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
28 citations as recorded by crossref.
- The influence of climate on water chemistry states and dynamics in rivers across Australia A. Lintern et al. 10.1002/hyp.14423
- 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. 10.1016/j.envpol.2021.117497
- 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. 10.1080/10807039.2024.2379046
- Data-Driven System Dynamics Model for Simulating Water Quantity and Quality in Peri-Urban Streams G. Lemaire et al. 10.3390/w13213002
- Large-scale prediction of stream water quality using an interpretable deep learning approach H. Zheng et al. 10.1016/j.jenvman.2023.117309
- The impact of stormwater biofilter design and operational variables on nutrient removal - a statistical modelling approach K. Zhang et al. 10.1016/j.watres.2020.116486
- 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
- Seasonal variation in land cover estimates reveals sensitivities and opportunities for environmental models D. Myers et al. 10.5194/hess-28-5295-2024
- 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
- 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. 10.1016/j.sciaf.2024.e02063
- Shifts in stream salt loads during and after prolonged droughts A. Lintern et al. 10.1002/hyp.14901
- Exploring key factors driving farm-level seasonal irrigation water usage with Bayesian hierarchical modelling Z. Gao et al. 10.1016/j.agwat.2024.108715
- Innovative Water Quality and Ecology Monitoring Using Underwater Unmanned Vehicles: Field Applications, Challenges and Feedback from Water Managers R. de Lima et al. 10.3390/w12041196
- 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. 10.5194/hess-25-2663-2021
- 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. 10.1016/j.envpol.2021.117337
- Widespread deoxygenation in warming rivers W. Zhi et al. 10.1038/s41558-023-01793-3
- Predicting quantiles of water quality from catchment characteristics D. Guo et al. 10.1002/hyp.13996
- Deep learning for water quality W. Zhi et al. 10.1038/s44221-024-00202-z
- 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 10.1016/j.ecolind.2024.112156
- Detecting and explaining long‐term changes in river water quality in south‐eastern Australia Z. He et al. 10.1002/hyp.14741
- Dispersed Urban‐Stormwater Control Improved Stream Water Quality in a Catchment‐Scale Experiment C. Walsh et al. 10.1029/2022WR032041
- Nitrate concentrations predominantly driven by human, climate, and soil properties in US rivers K. Sadayappan et al. 10.1016/j.watres.2022.119295
- Research on a Prediction Model of Water Quality Parameters in a Marine Ranch Based on LSTM-BP H. Xu et al. 10.3390/w15152760
- Modeling Biological Oxygen Demand Load Capacity in a Data-Scarce Basin with Important Anthropogenic Interventions A. Zurita et al. 10.3390/w13172379
- Drivers of water quality in Afromontane-savanna rivers E. Wanderi et al. 10.3389/fenvs.2022.972153
- SWAT-SF: A flexible SWAT-based model for watershed-scale water and soil salinity modeling M. Maleki Tirabadi et al. 10.1016/j.jconhyd.2021.103893
- An advanced approach for the precise prediction of water quality using a discrete hidden markov model D. Li et al. 10.1016/j.jhydrol.2022.127659
- Research progress in water quality prediction based on deep learning technology: a review W. Li et al. 10.1007/s11356-024-33058-7
27 citations as recorded by crossref.
- The influence of climate on water chemistry states and dynamics in rivers across Australia A. Lintern et al. 10.1002/hyp.14423
- 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. 10.1016/j.envpol.2021.117497
- 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. 10.1080/10807039.2024.2379046
- Data-Driven System Dynamics Model for Simulating Water Quantity and Quality in Peri-Urban Streams G. Lemaire et al. 10.3390/w13213002
- Large-scale prediction of stream water quality using an interpretable deep learning approach H. Zheng et al. 10.1016/j.jenvman.2023.117309
- The impact of stormwater biofilter design and operational variables on nutrient removal - a statistical modelling approach K. Zhang et al. 10.1016/j.watres.2020.116486
- 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
- Seasonal variation in land cover estimates reveals sensitivities and opportunities for environmental models D. Myers et al. 10.5194/hess-28-5295-2024
- 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
- 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. 10.1016/j.sciaf.2024.e02063
- Shifts in stream salt loads during and after prolonged droughts A. Lintern et al. 10.1002/hyp.14901
- Exploring key factors driving farm-level seasonal irrigation water usage with Bayesian hierarchical modelling Z. Gao et al. 10.1016/j.agwat.2024.108715
- Innovative Water Quality and Ecology Monitoring Using Underwater Unmanned Vehicles: Field Applications, Challenges and Feedback from Water Managers R. de Lima et al. 10.3390/w12041196
- 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. 10.5194/hess-25-2663-2021
- 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. 10.1016/j.envpol.2021.117337
- Widespread deoxygenation in warming rivers W. Zhi et al. 10.1038/s41558-023-01793-3
- Predicting quantiles of water quality from catchment characteristics D. Guo et al. 10.1002/hyp.13996
- Deep learning for water quality W. Zhi et al. 10.1038/s44221-024-00202-z
- 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 10.1016/j.ecolind.2024.112156
- Detecting and explaining long‐term changes in river water quality in south‐eastern Australia Z. He et al. 10.1002/hyp.14741
- Dispersed Urban‐Stormwater Control Improved Stream Water Quality in a Catchment‐Scale Experiment C. Walsh et al. 10.1029/2022WR032041
- Nitrate concentrations predominantly driven by human, climate, and soil properties in US rivers K. Sadayappan et al. 10.1016/j.watres.2022.119295
- Research on a Prediction Model of Water Quality Parameters in a Marine Ranch Based on LSTM-BP H. Xu et al. 10.3390/w15152760
- Modeling Biological Oxygen Demand Load Capacity in a Data-Scarce Basin with Important Anthropogenic Interventions A. Zurita et al. 10.3390/w13172379
- Drivers of water quality in Afromontane-savanna rivers E. Wanderi et al. 10.3389/fenvs.2022.972153
- SWAT-SF: A flexible SWAT-based model for watershed-scale water and soil salinity modeling M. Maleki Tirabadi et al. 10.1016/j.jconhyd.2021.103893
- An advanced approach for the precise prediction of water quality using a discrete hidden markov model D. Li et al. 10.1016/j.jhydrol.2022.127659
1 citations as recorded by crossref.
Latest update: 21 Jan 2025
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...
