Cheng et al. (2026) Revealing water supply-demand responses to varying inflow frequencies in inter-basin water diversion projects using a non-iterative deterministic sampling algorithm

Identification

• Journal: Journal of Hydrology Regional Studies
• Year: 2026
• Date: 2026-09-21
• Authors: Haomiao Cheng, Xiaoman Sun, Zuping Xu, Zhiming Qi, Matthew Tom Harrison, Matthew W. Sima, Yi Gong, Liang Wang, Jilin Cheng
• DOI: 10.1016/j.ejrh.2026.103985

Research Groups

• College of Hydraulic Science and Engineering, Institute of Modern Rural Water Conservancy, Yangzhou University (China)
• Department of Electrical Engineering, Taizhou University (China)
• Department of Bioresource Engineering, McGill University (Canada)
• Tasmanian Institute of Agriculture, University of Tasmania (Australia)
• Department of Civil and Environmental Engineering, Princeton University (USA)

Short Summary

This study proposes a non-iterative deterministic sampling algorithm, the Orthogonal Latin Neighborhood Algorithm (OLNA), to optimize multi-year multi-reservoir scheduling problems under varying hydrological conditions. The universality and efficiency of OLNA were verified to be superior to mainstream heuristic algorithms in solving high-dimensional nonlinear optimization problems.

Objective

• To develop a universal framework for minimizing water supply-demand gaps in inter-basin water diversion projects (IWDPs) by optimizing multi-reservoir water supply scheduling.
• To reveal the water supply-demand responses to varying inflow frequencies in IWDPs using OLNA.

Study Configuration

• Spatial Scale: The study focuses on the Eastern Route of the South-to-North Water Diversion Project (ER-SNWDP), China, which is a large-scale inter-basin water diversion project involving multiple reservoirs and water-receiving districts.
• Temporal Scale: The study considers a multi-year time horizon, with data collected for the period 1951-2020.

Methodology and Data

• Models used: A non-iterative deterministic sampling algorithm, the Orthogonal Latin Neighborhood Algorithm (OLNA), was proposed to optimize multi-year multi-reservoir scheduling problems.
• Data sources: Hydrological data, meteorological data, and pumping station system data were collected from various sources, including the China Annual Hydrological Reports, National Meteorological Information Center, and Jiangsu Water Resources Bulletin.

Main Results

• The universality and efficiency of OLNA were verified to be superior to mainstream heuristic algorithms in solving high-dimensional nonlinear optimization problems.
• The study revealed that the water supply-demand gap (WSG) of the ER-SNWDP has the potential to be reduced by 1.28 × 10^10 m^3 under 75% inflow frequency.
• Escalating water demand during high inflow frequency (hydrological droughts) exacerbates supply-demand imbalances despite optimized strategies.

Contributions

• This study provides a novel approach for optimizing multi-year multi-reservoir scheduling problems using a non-iterative deterministic sampling algorithm, OLNA.
• The universality and efficiency of OLNA were verified to be superior to mainstream heuristic algorithms in solving high-dimensional nonlinear optimization problems.
• The study reveals the water supply-demand responses to varying inflow frequencies in IWDPs, providing guidance for water regulation and drought-risk mitigation.

Funding

• This research was funded by the National Natural Science Foundation of China (Grant No. 51979006).
• The authors acknowledge the support from the Ministry of Water Resources, China, and the Jiangsu Water Resources Bureau.
• The study also received funding from the University of Tasmania and Princeton University.

Citation

@article{Cheng2026Revealing,
  author = {Cheng, Haomiao and Sun, Xiaoman and Xu, Zuping and Qi, Zhiming and Harrison, Matthew Tom and Sima, Matthew W. and Gong, Yi and Wang, Liang and Cheng, Jilin},
  title = {Revealing water supply-demand responses to varying inflow frequencies in inter-basin water diversion projects using a non-iterative deterministic sampling algorithm},
  journal = {Journal of Hydrology Regional Studies},
  year = {2026},
  doi = {10.1016/j.ejrh.2026.103985},
  url = {https://doi.org/10.1016/j.ejrh.2026.103985}
}