URBAN STORMWATER QUANTITY/QUALITY MODELING USING THE SCS METHOD AND EMPIRICAL EQUATIONS<sup>1</sup>

Tsihrintzis, Vassiliοs A.; Hamid, Rizwan

doi:10.1111/j.1752-1688.1997.tb04093.x

Cited by 36 publications

(26 citation statements)

References 8 publications

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“…All this data was compiled by USGS following detailed ®eld surveying which provided size and elevations of the storm drain system and the inlets, as well as horizontal and vertical control for aerial photographing, and topographic and other map generation. The physical and general characteristics of each catchment, given by Miller (1979), are summarized in Table I (Tsihrintzis and Hamid, 1997b).…”

Section: Site and Data Descriptionmentioning

confidence: 99%

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Runoff quality prediction from small urban catchments using SWMM

Tsihrintzis

Hamid²

1998

Hydrol. Process.

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174

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Abstract:The RUNOFF block of EPA's storm water management model (SWMM) was used to simulate the quantity and quality of urban storm water runo from four relatively small sites (i.e. 5 . 97±23 . 56 ha) in South Florida, each with a speci®c predominant land use (i.e. low density residential, high density residential, highway and commercial). The objectives of the study were to test the applicability of this model in small subtropical urban catchments and provide modellers with a way to select appropriate input parameters to be used in planning studies. A total of 58 storm events, measured by the US Geological Survey (USGS), provided hyetographs, hydrographs and pollutant loadings for biological oxygen demand (BOD 5 ), total suspended solids (TSS), total Kjeldahl nitrogen (TKN) and lead (Pb), and were used for calibration of the model. Several other catchment characteristics, also measured or estimated by USGS, were used in model input preparation. Application of the model was done using the Green±Ampt equation for in®ltration loss computation, a pollutant accumulation equation using a power build-up equation dependent on the number of dry days, and a power wash-o equation dependent on the predicted runo rate. Calibrated quantity input parameters are presented and compared with suggested values in the literature. The impervious depression storage was generally found to be the most sensitive calibration parameter, followed by the Manning's roughness coecients of conduit and overland¯ow, the Green±Ampt in®ltration parameters and, ®nally, the pervious depression storage. Calibrated quality input parameters are presented in the form of regression equations, as a function of rainfall depth and the number of antecedent dry days. A total of 16 independent rainfall events were used for veri®cation of the model, which showed a good comparison with observed data for both hydrographs and pollutant loadings.

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Section: Site and Data Descriptionmentioning

confidence: 99%

“…A location map is presented in Figure 1 (Miller, 1979;Tsihrintzis and Hamid, 1997b). The sites and predominant land uses were: Site 1 Ð low density single family residential (LDR) area (16 .…”

Section: Site and Data Descriptionmentioning

confidence: 99%

Runoff quality prediction from small urban catchments using SWMM

Tsihrintzis

Hamid²

1998

Hydrol. Process.

Self Cite

174

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“…The SCS-CN model has been used with agricultural watersheds and also functions well in urban areas. Many urban ecohydrology studies have used this model to simulate various runoff scenarios, ranging from small urban sites (e.g., highway or residential lots) to neighborhoods to large urban watersheds (Tsihrintzis and Hamid, 1997;Brander et al, 2004;Sjöman and Gill, 2013). This model is based on empirical studies of ungauged watersheds to estimate runoff from rainfall events (NRCS, 1986).…”

Section: Model Preparationmentioning

confidence: 99%

Potential reduction in urban runoff by green spaces in Beijing: A scenario analysis

Yao

Chen

Wei

et al. 2015

Urban Forestry & Urban Greening

118

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a b s t r a c tUrban green space provides multiple ecological benefits, among which the reduction of rainfall runoff is important for sustainable urban development, particularly for cities experiencing severe flooding and water hazards. However, the effectiveness of urban green space in mitigating runoff has not been fully determined. We evaluated potential reductions in surface runoff associated with urban green space in central Beijing under a greening scenario using the Soil Conservation Service Curve Number method. The results show that urban green space offers significant potential for runoff mitigation. In 2012, a total of 97.9 million m 3 of excess surface runoff was retained by urban green space; adding nearly 11% more tree canopy was projected to increase runoff retention by >30%, contributing to considerable benefits of urban rainwater regulation. At a more detailed scale, there were apparent internal variations. Urban function zones with >70% developed land showed less mitigation of runoff, while green zones (vegetation >60%), which occupied only 15.54% of the total area, contributed 31.07% of runoff reduction. Runoff reduction by urban green space, however, is influenced by many factors, such as rainfall, soil condition, and urban morphology. In regulating urban runoff, therefore, the priorities and integrating adaptive approaches to urban greening should be combined with compensatory and complementary measures.

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“…This method was chosen because of its relatively complete database, as well as the fact that it has been widely used for decades (Chung et al, 2010;Tsihrintzis and Hamid, 1997). This method is shown as Equations 1, 2 and 3.…”

Section: Calculation Methodsmentioning

confidence: 99%

Integrated design workflow and a new tool for urban rainwater management

Chen

Samuelson

Tong

2016

Journal of Environmental Management

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For stormwater management, Low Impact Development (LID) practices provide more sustainable solutions than traditional piping and storm ponds. However, to be effective, LID practices must be integrated into planning at the beginning of the design process; yet architects and related design professionals making decisions early in the design process are not equipped to consider runoff calculations given their current tools. In response to this dilemma, we have developed an open source stormwater runoff evaluation and management tool: Rainwater+. It is seamlessly integrated into computer-aided design (CAD) software to receive instant estimate on the stormwater runoff volume of architecture and landscape designs. Designers can thereby develop appropriate rainwater management strategies for the project-based on local precipitation data, specific standards, site conditions and economic considerations. We employed Rainwater+ to conduct two case studies illustrating the importance of considering storm water runoff in the early design stage. The first case study showed the importance of integrating rainwater management into design modeling software for the reason that the specific site condition is critical for determining LID practice. The second case study demonstrated the need of visualizing runoff flow direction in assisting the placement of LID practices at proper locations when the terrain is of great complexity.

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URBAN STORMWATER QUANTITY/QUALITY MODELING USING THE SCS METHOD AND EMPIRICAL EQUATIONS¹

Cited by 36 publications

References 8 publications

Runoff quality prediction from small urban catchments using SWMM

Runoff quality prediction from small urban catchments using SWMM

Potential reduction in urban runoff by green spaces in Beijing: A scenario analysis

Integrated design workflow and a new tool for urban rainwater management

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URBAN STORMWATER QUANTITY/QUALITY MODELING USING THE SCS METHOD AND EMPIRICAL EQUATIONS1

Cited by 36 publications

References 8 publications

Runoff quality prediction from small urban catchments using SWMM

Runoff quality prediction from small urban catchments using SWMM

Potential reduction in urban runoff by green spaces in Beijing: A scenario analysis

Integrated design workflow and a new tool for urban rainwater management

Contact Info

Product

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URBAN STORMWATER QUANTITY/QUALITY MODELING USING THE SCS METHOD AND EMPIRICAL EQUATIONS¹