Li et al. (2026) Multi-objective phased optimization framework of gray-green-blue infrastructure for synergistic runoff control in data-scarce regions

Identification

• Journal: Journal of Environmental Management
• Year: 2026
• Date: 2026-01-13
• Authors: Huayue Li, Qinghua Luan, Jun Liu, Hong Zhou, C. Gao
• DOI: 10.1016/j.jenvman.2026.128618

Research Groups

• State Key Laboratory of Water Cycle and Water Security in River Basin, Hohai University, Nanjing, China
• College of Hydrology and Water Resources, Hohai University, Nanjing, China
• Key Laboratory of Flood Disaster Prevention and Control of the Ministry of Emergency Management in China, Hohai University, Nanjing, China
• The National Key Laboratory of Water Disaster Prevention, Nanjing Hydraulic Research Institute, Nanjing, China

Short Summary

This study develops a two-phased multi-objective optimization framework for Gray-Green-Blue Infrastructure (GGBI) to synergistically control urban runoff in data-scarce regions, integrating pumping station drainage with GGBI design to minimize annual total cost and maximize runoff volume reduction.

Objective

• To develop a multi-objective phased optimization framework for Gray-Green-Blue Infrastructure (GGBI) that integrates pumping station drainage, using water surface ratio and Green Infrastructure (GI) as decision variables, to optimize annual total cost (ATC) and runoff volume reduction rate (RVRR), particularly for data-scarce regions.

Study Configuration

• Spatial Scale: Urban scale, specifically Handan, Hebei Province, China.
• Temporal Scale: Annual (for cost and runoff reduction objectives); optimization determines long-term design parameters.

Methodology and Data

• Models used: Multi-objective phased optimization framework, Dingo Optimization Algorithm (DOA).
• Data sources: The framework is designed for data-scarce regions. Pumping station drainage flow is determined through fitting relationships, implying derived or empirical data rather than extensive hydrological model inputs.

Main Results

• Pumping stations effectively enable rapid river discharge in limited Green-Blue Infrastructure (GBI) space, controlling water levels below 5.1 meters.
• Phase I optimization established decision variable ranges for preliminary planning: 16.09–37.13 cubic meters per second (m³/s) for pumping station drainage flow, 1 % for lake and 5 % for river water surface ratios, 31.6 %–69 % for sunken green space, and 10 %–26.4 % for permeable pavement ratios. These ranges serve as scientific constraints for Phase II spatial optimization.

Contributions

• Provides an innovative multi-objective phased optimization model for flood control in data-scarce regions, addressing limitations of conventional models that overlook pumping stations and require extensive data.
• Transforms pumping station drainage capacity from a fixed value into a dynamic variable determined by Green-Blue Infrastructure scale, offering a quantitative decision-making basis for GGBI under budget and space constraints.

Funding

• Not specified in the provided text.

Citation

@article{Li2026Multiobjective,
  author = {Li, Huayue and Luan, Qinghua and Liu, Jun and Zhou, Hong and Gao, C.},
  title = {Multi-objective phased optimization framework of gray-green-blue infrastructure for synergistic runoff control in data-scarce regions},
  journal = {Journal of Environmental Management},
  year = {2026},
  doi = {10.1016/j.jenvman.2026.128618},
  url = {https://doi.org/10.1016/j.jenvman.2026.128618}
}