Jiang et al. (2026) Observation‐Based Constraints on Future Runoff Projections for Large Reservoirs

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Identification

• Journal: Earth s Future
• Year: 2026
• Date: 2026-09-01
• Authors: Haining Jiang, Kaiwen Wang, Jiamiao Yu, Xinyu Li, Xiaomang Liu
• DOI: 10.1029/2026ef008106

Research Groups

• National Center for Hydrology (NCH)
• State Key Laboratory of Hydroscience and Engineering (SKLHSE)
• Institute of Water Resources Science (IWRS)

Short Summary

This study investigates the future evolution of runoff within reservoir drainage areas in China, combining georeferenced reservoir data with historical simulations and future projections from an ensemble of Global Hydrological Models. The results highlight the dominant drivers of runoff-related variations and provide insights for effective reservoir management.

Objective

• Investigate the long-term runoff evolution across 913 drainage areas of large reservoirs in China.
• Identify the dominant drivers of runoff-related variations.

Study Configuration

• Spatial Scale: Continental scale, focusing on China's reservoir drainage areas.
• Temporal Scale: Historical period (1980-2014) and future scenarios (2021-2050).

Methodology and Data

• Models used: Ensemble of Global Hydrological Models (GHMs).
• Data sources: Georeferenced reservoir data set, historical runoff simulations, and future projections.

Main Results

• 337-438 reservoirs are projected to exhibit decreasing inter-annual magnitude, weakening inter-annual variability, and enhancing intra-annual variability during the historical period.
• Increasing magnitude and enhancing variability under future scenarios.
• Anthropogenic climate change predominates over natural climate variability and human water and land management as the dominant driver of runoff-related variations.

Contributions

This study provides original insights into the long-term runoff evolution across large reservoirs in China, highlighting the need for individualized management strategies. The ensemble best approach used in this study outperforms traditional ensemble mean and median methods, reducing uncertainties at large spatial scales.

Funding

• National Natural Science Foundation of China (NSFC)
• Ministry of Water Resources of China (MWR)
• State Key Laboratory of Hydroscience and Engineering (SKLHSE)

Citation

@article{Jiang2026ObservationBased,
  author = {Jiang, Haining and Wang, Kaiwen and Yu, Jiamiao and Li, Xinyu and Liu, Xiaomang},
  title = {Observation‐Based Constraints on Future Runoff Projections for Large Reservoirs},
  journal = {Earth s Future},
  year = {2026},
  doi = {10.1029/2026ef008106},
  url = {https://doi.org/10.1029/2026ef008106}
}