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.
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.
the Creative Commons Attribution 4.0 License.
Research article
| 
21 Oct 2025
Research article |
| 21 Oct 2025
| 21 Oct 2025
Improving streamflow simulation through machine learning-powered data integration and its potential for forecasting in the Western U.S.
Yuan Yang
CORRESPONDING AUTHOR
Center for Western Weather and Water Extremes, Scripps Institution of Oceanography, University of California San Diego, CA, USA
Center for Western Weather and Water Extremes, Scripps Institution of Oceanography, University of California San Diego, CA, USA
Dapeng Feng
Department of Earth System Science, Stanford University, Stanford, CA, USA
Mu Xiao
Center for Western Weather and Water Extremes, Scripps Institution of Oceanography, University of California San Diego, CA, USA
Taylor Dixon
Center for Western Weather and Water Extremes, Scripps Institution of Oceanography, University of California San Diego, CA, USA
Robert Hartman
Robert K. Hartman Consulting Services, Roseville, CA, USA
Chaopeng Shen
Civil and Environmental Engineering, Pennsylvania State University, PA, USA
Yalan Song
Civil and Environmental Engineering, Pennsylvania State University, PA, USA
Agniv Sengupta
Center for Western Weather and Water Extremes, Scripps Institution of Oceanography, University of California San Diego, CA, USA
Luca Delle Monache
Center for Western Weather and Water Extremes, Scripps Institution of Oceanography, University of California San Diego, CA, USA
F. Martin Ralph
Center for Western Weather and Water Extremes, Scripps Institution of Oceanography, University of California San Diego, CA, USA
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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.
We explore a machine learning-based data integration method that integrates streamflow (Q) and...
