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    Optimal Capacity of a Stormwater Reservoir for Flood Peak Reduction

    Source: Journal of Hydrologic Engineering:;2018:;Volume ( 023 ):;issue: 004
    Author:
    Kiczko Adam;Szeląg Bartosz;Kozioł Adam P.;Krukowski Marcin;Kubrak Elżbieta;Kubrak Janusz;Romanowicz Renata J.
    DOI: 10.1061/(ASCE)HE.1943-5584.0001636
    Publisher: American Society of Civil Engineers
    Abstract: Stormwater reservoirs are used to control the runoff volume and to attenuate the peak flow rate from urban catchments. The required capacity of a stormwater reservoir and its dimensions are calculated with the help of rainfall-runoff models, using a time series of observed rainfall or a design rainfall. However, many studies have revealed that the reliability of models of urbanized basins is far from perfect. This study presents an optimization of the capacity of a stormwater reservoir in a small urbanized basin, taking into account the predictive uncertainty of the rainfall-runoff model. An analysis of the model uncertainty using the generalized likelihood uncertainty estimation (GLUE) framework allowed for an assessment of the optimal capacity of the stormwater reservoir required to ensure a given safety level. Such a probabilistic approach is compared with a deterministic approach, which is commonly used in practice, where the effect of uncertainty of the rainfall-runoff model is usually neglected. The numerical experiment showed that stochastic and deterministic approaches are not equivalent as the calculated capacities of the reservoirs were different. The deterministic solution leads to an overestimation of the reservoir capacity from 2 to 15%, compared to the mean capacity obtained using a stochastic formulation. The results indicate that the uncertainty of the runoff model might potentially be a significant factor in stormwater reservoir design.
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      Optimal Capacity of a Stormwater Reservoir for Flood Peak Reduction

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    contributor authorKiczko Adam;Szeląg Bartosz;Kozioł Adam P.;Krukowski Marcin;Kubrak Elżbieta;Kubrak Janusz;Romanowicz Renata J.
    date accessioned2019-02-26T07:59:50Z
    date available2019-02-26T07:59:50Z
    date issued2018
    identifier other%28ASCE%29HE.1943-5584.0001636.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4250758
    description abstractStormwater reservoirs are used to control the runoff volume and to attenuate the peak flow rate from urban catchments. The required capacity of a stormwater reservoir and its dimensions are calculated with the help of rainfall-runoff models, using a time series of observed rainfall or a design rainfall. However, many studies have revealed that the reliability of models of urbanized basins is far from perfect. This study presents an optimization of the capacity of a stormwater reservoir in a small urbanized basin, taking into account the predictive uncertainty of the rainfall-runoff model. An analysis of the model uncertainty using the generalized likelihood uncertainty estimation (GLUE) framework allowed for an assessment of the optimal capacity of the stormwater reservoir required to ensure a given safety level. Such a probabilistic approach is compared with a deterministic approach, which is commonly used in practice, where the effect of uncertainty of the rainfall-runoff model is usually neglected. The numerical experiment showed that stochastic and deterministic approaches are not equivalent as the calculated capacities of the reservoirs were different. The deterministic solution leads to an overestimation of the reservoir capacity from 2 to 15%, compared to the mean capacity obtained using a stochastic formulation. The results indicate that the uncertainty of the runoff model might potentially be a significant factor in stormwater reservoir design.
    publisherAmerican Society of Civil Engineers
    titleOptimal Capacity of a Stormwater Reservoir for Flood Peak Reduction
    typeJournal Paper
    journal volume23
    journal issue4
    journal titleJournal of Hydrologic Engineering
    identifier doi10.1061/(ASCE)HE.1943-5584.0001636
    page4018008
    treeJournal of Hydrologic Engineering:;2018:;Volume ( 023 ):;issue: 004
    contenttypeFulltext
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