Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 30, issue 3
Articles | Volume 30, issue 3
https://doi.org/10.5194/hess-30-757-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-30-757-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 30, issue 3
Research article
 | 
09 Feb 2026
Research article |  | 09 Feb 2026

Better continental-scale streamflow predictions for Australia: LSTM as a land surface model post-processor and standalone hydrological model

Ashkan Shokri, James C. Bennett, David E. Robertson, Jean-Michel Perraud, Andrew J. Frost, and Eric A. Lehmann

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Short summary
Predicting river flow accurately is crucial for managing water resources, especially in a changing climate. This study used deep learning to improve streamflow predictions across Australia. By either enhancing existing models or working independently with climate data, the deep learning approaches provided more reliable results than traditional methods. These findings can help water managers better plan for floods, droughts, and long-term water availability.
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