Articles | Volume 28, issue 5
https://doi.org/10.5194/hess-28-1191-2024
https://doi.org/10.5194/hess-28-1191-2024
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

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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. 
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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.
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