scholarly journals Key Factors Affecting Temporal Variability in Stream Water Quality

2019 ◽  
Vol 55 (1) ◽  
pp. 112-129 ◽  
Author(s):  
D. Guo ◽  
A. Lintern ◽  
J. A. Webb ◽  
D. Ryu ◽  
S. Liu ◽  
...  
Keyword(s):  
Water Quality ◽  
Temporal Variability ◽  
Stream Water ◽  
Key Factors ◽  
Stream Water Quality ◽  
Factors Affecting

scholarly journals A Bayesian approach to understanding the key factors influencing temporal variability in stream water quality: a case study in the Great Barrier Reef catchments

2021 ◽  
Author(s):  
Shuci Liu ◽  
Dongryeol Ryu ◽  
J. Anugs Webb ◽  
Anna Lintern ◽  
Danlu Guo ◽  
...  
Keyword(s):  
Water Quality ◽  
Great Barrier Reef ◽  
Temporal Variation ◽  
Temporal Variability ◽  
Stream Water ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Barrier Reef ◽  
Key Factors ◽  
Stream Water Quality

Abstract. Stream water quality is highly variable both across space and time. Water quality monitoring programs have collected a large amount of data that provide a good basis to investigate the key drivers of spatial and temporal variability. Event-based water quality monitoring data in the Great Barrier Reef catchments in northern Australia provides an opportunity to further our understanding of water quality dynamics in sub-tropical and tropical regions. This study investigated nine water quality constituents, including sediments, nutrients and salinity, with the aim of: 1) identifying the influential environmental drivers of temporal variation in flow event concentrations; and 2) developing a modelling framework to predict the temporal variation in water quality at multiple sites simultaneously. This study used a hierarchical Bayesian model averaging framework to explore the relationship between event concentration and catchment-scale environmental variables (e.g., runoff, rainfall and groundcover conditions). Key factors affecting the temporal changes in water quality varied among constituent concentrations, as well as between catchments. Catchment rainfall and runoff affected in-stream particulate constituents, while catchment wetness and vegetation cover had more impact on dissolved nutrient concentration and salinity. In addition, in large dry catchments, antecedent catchment soil moisture and vegetation had a large influence on dissolved nutrients, which highlights the important effect of catchment hydrological connectivity on pollutant mobilisation and delivery.

scholarly journals A Bayesian approach to understanding the key factors influencing temporal variability in stream water quality – a case study in the Great Barrier Reef catchments

2021 ◽  
Vol 25 (5) ◽  
pp. 2663-2683
Author(s):  
Shuci Liu ◽  
Dongryeol Ryu ◽  
J. Angus Webb ◽  
Anna Lintern ◽  
Danlu Guo ◽  
...  
Keyword(s):  
Water Quality ◽  
Great Barrier Reef ◽  
Temporal Variation ◽  
Temporal Variability ◽  
Stream Water ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Barrier Reef ◽  
Key Factors ◽  
Stream Water Quality

Abstract. Stream water quality is highly variable both across space and time. Water quality monitoring programmes have collected a large amount of data that provide a good basis for investigating the key drivers of spatial and temporal variability. Event-based water quality monitoring data in the Great Barrier Reef catchments in northern Australia provide an opportunity to further our understanding of water quality dynamics in subtropical and tropical regions. This study investigated nine water quality constituents, including sediments, nutrients and salinity, with the aim of (1) identifying the influential environmental drivers of temporal variation in flow event concentrations and (2) developing a modelling framework to predict the temporal variation in water quality at multiple sites simultaneously. This study used a hierarchical Bayesian model averaging framework to explore the relationship between event concentration and catchment-scale environmental variables (e.g. runoff, rainfall and groundcover conditions). Key factors affecting the temporal changes in water quality varied among constituent concentrations and between catchments. Catchment rainfall and runoff affected in-stream particulate constituents, while catchment wetness and vegetation cover had more impact on dissolved nutrient concentration and salinity. In addition, in large dry catchments, antecedent catchment soil moisture and vegetation had a large influence on dissolved nutrients, which highlights the important effect of catchment hydrological connectivity on pollutant mobilisation and delivery.

scholarly journals A bayesian hierarchical model to predict spatio-temporal variability in river water quality at 102 catchments

2020 ◽  
Author(s):  
Danlu Guo ◽  
Anna Lintern ◽  
Angus Webb ◽  
Dongryeol Ryu ◽  
Ulrike Bende-Michl ◽  
...  
Keyword(s):  
Water Quality ◽  
Statistical Model ◽  
Temporal Variability ◽  
Stream Water ◽  
Spatial Scales ◽  
Water Quality Monitoring ◽  
Catchment Management ◽  
Stream Water Quality ◽  
Bayesian Hierarchical ◽  
Spatio Temporal

<div> <div> <div> <div>Our current capacity to model stream water quality is limited particularly at large spatial scales across multiple catchments. To address this, we developed a Bayesian hierarchical statistical model to simulate the spatio-temporal variability in stream water quality across the state of Victoria, Australia. The model was developed using monthly water quality monitoring data over 21 years, across 102 catchments, which span over 130,000 km<sup>2</sup>. The modelling focused on six key water quality constituents: total suspended solids (TSS), total phosphorus (TP), filterable reactive phosphorus (FRP), total Kjeldahl nitrogen (TKN), nitrate-nitrite (NO<sub>x</sub>), and electrical conductivity (EC). The model structure was informed by knowledge of the key factors driving water quality variation, which had been identified in two preceding studies using the same dataset. Apart from FRP, which is hardly explainable (19.9%), the model explains 38.2% (NO<sub>x</sub>) to 88.6% (EC) of total spatio-temporal variability in water quality. Across constituents, the model generally captures over half of the observed spatial variability; temporal variability remains largely unexplained across all catchments, while long-term trends are well captured. The model is best used to predict proportional changes in water quality in a Box-Cox transformed scale, but can have substantial bias if used to predict absolute values for high concentrations. This model can assist catchment management by (1) identifying hot-spots and hot moments for waterway pollution; (2) predicting effects of catchment changes on water quality e.g. urbanization or forestation; and (3) identifying and explaining major water quality trends and changes. Further model improvements should focus on: (1) alternative statistical model structures to improve fitting for truncated data, for constituents where a large amount of data below the detection-limit; and (2) better representation of non-conservative constituents (e.g. FRP) by accounting for important biogeochemical processes.</div> </div> </div> </div>

scholarly journals A predictive model for spatio-temporal variability in stream water quality

2019 ◽  
Author(s):  
Danlu Guo ◽  
Anna Lintern ◽  
J. Angus Webb ◽  
Dongryeol Ryu ◽  
Ulrike Bende-Michl ◽  
...  
Keyword(s):  
Water Quality ◽  
Statistical Model ◽  
Temporal Variability ◽  
Stream Water ◽  
Model Performance ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Quality Data ◽  
Stream Water Quality ◽  
Spatio Temporal

Abstract. Degraded water quality in rivers and streams can have large economic, societal and ecological impacts. Stream water quality can be highly variable both over space and time. To develop effective management strategies for riverine water quality, it is critical to be able to predict these spatio-temporal variabilities. However, our current capacity to model stream water quality is limited, particularly at large spatial scales across multiple catchments. This is due to a lack of understanding of the key controls that drive spatio-temporal variabilities of stream water quality. To address this, we developed a Bayesian hierarchical statistical model to analyse the spatio-temporal variability in stream water quality across the state of Victoria, Australia. The model was developed based on monthly water quality monitoring data collected at 102 sites over 21 years. The modelling focused on six key water quality constituents: total suspended solids (TSS), total phosphorus (TP), filterable reactive phosphorus (FRP), total Kjeldahl nitrogen (TKN), nitrate-nitrite (NOx), and electrical conductivity (EC). Among the six constituents, the models explained varying proportions of variation in water quality. EC was the most predictable constituent (88.6 % variability explained) and FRP had the lowest predictive performance (19.9 % variability explained). The models were validated for multiple sets of calibration/validation sites and showed robust performance. Temporal validation revealed a systematic change in the TSS model performance across most catchments since an extended drought period in the study region, highlighting potential shifts in TSS dynamics over the drought. Further improvements in model performance need to focus on: (1) alternative statistical model structures to improve fitting for the low concentration data, especially records below the detection limit; and (2) better representation of non-conservative constituents by accounting for important biogeochemical processes. We also recommend future improvements in water quality monitoring programs which can potentially enhance the model capacity, via: (1) improving the monitoring and assimilation of high-frequency water quality data; and (2) improving the availability of data to capture land use and management changes over time.

scholarly journals Key factors influencing differences in stream water quality across space

2017 ◽  
Vol 5 (1) ◽  
pp. e1260 ◽  
Author(s):  
A. Lintern ◽  
J.A. Webb ◽  
D. Ryu ◽  
S. Liu ◽  
U. Bende-Michl ◽  
...  
Keyword(s):  
Water Quality ◽  
Stream Water ◽  
Key Factors ◽  
Stream Water Quality ◽  
Factors Influencing
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