Abstract
Green stormwater infrastructure (GSI) can provide multiple benefits in addition to stormwater management. However, there is a need to improve GSI siting to ensure these benefits are realized. We present a planning algorithm that hones in on ‘sweet spots’ of GSI implementation that are hydrologically optimal, feasible, and provide more equitable access to the benefits of GSI. We apply this approach in Lancaster, a city in Pennsylvania, US, with multiple stormwater-related challenges. To identify sweet spots, we first leveraged available spatial data to derive maps of five key criteria, including hydrology, vegetation, property ownership, sewer system type, and social vulnerability. We then normalized each layer and combined them using two different weighting schemes, including an ‘Even Weights’ and a ‘People’s Choice’ scenario based on a choice experiment embedded in a community survey. The survey indicated a preference for prioritizing the hydrology and sewer system criteria. Sweet spots for GSI implementation under each scenario were mapped based on the 90th percentile of the final combined key criteria layers. Comparisons between the two weighting schemes indicated a 73% overlap in sweet spot locations. We also found a small percentage (16%) of existing GSI in Lancaster overlapped with the sweet spots, indicating an opportunity to target future GSI implementation in the remaining sweet spots. Despite being demonstrated in a specific city, this relatively simple approach leveraging widely available spatial data can be applied and customized elsewhere and help improve future GSI siting methods.
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Data Availability Statement
All data, models, and code generated or used during the study appear in the published article.
Acknowledgments
This work was supported by the Penn State Institutes of Energy and the Environment through a 2019 Seed Grant. We wish to acknowledge input from current and former staff at the City of Lancaster, including Ruth Hocker, Kathryn Austin, and Angela Brackbill. We also thank Sina Sanei for assistance with some data analyses, and Matt Royer for contributions to early discussions on the project scope.
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Received: Jan 19, 2023
Accepted: May 18, 2023
Published online: Jun 15, 2023
Published in print: Aug 1, 2023
Discussion open until: Nov 15, 2023
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