Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 28, issue 5
Articles | Volume 28, issue 5
https://doi.org/10.5194/hess-28-1191-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-28-1191-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 28, issue 5
Research article
 | 
13 Mar 2024
Research article |  | 13 Mar 2024

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

Related authors

Streamflow elasticity as a function of aridity
Vazken Andréassian, Guilherme Mendoza Guimarães, Julien Lerat, and Alban de Lavenne
EGUsphere, https://doi.org/10.5194/egusphere-2025-4912,https://doi.org/10.5194/egusphere-2025-4912, 2025
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
Short summary
Time shift between precipitation and evaporation has more impact on annual streamflow variability than the elasticity of potential evaporation
Vazken Andréassian, Guilherme Mendoza Guimarães, Alban de Lavenne, and Julien Lerat
Hydrol. Earth Syst. Sci., 29, 5477–5491, https://doi.org/10.5194/hess-29-5477-2025,https://doi.org/10.5194/hess-29-5477-2025, 2025
Short summary
Technical note: Quadratic Solution of the Approximate Reservoir Equation (QuaSoARe)
Julien Lerat
Hydrol. Earth Syst. Sci., 29, 2003–2021, https://doi.org/10.5194/hess-29-2003-2025,https://doi.org/10.5194/hess-29-2003-2025, 2025
Short summary
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
EGUsphere, https://doi.org/10.5194/egusphere-2025-805,https://doi.org/10.5194/egusphere-2025-805, 2025
Short summary

Cited articles

Abbas, A., Boithias, L., Pachepsky, Y., Kim, K., Chun, J. A., and Cho, K. H.: AI4Water v1.0: an open-source python package for modeling hydrological time series using data-driven methods, Geosci. Model Dev., 15, 3021–3039, https://doi.org/10.5194/gmd-15-3021-2022, 2022. 
Australian Water Outlook: https://awo.bom.gov.au/, last access: February 2022. 
Bennett, J. C., Wang, Q. J., Robertson, D. E., Schepen, A., Li, M., and Michael, K.: Assessment of an ensemble seasonal streamflow forecasting system for Australia, Hydrol. Earth Syst. Sci., 21, 6007–6030, https://doi.org/10.5194/hess-21-6007-2017, 2017. 
Choi, J., Lee, J., and Kim, S.: Utilization of the Long Short-Term Memory network for predicting streamflow in ungauged basins in Korea, Ecol. Eng., 182, 106699, https://doi.org/10.1016/j.ecoleng.2022.106699, 2022. 
Clark, M. P., Vogel, R. M., Lamontagne, J. R., Mizukami, N., Knoben, W. J., Tang, G., Gharari, S., Freer, J. E., Whitfield, P. H., and Shook, K. R.: The abuse of popular performance metrics in hydrologic modeling, Water Resour. Res., 57, e2020WR029001, https://doi.org/10.1029/2020WR029001, 2021. 
More articles (53)
Download
Short summary
To determine if deep learning models are in general a viable alternative to traditional hydrologic modelling techniques in Australian catchments, a comparison of river–runoff predictions is made between traditional conceptual models and deep learning models in almost 500 catchments spread over the continent. It is found that the deep learning models match or outperform the traditional models in over two-thirds of the river catchments, indicating feasibility in a wide variety of conditions.
Read more
Share