Infrastructure Engineering - Research Publications

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    Key factors influencing differences in stream water quality across space
    Lintern, A ; Webb, JA ; Ryu, D ; Liu, S ; Bende-Michl, U ; Waters, D ; Leahy, P ; Wilson, P ; Western, AW (WILEY, 2018-01-01)
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    Predicting quantiles of water quality from catchment characteristics
    Guo, D ; Liu, S ; Singh, D ; Western, AW (WILEY, 2020-12-10)
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    A bayesian hierarchical model to predict spatio-temporal variability in river water quality at 102 catchments
    Guo, D ; Lintern, A ; Webb, A ; Ryu, D ; Bende-Michl, U ; Liu, S ; Western, A (Copernicus GmbH, 2020)
    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 km2. 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). 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% (NOx) 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.
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    Supplementary material to: "A predictive model for spatio-temporal variability in stream water quality"
    Guo, D ; Lintern, A ; Webb, JA ; Ryu, D ; Bende-Michl, U ; Liu, S ; Western, AW (Copernicus Publications, 2019)
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    A data-based predictive model for spatiotemporal variability in stream water quality
    Guo, D ; Lintern, A ; Webb, JA ; Ryu, D ; Bende-Michl, U ; Liu, S ; Western, AW (COPERNICUS GESELLSCHAFT MBH, 2020-02-24)
    Abstract. 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 spatiotemporal variability in stream water quality across the state of Victoria, Australia. The model was developed using monthly water quality monitoring data over 21 years and across 102 catchments (which span over 130 000 km2). The modeling 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). The model structure was informed by knowledge of the key factors driving water quality variation, which were identified in two preceding studies using the same dataset. Apart from FRP, which is hardly explained (19.9 %), the model explains 38.2 % (NOx) to 88.6 % (EC) of the total spatiotemporal variability in water quality. Across constituents, the model generally captures over half of the observed spatial variability; the temporal variability remains largely unexplained across all catchments, although long-term trends are well captured. The model is best used to predict proportional changes in water quality on a Box–Cox-transformed scale, but it 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 the 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 the following: (1) alternative statistical model structures to improve fitting for truncated data (for constituents where a large amount of data fall below the detection limit); and (2) better representation of nonconservative constituents (e.g., FRP) by accounting for important biogeochemical processes.
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    A web-based interface to visualize and model spatio-temporal variability of stream water quality
    Guo, D ; Lintern, A ; Webb, J ; Ryu, D ; Liu, S ; Bende-Michl, U ; Leahy, P ; Waters, D ; Watson, M ; Wilson, P ; Western, A ; Vietz, G ; Rutherfurd, I (River Basement Management Society, 2018)
    Understanding the spatio-temporal variability in stream water quality is critical for designing effective water quality management strategies. To facilitate this, we developed a web-based interface to visualize and model the spatio-temporal variability of stream water quality in Victoria. We used a dataset of long-term monthly water quality measurements from 102 monitoring sites in Victoria, focusing on six water quality constituents: total suspended solids (TSS), total phosphorus (TP), filterable reactive phosphorus (FRP), total Kjedahl nitrogen (TKN), nitrate-nitrite (NOx), and electrical conductivity (EC). The interface models spatio-temporal variability in water quality via a Bayesian hierarchical modelling framework, and produces summaries of (1) the key driving factors of spatio-temporal variability and (2) model performance assessed by multiple metrics. Additional features include predicting the time-averaged mean concentration at an un-sampled site, and testing the impact of land-use changes on the mean concentration at existing sites. This tool can be very useful in supporting the decision-making processes of catchment managers in (1) understanding the key drivers of changes in water quality and (2) designing water quality mitigation and restoration strategies.
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    What Are the Key Catchment Characteristics Affecting Spatial Differences in Riverine Water Quality?
    Lintern, A ; Webb, JA ; Ryu, D ; Liu, S ; Waters, D ; Leahy, P ; Bende-Michl, U ; Western, AW (AMER GEOPHYSICAL UNION, 2018-10-01)
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    Characterisation of spatial variability in water quality in the Great Barrier Reef catchments using multivariate statistical analysis
    Liu, S ; Ryu, D ; Webb, JA ; Lintern, A ; Waters, D ; Guo, D ; Western, AW (PERGAMON-ELSEVIER SCIENCE LTD, 2018-12-01)
    Water quality monitoring is important to assess changes in inland and coastal water quality. The focus of this study was to improve understanding of the spatial component of spatial-temporal water quality dynamics, particularly the spatial variability in water quality and the association between this spatial variability and catchment characteristics. A dataset of nine water quality constituents collected from 32 monitoring sites over a 11-year period (2006-2016), across the Great Barrier Reef catchments (Queensland, Australia), were evaluated by multivariate techniques. Two clusters were identified, which were strongly associated with catchment characteristics. A two-step Principal Component Analysis/Factor Analysis revealed four groupings of constituents with similar spatial pattern and allowed the key catchment characteristics affecting water quality to be determined. These findings provide a more nuanced view of spatial variations in water quality compared with previous understanding and an improved basis for water quality management to protect nearshore marine ecosystem.
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    Using a data-driven approach to understand the interaction between catchment characteristics and water quality responses
    Lintern, A ; Webb, JA ; Ryu, D ; Liu, S ; Bende-Michl, U ; Leahy, P ; Wilson, P ; Western, A ; Vietz, G ; Flatley, A ; Rutherfurd, I (River Basin Management Society, 2016)