Uncertainty and Sensitivity Analysis of SWMM Model in Computation of Manhole Water Depth and Subcatchment Peak Flood

Sharifan, Reza Afshin; Roshan, A.; Aflatoni, M.; A.Jahedi,; Zolghadr, Masih

doi:10.1016/j.sbspro.2010.05.205

Cited by 32 publications

(14 citation statements)

References 2 publications

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“…Some authors, such as Baek et al and Sharifan et al [42,43], suggest Nimperv and ManN as influential parameters in modeling with SWMM. Other researchers like Randall et al [8] identified Horton's infiltration speeds as relatively sensitive parameters.…”

Section: Discussionmentioning

confidence: 99%

Sensitivity Analysis of the Rainfall–Runoff Modeling Parameters in Data-Scarce Urban Catchment

et al. 2020

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Rainfall–runoff phenomena are among the main processes within the hydrological cycle. In urban zones, the increases in imperviousness cause increased runoff, originating floods. It is fundamental to know the sensitivity of parameters in the modeling of an urban basin, which makes the calibration process more efficient by allowing one to focus only on the parameters for which the modeling results are sensitive. This research presents a formal sensitivity analysis of hydrological and hydraulic parameters—absolute–relative, relative–absolute, relative–relative sensitivity and R2—applied to an urban basin. The urban basin of Tuxtla Gutiérrez, Chiapas, in Mexico is an area prone to flooding caused by extreme precipitation events. The basin has little information in which the records (with the same time resolution) of precipitation and hydrometry match. The basin model representing an area of 355.07 km2 was characterized in the Stormwater Management Model (SWMM). The sensitivity analysis was performed for eight hydrological parameters and one hydraulic for two precipitation events and their impact on the depths of the Sabinal River. Based on the analysis, the parameters derived from the analysis that stand out as sensitive are the Manning coefficient of impervious surface and the minimum infiltration speed with R2 > 0.60. The results obtained demonstrate the importance of knowing the sensitivity of the parameters and their selection to perform an adequate calibration.

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Section: Discussionmentioning

confidence: 99%

Sensitivity Analysis of the Rainfall–Runoff Modeling Parameters in Data-Scarce Urban Catchment

et al. 2020

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“…The sensitivities of the two water quality parameters are ranked as: decay constant > dispersion factor. The decay constant is a sensitive parameter, and the dispersion factor is an insensitive parameter [12]. Based on the above analysis results, each parameter of hydraulic and water quality can be optimized and adjusted, which can greatly reduce the workload of model parameter calibration and verification.…”

Section: Sensitivity Analysis Of Model Parametersmentioning

confidence: 99%

Simulation of Rain Garden Effects in Urbanized Area Based on Mike Flood

Zhang

et al. 2018

Water

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An urban storm and surface water pollution model (MIKE FLOOD) was used to assess the impact of rain gardens on water quantity and quality for an urban area in Xi'an. After the rain garden measures were added, the results show that: (1) In the case where the total proportion of rain gardens was 2%, the overflow reduction rate was 6.74% to 65.23%, the number of overflow points reduction rate was 1.79% to 65.63%, the overload pipes reduction rate was 0% to 11.15%, the runoff reduction rate was 1.93% to 9.69%; (2) Under different rainfall conditions, the load reduction rate of suspended solids (SS), chemical oxygen demand (COD), total nitrogen (TN), and total phosphorus (TP) were 2.36% to 30.35%, 2.37% to 30.11%, 2.34% to 30.08%, and 2.32% to 31.35%, respectively; (3) The submersion ranges of different submerged depths and submerged durations were reduced by 0.30% to 64.18% and 7.12% to 100%, respectively. The statistics of the automatic modeling and intelligent analysis system (AMIAS) showed that the rain garden regulation range of the waterlogging risk area was 0.78% to 100%. The rain garden has a good control effect on urban storm runoff in terms of water volume and water quality, but as the rainfall recurrence interval increases, the control effect will decrease.

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“…The variable x i is randomly changed within the value range of the variable and the result of the objective function y (x i ) corresponding to different x i obtained by running the model. The influence value e i is used to indicate the influence degree of the parameter variation on the model simulation results [22][23]. …where e i is the Morris coefficient, y is the model output values after the parameters changed, y 0 is the model output values before the parameters changed, and Δ i is the amplitude of parameter i.…”

Section: Sensitivity Analysismentioning

confidence: 99%

Evaluating Hydrological and Environmental Effects for Low-Impact Development of a Sponge City

Li¹,

Ma²,

Li³

et al. 2020

Pol. J. Environ. Stud.

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Uncertainty and Sensitivity Analysis of SWMM Model in Computation of Manhole Water Depth and Subcatchment Peak Flood

Cited by 32 publications

References 2 publications

Sensitivity Analysis of the Rainfall–Runoff Modeling Parameters in Data-Scarce Urban Catchment

Sensitivity Analysis of the Rainfall–Runoff Modeling Parameters in Data-Scarce Urban Catchment

Simulation of Rain Garden Effects in Urbanized Area Based on Mike Flood

Evaluating Hydrological and Environmental Effects for Low-Impact Development of a Sponge City

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