Wang et al. (2026) A multi-source precipitation blending method combining hydrological model-guided precipitation adjustment and double transfer learning-based data merging

Identification

• Journal: Journal of Hydrology
• Year: 2026
• Date: 2026-04-01
• Authors: J. H. Wang, Xiang Li, Ruiyan Wu, Xiangpeng Mu, Jili Wei, Yan Huang, Shenglong Gu, Dongqin Yin, Xin Tao, Keyan Xu
• DOI: 10.1016/j.jhydrol.2026.135448

Research Groups

[Affiliations not provided in the given text.]

Short Summary

This paper introduces a novel multi-source precipitation blending method that integrates hydrological model-guided precipitation adjustment with a double transfer learning-based data merging approach to enhance precipitation data accuracy.

Objective

• To develop and evaluate a new multi-source precipitation blending method by combining hydrological model-guided precipitation adjustment and double transfer learning-based data merging.

Study Configuration

• Spatial Scale: Not specified in the provided text.
• Temporal Scale: Not specified in the provided text.

Methodology and Data

• Models used: A hydrological model (for precipitation adjustment) and a double transfer learning model (for data merging). Specific model names are not provided.
• Data sources: Multi-source precipitation data. Specific sources (e.g., satellite, gauge, reanalysis) are not detailed.

Main Results

• Not available in the provided pre-proof text.

Contributions

• Proposes an innovative integrated method for blending multi-source precipitation data, combining the strengths of hydrological modeling for adjustment and double transfer learning for merging, which is expected to improve the accuracy and utility of precipitation products.

Funding

• Not specified in the provided text.

Citation

@article{Wang2026multisource,
  author = {Wang, J. H. and Li, Xiang and Wu, Ruiyan and Mu, Xiangpeng and Wei, Jili and Huang, Yan and Gu, Shenglong and Yin, Dongqin and Tao, Xin and Xu, Keyan},
  title = {A multi-source precipitation blending method combining hydrological model-guided precipitation adjustment and double transfer learning-based data merging},
  journal = {Journal of Hydrology},
  year = {2026},
  doi = {10.1016/j.jhydrol.2026.135448},
  url = {https://doi.org/10.1016/j.jhydrol.2026.135448}
}