Speaker
Description
In recent years, global climate change has intensified the frequency and severity of extreme rainfall events, while rapid urbanization has substantially expanded impervious surfaces. This expansion has led to elevated surface runoff, which poses a substantial threat to urban hydrological security. Synergetic grey-green infrastructure (SGGI) offers a promising paradigm for the effective management of stormwater in densely populated urban areas. This approach synergistically merges the sustainability and adaptability of Green Infrastructure (GI) with the reliability of Grey Infrastructure (GREI) (Lu et al., 2023). The optimal integration of GI and GREI presents a multi-objective optimization challenge that demands a rigorous evaluation of technical, economic, and ecological dimensions, while also navigating inherent trade-offs and constraints. Recent advances, particularly the integration of hydrological modeling with multi-objective optimization algorithms, have proven effective in advancing SGGI coupling strategies, offering a robust framework for addressing these challenges (Chen et al.,2024;Tansar et al,2023). For instance, Gao et al.(2022) highlighted the development of a novel multi-objective optimization framework for green-grey infrastructure, which emphasized synchronized optimization using the SWMM and SUSTAIN models and demonstrates reduced costs and enhanced rainwater utilization. Zhu et al. (2023) proposed a life-cycle evaluation framework coupled with a multi-objective optimization algorithm (NSGA-II) and SWMM for GI layout optimization. Some scholars have observed that a one-size-fits-all approach is inadequate, as existing research primarily focuses on optimizing and evaluating SGGI in the context of individual rainfall events, while overlooking the non-stationary effects of long-term climate change, complicating infrastructure planning.
To fill this gap, this study proposed a multi-stage optimization framework for SGGI layouts based on shared socio-economic pathways, utilizing graph theory and genetic algorithms to identify optimal solutions through life cycle cost (LCC) , hydraulic reliability and environmental pollution control in response to varying climate change scenarios. A case study of Shanghai, China, was conducted to validate this method. This approach provided a progressive grey-green coupling strategy, equipping decision-makers with a structured, step-by-step framework to adapt infrastructure to evolving climate conditions over time. By accurately forecasting extended rainfall patterns and identifying phase shifts in precipitation induced by climate change, the multi-stage optimization framework allowed for adaptive planning that aligns stage-specific improvements with long-term system-wide objectives.
This iterative approach ensured that interventions were integrated, contributing cumulatively to the enhancement of overall resilience and cost-efficiency in urban stormwater systems. This study aimed to: (1) segment long-term rainfall sequences to reveal trends and phase-specific precipitation patterns; (2) identify the optimal spatial configuration of decentralized SGGI based on LCC, hydraulic reliability and environmental pollution control; (3) evaluate and optimize the long-term hydrological performance,environmental benefits and investment costs of SGGI strategies within a multi-stage planning framework. This study's findings will enhance the understanding of SGGI optimization, specifically regarding flood resilience and cost-effectiveness amid future climate uncertainties. The multi-stage SGGI optimization framework proposed in this study can effectively improve the reliability and adaptability of flood management under the uncertainties of long-term climate change.
References
Chen, W., Wang, W., Mei, C., Chen, Y., Zhang, P. and Cong, P. (2024). Multi-objective decision-making for green infrastructure planning: Impacts of rainfall characteristics and infrastructure configuration. Journal of Hydrology, [online] 628, p.130572. doi:https://doi.org/10.1016/j.jhydrol.2023.130572.
Gao, Z., Zhang, Q.H., Xie, Y.D., Wang, Q., Dzakpasu, M., Xiong, J.Q. and Wang, X.C. (2022). A novel multi-objective optimization framework for urban green-gray infrastructure implementation under impacts of climate change. Science of The Total Environment, 825, p.153954. doi:https://doi.org/10.1016/j.scitotenv.2022.153954.
Lu, P., Sun, Y. and Steffen, N. (2023). Scenario-based performance assessment of green-grey-blue infrastructure for flood-resilient spatial solution: A case study of Pazhou, Guangzhou, greater Bay area. Landscape and Urban Planning, [online] 238, p.104804. doi:https://doi.org/10.1016/j.landurbplan.2023.104804.
Tansar, H., Duan, H.-F. and Mark, O. (2023). A multi-objective decision-making framework for implementing green-grey infrastructures to enhance urban drainage system resilience. Journal of Hydrology, 620, p.129381. doi:https://doi.org/10.1016/j.jhydrol.2023.129381.
Zhu, Y., Xu, C., Liu, Z., Yin, D., Jia, H. and Guan, Y. (2023). Spatial layout optimization of green infrastructure based on life-cycle multi-objective optimization algorithm and SWMM model. Resources, Conservation and Recycling, 191, p.106906. doi:https://doi.org/10.1016/j.resconrec.2023.106906.
| Keywords | Urban stormwater management; Synergetic grey-green infrastructure; Climate change; Multi-stage optimization |
|---|---|
| Best Congress Paper Award | Yes |
