Articles | Volume 22, issue 12
https://doi.org/10.5194/hess-22-6257-2018
© Author(s) 2018. 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-22-6257-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Evaluating post-processing approaches for monthly and seasonal streamflow forecasts
Fitsum Woldemeskel
CORRESPONDING AUTHOR
Bureau of Meteorology, VIC, Melbourne, Australia
David McInerney
School of Civil, Environmental and Mining Engineering, University
of Adelaide, SA, Australia
Julien Lerat
Bureau
of Meteorology, ACT, Canberra, Australia
Mark Thyer
School of Civil, Environmental and Mining Engineering, University
of Adelaide, SA, Australia
Dmitri Kavetski
School of Civil, Environmental and Mining Engineering, University
of Adelaide, SA, Australia
School
of Engineering, University of Newcastle, Callaghan, NSW, Australia
Daehyok Shin
Bureau of Meteorology, VIC, Melbourne, Australia
Narendra Tuteja
Bureau
of Meteorology, ACT, Canberra, Australia
George Kuczera
School
of Engineering, University of Newcastle, Callaghan, NSW, Australia
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- Towards an Extension of the Model Conditional Processor: Predictive Uncertainty Quantification of Monthly Streamflow via Gaussian Mixture Models and Clusters J. Romero-Cuellar et al. 10.3390/w14081261
- Development of a national 7-day ensemble streamflow forecasting service for Australia H. Hapuarachchi et al. 10.5194/hess-26-4801-2022
- Multi‐temporal Hydrological Residual Error Modeling for Seamless Subseasonal Streamflow Forecasting D. McInerney et al. 10.1029/2019WR026979
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- Estimating the added value of GRACE total water storage and uncertainty quantification in seasonal streamflow forecasting L. Liu et al. 10.1080/02626667.2021.1998510
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- Towards robust seasonal streamflow forecasts in mountainous catchments: impact of calibration metric selection in hydrological modeling D. Araya et al. 10.5194/hess-27-4385-2023
- Improving medium-range streamflow forecasts over South Korea with a dual-encoder transformer model D. Lee & K. Ahn 10.1016/j.jenvman.2024.122114
- A Bayesian Hierarchical Framework for Postprocessing Daily Streamflow Simulations across a River Network Á. Ossandón et al. 10.1175/JHM-D-21-0167.1
- How to assess climate change impact models: uncertainty analysis of streamflow statistics via approximate Bayesian computation (ABC) J. Romero-Cuellar & F. Francés 10.1080/02626667.2023.2231437
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- A robust approach for calibrating a daily rainfall-runoff model to monthly streamflow data J. Lerat et al. 10.1016/j.jhydrol.2020.125129
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- Probabilistic prediction of the time to hard freeze using seasonal weather forecasts and survival time methods T. Roksvåg et al. 10.1002/qj.4403
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Latest update: 22 Nov 2024
Short summary
This paper evaluates several schemes for post-processing monthly and seasonal streamflow forecasts using the Australian Bureau of Meteorology's streamflow forecasting system. Through evaluation across 300 catchments, the best-performing scheme has been identified, which is found to substantially improve important aspects of the forecast quality. This finding is significant because the improved forecasts help water managers and users of the service to make better-informed decisions.
This paper evaluates several schemes for post-processing monthly and seasonal streamflow...