Articles | Volume 26, issue 18
https://doi.org/10.5194/hess-26-4801-2022
© Author(s) 2022. 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-26-4801-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
29 Sep 2022
Research article |
| 29 Sep 2022
| 29 Sep 2022
Development of a national 7-day ensemble streamflow forecasting service for Australia
Hapu Arachchige Prasantha Hapuarachchi
CORRESPONDING AUTHOR
Bureau of Meteorology, 700 Collins Street, Docklands, VIC 3008,
Australia
Mohammed Abdul Bari
Bureau of Meteorology, 1 Ord Street, West Perth, WA 6005, Australia
Aynul Kabir
Bureau of Meteorology, 700 Collins Street, Docklands, VIC 3008,
Australia
Mohammad Mahadi Hasan
Bureau of Meteorology, The Treasury Building, Parkes Place West,
Canberra, ACT 2600, Australia
Fitsum Markos Woldemeskel
Bureau of Meteorology, 700 Collins Street, Docklands, VIC 3008,
Australia
Nilantha Gamage
Bureau of Meteorology, 700 Collins Street, Docklands, VIC 3008,
Australia
Patrick Daniel Sunter
Bureau of Meteorology, 700 Collins Street, Docklands, VIC 3008,
Australia
Xiaoyong Sophie Zhang
Bureau of Meteorology, 700 Collins Street, Docklands, VIC 3008,
Australia
David Ewen Robertson
Commonwealth Scientific and Industrial Research Organization, Research
Way, Clayton, VIC 3168, Australia
James Clement Bennett
Commonwealth Scientific and Industrial Research Organization, Research
Way, Clayton, VIC 3168, Australia
Paul Martinus Feikema
Bureau of Meteorology, 700 Collins Street, Docklands, VIC 3008,
Australia
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Cited
12 citations as recorded by crossref.
- A review of hybrid deep learning applications for streamflow forecasting K. Ng et al. 10.1016/j.jhydrol.2023.130141
- Changes in Magnitude and Shifts in Timing of Australian Flood Peaks M. Bari et al. 10.3390/w15203665
- Hybrid multi-model ensemble learning for reconstructing gridded runoff of Europe for 500 years U. Singh et al. 10.1016/j.inffus.2023.101807
- Monthly streamflow forecasting by machine learning methods using dynamic weather prediction model outputs over Iran M. Akbarian et al. 10.1016/j.jhydrol.2023.129480
- Hybrid forecasting: blending climate predictions with AI models L. Slater et al. 10.5194/hess-27-1865-2023
- Comprehensive Overview of Flood Modeling Approaches: A Review of Recent Advances V. Kumar et al. 10.3390/hydrology10070141
- Advancing Medium-Range Streamflow Forecasting for Large Hydropower Reservoirs in Brazil by Means of Continental-Scale Hydrological Modeling A. Kolling Neto et al. 10.3390/w15091693
- Simulation of Gauged and Ungauged Streamflow of Coastal Catchments across Australia M. Bari et al. 10.3390/w16040527
- Simplified Approach to Mixed-Integer Chance-Constrained Optimization with Ensemble Streamflow Forecasts for Risk-Based Dam Operation T. Ng et al. 10.1061/JWRMD5.WRENG-5885
- Stream flow prediction using TIGGE ensemble precipitation forecast data for Sabarmati river basin A. Patel & S. Yadav 10.2166/ws.2022.362
- Using Deep Learning Algorithms for Intermittent Streamflow Prediction in the Headwaters of the Colorado River, Texas F. Forghanparast & G. Mohammadi 10.3390/w14192972
- Predicting Daily Streamflow in a Cold Climate Using a Novel Data Mining Technique: Radial M5 Model Tree O. Kisi et al. 10.3390/w14091449
10 citations as recorded by crossref.
- A review of hybrid deep learning applications for streamflow forecasting K. Ng et al. 10.1016/j.jhydrol.2023.130141
- Changes in Magnitude and Shifts in Timing of Australian Flood Peaks M. Bari et al. 10.3390/w15203665
- Hybrid multi-model ensemble learning for reconstructing gridded runoff of Europe for 500 years U. Singh et al. 10.1016/j.inffus.2023.101807
- Monthly streamflow forecasting by machine learning methods using dynamic weather prediction model outputs over Iran M. Akbarian et al. 10.1016/j.jhydrol.2023.129480
- Hybrid forecasting: blending climate predictions with AI models L. Slater et al. 10.5194/hess-27-1865-2023
- Comprehensive Overview of Flood Modeling Approaches: A Review of Recent Advances V. Kumar et al. 10.3390/hydrology10070141
- Advancing Medium-Range Streamflow Forecasting for Large Hydropower Reservoirs in Brazil by Means of Continental-Scale Hydrological Modeling A. Kolling Neto et al. 10.3390/w15091693
- Simulation of Gauged and Ungauged Streamflow of Coastal Catchments across Australia M. Bari et al. 10.3390/w16040527
- Simplified Approach to Mixed-Integer Chance-Constrained Optimization with Ensemble Streamflow Forecasts for Risk-Based Dam Operation T. Ng et al. 10.1061/JWRMD5.WRENG-5885
- Stream flow prediction using TIGGE ensemble precipitation forecast data for Sabarmati river basin A. Patel & S. Yadav 10.2166/ws.2022.362
2 citations as recorded by crossref.
- Using Deep Learning Algorithms for Intermittent Streamflow Prediction in the Headwaters of the Colorado River, Texas F. Forghanparast & G. Mohammadi 10.3390/w14192972
- Predicting Daily Streamflow in a Cold Climate Using a Novel Data Mining Technique: Radial M5 Model Tree O. Kisi et al. 10.3390/w14091449
Latest update: 24 Apr 2024
Short summary
Methodology for developing an operational 7-day ensemble streamflow forecasting service for Australia is presented. The methodology is tested for 100 catchments to learn the characteristics of different NWP rainfall forecasts, the effect of post-processing, and the optimal ensemble size and bootstrapping parameters. Forecasts are generated using NWP rainfall products post-processed by the CHyPP model, the GR4H hydrologic model, and the ERRIS streamflow post-processor inbuilt in the SWIFT package
Methodology for developing an operational 7-day ensemble streamflow forecasting service for...
