Zhang Yu, Yin Haiwei, Liu Ming, Kong Fanhua, Xu Jiangang
School of Architecture and Urban Planning, Nanjing University, No 22, Hankou Road, Nanjing 210093, PR China; Key Laboratory of Urban AI and Green Built Environment of Provincial Higher Education Institutes, Nanjing University, No 22, Hankou Road, Nanjing 210093, PR China.
School of Architecture and Urban Planning, Nanjing University, No 22, Hankou Road, Nanjing 210093, PR China; Key Laboratory of Urban AI and Green Built Environment of Provincial Higher Education Institutes, Nanjing University, No 22, Hankou Road, Nanjing 210093, PR China; School of Architecture and Planning, Anhui Jianzhu University, Hefei 230022, PR China; Anhui Collaborative Innovation Center for Urbanization Construction, Hefei 230022, PR China.
Water Res. 2025 Mar 15;272:122932. doi: 10.1016/j.watres.2024.122932. Epub 2024 Dec 7.
Green-grey infrastructure is recommended as an innovative stormwater management strategy in response to urban flooding and climate change. Currently, the indicators used to optimize sustainable green-grey infrastructure and evaluate its stormwater management performance have been limited and based on self-defined criteria. In this study, we developed a comprehensive environmental sustainability indicator that integrates reliability, resilience, vulnerability, and hydrological sustainability as one of the objectives for optimizing green-grey infrastructure layout. The new indicator fully considered both system-level and component-level failures. Graph-theoretic algorithm coupled with NSGA-Ⅱ was applied to support the layout design and optimization. Additionally, the hydro-hydraulic performance of representative optimized layouts under extreme storms and climate change scenarios was re-evaluated to compare the effectiveness of various self-defined environmental sustainability indicators. The results demonstrated that under the same budget conditions, layouts optimized using the environmental sustainability indicator proposed in this study demonstrated superior performance, primarily reflected in less flood severity, flood duration, and conduit surcharge. Furthermore, it was effective and necessary to comprehensively consider the system-level overload consequences, the component-level failure-recovery process, and the extent of restoration to the natural hydrological state in the green-grey optimization process. This framework aims to address the stormwater management challenges posed by short-term extreme storms and long-term climate changes, while balancing sustainable economic and natural hydrological states.
绿色-灰色基础设施被推荐作为应对城市内涝和气候变化的一种创新型雨水管理策略。目前，用于优化可持续绿色-灰色基础设施并评估其雨水管理性能的指标有限，且基于自行定义的标准。在本研究中，我们开发了一个综合环境可持续性指标，将可靠性、恢复力、脆弱性和水文可持续性整合为优化绿色-灰色基础设施布局的目标之一。新指标充分考虑了系统层面和组件层面的故障。应用图论算法结合NSGA-Ⅱ来支持布局设计和优化。此外，对极端风暴和气候变化情景下代表性优化布局的水流水力性能进行了重新评估，以比较各种自行定义的环境可持续性指标的有效性。结果表明，在相同预算条件下，使用本研究提出的环境可持续性指标优化的布局表现出更优性能，主要体现在洪水严重程度更低、洪水持续时间更短以及管道超载情况更少。此外，在绿色-灰色优化过程中全面考虑系统层面的过载后果、组件层面的故障恢复过程以及恢复到自然水文状态的程度是有效且必要的。该框架旨在应对短期极端风暴和长期气候变化带来的雨水管理挑战，并在可持续经济和自然水文状态之间取得平衡。
