Document Type : Research Paper
Authors
1 Department of Water Engineering, Faculty of Agricultural Sciences, University of Guilan, Rasht, Iran.
2 Department of Irrigation and Reclamation Engineering, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran
Abstract
Keywords
Main Subjects
Despite significant investments in traditional flood control infrastructure (FCI), urban flooding remains a critical challenge, especially during rainfall events that exceed the design storm. Low Impact Development (LID) strategies offer a decentralized, environmentally friendly approach to managing urban runoff and reducing pressure on conventional drainage systems. However, selecting the optimal type, combination, and spatial extent of LIDs is a complex task. This study aims to develop and demonstrate a systematic, integrated framework for selecting the best LID strategies to enhance the resilience of existing urban drainage networks under extreme rainfall conditions.
The research employs a comprehensive framework that combines detailed hydrological-hydraulic simulation with Multi-Criteria Decision-Making (MCDM) analysis. The study introduces three dynamic and holistic performance indicators—reliability, resilience, and vulnerability (RRV)—alongside an economic indicator (annualized life cycle cost), moving beyond static performance measures commonly used in previous studies.
The approach was applied to a 62-hectare urban drainage network in Rasht, Iran. Using the Storm Water Management Model (SWMM 5.2), the hydraulic performance of 12 different LID scenarios was simulated. These scenarios involved three LID practices—Green Roofs (GR), Permeable Pavements (PP), and Rain Barrels (RB)—implemented individually and in combination at low (L), medium (M), and high (H) spatial coverage levels.
A novel operational framework was defined for the drainage network, using the design storm intensity as a threshold to identify critical periods during a severe 43-hour evaluation storm. This allowed for a more precise calculation of the RRV indicators at each network node, reflecting the system’s dynamic performance under stress.
The 12 scenarios were ranked using three different MCDM methods: the Analytic Hierarchy Process (AHP), Linear Assignment (LA), and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The weighting of criteria was handled differently: the Eigenvector method (incorporating decision-maker judgment) was used for AHP and LA, while the Entropy method (objective, data-based) was used for TOPSIS.
Implementing LIDs, especially combinations with high spatial coverage, significantly improved the drainage network’s performance. The scenario combining all three LIDs (GR+PP+RB, denoted as GPR) at high coverage (GPR-H) achieved a 42.26% reduction in total runoff volume at the network outlet compared to the base scenario (no LIDs). The use of RRV indicators proved superior to single-metric assessments (e.g., peak flow reduction). They provided a more holistic and realistic evaluation by accounting for the duration, frequency, and severity of flooding events, leading to more informed decision-making. The choice of MCDM method significantly influenced the results due to the underlying weighting techniques. AHP and LA methods, which assigned higher weights to reliability and vulnerability, consistently prioritized scenarios with combined LIDs and high coverage (e.g., GPR-H, GR+PP at high coverage denoted as GP-H) as optimal, favoring superior hydraulic performance over cost. Due to the Entropy method’s objective weighting, the reliability indicator received a very low weight (because of its low variability across scenarios), causing TOPSIS to favor single LID scenarios with lower costs (e.g., GR-H, PP-M), despite their weaker hydraulic performance. A comparative analysis of the top-ranked scenario from each MCDM method showed that the scenario selected by AHP delivered the best hydraulic performance (a 1.19 m³/s reduction in peak flow) at a reasonable cost, demonstrating its effectiveness for this application.
This study successfully developed a robust framework for selecting optimal LID strategies. It highlights the critical importance of using dynamic performance indicators like reliability, resilience, and vulnerability for a realistic assessment of urban drainage systems. The research also underscores that the weighting of criteria in MCDM is a pivotal step that can drastically alter the outcome. The proposed AHP-based approach, which effectively balances performance and cost, is recommended as a powerful decision-support tool for urban planners and managers seeking to enhance the resilience of stormwater drainage networks against climate change and increasing urbanization.
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Conceptualization, methodology, and data curation were collaboratively designed and executed by all authors. Software, investigation and resources was conducted by Gita Mirzaei and validated by Ali Moussavi and Afshin Ashrafzadeh. All authors contributed to writing, reviewing, and editing the manuscript, ensuring a cohesive final submission.
During the preparation of this work the authors did not use Generative AI and AI-assisted technologies.
Data available on request from the authors.
The authors extend their gratitude to University of Guilan for its support.
The authors confirm that the study was conducted in accordance with ethical principles, and no data fabrication, falsification, plagiarism, or misconduct occurred.
The authors declare no conflict of interest.
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