Assessing and optimizing the hydrological performance of Grey-Green infrastructure systems in response to climate change and non-stationary time series

Mo Wang; Ming Liu; Dongqing Zhang; Jinda Qi; Weicong Fu; Yu Zhang; Qiuyi Rao; Amin E Bakhshipour; Soon Keat Tan

doi:10.1016/j.watres.2023.119720

Assessing and optimizing the hydrological performance of Grey-Green infrastructure systems in response to climate change and non-stationary time series

Water Res. 2023 Apr 1:232:119720. doi: 10.1016/j.watres.2023.119720. Epub 2023 Feb 8.

Authors

Mo Wang¹, Ming Liu², Dongqing Zhang³, Jinda Qi⁴, Weicong Fu⁵, Yu Zhang², Qiuyi Rao¹, Amin E Bakhshipour⁶, Soon Keat Tan⁷

Affiliations

¹ College of Architecture and Urban Planning, Guangzhou University, Guangzhou 510006, China; Architectural Design and Research Institute of Guangzhou University, Guangzhou 510499, China.
² College of Architecture and Urban Planning, Guangzhou University, Guangzhou 510006, China.
³ Guangdong Provincial Key Laboratory of Petrochemical Pollution Processes and Control, School of Environmental Science and Engineering, Guangdong University of Petrochemical Technology, Maoming, Guangdong 525000, China.
⁴ Department of Architecture, National University of Singapore, 117575, Singapore. Electronic address: jindaqi@nus.edu.sg.
⁵ College of Landscape Architecture, Fujian Agriculture and Forestry University, Fuzhou 350002, China.
⁶ Civil Engineering, Institute of Urban Water Management, University of Kaiserslautern, Kaiserslautern 67663, Germany.
⁷ School of Civil and Environmental Engineering, Nanyang Technological University, 639798, Singapore.

PMID: 36774753
DOI: 10.1016/j.watres.2023.119720

Abstract

Climate change has led to the increased intensity and frequency of extreme meteorological events, threatening the drainage capacity in urban catchments and densely built-up cities. To alleviate urban flooding disasters, strategies coupled with green and grey infrastructure have been proposed to support urban stormwater management. However, most strategies rely largely on diachronic rainfall data and ignore long-term climate change impacts. This study described a novel framework to assess and to identify the optimal solution in response to uncertainties following climate change. The assessment framework consists of three components: (1) assess and process climate data to generate long-term time series of meteorological parameters under different climate conditions; (2) optimise the design of Grey-Green infrastructure systems to establish the optimal design solutions; and (3) perform a multi-criteria assessment of economic and hydrological performance to support decision-making. A case study in Guangzhou, China was carried out to demonstrate the usability and application processes of the framework. The results of the case study illustrated that the optimised Grey-Green infrastructure could save life cycle costs and reduce total outflow (56-66%), peak flow (22-85%), and TSS (more than 60%) compared to the fully centralised grey infrastructure system, indicating its high superior in economic competitiveness and hydrological performance under climate uncertainties. In terms of spatial configuration, the contribution of green infrastructure appeared not as critical as the adoption of decentralisation of the drainage networks. Furthermore, under extreme drought scenarios, the decentralised infrastructure system exhibited an exceptionally high degree of removal performance for non-point source pollutants.

Keywords: Antecedent dry days; Climate change; Coupled Grey-Green system; Green infrastructure; Regional climate model; Urban stormwater management.

MeSH terms

China
Cities
Climate Change*
Rain*
Time Factors