitigation Model, and the Arc-Malstrøm Bluespot model to predict flood risk in the metropolitan and surrounding areas of Richmond, Virginia. In partnership with PlanRVA and Groundwork RVA, we worked with local governments and community organizations to pinpoint specific streets and blocks that are more susceptible to flooding, which will help inform community planners’ flood mitigation efforts. For these areas of concern, we created detailed maps depicting flood risk and predicted flood depth for various precipitation levels. We concluded that census tracts in urban areas tend to have higher flood risk and social vulnerability. In this context, urban areas were defined as densely populated, low-income communities with larger populations of people of color. One limitation of this study was the use of simplistic models that did not account for runoff processes, antecedent moisture conditions, or existing drainage infrastructure. While these models required many assumptions and produced errors, the results were sufficient for rough regional analyses and for narrowing the scope of future work. PlanRVA used these results to enhance existing flood risk information, improve resilience planning, and develop strategies for infrastructure projects. My focus for the future is to replicate this study in coastal southeast Louisiana to learn how enhanced flood modeling can help mitigation and emergency preparedness efforts.References:Arkema, K. K., Griffin, R., Maldonado, S., Silver, J., Suckale, J., & Guerry, A. D. (2017). InVEST Urban Flood Risk Mitigation Model. Annals of the New York Academy of Sciences, 1399(1), 5–26. https://doi.org/10.1111/nyas.13322 Balstrøm, T., & Crawford, D. (2018). Arc-Malstrøm: A 1D hydrologic screening method for stormwater assessments based on geometric networks. Computers & Geosciences, 116, 64–73. https://doi.org/10.1016/j.cageo.2018.04.010Bulti, D. T., & Abebe, B. G. (2020). A review of flood modeling methods for urban pluvial flood application. Modeling Earth Systems and Environment, 6(3), 1293–1302. https://doi.org/10.1007/s40808-020-00803-z
Funder Acknowledgement(s): Funder Acknowledgements: I thank K. Ross and L. Gleason at the NASA Langley Research Center for their support regarding this research. K. Tanner, B. Thiele, and M. Lincoln for their contributions to this work. Funding was provided by the NASA DEVELOP Program.
Faculty Advisor: Dr. Margaret Reams, mreams@lsu.edu
Role: While we all contributed significantly to each section of this research, I was directly responsible for the InVEST Urban Flood Risk Mitgation Model and all bivariate analyses.