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Data for: Do baseline assumptions alter the efficacy of green stormwater infrastructure to reduce combined sewer overflows?
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Swiss Federal Institute of Aquatic Science and Technology (Eawag) - view all
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Last updated3 weeks ago
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Overview

Green stormwater infrastructure (GSI) is growing in popularity to reduce combined sewer overflows (CSOs) and hydrologic simulation models are a tool to assess this reduction potential. Given the numerous and interacting water flows that contribute to CSOs, such as evapotranspiration (ET) and groundwater (GW), these models should ideally account for them. However, due to the complexity, simplified models are often used, and it is currently unknown how these assumptions affect estimates of CSOs, GSI effectiveness, and ultimately planning guidance. This study evaluates the effect on estimates of CSOs and GSI effectiveness when different flows and hydrologic processes are neglected. We modified an existing EPA SWMM model of a combined sewer system in Switzerland to include ET, GW, and upstream inflows. Historical rainfall data over 30 years are used to assess volume and duration of CSOs with and without three types of GSI (bioretention basins, permeable pavements and green roofs). Results demonstrate that neglect of certain flows in modelling can alter CSO volumes from -15% to 40% and duration from -40% to 5%. GSI effectiveness also varies considerably when different processes are represented in the model, resulting in differences in potential volume reduction of CSOs from 8 to 35%, depending on the GSI type and model structure. Representation of GW within models is particularly crucial when infiltrating GSI are present, as CSOs could increase in certain subcatchments due to higher GW levels from increased infiltration. When basing GSI planning decisions on modeled estimates of CSOs, all relevant hydrologic processes should be included to the extent possible and uncertainty and assumptions should always be considered.

Blue-Green infrastructureSWMMcombined sewer overflowcombined sewer systemsevapotranspirationgroundwatermodeling assumptionsstormwater management
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KeyValue
Harvest Object Id29c57b01-4232-42a3-a2a2-7f08a2497f34
Harvest Source Idd0230d8d-fb2c-4caf-94e8-8ad52bd38ad9
Harvest Source TitleThe Eawag Research Data Institutional Repository
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