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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Cited
14 citations as recorded by crossref.
- Quantification of the effect of hydrological drivers on actual evapotranspiration using the Bayesian model averaging approach for various landscapes over Northeast Asia Y. Hao et al. 10.1016/j.jhydrol.2022.127543
- Explainable AI approach with original vegetation data classifies spatio-temporal nitrogen in flows from ungauged catchments to the Great Barrier Reef C. O’Sullivan et al. 10.1038/s41598-023-45259-0
- Divergent trends of ecosystem status and services in the Hexi Corridor H. Zhu et al. 10.3389/fenvs.2022.1008441
- Exploring key factors driving farm-level seasonal irrigation water usage with Bayesian hierarchical modelling Z. Gao et al. 10.1016/j.agwat.2024.108715
- Trade-Off and Synergy Relationships and Driving Factor Analysis of Ecosystem Services in the Hexi Region S. Xiao et al. 10.3390/rs16173147
- Synthesizing the impacts of baseflow contribution on concentration–discharge (<i>C</i>–<i>Q</i>) relationships across Australia using a Bayesian hierarchical model D. Guo et al. 10.5194/hess-26-1-2022
- Effects of detection limits on spatial modeling of water quality in lakes Z. Song et al. 10.1016/j.scitotenv.2022.161052
- Spatially adaptive machine learning models for predicting water quality in Hong Kong Q. Wang et al. 10.1016/j.jhydrol.2023.129649
- Pattern recognition describing spatio-temporal drivers of catchment classification for water quality C. O’Sullivan et al. 10.1016/j.scitotenv.2022.160240
- Improved export coefficient model for identification of watershed environmental risk areas M. Wang et al. 10.1007/s11356-022-24499-z
- Temporal and spatial variation in water quality in the Yangtze Estuary from 2012 to 2018 L. Chong et al. 10.1007/s11356-022-21122-z
- Sampling frequency optimization of the water quality monitoring network in São Paulo State (Brazil) towards adaptive monitoring in a developing country R. de Almeida et al. 10.1007/s11356-023-29998-1
- Controls on Spatial Variability in Mean Concentrations and Export Patterns of River Chemistry Across the Australian Continent S. Liu et al. 10.1029/2022WR032365
- Surface Water Monitoring Systems—The Importance of Integrating Information Sources for Sustainable Watershed Management Y. Sudriani et al. 10.1109/ACCESS.2023.3263802
14 citations as recorded by crossref.
- Quantification of the effect of hydrological drivers on actual evapotranspiration using the Bayesian model averaging approach for various landscapes over Northeast Asia Y. Hao et al. 10.1016/j.jhydrol.2022.127543
- Explainable AI approach with original vegetation data classifies spatio-temporal nitrogen in flows from ungauged catchments to the Great Barrier Reef C. O’Sullivan et al. 10.1038/s41598-023-45259-0
- Divergent trends of ecosystem status and services in the Hexi Corridor H. Zhu et al. 10.3389/fenvs.2022.1008441
- Exploring key factors driving farm-level seasonal irrigation water usage with Bayesian hierarchical modelling Z. Gao et al. 10.1016/j.agwat.2024.108715
- Trade-Off and Synergy Relationships and Driving Factor Analysis of Ecosystem Services in the Hexi Region S. Xiao et al. 10.3390/rs16173147
- Synthesizing the impacts of baseflow contribution on concentration–discharge (<i>C</i>–<i>Q</i>) relationships across Australia using a Bayesian hierarchical model D. Guo et al. 10.5194/hess-26-1-2022
- Effects of detection limits on spatial modeling of water quality in lakes Z. Song et al. 10.1016/j.scitotenv.2022.161052
- Spatially adaptive machine learning models for predicting water quality in Hong Kong Q. Wang et al. 10.1016/j.jhydrol.2023.129649
- Pattern recognition describing spatio-temporal drivers of catchment classification for water quality C. O’Sullivan et al. 10.1016/j.scitotenv.2022.160240
- Improved export coefficient model for identification of watershed environmental risk areas M. Wang et al. 10.1007/s11356-022-24499-z
- Temporal and spatial variation in water quality in the Yangtze Estuary from 2012 to 2018 L. Chong et al. 10.1007/s11356-022-21122-z
- Sampling frequency optimization of the water quality monitoring network in São Paulo State (Brazil) towards adaptive monitoring in a developing country R. de Almeida et al. 10.1007/s11356-023-29998-1
- Controls on Spatial Variability in Mean Concentrations and Export Patterns of River Chemistry Across the Australian Continent S. Liu et al. 10.1029/2022WR032365
- Surface Water Monitoring Systems—The Importance of Integrating Information Sources for Sustainable Watershed Management Y. Sudriani et al. 10.1109/ACCESS.2023.3263802
Latest update: 20 Nov 2024
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...
