Cao et al. (2026) Anthropogenic dominance of water storage variability in the Yellow River Basin: A machine learning synthesis of multi-source data (1981–2031)

Identification

• Journal: Journal of Hydrology
• Year: 2026
• Date: 2026-09-01
• Authors: Yanping Cao, Zunyi Xie, Dandan Liu, Chaolin Mu, Jielun Wang, Yihao Chang, Yingjun Pang
• DOI: 10.1016/j.jhydrol.2026.136347

Research Groups

Not specified in the provided text.

Short Summary

This study utilizes a machine learning synthesis of multi-source data to analyze water storage variability in the Yellow River Basin from 1981 to 2031, concluding that anthropogenic factors are the dominant driver.

Objective

• To investigate the drivers of water storage variability in the Yellow River Basin and quantify the extent of anthropogenic dominance.

Study Configuration

• Spatial Scale: Yellow River Basin.
• Temporal Scale: 1981–2031.

Methodology and Data

• Models used: Machine learning synthesis.
• Data sources: Multi-source data (specific sources not detailed in the provided text).

Main Results

• Anthropogenic factors dominate the variability of water storage within the Yellow River Basin.

Contributions

• Provides a long-term synthesis and projection (1981–2031) of water storage variability in the Yellow River Basin, highlighting the primary role of human activities over natural variability through a machine learning approach.

Funding

Not specified in the provided text.

Citation

@article{Cao2026Anthropogenic,
  author = {Cao, Yanping and Xie, Zunyi and Liu, Dandan and Mu, Chaolin and Wang, Jielun and Chang, Yihao and Pang, Yingjun},
  title = {Anthropogenic dominance of water storage variability in the Yellow River Basin: A machine learning synthesis of multi-source data (1981–2031)},
  journal = {Journal of Hydrology},
  year = {2026},
  doi = {10.1016/j.jhydrol.2026.136347},
  url = {https://doi.org/10.1016/j.jhydrol.2026.136347}
}