Loading [MathJax]/jax/output/HTML-CSS/fonts/TeX/fontdata.js
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

Related authors

How does the time shift between precipitation and evaporation affect annual streamflow variability? A large sample elasticity study
Vazken Andréassian, Guilherme Mendoza Guimarães, Alban de Lavenne, and Julien Lerat
EGUsphere, https://doi.org/10.5194/egusphere-2025-414,https://doi.org/10.5194/egusphere-2025-414, 2025
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
Short summary
Technical note: Quadratic solution of the approximate reservoir equation (QuaSoARe)
Julien Lerat
EGUsphere, https://doi.org/10.5194/egusphere-2024-3184,https://doi.org/10.5194/egusphere-2024-3184, 2024
Short summary

Related subject area

Subject: Catchment hydrology | Techniques and Approaches: Modelling approaches
Catchments do not strictly follow Budyko curves over multiple decades, but deviations are minor and predictable
Muhammad Ibrahim, Miriam Coenders-Gerrits, Ruud van der Ent, and Markus Hrachowitz
Hydrol. Earth Syst. Sci., 29, 1703–1723, https://doi.org/10.5194/hess-29-1703-2025,https://doi.org/10.5194/hess-29-1703-2025, 2025
Short summary
Scale dependency in modeling nivo-glacial hydrological systems: the case of the Arolla basin, Switzerland
Anne-Laure Argentin, Pascal Horton, Bettina Schaefli, Jamal Shokory, Felix Pitscheider, Leona Repnik, Mattia Gianini, Simone Bizzi, Stuart N. Lane, and Francesco Comiti
Hydrol. Earth Syst. Sci., 29, 1725–1748, https://doi.org/10.5194/hess-29-1725-2025,https://doi.org/10.5194/hess-29-1725-2025, 2025
Short summary
Extended-range forecasting of stream water temperature with deep-learning models
Ryan S. Padrón, Massimiliano Zappa, Luzi Bernhard, and Konrad Bogner
Hydrol. Earth Syst. Sci., 29, 1685–1702, https://doi.org/10.5194/hess-29-1685-2025,https://doi.org/10.5194/hess-29-1685-2025, 2025
Short summary
Technical note: An approach for handling multiple temporal frequencies with different input dimensions using a single LSTM cell
Eduardo Acuña Espinoza, Frederik Kratzert, Daniel Klotz, Martin Gauch, Manuel Álvarez Chaves, Ralf Loritz, and Uwe Ehret
Hydrol. Earth Syst. Sci., 29, 1749–1758, https://doi.org/10.5194/hess-29-1749-2025,https://doi.org/10.5194/hess-29-1749-2025, 2025
Short summary
Projections of streamflow intermittence under climate change in European drying river networks
Louise Mimeau, Annika Künne, Alexandre Devers, Flora Branger, Sven Kralisch, Claire Lauvernet, Jean-Philippe Vidal, Núria Bonada, Zoltán Csabai, Heikki Mykrä, Petr Pařil, Luka Polović, and Thibault Datry
Hydrol. Earth Syst. Sci., 29, 1615–1636, https://doi.org/10.5194/hess-29-1615-2025,https://doi.org/10.5194/hess-29-1615-2025, 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. 
Download
Short summary
To determine if deep learning models are in general a viable alternative to traditional...
Share