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

Related authors

Design and trial implementation of a continental-scale, kilometre-resolution hourly precipitation analysis for Australia using satellite, radar and gauges
Yuhang Zhang, Quan J. Wang, Andrew J. Frost, Jayaram Pudashine, Blair Trewin, Carlos Velasco-Forero, Chun-Hsu Su, and Vincent Villani
EGUsphere, https://doi.org/10.5194/egusphere-2026-666,https://doi.org/10.5194/egusphere-2026-666, 2026
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
Short summary
Comparison of BARRA and ERA5 in replicating mean and extreme precipitation over Australia
Kevin K. W. Cheung, Fei Ji, Nidhi Nishant, Jin Teng, James Bennett, and De Li Liu
Hydrol. Earth Syst. Sci., 29, 3527–3543, https://doi.org/10.5194/hess-29-3527-2025,https://doi.org/10.5194/hess-29-3527-2025, 2025
Short summary
Uncovering a Key Predictors for Enhancing Daily Streamflow Simulation Using Machine Learning
Arash Aghakhani, David E. Robertson, and Valentijn R. N. Pauwels
EGUsphere, https://doi.org/10.5194/egusphere-2025-553,https://doi.org/10.5194/egusphere-2025-553, 2025
Short summary
Deep learning for monthly rainfall–runoff modelling: a large-sample comparison with conceptual models across Australia
Stephanie R. Clark, Julien Lerat, Jean-Michel Perraud, and Peter Fitch
Hydrol. Earth Syst. Sci., 28, 1191–1213, https://doi.org/10.5194/hess-28-1191-2024,https://doi.org/10.5194/hess-28-1191-2024, 2024
Short summary
Spatial and temporal dynamics of suspended sediment concentrations in coastal waters of the South China Sea, off Sarawak, Borneo: ocean colour remote sensing observations and analysis
Jenny Choo, Nagur Cherukuru, Eric Lehmann, Matt Paget, Aazani Mujahid, Patrick Martin, and Moritz Müller
Biogeosciences, 19, 5837–5857, https://doi.org/10.5194/bg-19-5837-2022,https://doi.org/10.5194/bg-19-5837-2022, 2022
Short summary
More articles (2)

Cited articles

Coron, L., Andréassian, V., Perrin, C., Lerat, J., Vaze, J., Bourqui, M., and Hendrickx, F.: Crash testing hydrological models in contrasted climate conditions: An experiment on 216 Australian catchments, Water. Resour. Res., 48, https://doi.org/10.1029/2011WR011721, 2012. 
Duan, Q., Sorooshian, S., and Gupta, V. K.: Optimal use of the SCE-UA global optimization method for calibrating watershed models, J. Hydrol., 158, 265–284, https://doi.org/10.1016/0022-1694(94)90057-4, 1994. 
Fowler, K. J. A., Acharya, S. C., Addor, N., Chou, C., and Peel, M. C.: CAMELS-AUS: hydrometeorological time series and landscape attributes for 222 catchments in Australia, Earth Syst. Sci. Data, 13, 3847–3867, https://doi.org/10.5194/essd-13-3847-2021, 2021. 
Fowler, K. J. A., Zhang, Z., and Hou, X.: CAMELS-AUS v2: updated hydrometeorological time series and landscape attributes for an enlarged set of catchments in Australia, Earth Syst. Sci. Data, 17, 4079–4095, https://doi.org/10.5194/essd-17-4079-2025, 2025. 
Frame, J. M., Kratzert, F., Raney, A., Rahman, M., Salas, F. R., and Nearing, G. S.: Post-Processing the National Water Model with Long Short-Term Memory Networks for Streamflow Predictions and Model Diagnostics, J. Am. Water. Resour. Assoc., 57, https://doi.org/10.1111/1752-1688.12964, 2021. 
More articles (33)
Download
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.
Read more
Share