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[论文]胡潭高等人.Investigating relationships between landscape patterns and surface runoff from a spatial distribution and intensity perspective

时间:2023-02-13 13:28:44 文章来源 :学科 浏览量:0

Investigating relationships between landscape patterns and surface runoff from a spatial distribution and intensity perspective

L. Wang, H. Hou, Y. Li, J. Pan, P. Wang, B. Wang, et al.

J Environ Manage 2023 Vol. 325 Issue Pt B Pages 116631

Accession Number: 36347186 DOI: 10.1016/j.jenvman.2022.116631

https://www.ncbi.nlm.nih.gov/pubmed/36347186

Rapid urbanization changes landscape patterns and results in frequent urban waterlogging issues, which affect citizens' daily lives and cause economic loss. Understanding the spatial patterns and impact factors associated with urban waterlogging under different rainfall intensities has significant implications for mitigating this hazard. In this study, the runoff depth calculated according to the Storm Water Management Model (SWMM) simulation results was used to investigate the spatial characteristics of urban waterlogging. Multiple scenario-based designs, a correlation analysis, and a stepwise regression model were employed to detect the relationship between surface runoff depth and landscape patterns under different rainfall intensities. The results show that when the rainfall intensity reached 12.5 mm/12 h, the conversion rate of rainfall to runoff increased significantly, indicating an increased waterlogging risk. Areas with impervious surface proportions of 25-50% and 75-100% were shown to require more attention due to the strong sensitivity of the surface runoff depth to an increase in the impervious surface. It is most cost-effective to maintain the original high-density vegetation or increase the vegetation density from 0-25% to 25-50% for urban green space. Additionally, the landscape configuration also affects the surface runoff depth. The fragmented, scattered, or regular shape of impervious surface patches can reduce surface runoff effectively; larger and less fragmented green space was also shown to have a surface runoff controlling. The adjusted R(2) values were greater than 0.6 for all stepwise regression models, indicating that the landscape variables selected in the study can effectively predict the surface runoff depth. These models also showed that the landscape composition had a more profound contribution than the landscape configuration on runoff depth. These findings provide meaningful insights and perspectives for urban waterlogging hazard mitigation, quantitative landscape planning, and risk management. The method proposed by this study provides a referable framework for future studies on urban waterlogging and its response to the landscape in the context of global climate change.