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dc.contributor.authorZhou, Bowen
dc.contributor.authorRadosavljevic, Jovana
dc.contributor.authorParsons, Chris T.
dc.contributor.authorRezanezhad, Fereidoun
dc.contributor.authorPasseport, Elodie
dc.contributor.authorVan Cappellen, Philippe 14:05:31 (GMT) 14:05:31 (GMT)
dc.description.abstractA variety of best management practices (BMPs) are being implemented to attenuate the increasing eutrophication risk caused by excessive phosphorus (P) export via urban stormwater runoff. However, P reduction performance of the BMPs are highly variable under different climatic, watershed, and design settings. Many of BMPs are actually reported to enrich P concentrations, which questions their efficiency to reduce P load export from urban watersheds. In this study, we developed a data-driven machine learning model to predict P reduction or enrichment in urban stormwater BMPs. The model is trained and validated using hydrologic and P concentration data for several typical urban stormwater BMPs, including traditional systems (retention pond, wetland basin and detention basin) and low-impact development (LID) systems (bioretention cell, grass swale and grass strip), from the International Stormwater BMP Database. Unlike other models in previous studies, our model can simulate both P reduction and enrichment by urban BMPs under specific input and climatic, watershed, and BMP design conditions. Additionally, a PCSWMM (Stormwater Management Model) was developed for a small urban watershed in Southern Ontario (Lake Wilcox (LW) watershed) to provide representation of the rainfall–runoff processes in the watershed. Parameters obtained by PCSWMM were calibrated using the observed data, and these will be used along with data-driven BMP P model for LW to estimate projected changes in P export under different BMPs application and climatic scenarios at the watershed scale. This study will propose an innovative and more robust method to estimate attenuation of P export by BMPs at watershed scale. It will also improve our understanding about critical climatic, watershed and BMP design variables that control BMPs P reduction performances.en
dc.description.sponsorshipThis work was supported by the Managing Urban Eutrophication Risks under Climate Change project under the Global Water Futures (GWF) program funded by the Canada First Research Excellence Fund (CFREF), and by funding from the Natural Sciences and Engineering Research Council of Canada (NSERC) Strategic Partnership Grant (STPGP 521515-18). We would like to acknowledge the Water Research Foundation for providing the data.en
dc.publisherUniversity of Waterlooen
dc.relation.ispartofseriesGlobal Water Futures;
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.subjectGWF AOSM 2023en
dc.subjectstormwater best management practicesen
dc.subjectmachine learningen
dc.titleModelling reduction and enrichment effects of urban stormwater best management practices on phosphorus at the watershed scaleen
dc.typeConference Posteren
dcterms.bibliographicCitationZhou, B; Radosavljevic, J; Parsons, C; Rezanezhad, F; Passeport, E & Van Cappellen, P. (2023). Modelling reduction and enrichment effects on urban stormwater best management practices on phosphorus at the watershed scale. Global Water Futures (GWF) Annual Open Science Meeting Conference. University of Waterloo.en
uws.contributor.affiliation1Faculty of Scienceen
uws.contributor.affiliation2Earth and Environmental Sciencesen

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