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.
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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Kevin K. W. Cheung, Fei Ji, Nidhi Nishant, Jin Teng, James Bennett, and De Li Liu
Hydrol. Earth Syst. Sci., 29, 3527–3543, https://doi.org/10.5194/hess-29-3527-2025, https://doi.org/10.5194/hess-29-3527-2025, 2025
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This study evaluates two reanalysis datasets, which are critical in climate, weather research, and water resources analysis, for the Australian region in terms of simulating daily mean precipitation and six other selected precipitation extremes. While spatial patterns of mean precipitation are well reproduced, substantial biases exist in precipitation variability, trends, and extremes. Caution in applying these datasets is thus advised in terms of the latter aspects.
Christopher A. Pickett-Heaps, Patrick Sunter, Wendy Sharples, Michael Pegios, Catherine Wilson, Alex Cornish, Richard Laugesen, and Elisabetta Carrara
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This study evaluates seasonal forecast skill of river discharge from (1) a gridded hydrological model coupled with statistical post-processing and (2) a locally calibrated statistical hydrological model dependant on recent hydrological observations. Results indicate a similar level of forecast skill. The statistical post-processor is not dependant on recent observations to maintain forecast skill, a finding that will have a positive impact on operational hydrological forecasting.
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
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Predicting river flow accurately is crucial for managing water resources, especially in a changing climate. This study used deep learning to improve streamflow predictions across Australia. By either enhancing existing models or working independently with climate data, the deep learning approaches provided more reliable results than traditional methods. These findings can help water managers better plan for floods, droughts, and long-term water availability.
Arash Aghakhani, David E. Robertson, and Valentijn R. N. Pauwels
EGUsphere, https://doi.org/10.5194/egusphere-2025-553, https://doi.org/10.5194/egusphere-2025-553, 2025
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Australia's shifting climate, with recurring droughts and wet periods, makes streamflow prediction challenging. This study combines GR4J model with machine learning to improve daily streamflow forecasts in Western Victoria. By identifying key factors affecting river flow, it offers valuable insights for water management. The findings show that machine learning can reveal limitations in traditional models, leading to more accurate predictions in drought-prone regions.
Gnanathikkam Emmanuel Amirthanathan, Mohammed Abdul Bari, Fitsum Markos Woldemeskel, Narendra Kumar Tuteja, and Paul Martinus Feikema
Hydrol. Earth Syst. Sci., 27, 229–254, https://doi.org/10.5194/hess-27-229-2023, https://doi.org/10.5194/hess-27-229-2023, 2023
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We used statistical tests to detect annual and seasonal streamflow trends and step changes across Australia. The Murray–Darling Basin and other rivers in the southern and north-eastern areas showed decreasing trends. Only rivers in the Timor Sea region in northern Australia showed significant increasing trends. Our results assist with infrastructure planning and management of water resources. This study was undertaken by the Bureau of Meteorology with its responsibility under the Water Act 2007.
David McInerney, Mark Thyer, Dmitri Kavetski, Richard Laugesen, Fitsum Woldemeskel, Narendra Tuteja, and George Kuczera
Hydrol. Earth Syst. Sci., 26, 5669–5683, https://doi.org/10.5194/hess-26-5669-2022, https://doi.org/10.5194/hess-26-5669-2022, 2022
Short summary
Short summary
Streamflow forecasts a day to a month ahead are highly valuable for water resources management. Current practice often develops forecasts for specific lead times and aggregation timescales. In contrast, a single, seamless forecast can serve multiple lead times/timescales. This study shows seamless forecasts can match the performance of forecasts developed specifically at the monthly scale, while maintaining quality at other lead times. Hence, users need not sacrifice capability for performance.
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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...