Abstract
Design storms are an essential element in the design of urban drainage systems. While it is accepted that systems optimized with respect to design storms should perform well under similar rainfall conditions, there is a lack of knowledge on when and why design storms may fail to produce robust solutions—namely, drainage systems that perform consistently under a broad range of rainfall events. To address this question, we contribute a computational framework that evaluates the robustness of drainage systems optimized for a design storm. The framework consists of four building blocks. First, we use sensitivity analysis to identify the most important decision variables [e.g., pipe expansions and low impact development (LID)], thereby reducing the complexity of the design problem. Second, we solve the problem using a multiobjective simulation-optimization scheme, which yields a set of Pareto-efficient solutions optimizing various measures of performance. Third, we simulate each solution under stochastic rainfall events characterized by different duration, intensity, and profile, and finally evaluate their robustness (i.e., the capability of attaining the performance they are designed for). The application of the proposed framework to the Nhieu Loc-Thi Nghe basin (Ho Chi Minh City, Vietnam) indicates that none of the Pareto-efficient solutions are robust across all rainfall events. In particular, we find that the optimized solutions underperform during (1) rainfall events with smaller intensity, but greater depth, than the design storm, and (2) small, but frequent, rainfall events. Results for our case study also suggest that the expansion of the drainage network is more likely to give a robust design than the implementation of LID technologies. Overall, our work elucidates the need to include stochastic rainfall events throughout the design process so as to obtain robust drainage solutions.
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Data Availability Statement
Some or all data, models, or code generated or used during the study are proprietary or confidential in nature and may only be provided with restrictions (e.g., anonymized data).
Acknowledgments
This research is supported by Veolia City Modeling Centre through the Singapore Economic Development Board’s Industrial Postgraduate Programme (Project No. IGIPVCMC1601). The authors would also like to acknowledge Veolia Recherche et Innovation (VERI) for their support.
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Journal of Water Resources Planning and Management
Volume 146 • Issue 3 • March 2020
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©2020 American Society of Civil Engineers.
History
Received: Mar 18, 2019
Accepted: Jul 31, 2019
Published online: Jan 7, 2020
Published in print: Mar 1, 2020
Discussion open until: Jun 7, 2020
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