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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This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
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Jiangtao Liu, David Hughes, Farshid Rahmani, Kathryn Lawson, and Chaopeng Shen
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Under-monitored regions like Africa need high-quality soil moisture predictions to help with food production, but it is not clear if soil moisture processes are similar enough around the world for data-driven models to maintain accuracy. We present a deep-learning-based soil moisture model that learns from both in situ data and satellite data and performs better than satellite products at the global scale. These results help us apply our model globally while better understanding its limitations.
Sara Sadri, James S. Famiglietti, Ming Pan, Hylke E. Beck, Aaron Berg, and Eric F. Wood
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A farm-scale hydroclimatic machine learning framework to advise farmers was developed. FarmCan uses remote sensing data and farmers' input to forecast crop water deficits. The 8 d composite variables are better than daily ones for forecasting water deficit. Evapotranspiration (ET) and potential ET are more effective than soil moisture at predicting crop water deficit. FarmCan uses a crop-specific schedule to use surface or root zone soil moisture.
Hylke E. Beck, Ming Pan, Diego G. Miralles, Rolf H. Reichle, Wouter A. Dorigo, Sebastian Hahn, Justin Sheffield, Lanka Karthikeyan, Gianpaolo Balsamo, Robert M. Parinussa, Albert I. J. M. van Dijk, Jinyang Du, John S. Kimball, Noemi Vergopolan, and Eric F. Wood
Hydrol. Earth Syst. Sci., 25, 17–40, https://doi.org/10.5194/hess-25-17-2021, https://doi.org/10.5194/hess-25-17-2021, 2021
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We evaluated the largest and most diverse set of surface soil moisture products ever evaluated in a single study. We found pronounced differences in performance among individual products and product groups. Our results provide guidance to choose the most suitable product for a particular application.
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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...
