Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 29, issue 23
Articles | Volume 29, issue 23
https://doi.org/10.5194/hess-29-6829-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-29-6829-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 29, issue 23
Research article
 | 
01 Dec 2025
Research article |  | 01 Dec 2025

Ensembling differentiable process-based and data-driven models with diverse meteorological forcing datasets to advance streamflow simulation

Peijun Li, Yalan Song, Ming Pan, Kathryn Lawson, and Chaopeng Shen

Viewed

Total article views: 5,125 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
3,860 1,138 127 5,125 147 178
  • HTML: 3,860
  • PDF: 1,138
  • XML: 127
  • Total: 5,125
  • BibTeX: 147
  • EndNote: 178
Views and downloads (calculated since 10 Mar 2025)
Cumulative views and downloads (calculated since 10 Mar 2025)

Viewed (geographical distribution)

Total article views: 5,125 (including HTML, PDF, and XML) Thereof 5,033 with geography defined and 92 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Saved (final revised paper)

Latest update: 08 Jun 2026
Download
Short summary
This study explores how combining different model types improves streamflow predictions, especially in data-sparse scenarios. By integrating two highly accurate models with distinct mechanisms and leveraging multiple meteorological datasets, we highlight their unique strengths and set new accuracy benchmarks across spatiotemporal conditions. Our findings enhance the understanding of how diverse models and multi-source data can be effectively used to improve hydrological predictions.
Read more
Share