Planning green infrastructure placement based on projected precipitation data

Journal Article · 09 December 2020 · Journal of Environmental Management

DOI: https://doi.org/10.1016/j.jenvman.2020.111718 · OSTI ID:1737482

Omitaomu, Olufemi A.  ^[1]; Kotikot, Susan M. ^[2]; Parish, Esther S.  ^[1]

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1. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
2. Pennsylvania State Univ., University Park, PA (United States)

Continued urbanization has led to tremendous changes on the landscape. These changes have exacerbated the effects of extreme climatic events such as flooding because of constrained water infiltration and increased surface flow. Typical runoff control measures involve sophisticated gray infrastructure that guide excess surface flow into storage and disposal sites. In a dynamic climate system, these measures are not sustainable since they cannot be easily modified to accommodate large volumes of runoff. Green Infrastructure (GI) is an adaptable technique that can be used to minimize runoff, in addition to offering an array of additional benefits (urban heat regulation, aesthetics, improved air quality etc.). Strategic placement of GI is key to achieving maximum utility. While physical site characteristics play a major role in determining suitable GI placement sites, knowledge of future precipitation patterns is crucial to ensure successful flood mitigation. Here, suitable GI sites within the city of Knoxville, Tennessee, were determined based on potential impact of an extreme flood event as indicated by site characteristics. Then, the relative potential likelihood of a flood event was determined based on projected precipitation data and knowledge of existing flood zones. By combining potential impact with likelihood information, low, medium, and high priority GI implementation sites were established. Results indicate that high priority sites are in the central parts of the city with priority decreasing outward. The GI prioritization scheme presented here, offers valuable guidance to city planners and policy makers who wish to exploit the GI approach for flood mitigation.

• Research Organization: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
• Sponsoring Organization: USDOE Office of Science (SC), Biological and Environmental Research (BER); USDOE Laboratory Directed Research and Development (LDRD) Program
• Grant/Contract Number: AC05-00OR22725
• OSTI ID: 1737482
• Journal Information: Journal of Environmental Management, Journal Name: Journal of Environmental Management Vol. 279; ISSN 0301-4797
• Publisher: ElsevierCopyright Statement
• Country of Publication: United States
• Language: English