| Literature DB >> 32570310 |
Linyuan Leng1, Xuhui Mao2, Haifeng Jia3, Te Xu4, Albert S Chen5, Dingkun Yin2, Guangtao Fu6.
Abstract
In recent years, Sponge City has gained significant interests as a way of urban water management. The kernel of Sponge City is to develop a coupled green-grey-blue system which consists of green infrastructure at the source, grey infrastructure (i.e. drainage system) at the midway and receiving water bodies as the blue part at the terminal. However, the current approaches for assessing the performance of Sponge City construction are confined to green-grey systems and do not adequately reflect the effectiveness in runoff reduction and the impacts on receiving water bodies. This paper proposes an integrated assessment framework of coupled green-grey-blue systems on compliance of water quantity and quality control targets in Sponge City construction. Rainfall runoff and river system models are coupled to provide quantitative simulation evaluations of a number of indicators of land-based and river quality. A multi-criteria decision-making method, i.e., Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is adopted to rank design alternatives and identify the optimal alternative for Sponge City construction. The effectiveness of this framework is demonstrated in a typical plain river network area of Suzhou, China. The results demonstrate that the performance of Sponge City strategies increases with large scale deployment under smaller rainfall events. In addition, though surface runoff has a dilution effect on the river water quality, the control of surface pollutants can play a significant role in the river water quality improvement. This framework can be applied to Sponge City projects to achieve the enhancement of urban water management.Entities:
Keywords: Green-grey-blue system; Low impact development; Performance assessment; Sponge City; TOPSIS
Year: 2020 PMID: 32570310 DOI: 10.1016/j.scitotenv.2020.138608
Source DB: PubMed Journal: Sci Total Environ ISSN: 0048-9697 Impact factor: 7.963