Optimal siting of rainwater harvesting systems for reducing combined sewer overflows at city scale.

Autor: Ghodsi SH; Department of Civil, Structural and Environmental Engineering, University at Buffalo, Buffalo, NY, USA., Zhu Z; Department of Civil, Structural and Environmental Engineering, University at Buffalo, Buffalo, NY, USA. Electronic address: zhenduoz@buffalo.edu., Matott LS; Northrop Grumman Amherst Systems, Buffalo, NY, USA., Rabideau AJ; Department of Civil, Structural and Environmental Engineering, University at Buffalo, Buffalo, NY, USA., Torres MN; Department of Civil, Structural and Environmental Engineering, University at Buffalo, Buffalo, NY, USA.
Jazyk: angličtina
Zdroj: Water research [Water Res] 2023 Feb 15; Vol. 230, pp. 119533. Date of Electronic Publication: 2022 Dec 26.
DOI: 10.1016/j.watres.2022.119533
Abstrakt: The installation of green infrastructure (GI) is an effective approach to manage urban stormwater and combined sewer overflow (CSO) by restoring pre-development conditions in urban areas. Research on simulation-optimization techniques to aid with GI planning decision-making is expanding. However, due to high computational expense, the simulation-optimization methods are often based on design storm events, and it is unclear how much different rainfall scenarios (i.e., design storm events vs. long-term historical rainfall data) impact the optimal siting of GI. The Parallel Pareto Archived Dynamically Dimensioned Search (ParaPADDS) algorithm in a novel simulation-optimization tool OSTRICH-SWMM was used to leverage distributed computing resources. A case study was conducted to optimally site rainwater harvesting cisterns within 897 potential subcatchments throughout the City of Buffalo, New York. Seven design storm events with different return periods and rainfall durations and a one-month historical rainfall time series were considered. The results showed that the optimal solutions of siting cisterns using event-based scenarios, though less computationally expensive, may not perform well under continuous rainfall scenarios, suggesting design rainfall scenarios should be carefully considered for optimizing GI planning. The impact of rainfall scenarios was particularly significant in the middle region of the Pareto front of multi-objective optimization. Utilizing high-performance parallel computing, OSTRICH-SWMM is a promising tool to optimize GI at large spatial and temporal scales.
Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2022. Published by Elsevier Ltd.)
Databáze: MEDLINE
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