Integrating Social Equity into Multiobjective Optimization of Urban Stormwater Low-Impact Development
Publication: Journal of Water Resources Planning and Management
Volume 149, Issue 8
Abstract
Recent studies have demonstrated some advantages of using advanced heuristic algorithms to identify near-Pareto-optimal future locations, types, and sizes for stormwater low-impact development and green infrastructure (LID/GI) across a given urban landscape. However, previous optimization studies did not consider social equity as an objective, which poses problems because urban green infrastructure often is distributed inequitably. Increasing access to LID/GI in historically marginalized areas is a prominent environmental justice issue, and increasingly is becoming a primary consideration when prioritizing future locations, types, and sizes of urban LID/GI. This study integrated a novel spatial social equity objective [LID/GI–Social Vulnerability Index (SVI) correlation objective, ] into a multiobjective LID/GI optimization model. The LID/GI-SVI correlation is an objective that directs the optimization algorithm to search for LID/GI distributions that maximize the linear correlation between LID/GI implementation and subbasins with higher estimated percentages of historically marginalized people. Our analysis focused on understanding the impacts of the LID/GI-SVI correlation objective on a LID/GI optimization model. This modeling study demonstrates that (1) the LID/GI-SVI correlation objective can be used to direct optimization algorithms to search for LID/GI distributions that can achieve runoff management objectives, increase green LID/GI implementation in more marginalized areas, and explore the potential trade-offs or synergies between hydrologic and equity goals; (2) LID/GI optimization formulations that consider only hydrologic objectives likely will not result in equitable LID/GI distributions; (3) LID/GI distributions that perform well on the LID/GI-SVI correlation may be composed of different types of LID/GI than less-equitable but more hydrologically favorable LID/GI distributions; and (4) for our study area, including spatial equity as an objective resulted in modest reductions in the hydrologic performance of near-Pareto-optimal LID/GI distributions.
Practical Applications
Low-impact development or stormwater green infrastructure (LID/GI) has been implemented across the globe to address the environmental issues of increased impervious surfaces, aging infrastructure, elevated stormwater runoff due to climate change, and to meet non-point-source discharge permit requirements. Recent research has demonstrated the use of advanced algorithms to help identify optimal locations, sizes, and types of LID/GI that will maximize local runoff reduction benefits. Although runoff control is a primary objective of LID/GI, many cities in the US have been striving to prioritize local areas with high proportions of historically underserved or vulnerable populations for implementation. In tune with this trend, we developed a novel equity objective function that forces a proportional relationship between LID/GI implementation and indicators of social vulnerability. We tested the impact of this new objective within a multiobjective LID/GI optimization algorithm and found minimal trade-offs between runoff and equity objectives in our study area.
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Data Availability Statement
Some or all data, models, or code generated or used during the study are available in a repository online in accordance with funder data retention policies at https://github.com/rsh6pb/Optimal-Equitable-LID-Mgt.
Acknowledgments
Funding for this project was provided by the University of Virginia through a “3-Cavaliers” grant and Dean’s Scholar Fellowship funding. The authors acknowledge Research Computing (RC) at the University of Virginia for providing computational resources and technical support that contributed to the results reported within this publication (https://rc.virginia.edu). Computational Hydraulics International (CHI) provided access to PCSWMM through their educational grant program. The authors thank Kazi Tamadunn, Zach Perkins, Emilia Torrellas, Ruoyu Zhang, Meredith Hoos, and other graduate students at the University of Virginia for providing field monitoring and GIS support over the duration of this project. The authors also thank Greg Harper, Chief of Environmental Services for Albemarle County, and Andrea Henry, PE, Water Resources Protection Administrator from the Public Works Department in the City of Charlottesville, for providing information on local LID/GI planning, preliminary SWMM model data, and drainage documentation for the study area.
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Journal of Water Resources Planning and Management
Volume 149 • Issue 8 • August 2023
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© 2023 American Society of Civil Engineers.
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Received: Sep 14, 2022
Accepted: Mar 27, 2023
Published online: Jun 6, 2023
Published in print: Aug 1, 2023
Discussion open until: Nov 6, 2023
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