Assessing stormwater control measure inventories from 23 cities in the United States
Name:
Choat_2023_Environ_Res_Infrast ...
Size:
1.668Mb
Format:
PDF
Description:
Final Published Version
Author
Choat, B.Pulido, A.
Bhaskar, A.S.
Hale, R.L.
Zhang, H.X.
Meixner, T.

McPhillips, L.
Hopkins, K.
Cherrier, J.
Cheng, C.
Affiliation
Department of Hydrology and Atmospheric Sciences, University of ArizonaThe Design School, Arizona State University
Issue Date
2023-04-27Keywords
best management practicesgreen infrastructure
statistical analysis
stormwater control measures
stormwater infrastructure
stormwater management
Metadata
Show full item recordPublisher
Institute of PhysicsCitation
Benjamin Choat et al 2023 Environ. Res.: Infrastruct. Sustain. 3 025003Rights
© 2023 The Author(s). Published by IOP Publishing Ltd. Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence.Collection Information
This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.Abstract
Since the 1987 Clean Water Act Section 319 amendment, the US Government has required and funded the development of nonpoint source pollution programs with about $5 billion dollars. Despite these expenditures, nonpoint source pollution from urban watersheds is still a significant cause of impaired waters in the United States. Urban stormwater management has rapidly evolved over recent decades with decision-making made at a local or city scale. To address the need for a better understanding of how stormwater management has been implemented in different cities, we used stormwater control measure (SCM) network data from 23 US cities and assessed what physical, climatic, socioeconomic, and/or regulatory explanatory variables, if any, are related to SCM assemblages at the municipal scale. Spearman’s correlation and Wilcoxon rank-sum tests were used to investigate relationships between explanatory variables and SCM types and assemblages of SCMs in each city. The results from these analyses showed that for the cities assessed, physical explanatory variables (e.g. impervious percentage and depth to water table) explained the greatest portion of variability in SCM assemblages. Additionally, it was found that cities with combined sewers favored filters, swales and strips, and infiltrators over basins, and cities that are under consent decrees with the Environmental Protection Agency tended to include filters more frequently in their SCM inventories. Future work can build on the SCM assemblages used in this study and their explanatory variables to better understand the differences and drivers of differences in SCM effectiveness across cities, improve watershed modeling, and investigate city- and watershed-scale impacts of SCM assemblages. © 2023 The Author(s). Published by IOP Publishing Ltd.Note
Open access journalISSN
2634-4505Version
Final Published Versionae974a485f413a2113503eed53cd6c53
10.1088/2634-4505/acc759
Scopus Count
Collections
Except where otherwise noted, this item's license is described as © 2023 The Author(s). Published by IOP Publishing Ltd. Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence.