Robson L. Pachaly, Don Guy V.V. Biessan, Jose G. Vasconcelos, Frances C. O’Donnell, Benjamin F. Bowers
{"title":"Continuous Hydrologic Modeling of a Parking Lot and Related Best Management Practices with PCSWMM","authors":"Robson L. Pachaly, Don Guy V.V. Biessan, Jose G. Vasconcelos, Frances C. O’Donnell, Benjamin F. Bowers","doi":"10.1111/j.1936-704X.2022.3382.x","DOIUrl":null,"url":null,"abstract":"<div>\n \n <p>Permeable pavements are a green infrastructure stormwater management practice that can serve as a functional component of the site design. However, previous field studies suggest high uncertainty in the parameters used for performing hydrologic calculations for permeable pavements. The Environmental Protection Agency (EPA) Storm Water Management Model (SWMM) within the PCSWMM software package was used to simulate the hydrologic dynamics of a parking lot that is 25% covered with permeable interlocking concrete pavers in Auburn, AL. The model was calibrated to field observations of water level at two points where the pavement system outflows to a bioretention basin and rainfall data from a nearby weather station. The use of the Curve Number (CN) method within SWMM resulted in good prediction of pavement outflow by the calibrated model, with R<sup>2</sup> and Nash-Sutcliffe model efficiency both greater than 0.8, except where issues with precipitation data coverage occurred. This demonstrates that permeable pavements can be modeled as a land cover type rather than as detention storage. The calibrated value of the runoff CN for permeable pavement was 60, much lower than what is recommended in many design guidelines for the underlying soil type at the research site, which is hydrologic soil group B. Based on evaluation of alternative model scenarios, the permeable pavement reduced runoff by 11-38% across contrasting rain events.</p></div>","PeriodicalId":45920,"journal":{"name":"Journal of Contemporary Water Research & Education","volume":null,"pages":null},"PeriodicalIF":0.9000,"publicationDate":"2023-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/j.1936-704X.2022.3382.x","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Contemporary Water Research & Education","FirstCategoryId":"1085","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/j.1936-704X.2022.3382.x","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"WATER RESOURCES","Score":null,"Total":0}
引用次数: 0
Abstract
Permeable pavements are a green infrastructure stormwater management practice that can serve as a functional component of the site design. However, previous field studies suggest high uncertainty in the parameters used for performing hydrologic calculations for permeable pavements. The Environmental Protection Agency (EPA) Storm Water Management Model (SWMM) within the PCSWMM software package was used to simulate the hydrologic dynamics of a parking lot that is 25% covered with permeable interlocking concrete pavers in Auburn, AL. The model was calibrated to field observations of water level at two points where the pavement system outflows to a bioretention basin and rainfall data from a nearby weather station. The use of the Curve Number (CN) method within SWMM resulted in good prediction of pavement outflow by the calibrated model, with R2 and Nash-Sutcliffe model efficiency both greater than 0.8, except where issues with precipitation data coverage occurred. This demonstrates that permeable pavements can be modeled as a land cover type rather than as detention storage. The calibrated value of the runoff CN for permeable pavement was 60, much lower than what is recommended in many design guidelines for the underlying soil type at the research site, which is hydrologic soil group B. Based on evaluation of alternative model scenarios, the permeable pavement reduced runoff by 11-38% across contrasting rain events.