Articles | Volume 25, issue 5
https://doi.org/10.5194/hess-25-2663-2021
© Author(s) 2021. 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-25-2663-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
20 May 2021
Research article |
| 20 May 2021
| 20 May 2021
A Bayesian approach to understanding the key factors influencing temporal variability in stream water quality – a case study in the Great Barrier Reef catchments
Department of Infrastructure Engineering, The University of Melbourne, Parkville, VIC 3010, Australia
Dongryeol Ryu
Department of Infrastructure Engineering, The University of Melbourne, Parkville, VIC 3010, Australia
J. Angus Webb
Department of Infrastructure Engineering, The University of Melbourne, Parkville, VIC 3010, Australia
Anna Lintern
Department of Infrastructure Engineering, The University of Melbourne, Parkville, VIC 3010, Australia
Department of Civil Engineering, Monash University, VIC 3800, Australia
Danlu Guo
Department of Infrastructure Engineering, The University of Melbourne, Parkville, VIC 3010, Australia
David Waters
Queensland Department of Resources, Toowoomba, QLD 4350, Australia
Andrew W. Western
Department of Infrastructure Engineering, The University of Melbourne, Parkville, VIC 3010, Australia
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Short summary
Riverine water quality can change markedly at one particular location. This study developed predictive models to represent the temporal variation in stream water quality across the Great Barrier Reef catchments, Australia. The model structures were informed by a data-driven approach, which is useful for identifying important factors determining temporal changes in water quality and, in turn, providing critical information for developing management strategies.
Riverine water quality can change markedly at one particular location. This study developed...
