Articles | Volume 24, issue 2
https://doi.org/10.5194/hess-24-827-2020
© Author(s) 2020. 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-24-827-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
24 Feb 2020
Research article |
| 24 Feb 2020
| 24 Feb 2020
A data-based predictive model for spatiotemporal variability in stream water quality
Department of Infrastructure Engineering, The University of
Melbourne, Parkville, VIC, Australia
Anna Lintern
Department of Infrastructure Engineering, The University of
Melbourne, Parkville, VIC, Australia
Department of Civil Engineering, Monash University, Clayton, VIC,
Australia
J. Angus Webb
Department of Infrastructure Engineering, The University of
Melbourne, Parkville, VIC, Australia
Dongryeol Ryu
Department of Infrastructure Engineering, The University of
Melbourne, Parkville, VIC, Australia
Ulrike Bende-Michl
Bureau of Meteorology, Parkes, ACT, Australia
Shuci Liu
Department of Infrastructure Engineering, The University of
Melbourne, Parkville, VIC, Australia
Andrew William Western
Department of Infrastructure Engineering, The University of
Melbourne, Parkville, VIC, Australia
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Cited
40 citations as recorded by crossref.
- The influence of climate on water chemistry states and dynamics in rivers across Australia A. Lintern et al.
- Australian water quality trends over two decades show deterioration in the Great Barrier Reef region and recovery in the Murray-Darling Basin D. Guo et al.
- Identification of point source emission in river pollution incidents based on Bayesian inference and genetic algorithm: Inverse modeling, sensitivity, and uncertainty analysis Y. Zhu et al.
- Technical note: Image processing for continuous river turbidity monitoring – full-scale tests and potential applications D. Miglino et al.
- Environmental implication identification on water quality variation and human health risk assessment in the middle and lower reaches of the Hanjiang River, China X. Nong et al.
- Temporal and spatial feature extraction using graph neural networks for multi-point water quality prediction in river network areas H. Wan et al.
- Full historic record true colour changes in Greater Sydney drinking water supply systems in Australia T. Ramanathan et al.
- Data-Driven System Dynamics Model for Simulating Water Quantity and Quality in Peri-Urban Streams G. Lemaire et al.
- Large-scale prediction of stream water quality using an interpretable deep learning approach H. Zheng et al.
- The impact of stormwater biofilter design and operational variables on nutrient removal - a statistical modelling approach K. Zhang et al.
- Synthesizing the impacts of baseflow contribution on concentration–discharge (C–Q) relationships across Australia using a Bayesian hierarchical model D. Guo et al.
- Seasonal variation in land cover estimates reveals sensitivities and opportunities for environmental models D. Myers et al.
- Temporal and spatial variation in water quality in the Yangtze Estuary from 2012 to 2018 L. Chong et al.
- Information synthesis to identify water quality issues and select applicable in-stream water quality model for the Awash River basin in Ethiopia: A perspective from developing countries E. Zinabu et al.
- Shifts in stream salt loads during and after prolonged droughts A. Lintern et al.
- Exploring key factors driving farm-level seasonal irrigation water usage with Bayesian hierarchical modelling Z. Gao et al.
- Innovative Water Quality and Ecology Monitoring Using Underwater Unmanned Vehicles: Field Applications, Challenges and Feedback from Water Managers R. de Lima et al.
- Developing Predictive Models Using Sonde Data To Estimate Fecal Contamination in Estuarine Waters J. Harrison et al.
- A Bayesian approach to understanding the key factors influencing temporal variability in stream water quality – a case study in the Great Barrier Reef catchments S. Liu et al.
- Unveiling the impact of the COVID-19 pandemic on water quality: a case study on Ain-Silan and Ain-Karma sources N. Mizane et al.
- A multi-model approach to assessing the impacts of catchment characteristics on spatial water quality in the Great Barrier Reef catchments S. Liu et al.
- Widespread deoxygenation in warming rivers W. Zhi et al.
- Predicting quantiles of water quality from catchment characteristics D. Guo et al.
- Deep learning for water quality W. Zhi et al.
- Machine learning and remote sensing-based hierarchical framework for assessing non-optically observable riverine water quality parameters K. Singh et al.
- Building an XGBoost model based on landscape metrics and meteorological data for nonpoint source pollution management in the Nakdong river watershed S. Hee Shim & J. Hyun Choi
- Hybrid process-based and deep learning for river nutrient prediction under limited monitoring data J. Tang et al.
- Detecting and explaining long‐term changes in river water quality in south‐eastern Australia Z. He et al.
- Dispersed Urban‐Stormwater Control Improved Stream Water Quality in a Catchment‐Scale Experiment C. Walsh et al.
- Nitrate concentrations predominantly driven by human, climate, and soil properties in US rivers K. Sadayappan et al.
- Research on a Prediction Model of Water Quality Parameters in a Marine Ranch Based on LSTM-BP H. Xu et al.
- Targeting critical zones: A review of heterogeneity identification methods and driving mechanisms of watershed phosphorus transport Q. Wu et al.
- Spatiotemporal prediction of water quality and ecological risk assessment in a river basin using T-GCN based on river network topology constraints L. Li
- Modeling Biological Oxygen Demand Load Capacity in a Data-Scarce Basin with Important Anthropogenic Interventions A. Zurita et al.
- Drivers of water quality in Afromontane-savanna rivers E. Wanderi et al.
- SWAT-SF: A flexible SWAT-based model for watershed-scale water and soil salinity modeling M. Maleki Tirabadi et al.
- An advanced approach for the precise prediction of water quality using a discrete hidden markov model D. Li et al.
- Spatiotemporal Dynamics and Drivers of Microbial Contaminants in Hurricane Florence Floodwaters E. Fidan et al.
- Explainable AI for Water Quality Monitoring: A Systematic Review of Transparency, Interpretability, and Trust I. Aderemi et al.
- Accurate and robust estimation of TDS, TOC, Chl-a and surface water temperature using Landsat-8, Sentinel-2, MODIS, and ASTER sensors A. Quevedo-Castro et al.
40 citations as recorded by crossref.
- The influence of climate on water chemistry states and dynamics in rivers across Australia A. Lintern et al.
- Australian water quality trends over two decades show deterioration in the Great Barrier Reef region and recovery in the Murray-Darling Basin D. Guo et al.
- Identification of point source emission in river pollution incidents based on Bayesian inference and genetic algorithm: Inverse modeling, sensitivity, and uncertainty analysis Y. Zhu et al.
- Technical note: Image processing for continuous river turbidity monitoring – full-scale tests and potential applications D. Miglino et al.
- Environmental implication identification on water quality variation and human health risk assessment in the middle and lower reaches of the Hanjiang River, China X. Nong et al.
- Temporal and spatial feature extraction using graph neural networks for multi-point water quality prediction in river network areas H. Wan et al.
- Full historic record true colour changes in Greater Sydney drinking water supply systems in Australia T. Ramanathan et al.
- Data-Driven System Dynamics Model for Simulating Water Quantity and Quality in Peri-Urban Streams G. Lemaire et al.
- Large-scale prediction of stream water quality using an interpretable deep learning approach H. Zheng et al.
- The impact of stormwater biofilter design and operational variables on nutrient removal - a statistical modelling approach K. Zhang et al.
- Synthesizing the impacts of baseflow contribution on concentration–discharge (C–Q) relationships across Australia using a Bayesian hierarchical model D. Guo et al.
- Seasonal variation in land cover estimates reveals sensitivities and opportunities for environmental models D. Myers et al.
- Temporal and spatial variation in water quality in the Yangtze Estuary from 2012 to 2018 L. Chong et al.
- Information synthesis to identify water quality issues and select applicable in-stream water quality model for the Awash River basin in Ethiopia: A perspective from developing countries E. Zinabu et al.
- Shifts in stream salt loads during and after prolonged droughts A. Lintern et al.
- Exploring key factors driving farm-level seasonal irrigation water usage with Bayesian hierarchical modelling Z. Gao et al.
- Innovative Water Quality and Ecology Monitoring Using Underwater Unmanned Vehicles: Field Applications, Challenges and Feedback from Water Managers R. de Lima et al.
- Developing Predictive Models Using Sonde Data To Estimate Fecal Contamination in Estuarine Waters J. Harrison et al.
- A Bayesian approach to understanding the key factors influencing temporal variability in stream water quality – a case study in the Great Barrier Reef catchments S. Liu et al.
- Unveiling the impact of the COVID-19 pandemic on water quality: a case study on Ain-Silan and Ain-Karma sources N. Mizane et al.
- A multi-model approach to assessing the impacts of catchment characteristics on spatial water quality in the Great Barrier Reef catchments S. Liu et al.
- Widespread deoxygenation in warming rivers W. Zhi et al.
- Predicting quantiles of water quality from catchment characteristics D. Guo et al.
- Deep learning for water quality W. Zhi et al.
- Machine learning and remote sensing-based hierarchical framework for assessing non-optically observable riverine water quality parameters K. Singh et al.
- Building an XGBoost model based on landscape metrics and meteorological data for nonpoint source pollution management in the Nakdong river watershed S. Hee Shim & J. Hyun Choi
- Hybrid process-based and deep learning for river nutrient prediction under limited monitoring data J. Tang et al.
- Detecting and explaining long‐term changes in river water quality in south‐eastern Australia Z. He et al.
- Dispersed Urban‐Stormwater Control Improved Stream Water Quality in a Catchment‐Scale Experiment C. Walsh et al.
- Nitrate concentrations predominantly driven by human, climate, and soil properties in US rivers K. Sadayappan et al.
- Research on a Prediction Model of Water Quality Parameters in a Marine Ranch Based on LSTM-BP H. Xu et al.
- Targeting critical zones: A review of heterogeneity identification methods and driving mechanisms of watershed phosphorus transport Q. Wu et al.
- Spatiotemporal prediction of water quality and ecological risk assessment in a river basin using T-GCN based on river network topology constraints L. Li
- Modeling Biological Oxygen Demand Load Capacity in a Data-Scarce Basin with Important Anthropogenic Interventions A. Zurita et al.
- Drivers of water quality in Afromontane-savanna rivers E. Wanderi et al.
- SWAT-SF: A flexible SWAT-based model for watershed-scale water and soil salinity modeling M. Maleki Tirabadi et al.
- An advanced approach for the precise prediction of water quality using a discrete hidden markov model D. Li et al.
- Spatiotemporal Dynamics and Drivers of Microbial Contaminants in Hurricane Florence Floodwaters E. Fidan et al.
- Explainable AI for Water Quality Monitoring: A Systematic Review of Transparency, Interpretability, and Trust I. Aderemi et al.
- Accurate and robust estimation of TDS, TOC, Chl-a and surface water temperature using Landsat-8, Sentinel-2, MODIS, and ASTER sensors A. Quevedo-Castro et al.
Saved (final revised paper)
Latest update: 11 May 2026
Short summary
This study developed predictive models to represent the spatial and temporal variation of stream water quality across Victoria, Australia. The model structures were informed by a data-driven approach, which identified the key controls of water quality variations from long-term records. These models are helpful to identify likely future changes in water quality and, in turn, provide critical information for developing management strategies to improve stream water quality.
This study developed predictive models to represent the spatial and temporal variation of stream...
