Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 29, issue 20
Articles | Volume 29, issue 20
https://doi.org/10.5194/hess-29-5453-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-29-5453-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 29, issue 20
Research article
 | 
21 Oct 2025
Research article |  | 21 Oct 2025

Improving streamflow simulation through machine learning-powered data integration and its potential for forecasting in the Western U.S.

Yuan Yang, Ming Pan, Dapeng Feng, Mu Xiao, Taylor Dixon, Robert Hartman, Chaopeng Shen, Yalan Song, Agniv Sengupta, Luca Delle Monache, and F. Martin Ralph

Viewed

Total article views: 1,198 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
946 224 28 1,198 28 48
  • HTML: 946
  • PDF: 224
  • XML: 28
  • Total: 1,198
  • BibTeX: 28
  • EndNote: 48
Views and downloads (calculated since 30 Apr 2025)
Cumulative views and downloads (calculated since 30 Apr 2025)

Viewed (geographical distribution)

Total article views: 1,198 (including HTML, PDF, and XML) Thereof 1,198 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 21 Oct 2025
Download
Short summary
We explore a machine learning-based data integration method that integrates streamflow (Q) and snow water equivalent (SWE) to improve streamflow estimates at various lag times (1–10 d, 1–6 months) and timescales (daily and monthly) over Western US basins. Benefits rank as: integrating Q at the daily scale > Q at the monthly scale > SWE at the monthly scale > SWE at the daily scale. Results highlight the method’s potential for short- and long-term streamflow forecasting in the Western US.
Read more
Share