Zhang Wenli, Qiu Suixuan, Lin Zhuochun, Chen Zhixin, Yang Yuchen, Lin Jinyao, Li Shaoying
School of Geography and Remote Sensing, Guangzhou University, Guangzhou 510006, China.
School of Geography and Remote Sensing, Guangzhou University, Guangzhou 510006, China; Huangpu Research School of Guangzhou University, Guangzhou 510006, China.
Environ Res. 2025 Feb 1;266:120561. doi: 10.1016/j.envres.2024.120561. Epub 2024 Dec 6.
The extensive expansion of impervious surfaces encroaches on green spaces and causes frequent urban waterlogging disasters. Previous studies have focused mainly on the influence of green space landscape pattern on waterlogging, with less attention given to green space morphological spatial pattern (MSPA). MSPA can be used to differentiate various types of land use morphologies from a microscopic perspective and reveal visualized spatial characteristics. Therefore, this study selected Shenzhen, a city with serious waterlogging problems, as the study area. The anthropogenic/natural environments and green space morphological spatial pattern were considered. Pearson correlation analysis and random forest regression were combined to investigate the influence of these drivers on the density of waterlogging hotspots and quantify the degree of importance for each driver. The results were supplemented with explanations using SHapley Additive exPlanations and Partial Dependence Plots. Pearson correlation analysis revealed that green space morphological spatial pattern, the proportion of green spaces, and the proportion of impervious surfaces were the dominant drivers. Additionally, the random forest regression showed that incorporating green space morphological spatial pattern and average tree height as potential drivers could strengthen the model's goodness-of-fit. While the proportion of impervious surfaces, the proportion of green spaces, and population density were important drivers, the green space morphological spatial pattern, specifically the "loop", "edge", and "core", was even more crucial and had an optimal design range. Therefore, green space morphological spatial pattern should be emphasized during the planning of "sponge cities" to maximize the ability of green spaces to mitigate waterlogging. In summary, our findings are expected to provide feasible suggestions for waterlogging control and green space planning.
不透水表面的广泛扩张侵占了绿地，导致城市内涝灾害频发。以往的研究主要集中在绿地景观格局对内涝的影响上，而对绿地形态空间格局（MSPA）的关注较少。MSPA可以从微观角度区分不同类型的土地利用形态，并揭示可视化的空间特征。因此，本研究选取内涝问题严重的城市深圳作为研究区域。考虑了人为/自然环境和绿地形态空间格局。结合皮尔逊相关分析和随机森林回归，研究这些驱动因素对内涝热点密度的影响，并量化每个驱动因素的重要程度。结果用SHapley加性解释和部分依赖图进行补充说明。皮尔逊相关分析表明，绿地形态空间格局、绿地比例和不透水表面比例是主要驱动因素。此外，随机森林回归表明，将绿地形态空间格局和平均树高作为潜在驱动因素纳入模型可以增强模型的拟合优度。虽然不透水表面比例、绿地比例和人口密度是重要驱动因素，但绿地形态空间格局，特别是“环”“边缘”和“核心”更为关键，且有一个最优设计范围。因此，在“海绵城市”规划中应强调绿地形态空间格局，以最大限度地发挥绿地缓解内涝的能力。总之，我们的研究结果有望为内涝控制和绿地规划提供可行的建议。
