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.
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.
the Creative Commons Attribution 4.0 License.
Research article
| 
13 Mar 2024
Research article |
| 13 Mar 2024
| 13 Mar 2024
Deep learning for monthly rainfall–runoff modelling: a large-sample comparison with conceptual models across Australia
Stephanie R. Clark
CORRESPONDING AUTHOR
CSIRO, Environment, Sydney, NSW, Australia
Julien Lerat
CSIRO, Environment, Canberra, ACT, Australia
Jean-Michel Perraud
CSIRO, Environment, Canberra, ACT, Australia
Peter Fitch
CSIRO, Environment, Canberra, ACT, Australia
Viewed
Total article views: 3,260 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 30 May 2023)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 2,459 | 727 | 74 | 3,260 | 83 | 104 |
- HTML: 2,459
- PDF: 727
- XML: 74
- Total: 3,260
- BibTeX: 83
- EndNote: 104
Total article views: 2,149 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 13 Mar 2024)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 1,710 | 395 | 44 | 2,149 | 66 | 90 |
- HTML: 1,710
- PDF: 395
- XML: 44
- Total: 2,149
- BibTeX: 66
- EndNote: 90
Total article views: 1,111 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 30 May 2023)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 749 | 332 | 30 | 1,111 | 17 | 14 |
- HTML: 749
- PDF: 332
- XML: 30
- Total: 1,111
- BibTeX: 17
- EndNote: 14
Viewed (geographical distribution)
Total article views: 3,260 (including HTML, PDF, and XML)
Thereof 3,160 with geography defined
and 100 with unknown origin.
Total article views: 2,149 (including HTML, PDF, and XML)
Thereof 2,094 with geography defined
and 55 with unknown origin.
Total article views: 1,111 (including HTML, PDF, and XML)
Thereof 1,066 with geography defined
and 45 with unknown origin.
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
Cited
19 citations as recorded by crossref.
- Integration of Gaussian process regression and K means clustering for enhanced short term rainfall runoff modeling O. Kisi et al. 10.1038/s41598-025-91339-8
- Daily Runoff Prediction Method Based on Secondary Decomposition and the GTO-Informer-GRU Model H. Yu et al. 10.3390/w17182775
- Associations between deep learning runoff predictions and hydrogeological conditions in Australia S. Clark & J. Jaffrés 10.1016/j.jhydrol.2024.132569
- Investigation of the effect of climatic parameters in machine learning algorithms for streamflow predicting in Hamoon Helmand Catchment, Iran S. Vakili & S. Mousavi 10.1007/s12517-025-12243-z
- Toward improved deep learning-based regionalized streamflow modeling : Exploiting the power of basin similarity Y. Xu et al. 10.1016/j.envsoft.2025.106374
- Multi-decadal streamflow projections for catchments in Brazil based on CMIP6 multi-model simulations and neural network embeddings for linear regression models M. Scheuerer et al. 10.5194/hess-29-5099-2025
- Improving streamflow simulation through machine learning-powered data integration and its potential for forecasting in the Western U.S. Y. Yang et al. 10.5194/hess-29-5453-2025
- Runoff Simulation in Data-Scarce Alpine Regions: Comparative Analysis Based on LSTM and Physically Based Models J. Yue et al. 10.3390/w16152161
- A novel data cleaning and completion framework for NB-IoT water meters in district metered areas based on purification ratio coefficients J. Zhang et al. 10.2166/aqua.2025.035
- Assessing the effect of rating curve uncertainty in streamflow simulation on Kulfo watershed, Southern Ethiopia N. Bekele Mena et al. 10.2166/wcc.2024.645
- Water Inflow Forecasting Based on Visual MODFLOW and GS-SARIMA-LSTM Methods Z. Yang et al. 10.3390/w16192749
- Frequency-Domain Convolutional Network With Historical Data Fusion Module for Regional Streamflow Prediction Y. Zhang et al. 10.1109/TGRS.2025.3605332
- A novel framework for multi-step water level predicting by spatial–temporal deep learning models based on integrated physical models S. Zhang et al. 10.1016/j.jhydrol.2025.133683
- Advanced Framework for Predicting Rainfall-Runoff: Comparative Evaluation of AI Models for Enhanced Forecasting Accuracy H. Sanikhani et al. 10.1007/s11269-025-04106-9
- REAL-TIME FLOOD RUNOFF PREDICTION FOR A SMALL-TO-MEDIUM-SCALE URBAN RIVER WATERSHED USING A BIDIRECTIONAL LONG-SHORT-TERM MEMORY MODEL (BiLSTM) C. SUBRAMANIYAM et al. 10.2208/journalofjsce.24-27025
- Climate change impact assessment on a German lowland river using long short-term memory and conceptual hydrological models A. Ley et al. 10.1016/j.ejrh.2025.102426
- Spatially resolved rainfall streamflow modeling in central Europe M. Vischer et al. 10.5194/hess-29-5233-2025
- Assessment of monthly runoff simulations based on a physics-informed machine learning framework: The effect of intermediate variables in its construction C. Deng et al. 10.1016/j.jenvman.2024.121299
- Evaluation of LSTM vs. conceptual models for hourly rainfall runoff simulations with varied training period lengths M. Fathi et al. 10.1038/s41598-025-96577-4
18 citations as recorded by crossref.
- Integration of Gaussian process regression and K means clustering for enhanced short term rainfall runoff modeling O. Kisi et al. 10.1038/s41598-025-91339-8
- Daily Runoff Prediction Method Based on Secondary Decomposition and the GTO-Informer-GRU Model H. Yu et al. 10.3390/w17182775
- Associations between deep learning runoff predictions and hydrogeological conditions in Australia S. Clark & J. Jaffrés 10.1016/j.jhydrol.2024.132569
- Investigation of the effect of climatic parameters in machine learning algorithms for streamflow predicting in Hamoon Helmand Catchment, Iran S. Vakili & S. Mousavi 10.1007/s12517-025-12243-z
- Toward improved deep learning-based regionalized streamflow modeling : Exploiting the power of basin similarity Y. Xu et al. 10.1016/j.envsoft.2025.106374
- Multi-decadal streamflow projections for catchments in Brazil based on CMIP6 multi-model simulations and neural network embeddings for linear regression models M. Scheuerer et al. 10.5194/hess-29-5099-2025
- Improving streamflow simulation through machine learning-powered data integration and its potential for forecasting in the Western U.S. Y. Yang et al. 10.5194/hess-29-5453-2025
- Runoff Simulation in Data-Scarce Alpine Regions: Comparative Analysis Based on LSTM and Physically Based Models J. Yue et al. 10.3390/w16152161
- A novel data cleaning and completion framework for NB-IoT water meters in district metered areas based on purification ratio coefficients J. Zhang et al. 10.2166/aqua.2025.035
- Assessing the effect of rating curve uncertainty in streamflow simulation on Kulfo watershed, Southern Ethiopia N. Bekele Mena et al. 10.2166/wcc.2024.645
- Water Inflow Forecasting Based on Visual MODFLOW and GS-SARIMA-LSTM Methods Z. Yang et al. 10.3390/w16192749
- Frequency-Domain Convolutional Network With Historical Data Fusion Module for Regional Streamflow Prediction Y. Zhang et al. 10.1109/TGRS.2025.3605332
- A novel framework for multi-step water level predicting by spatial–temporal deep learning models based on integrated physical models S. Zhang et al. 10.1016/j.jhydrol.2025.133683
- Advanced Framework for Predicting Rainfall-Runoff: Comparative Evaluation of AI Models for Enhanced Forecasting Accuracy H. Sanikhani et al. 10.1007/s11269-025-04106-9
- REAL-TIME FLOOD RUNOFF PREDICTION FOR A SMALL-TO-MEDIUM-SCALE URBAN RIVER WATERSHED USING A BIDIRECTIONAL LONG-SHORT-TERM MEMORY MODEL (BiLSTM) C. SUBRAMANIYAM et al. 10.2208/journalofjsce.24-27025
- Climate change impact assessment on a German lowland river using long short-term memory and conceptual hydrological models A. Ley et al. 10.1016/j.ejrh.2025.102426
- Spatially resolved rainfall streamflow modeling in central Europe M. Vischer et al. 10.5194/hess-29-5233-2025
- Assessment of monthly runoff simulations based on a physics-informed machine learning framework: The effect of intermediate variables in its construction C. Deng et al. 10.1016/j.jenvman.2024.121299
Latest update: 26 Oct 2025
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.
To determine if deep learning models are in general a viable alternative to traditional...
