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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Doaa Aboelyazeed, Chonggang Xu, Forrest M. Hoffman, Jiangtao Liu, Alex W. Jones, Chris Rackauckas, Kathryn Lawson, and Chaopeng Shen
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Dapeng Feng, Hylke Beck, Kathryn Lawson, and Chaopeng Shen
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Powerful hybrid models (called δ or delta models) embrace the fundamental learning capability of AI and can also explain the physical processes. Here we test their performance when applied to regions not in the training data. δ models rivaled the accuracy of state-of-the-art AI models under the data-dense scenario and even surpassed them for the data-sparse one. They generalize well due to the physical structure included. δ models could be ideal candidates for global hydrologic assessment.
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Hybrid forecasting systems combine data-driven methods with physics-based weather and climate models to improve the accuracy of predictions for meteorological and hydroclimatic events such as rainfall, temperature, streamflow, floods, droughts, tropical cyclones, or atmospheric rivers. We review recent developments in hybrid forecasting and outline key challenges and opportunities in the field.
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
Hydrol. Earth Syst. Sci., 26, 5373–5390, https://doi.org/10.5194/hess-26-5373-2022, https://doi.org/10.5194/hess-26-5373-2022, 2022
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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.
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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...
