Mo et al. (2026) Multi-objective Joint Optimization Operation of Cascade Reservoirs Considering River Hydrological Health Assessment

Identification

• Journal: Water Resources Management
• Year: 2026
• Date: 2026-01-01
• Authors: Chongxun Mo, Zihan Lin, N. Li, Xiaoyu Wan, Gang Tang, Yi Huang
• DOI: 10.1007/s11269-025-04432-y

Research Groups

• College of Architecture and Civil Engineering, Guangxi University, Nanning, China
• Key Laboratory of Disaster Prevention and Structural Safety of Ministry of Education, Guangxi University, Nanning, China
• Guangxi Key Laboratory of Disaster Prevention and Engineering Safety, Guangxi University, Nanning, China
• Guangxi Provincial Engineering Research Center of Water Security and Intelligent Control for Karst Region, Guangxi University, Nanning, China
• Guangxi Water & Power Design Institute Co., Ltd, Nanning, China

Short Summary

This study developed a multi-objective optimization framework for cascade reservoir operation in the Haokun-Chengbi River Basin, integrating river hydrological health assessment to balance power generation, water supply, and ecological flow. The research found that optimal strategies vary significantly with hydrological conditions, with dry years showing the most pronounced improvement in river hydrological health (up to 57.35%) after optimization.

Objective

• To calculate the minimum and optimal ecological flows of the Haokun Reservoir and the Chengbi River Reservoir.
• To solve the multi-objective optimization problem of reservoirs using the NSGA-III algorithm.
• To select the optimal scheduling plan using a decision-making model (TOPSIS).
• To evaluate the optimal scheduling results of reservoirs by introducing the river hydrological health level theory.

Study Configuration

• Spatial Scale: Haokun-Chengbi River cascade reservoirs in the Chengbi River Basin, a tributary of the Youjiang section of the Yujiang main stream of the Pearl River system in Baise City, Guangxi Province, Southwest China.
• Temporal Scale: Daily runoff and water level data from 1963 to 2020 (58 years) were used for analysis and optimization across typical wet, normal, and dry years.

Methodology and Data

• Models used:
  • Multi-objective optimization model for cascade reservoirs.
  • NSGA-III (Non-dominated Sorting Genetic Algorithm III) for multi-objective optimization.
  • TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) for optimal solution decision-making.
  • Analytic Hierarchy Process (AHP) for subjective weighting of decision variables.
  • CRITIC method for objective weighting of decision variables.
  • Minimum information entropy method for combining subjective and objective weights.
  • Ecological flow calculation methods: Monthly minimum flow, intra-annual distribution, flow duration curve, driest month average runoff, NGPRP, improved monthly frequency, Range of Variability Approach (RVA), probability density maximization, and Tennant method (for verification).
  • River hydrological health level assessment based on ecological flow thresholds (Ma et al., 2019).
• Data sources:
  • Daily runoff and water level data measured at Pingtang Station (downstream of Haokun Reservoir, upstream of Chengbi River Reservoir) from 1963 to 2020.
  • Basic information (e.g., storage capacity, water levels) for the Haokun and Chengbi River reservoirs.

Main Results

• The flow duration curve method was selected for calculating minimum ecological flow, and the improved monthly frequency method for appropriate ecological flow.
• Pareto optimal solutions demonstrated varying dispersion across typical years, with the strongest dispersion observed in dry years, indicating more pronounced trade-offs.
• A strong linear relationship was found between ecological overflow/water shortage and power generation, with competition intensifying in dry years. Water supply was identified as a priority target, becoming more limited in dry years.
• The Haokun Reservoir significantly contributed to delaying flood peaks for the downstream Chengbi River Reservoir, with a more pronounced adjustment effect at lower water levels.
• The combined weighting of objectives across typical years ranked as: power generation > ecology > water supply. The weight of power generation decreased with decreasing water inflow, while water supply and ecology weights increased.
• Compared to historical operations, optimized schemes increased power generation and water supply in all typical years. Ecological overflow and water shortage decreased by 14.134% in normal years but increased by 19.831% in wet years.
• River hydrological health assessment showed that improvement was most pronounced in dry years, reaching up to 57.35% in September. However, hydrological health declined in some months, such as August in wet years (due to increased scour from flood discharge) and the first half of normal years (due to reduced discharge to meet water supply targets).

Contributions

• Established a novel framework that explicitly integrates multi-objective cascade reservoir optimization scheduling with river hydrological health assessment.
• Applied the NSGA-III algorithm and TOPSIS decision model to address complex, multi-objective, and nonlinear reservoir operation problems, considering ecological flow thresholds.
• Demonstrated the critical need for adaptive reservoir operation strategies tailored to specific hydrological conditions (wet, normal, dry years) due to shifting trade-offs between objectives.
• Provided a useful reference for sustainable development in reservoir operation and management, particularly in ecologically sensitive areas with limited ecological data.

Funding

• National Natural Science Foundation of China (Grant No. 52269002)
• Guangxi Key Technologies R&D Program (Grant No. AB24010047)
• Guangxi Water Resource Technology Promotion Foundation (Grant No. SK2022-021 and Grant No. SK2021-3-23)

Citation

@article{Mo2026Multiobjective,
  author = {Mo, Chongxun and Lin, Zihan and Li, N. and Wan, Xiaoyu and Tang, Gang and Huang, Yi},
  title = {Multi-objective Joint Optimization Operation of Cascade Reservoirs Considering River Hydrological Health Assessment},
  journal = {Water Resources Management},
  year = {2026},
  doi = {10.1007/s11269-025-04432-y},
  url = {https://doi.org/10.1007/s11269-025-04432-y}
}