Modeling of a lot scale rainwater tank system in XP-SWMM: A case study in Western Sydney, Australia

Sterren, Marlène van der; Rahman, Ataur; Ryan, G.L.

doi:10.1016/j.jenvman.2014.02.013

Cited by 28 publications

(16 citation statements)

References 28 publications

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“…The purpose of water quality investigations using SWMM have included (1) determining the effects from nonpoint pollution (Jia et al 2014;Oraei Zare et al 2012;Choi et al 2006), (2) BMP/LIDs treatment (Chang et al 2008;Jia et al 2014;Oraei Zare et al 2012;Van der Sterren et al 2014), (3) air quality interactions (Burian et al 2001(Burian et al , 2002Fallah Shorshani et al 2014), (4) climate/land use change effects (Borris et al 2014), (5) first flush effects (Chang et al 2008;Ouyang et al 2012;Young et al 2010), (6) monitoring the program design (Talei et al 2010), (7) traditional stormwater management structure performance (Borris et al 2014), and (8) spatial resolution effects (Fallah Shorshani et al 2014;Park et al 2008;.…”

Section: Water Quality and Transport Of Contaminantsmentioning

confidence: 99%

“…All of the models' results were found to be sufficiently accurate and reliable; however, the models were sensitive to the parameters used to simulate the buildup and wash-off processes. Valeo and Ho (2004) applied multiple methods/models, including the urban snow model (USM) and SWMM to predict the snow melt flow from a small residential community (25.48 ha) located in Calgary, Canada. They found that the USM performed better than SWMM as measured by time to peak, peak flow rate, and model efficiency.…”

Section: Comparison Of Swmm With Other Modelsmentioning

confidence: 99%

See 1 more Smart Citation

Storm Water Management Model: Performance Review and Gap Analysis

Niazi

Nietch

Maghrebi

et al. 2017

J. Sustainable Water Built Environ.

168

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The storm water management model (SWMM) is a widely used tool for urban drainage design and planning. Hundreds of peerreviewed articles and conference proceedings have been written describing applications of SWMM. This review focuses on collecting information on model performance with respect to calibration and validation in the peer-reviewed literature. The major developmental history and applications of the model are also presented. The results provide utility to others looking for a quick reference to gauge the integrity of their own unique SWMM application. A gap analysis assesses the model's ability to perform water-quality simulations considering green infrastructure (GI)/low impact development (LID) designs and effectiveness. It is concluded that the level of detail underlying the conceptual model of SWMM versus its overall computational parsimony is well balanced-making it an adequate model for large and medium-scale hydrologic applications. However, embedding a new mechanistic algorithm or providing user guidance for coupling with other models will be necessary to realistically simulate diffuse pollutant sources, their fate and transport, and the effectiveness of GI/LID implementation scenarios.

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Section: Water Quality and Transport Of Contaminantsmentioning

confidence: 99%

Section: Comparison Of Swmm With Other Modelsmentioning

confidence: 99%

Storm Water Management Model: Performance Review and Gap Analysis

Niazi

Nietch

Maghrebi

et al. 2017

J. Sustainable Water Built Environ.

168

View full text Add to dashboard Cite

show abstract

“…This procedure supported the identification of the most sensitive parameters in the model, thereby allowing these parameters to be used for calibration [22,23]. Sensitivity analyses can be broadly grouped into local and global approaches [24].…”

Section: Sensitivity Analysismentioning

confidence: 99%

Modeling the Quality and Quantity of Runoff in a Highly Urbanized Catchment Using Storm Water Management Model

Li¹,

Liu²,

Hu³

et al. 2016

Pol. J. Environ. Stud.

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“…Any modeling exercise should aim to minimize the associated errors. Common errors that can occur within modeling are random or systematic errors in the input data, including errors due to non-optimal data values and errors because of incomplete or biased model structure (van der Sterren et al 2014). Moreover, water level sensors provide realization of measurement process subject to normalized error, which is assumed in Equation 9:…”

Section: Model Calibration and Validationmentioning

confidence: 99%

Realtime Water Depth Logger Data as Input to PCSWMM to Estimate Tree Filter Performance

Ertezaei

Ponce

Dietrich

et al. 2018

JWMM

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In this study, water depth measurements were collected in storm water infrastructure during rain events using a pressure based water level sensor. Irregularities within the measured water level datasets required data smoothing to prepare the observed data for calibration. A rainfall-runoff model was created using a proprietary version of the U.S. Environmental Protection Agency's Storm Water Management Model, PCSWMM, to predict the performance of recently implemented green stormwater infrastructure with respect to runoff at the site. The PCSWMM model calibration was accomplished by comparing water level data collected on site to the PCSWMM output data produced by the uncalibrated model. Nash-Sutcliffe efficiency was used to assess the performance of the calibration procedure. Sensitivity analyses of the estimated parameters were performed to assess the impacts of the model parameters on overall model output. The overarching objective of the study was to demonstrate the value of inexpensive and readily available real-time pressure based water level sensor data to calibrate a PCSWMM model.

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Modeling of a lot scale rainwater tank system in XP-SWMM: A case study in Western Sydney, Australia

Cited by 28 publications

References 28 publications

Storm Water Management Model: Performance Review and Gap Analysis

Storm Water Management Model: Performance Review and Gap Analysis

Modeling the Quality and Quantity of Runoff in a Highly Urbanized Catchment Using Storm Water Management Model

Realtime Water Depth Logger Data as Input to PCSWMM to Estimate Tree Filter Performance

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