Wang et al. (2026) Multidimensional evaluation of the gridded precipitation datasets over the source region of the Yellow River

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Identification

• Journal: Journal of Hydrometeorology
• Year: 2026
• Date: 2026-04-21
• Authors: Jie Wang, Qian Zhang, Hongjun Bao, Xiaobo Yun, Yaping Chang, Qiuhong Tang
• DOI: 10.1175/jhm-d-26-0019.1

Research Groups

Not specified in the provided text.

Short Summary

The study evaluates five existing precipitation datasets in the Source Region of the Yellow River (SRYR) to develop a new, high-accuracy gridded dataset (CCLI) using the delta correction method. The resulting CCLI dataset outperformed the others in accuracy and uncertainty, revealing a long-term precipitation increase of 2.03 mm/yr in the region since the 1950s.

Objective

• To construct and validate a high-accuracy, long-term gridded precipitation dataset for the Source Region of the Yellow River (SRYR) to address the discrepancies and biases found in existing precipitation products in alpine regions.

Study Configuration

• Spatial Scale: Source Region of the Yellow River (SRYR); spatial resolution of 0.1° × 0.1°.
• Temporal Scale: 1951–2020.

Methodology and Data

• Models used: Delta correction method.
• Data sources: Gauge observations and five precipitation datasets: China Meteorological Forcing Dataset (CMFD), CMFDv2.0, CN05.1, ERA5-Land, and the Third Pole region high-resolution near-surface meteorological forcing dataset (TPMFD).

Main Results

• The newly developed CCLI dataset outperformed the five evaluated datasets across monthly, seasonal, and annual scales.
• CCLI exhibited the lowest uncertainty among all tested datasets, with grid uncertainty ranging from 0.66 to 7.89 mm $\text{month}^{-1}$.
• Among the existing datasets, CMFDv2.0 showed the best performance, while ERA5-Land, CN05.1, and TPMFD exhibited significant biases and high uncertainties.
• Long-term analysis using CCLI indicates a significant and consistent increase in precipitation in the SRYR at a rate of 2.03 mm/yr since the 1950s.

Contributions

• Provides a high-quality, long-term (1951–2020) gridded precipitation dataset (CCLI) specifically optimized for the SRYR alpine region.
• Establishes a benchmark for precipitation data accuracy in a region where observations are sparse, facilitating more reliable future water cycle and cryohydrological studies.

Funding

Not specified in the provided text.

Citation

@article{Wang2026Multidimensional,
  author = {Wang, Jie and Zhang, Qian and Bao, Hongjun and Yun, Xiaobo and Chang, Yaping and Tang, Qiuhong},
  title = {Multidimensional evaluation of the gridded precipitation datasets over the source region of the Yellow River},
  journal = {Journal of Hydrometeorology},
  year = {2026},
  doi = {10.1175/jhm-d-26-0019.1},
  url = {https://doi.org/10.1175/jhm-d-26-0019.1}
}