Articles | Volume 22, issue 1
https://doi.org/10.5194/hess-22-871-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/hess-22-871-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
01 Feb 2018
Research article |
| 01 Feb 2018
| 01 Feb 2018
State updating and calibration period selection to improve dynamic monthly streamflow forecasts for an environmental flow management application
Matthew S. Gibbs
CORRESPONDING AUTHOR
School of Civil, Environmental and Mining Engineering, The University
of Adelaide, North Terrace, Adelaide, South Australia, 5005, Australia
Department of Environment, Water and Natural Resources, Government of
South Australia, P.O. Box 1047, Adelaide, 5000, Australia
David McInerney
School of Civil, Environmental and Mining Engineering, The University
of Adelaide, North Terrace, Adelaide, South Australia, 5005, Australia
Greer Humphrey
School of Civil, Environmental and Mining Engineering, The University
of Adelaide, North Terrace, Adelaide, South Australia, 5005, Australia
Mark A. Thyer
School of Civil, Environmental and Mining Engineering, The University
of Adelaide, North Terrace, Adelaide, South Australia, 5005, Australia
Holger R. Maier
School of Civil, Environmental and Mining Engineering, The University
of Adelaide, North Terrace, Adelaide, South Australia, 5005, Australia
Graeme C. Dandy
School of Civil, Environmental and Mining Engineering, The University
of Adelaide, North Terrace, Adelaide, South Australia, 5005, Australia
Dmitri Kavetski
School of Civil, Environmental and Mining Engineering, The University
of Adelaide, North Terrace, Adelaide, South Australia, 5005, Australia
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- 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
- Time-varying network-based approach for capturing hydrological extremes under climate change with application on drought R. Dutta & R. Maity 10.1016/j.jhydrol.2021.126958
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- On how data are partitioned in model development and evaluation: Confronting the elephant in the room to enhance model generalization H. Maier et al. 10.1016/j.envsoft.2023.105779
- Achieving Robust and Transferable Performance for Conservation‐Based Models of Dynamical Physical Systems F. Zheng et al. 10.1029/2021WR031818
- A Stacking Ensemble Model of Various Machine Learning Models for Daily Runoff Forecasting M. Lu et al. 10.3390/w15071265
- Improved understanding of calibration efficiency, difficulty and parameter uniqueness of conceptual rainfall runoff models using fitness landscape metrics S. Zhu et al. 10.1016/j.jhydrol.2024.131586
- On Lack of Robustness in Hydrological Model Development Due to Absence of Guidelines for Selecting Calibration and Evaluation Data: Demonstration for Data‐Driven Models F. Zheng et al. 10.1002/2017WR021470
- Comparison of the alternative models SOURCE and SWAT for predicting catchment streamflow, sediment and nutrient loads under the effect of land use changes H. Nguyen et al. 10.1016/j.scitotenv.2019.01.286
- AI-based techniques for multi-step streamflow forecasts: application for multi-objective reservoir operation optimization and performance assessment Y. Guo et al. 10.5194/hess-25-5951-2021
- Dynamic runoff simulation in a changing environment: A data stream approach Q. Yang et al. 10.1016/j.envsoft.2018.11.007
- Inclusion of Ecological Water Requirements in Optimization of Water Resource Allocation Under Changing Climatic Conditions W. Yue et al. 10.1007/s11269-021-03039-3
- Conditioning ensemble streamflow prediction with the North Atlantic Oscillation improves skill at longer lead times S. Donegan et al. 10.5194/hess-25-4159-2021
- Operational Seasonal Water Supply and Water Level Forecasting for the Laurentian Great Lakes L. Fry et al. 10.1061/(ASCE)WR.1943-5452.0001214
- TALKS: A systematic framework for resolving model-data discrepancies M. Vilas et al. 10.1016/j.envsoft.2023.105668
- Temporal and spectral governing dynamics of Australian hydrological streamflow time series N. James & H. Bondell 10.1016/j.jocs.2022.101767
- The robustness of conceptual rainfall-runoff modelling under climate variability – A review H. Ji et al. 10.1016/j.jhydrol.2023.129666
- Benefits of Explicit Treatment of Zero Flows in Probabilistic Hydrological Modeling of Ephemeral Catchments D. McInerney et al. 10.1029/2018WR024148
- Toward Improved Probabilistic Predictions for Flood Forecasts Generated Using Deterministic Models X. Jiang et al. 10.1029/2019WR025477
- Temporal Networks‐Based Approach for Nonstationary Hydroclimatic Modeling and its Demonstration With Streamflow Prediction R. Dutta & R. Maity 10.1029/2020WR027086
- Jointly Calibrating Hydrologic Model Parameters and State Adjustments S. Kim et al. 10.1029/2020WR028499
- On the Robustness of Conceptual Rainfall‐Runoff Models to Calibration and Evaluation Data Set Splits Selection: A Large Sample Investigation D. Guo et al. 10.1029/2019WR026752
- Multi‐temporal Hydrological Residual Error Modeling for Seamless Subseasonal Streamflow Forecasting D. McInerney et al. 10.1029/2019WR026979
- Multi-driver ensemble to evaluate the water utility business interruption cost induced by hydrological drought risk scenarios in Brazil D. Guzmán et al. 10.1080/1573062X.2022.2058564
- Evaluating post-processing approaches for monthly and seasonal streamflow forecasts F. Woldemeskel et al. 10.5194/hess-22-6257-2018
- Improved data splitting methods for data-driven hydrological model development based on a large number of catchment samples J. Chen et al. 10.1016/j.jhydrol.2022.128340
27 citations as recorded by crossref.
- Framework for developing hybrid process-driven, artificial neural network and regression models for salinity prediction in river systems J. Hunter et al. 10.5194/hess-22-2987-2018
- A modelling framework and R-package for evaluating system performance under hydroclimate variability and change B. Bennett et al. 10.1016/j.envsoft.2021.104999
- 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
- Time-varying network-based approach for capturing hydrological extremes under climate change with application on drought R. Dutta & R. Maity 10.1016/j.jhydrol.2021.126958
- Integration and Evaluation of Forecast-Informed Multiobjective Reservoir Operations G. Yang et al. 10.1061/(ASCE)WR.1943-5452.0001229
- On how data are partitioned in model development and evaluation: Confronting the elephant in the room to enhance model generalization H. Maier et al. 10.1016/j.envsoft.2023.105779
- Achieving Robust and Transferable Performance for Conservation‐Based Models of Dynamical Physical Systems F. Zheng et al. 10.1029/2021WR031818
- A Stacking Ensemble Model of Various Machine Learning Models for Daily Runoff Forecasting M. Lu et al. 10.3390/w15071265
- Improved understanding of calibration efficiency, difficulty and parameter uniqueness of conceptual rainfall runoff models using fitness landscape metrics S. Zhu et al. 10.1016/j.jhydrol.2024.131586
- On Lack of Robustness in Hydrological Model Development Due to Absence of Guidelines for Selecting Calibration and Evaluation Data: Demonstration for Data‐Driven Models F. Zheng et al. 10.1002/2017WR021470
- Comparison of the alternative models SOURCE and SWAT for predicting catchment streamflow, sediment and nutrient loads under the effect of land use changes H. Nguyen et al. 10.1016/j.scitotenv.2019.01.286
- AI-based techniques for multi-step streamflow forecasts: application for multi-objective reservoir operation optimization and performance assessment Y. Guo et al. 10.5194/hess-25-5951-2021
- Dynamic runoff simulation in a changing environment: A data stream approach Q. Yang et al. 10.1016/j.envsoft.2018.11.007
- Inclusion of Ecological Water Requirements in Optimization of Water Resource Allocation Under Changing Climatic Conditions W. Yue et al. 10.1007/s11269-021-03039-3
- Conditioning ensemble streamflow prediction with the North Atlantic Oscillation improves skill at longer lead times S. Donegan et al. 10.5194/hess-25-4159-2021
- Operational Seasonal Water Supply and Water Level Forecasting for the Laurentian Great Lakes L. Fry et al. 10.1061/(ASCE)WR.1943-5452.0001214
- TALKS: A systematic framework for resolving model-data discrepancies M. Vilas et al. 10.1016/j.envsoft.2023.105668
- Temporal and spectral governing dynamics of Australian hydrological streamflow time series N. James & H. Bondell 10.1016/j.jocs.2022.101767
- The robustness of conceptual rainfall-runoff modelling under climate variability – A review H. Ji et al. 10.1016/j.jhydrol.2023.129666
- Benefits of Explicit Treatment of Zero Flows in Probabilistic Hydrological Modeling of Ephemeral Catchments D. McInerney et al. 10.1029/2018WR024148
- Toward Improved Probabilistic Predictions for Flood Forecasts Generated Using Deterministic Models X. Jiang et al. 10.1029/2019WR025477
- Temporal Networks‐Based Approach for Nonstationary Hydroclimatic Modeling and its Demonstration With Streamflow Prediction R. Dutta & R. Maity 10.1029/2020WR027086
- Jointly Calibrating Hydrologic Model Parameters and State Adjustments S. Kim et al. 10.1029/2020WR028499
- On the Robustness of Conceptual Rainfall‐Runoff Models to Calibration and Evaluation Data Set Splits Selection: A Large Sample Investigation D. Guo et al. 10.1029/2019WR026752
- Multi‐temporal Hydrological Residual Error Modeling for Seamless Subseasonal Streamflow Forecasting D. McInerney et al. 10.1029/2019WR026979
- Multi-driver ensemble to evaluate the water utility business interruption cost induced by hydrological drought risk scenarios in Brazil D. Guzmán et al. 10.1080/1573062X.2022.2058564
- Evaluating post-processing approaches for monthly and seasonal streamflow forecasts F. Woldemeskel et al. 10.5194/hess-22-6257-2018
Discussed (final revised paper)
Latest update: 17 Jul 2024
Short summary
This work developed models to predict how much water will be available in the next month to maximise environmental and social outcomes in southern Australia. Initialising the models with observed streamflow data, instead of warmed up by rainfall data, improved the results, even at a monthly lead time, making sure only data representative of the forecast period to develop the models were also important. If this step was ignored, and instead all data were used, poor predictions could be produced.
This work developed models to predict how much water will be available in the next month to...
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