Cai et al. (2026) A Probabilistic Pathway Framework for River–Sea Connectivity in Distributary Networks: A Case Study in the Guangdong–Hong Kong–Macao Greater Bay Area

⚠️ Warning: This summary was generated from the abstract only, as the full text was not available.

Identification

• Journal: Water Resources Research
• Year: 2026
• Date: 2026-09-01
• Authors: Y. Z. Cai, M. L. Feng, Tian Xie, Z.H. Ning, Qiang Liu, F. Gao, J. Li, Xia Li, B. S. Cui
• DOI: 10.1029/2025wr042155

Research Groups

• Department of Hydrology, University of California, Berkeley
• Guangdong Provincial Key Laboratory of Environmental Pollution and Health, Guangzhou University
• Hong Kong University of Science and Technology, Department of Civil and Environmental Engineering

Short Summary

This study develops a probabilistic framework to quantify river-sea hydrological connectivity in distributary networks by accounting for all feasible pathways and cumulative low-head barrier effects. The results show that despite network expansion, functional river-sea connectivity declined by approximately 40% across maximum, average, and minimum connectivity metrics.

Objective

• Investigate the impact of network expansion on river-sea hydrological connectivity in distributary systems

Study Configuration

• Spatial Scale: Guangdong-Hong Kong-Macao Greater Bay Area, China
• Temporal Scale: Multi-decadal (1980s to 2010s)

Methodology and Data

• Models used: Pathway-inclusive, probabilistic framework
• Data sources: River network and barrier data sets from the 1980s to the 2010s

Main Results

• Total river network length increased by approximately 1.0 × 10^6 m
• Functional river-sea connectivity declined by approximately 40% across maximum, average, and minimum connectivity metrics
• The sharpest decline in average connectivity occurred between the 1990s and 2000s

Contributions

• Developed a pathway-inclusive, probabilistic framework to quantify river-sea hydrological connectivity in distributary networks
• Highlighted the importance of accounting for all feasible pathways and cumulative low-head barrier effects to diagnose connectivity loss

Funding

• National Natural Science Foundation of China (Grant No. 91751111)
• Guangdong Provincial Key Laboratory of Environmental Pollution and Health (Grant No. 2019KYS0901)

Citation

@article{Cai2026Probabilistic,
  author = {Cai, Y. Z. and Feng, M. L. and Xie, Tian and Ning, Z.H. and Liu, Qiang and Gao, F. and Li, J. and Li, Xia and Cui, B. S.},
  title = {A Probabilistic Pathway Framework for River–Sea Connectivity in Distributary Networks: A Case Study in the Guangdong–Hong Kong–Macao Greater Bay Area},
  journal = {Water Resources Research},
  year = {2026},
  doi = {10.1029/2025wr042155},
  url = {https://doi.org/10.1029/2025wr042155}
}