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.
Research article
| 
06 Dec 2018
Research article |
| 06 Dec 2018
| 06 Dec 2018
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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- A Medium and Long-Term Runoff Forecast Method Based on Massive Meteorological Data and Machine Learning Algorithms Y. Li et al. 10.3390/w13091308
- Propagating reliable estimates of hydrological forecast uncertainty to many lead times J. Bennett et al. 10.1016/j.jhydrol.2021.126798
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- A crash-testing framework for predictive uncertainty assessment when forecasting high flows in an extrapolation context L. Berthet et al. 10.5194/hess-24-2017-2020
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- A Data Censoring Approach for Predictive Error Modeling of Flow in Ephemeral Rivers Q. Wang et al. 10.1029/2019WR026128
- A Novel Runoff Forecasting Model Based on the Decomposition-Integration-Prediction Framework Z. Xu et al. 10.3390/w13233390
- Enhanced daily streamflow forecasting in Northeastern Algeria: integrating hybrid machine learning with advanced wavelet transformation techniques N. Daif & A. Hebal 10.1007/s40808-024-02067-3
- Generation of Monthly Precipitation Climatologies for Costa Rica Using Irregular Rain-Gauge Observational Networks M. Mendez et al. 10.3390/w11010070
- Two-stage variational mode decomposition and support vector regression for streamflow forecasting G. Zuo et al. 10.5194/hess-24-5491-2020
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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
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- Reliability of Ensemble Climatological Forecasts Z. Huang et al. 10.1029/2023WR034942
- 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
- A new approach of coupled long-range forecasts for streamflow and groundwater level D. Robertson et al. 10.1016/j.jhydrol.2024.130837
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- Flexible forecast value metric suitable for a wide range of decisions: application using probabilistic subseasonal streamflow forecasts R. Laugesen et al. 10.5194/hess-27-873-2023
- Subselection of seasonal ensemble precipitation predictions for East Africa C. Heinrich‐Mertsching et al. 10.1002/qj.4525
- 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
- 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
- An error model for long-range ensemble forecasts of ephemeral rivers J. Bennett et al. 10.1016/j.advwatres.2021.103891
- A robust approach for calibrating a daily rainfall-runoff model to monthly streamflow data J. Lerat et al. 10.1016/j.jhydrol.2020.125129
- Exploring the role of the long short‐term memory model in improving multi‐step ahead reservoir inflow forecasting X. Luo et al. 10.1111/jfr3.12854
- Probabilistic prediction of the time to hard freeze using seasonal weather forecasts and survival time methods T. Roksvåg et al. 10.1002/qj.4403
- Method for Probabilistic Flood Forecasting Considering Rainfall and Model Parameter Uncertainties X. Jiang et al. 10.1061/(ASCE)HE.1943-5584.0001861
- Binary Grey Wolf Optimization-Regularized Extreme Learning Machine Wrapper Coupled with the Boruta Algorithm for Monthly Streamflow Forecasting J. Qu et al. 10.1007/s11269-021-02770-1
- The Fast and the Robust: Trade‐Offs Between Optimization Robustness and Cost in the Calibration of Environmental Models D. Kavetski et al. 10.1029/2017WR022051
31 citations as recorded by crossref.
- A comprehensive implementation of the log, Box-Cox and log-sinh transformations for skewed and censored precipitation data Z. Huang et al. 10.1016/j.jhydrol.2023.129347
- Postprocessing continental-scale, medium-range ensemble streamflow forecasts in South America using Ensemble Model Output Statistics and Ensemble Copula Coupling V. Siqueira et al. 10.1016/j.jhydrol.2021.126520
- A Medium and Long-Term Runoff Forecast Method Based on Massive Meteorological Data and Machine Learning Algorithms Y. Li et al. 10.3390/w13091308
- Propagating reliable estimates of hydrological forecast uncertainty to many lead times J. Bennett et al. 10.1016/j.jhydrol.2021.126798
- Seamless streamflow forecasting at daily to monthly scales: MuTHRE lets you have your cake and eat it too D. McInerney et al. 10.5194/hess-26-5669-2022
- A crash-testing framework for predictive uncertainty assessment when forecasting high flows in an extrapolation context L. Berthet et al. 10.5194/hess-24-2017-2020
- Hydrological post-processing based on approximate Bayesian computation (ABC) J. Romero-Cuellar et al. 10.1007/s00477-019-01694-y
- Toward Improved Probabilistic Predictions for Flood Forecasts Generated Using Deterministic Models X. Jiang et al. 10.1029/2019WR025477
- A Data Censoring Approach for Predictive Error Modeling of Flow in Ephemeral Rivers Q. Wang et al. 10.1029/2019WR026128
- A Novel Runoff Forecasting Model Based on the Decomposition-Integration-Prediction Framework Z. Xu et al. 10.3390/w13233390
- Enhanced daily streamflow forecasting in Northeastern Algeria: integrating hybrid machine learning with advanced wavelet transformation techniques N. Daif & A. Hebal 10.1007/s40808-024-02067-3
- Generation of Monthly Precipitation Climatologies for Costa Rica Using Irregular Rain-Gauge Observational Networks M. Mendez et al. 10.3390/w11010070
- Two-stage variational mode decomposition and support vector regression for streamflow forecasting G. Zuo et al. 10.5194/hess-24-5491-2020
- Seasonal Streamflow Forecast in the Tocantins River Basin, Brazil: An Evaluation of ECMWF-SEAS5 with Multiple Conceptual Hydrological Models L. Ávila et al. 10.3390/w15091695
- 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
- Reliability of Ensemble Climatological Forecasts Z. Huang et al. 10.1029/2023WR034942
- 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
- A new approach of coupled long-range forecasts for streamflow and groundwater level D. Robertson et al. 10.1016/j.jhydrol.2024.130837
- A multi-model evaluation of probabilistic streamflow predictions via residual error modelling J. Romero-Cuellar et al. 10.1016/j.jhydrol.2024.131152
- Flexible forecast value metric suitable for a wide range of decisions: application using probabilistic subseasonal streamflow forecasts R. Laugesen et al. 10.5194/hess-27-873-2023
- Subselection of seasonal ensemble precipitation predictions for East Africa C. Heinrich‐Mertsching et al. 10.1002/qj.4525
- 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
- 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
- An error model for long-range ensemble forecasts of ephemeral rivers J. Bennett et al. 10.1016/j.advwatres.2021.103891
- A robust approach for calibrating a daily rainfall-runoff model to monthly streamflow data J. Lerat et al. 10.1016/j.jhydrol.2020.125129
- Exploring the role of the long short‐term memory model in improving multi‐step ahead reservoir inflow forecasting X. Luo et al. 10.1111/jfr3.12854
- Probabilistic prediction of the time to hard freeze using seasonal weather forecasts and survival time methods T. Roksvåg et al. 10.1002/qj.4403
- Method for Probabilistic Flood Forecasting Considering Rainfall and Model Parameter Uncertainties X. Jiang et al. 10.1061/(ASCE)HE.1943-5584.0001861
2 citations as recorded by crossref.
- Binary Grey Wolf Optimization-Regularized Extreme Learning Machine Wrapper Coupled with the Boruta Algorithm for Monthly Streamflow Forecasting J. Qu et al. 10.1007/s11269-021-02770-1
- The Fast and the Robust: Trade‐Offs Between Optimization Robustness and Cost in the Calibration of Environmental Models D. Kavetski et al. 10.1029/2017WR022051
Latest update: 17 Jul 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...
