Technical Papers
Jun 22, 2019

Copula-Based Performance Assessment of Online and Offline Detention Ponds for Urban Stormwater Management

Publication: Journal of Hydrologic Engineering
Volume 24, Issue 9

Abstract

Detention ponds are known as an efficient low-impact development (LID) strategy for reducing the negative impacts of urban floods. Their performance in flood peak attenuation is directly related to the size, location, and type of connections to the main drainage system, as well as the shape of their inlet/outlet structures and operational procedures. In this paper, a methodology is developed to design multiple detention ponds in urban drainage systems considering rainfall uncertainties. Harmony search (HS), a popular metaheuristic algorithm, is coupled with USEPA’s Storm Water Management Model version 5.1 (SWMM 5.1) and run under a significant number of synthetic rainfalls generated by copulas, a kind of joint probability distribution, to find the optimal place and size of online/offline detention ponds in the network. The approach is tested on the east main stormwater drainage network of Tehran, the capital city of Iran. Results reveal that offline construction of ponds has generally better performance than online construction. An optimal design and arrangement of ponds efficiently decreases flooding in the whole network up to 12.5% and outperforms the previous design proposed by consultant engineers for the rehabilitation of the studied network.

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Information & Authors

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Go to Journal of Hydrologic Engineering
Journal of Hydrologic Engineering
Volume 24Issue 9September 2019

History

Received: Aug 4, 2018
Accepted: Mar 7, 2019
Published online: Jun 22, 2019
Published in print: Sep 1, 2019
Discussion open until: Nov 22, 2019

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Assistant Professor, Faculty of Civil, Water and Environmental Engineering, Shahid Beheshti Univ., Tehran 1658953571, Iran (corresponding author). Email: [email protected]
P. Khazaei
M.Sc. Student, Faculty of Civil, Water and Environmental Engineering, Shahid Beheshti Univ., Tehran 1658953571, Iran.

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