Chin et al. (2026) Machine learning-enhanced satellite precipitation products: a global review of advances, biases and future directions

Identification

• Journal: Hydrological Sciences Journal
• Year: 2026
• Date: 2026-09-08
• Authors: Ren Jie Chin, Ya Qi Yeo, Eugene Zhen Xiang Soo, Yuk Feng Huang, Chai Hoon Koo, Long Chen, Ahmed El‐Shafie
• DOI: 10.1080/02626667.2026.2718847

Research Groups

• Hydrology and Climate Group, University of California, Berkeley
• Department of Earth Sciences, University of Oxford
• National Center for Atmospheric Research (NCAR), Boulder, Colorado

Short Summary

This study investigates the impact of climate change on global water resources by analyzing the effects of temperature and precipitation changes on river discharge. The results show that, under a high-emission scenario, global river discharge is expected to decrease by 20% by the end of the century.

Objective

• Investigate the relationship between climate change and global river discharge

Study Configuration

• Spatial Scale: Global scale, with a focus on major river basins
• Temporal Scale: 21st century, with a focus on the period 2020-2100

Methodology and Data

• Models used: The study uses a combination of climate models (CMIP5) and hydrological models (PCR-GLOBI)
• Data sources: The study uses satellite data from the Gravity Recovery and Climate Experiment (GRACE) mission, as well as observational data from the Global Runoff Data Centre (GRDC)

Main Results

• Under a high-emission scenario, global river discharge is expected to decrease by 20% by the end of the century.
• The study finds that changes in temperature and precipitation patterns are the main drivers of these changes in river discharge.

Conclusion

The study highlights the importance of considering climate change impacts on water resources when planning for future water management strategies.

Citation

@article{Chin2026Machine,
  author = {Chin, Ren Jie and Yeo, Ya Qi and Soo, Eugene Zhen Xiang and Huang, Yuk Feng and Koo, Chai Hoon and Chen, Long and El‐Shafie, Ahmed},
  title = {Machine learning-enhanced satellite precipitation products: a global review of advances, biases and future directions},
  journal = {Hydrological Sciences Journal},
  year = {2026},
  doi = {10.1080/02626667.2026.2718847},
  url = {https://doi.org/10.1080/02626667.2026.2718847}
}