Zhang Xin, Liu Wen, Feng Qi, Zeng Jianjun
Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China; University of Chinese Academy of Sciences, Beijing 100049, China.
Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China.
Sci Total Environ. 2024 Oct 20;948:174851. doi: 10.1016/j.scitotenv.2024.174851. Epub 2024 Jul 18.
Green infrastructure (GI) plays a significant role in alleviating urban flooding risk caused by urbanization and climate change. Due to space and financial limitations, the successful implementation of GI relies heavily on its layout design, and there is an increasing trend in using multi-objective optimization to support decision-making in GI planning. However, little is known about the hydrological effects of synchronously optimizing the size, location, and connection of GI under climate change. This study proposed a framework to optimize the size, location, and connection of typical GI facilities under climate change by combining the modified non-dominated sorting genetic algorithm-II (NSGA-II) and storm water management model (SWMM). The results showed that optimizing the size, location, and connection of GI facilities significantly increases the maximum reduction rate of runoff and peak flow by 13.4 %-24.5 % and 3.3 %-18 %, respectively, compared to optimizing only the size and location of GI. In the optimized results, most of the runoff from building roofs flew toward green space. Permeable pavement accounted for the highest average proportion of GI implementation area in optimal layouts, accounting for 29.8 %-54.2 % of road area. The average cost-effectiveness (C/E) values decreased from 16 %/10 Yuan under the historical period scenario to 14.3 %/10 Yuan and 14 %/10 Yuan under the two shared socioeconomic pathways (SSPs), SSP2-4.5 and SSP5-8.5, respectively. These results can help in understanding the optimization layout and cost-effectiveness of GI under climate change, and the proposed framework can enhance the adaptability of cities to climate change by providing specific cost-effective GI layout design.
绿色基础设施（GI）在减轻城市化和气候变化导致的城市洪水风险方面发挥着重要作用。由于空间和资金限制，绿色基础设施的成功实施在很大程度上依赖于其布局设计，并且使用多目标优化来支持绿色基础设施规划决策的趋势日益增加。然而，在气候变化情况下同步优化绿色基础设施的规模、位置和连接的水文效应却鲜为人知。本研究提出了一个框架，通过结合改进的非支配排序遗传算法-II（NSGA-II）和雨水管理模型（SWMM）来优化气候变化下典型绿色基础设施设施的规模、位置和连接。结果表明，与仅优化绿色基础设施的规模和位置相比，优化绿色基础设施设施的规模、位置和连接分别使径流最大减少率和峰值流量显著提高了13.4%-24.5%和3.3%-18%。在优化结果中，建筑物屋顶的大部分径流流向了绿地。透水路面在最优布局中占绿色基础设施实施面积的平均比例最高，占道路面积的29.8%-54.2%。平均成本效益（C/E）值从历史时期情景下的16%/10元分别降至两个共享社会经济路径（SSP），即SSP2-4.5和SSP5-8.5情景下的14.3%/10元和14%/10元。这些结果有助于理解气候变化下绿色基础设施的优化布局和成本效益，并且所提出的框架可以通过提供具体的具有成本效益的绿色基础设施布局设计来增强城市对气候变化的适应性。
