Quantitative analysis of impact of green stormwater infrastructures on combined sewer overflow control and urban flooding control

Tao, Jinsong; Li, Zijian; Peng, Xinlai; Guo, Ying

doi:10.1007/s11783-017-0952-4

Cited by 35 publications

(25 citation statements)

References 17 publications

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“…Rosa et al 2015). Still, most watershed stormwater studies were hypothetical (i.e., without comparison to empirical data) with authors using SWMM to calculate the benefits of future GI scenarios: (Luan et al 2017;Tao et al 2017). Krebs et al (2013) presented parameter sensitivity analysis for a high-resolution, highly urbanized small subcatchment in Finland.…”

Section: Performance Of Models For Lid Unitsmentioning

confidence: 99%

Testing of the Storm Water Management Model Low Impact Development Modules

Platz

Simón

Tryby

2020

J American Water Resour Assoc

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Research Impact Statement: This work was performed to test the low impact development modules in SWMM, a model used by practitioners to assist in sizing stormwater and wastewater infrastructure.ABSTRACT: Stormwater infrastructure designers and operators rely heavily on the United States Environmental Protection Agency's Storm Water Management Model (SWMM) to simulate stormwater and wastewater infrastructure performance. Since its inception in the late 1970s, improvements and extensions have been tested and evaluated rigorously to verify the accuracy of the model. As a continuation of this progress, the main objective of this study was to quantify how accurately SWMM simulates the hydrologic activity of low impact development (LID) storm control measures. Model performance was evaluated by quantitatively comparing empirical data to model results using a multievent, multiobjective calibration method. The calibration methodology utilized the PEST software, a Parameter ESTimation tool, to determine unmeasured hydrologic parameters for SWMM's LID modules. The calibrated LID modules' Nash-Sutcliffe efficiencies averaged 0.81; average percent bias (PBIAS) À9%; average ratio of root mean square error to standard deviation of measured values 0.485; average index of agreement 0.94; and the average volume error, simulated vs. observed, was +9%. SWMM accurately predicted the timing of peak flows, but usually underestimated their magnitudes by 10%. The average volume reduction, measured outflow volume divided by inflow volume, was 48%. We had more difficulty in calibrating one study, an infiltration trench, which identified a significant limitation of the current version of the SWMM LID module; it cannot simulate lateral exfiltration of water out of the storage layers of a LID storm control measure. This limitation is especially severe for a deep LIDs, such as infiltration trenches. Nevertheless, SWMM satisfactorily simulated the hydrologic performance of eight of the nine LID practices.(

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Section: Performance Of Models For Lid Unitsmentioning

confidence: 99%

Testing of the Storm Water Management Model Low Impact Development Modules

Platz

Simón

Tryby

2020

J American Water Resour Assoc

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“…In the reviewed articles, NSGA-II is used in most of the studies to date. Wang et al, (2015) (Cipolla et al, 2016;Li et al, 2018b;Mei et al, 2018;Tao et al, 2017;Wu et al, 2018;Yang et al, 2018;Zhu and Chen, 2017) to address the optimisation problems.…”

Section: (2) Tools Based On Integration Between Optimization Algorithmentioning

confidence: 99%

Nature-Based Solutions for hydro-meteorological risk reduction: A state-of-the-art review of the research area

Ruangpan¹,

Vojinović²,

Sabatino³

et al. 2019

Preprint

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Abstract. Natural hazards such as severe floods, storm surges, landslides, avalanches, hail, windstorms, droughts, heat waves and forest fires occur almost daily. This situation is likely to become worse given the projected changes in climate, degradation of ecosystems, population growth and urbanisation. The new concepts such as Ecosystem-based Adaptation, Green Infrastructure and/or Nature-Based Solutions have emerged as effective means to respond to such challenges. The present paper provides a critical review of the literature and identifies current knowledge gaps and future research prospects. There has been an explosion of scientific publications on this topic with a more significant rise taking place from 2007 onwards. Hence, the review process started by sourcing 1381 articles from Scopus which were also cross-referenced with the articles sourced from Web of Science and Google Scholar. The full analysis was performed on 159 closely related articles. The analysis confirmed that numerous advancements have been achieved to date. These solutions have already proven to be valuable in providing sustainable, cost-effective, multi-purpose and flexible means for hydro-meteorological risk reduction. However, there are still many areas where further research and demonstration is needed in order to promote their upscaling and replication and to make them become mainstream solutions.

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“…Potential evapotranspiration represents the ET rate limited only by energy supply instead of water supply. In current practices such as stormwater management, it is common to use PET or pan evaporation to represent ET a [100,104,[113][114][115][116] and calculate other unknowns in the water balance [62]. However, without the adjustment for the substrate moisture content, ET a will be overestimated for unsaturated conditions [89,117].…”

Section: Actual Evapotranspiration Modelsmentioning

confidence: 99%

Evapotranspiration from Green Infrastructure: Benefit, Measurement, and Simulation

Feng¹

2019

Advanced Evapotranspiration Methods and Applications

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Green infrastructure (GI) is a common solution for stormwater management in an urban environment, with attached environmental benefits like flood control, urban heat island relief, adaptations to climate change, biodiversity protection, air pollution reduction, and food production. Evapotranspiration (ET) controls the GI's hydrologic performance and affects all related benefits. Essentially, ET constrains the turnover of moisture storage and determines the demand for supplemental irrigation and then the cost-effectiveness of a GI project. Considering the spatial heterogeneousness of an urban space and the GI's multi-layer designs, the classic ET equations have challenges in representing the ET variations from GI units. The underperformance of the existing ET models is partly due to the lack of corresponding high-quality field observations for each GI type in various urban settings. This chapter, therefore, summarizes the current research progress and existing challenges regarding the benefit, measurement, and simulation of ET process from GI.

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Quantitative analysis of impact of green stormwater infrastructures on combined sewer overflow control and urban flooding control

Cited by 35 publications

References 17 publications

Testing of the Storm Water Management Model Low Impact Development Modules

Testing of the Storm Water Management Model Low Impact Development Modules

Nature-Based Solutions for hydro-meteorological risk reduction: A state-of-the-art review of the research area

Evapotranspiration from Green Infrastructure: Benefit, Measurement, and Simulation

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