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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    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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    Integrated modelling of spatio-temporal variability in stream water quality across victorian catchments
    Guo, D ; Lintern, A ; Webb, JA ; Ryu, D ; Liu, S ; Western, AW (Engineers Australia, 2018-01-01)
    Degraded water quality in rivers and streams can have large economical, societal and ecological impacts. Stream water quality can be highly variable both over space and time, so understanding and modelling these spatio-temporal variabilities is critical to developing management and mitigation strategies to improve riverine water quality. However, there is currently limited capacity to model stream water quality due to the lack of understanding of the key factors driving spatio-temporal variability in water quality. To address this, a Bayesian hierarchical statistical model has been developed to describe the spatio-temporal variability in stream water quality across multiple catchments in the state of Victoria, Australia. We used monthly water quality monitoring data collected at 102 sites over 20 years. The modelling focused on three key water quality indicators: total suspended solids (TSS), nitrate-nitrite (NOx) and salinity (EC). It was found that both human-influenced catchment characteristics (land use) and other natural characteristics such as climate or topography are important drivers of spatial variabilities. The key drivers of temporal variability are changes in streamflow, climate and vegetation cover. These key drivers have been integrated into a spatio-temporal modelling framwork. These models can be applied at different spatial and temporal scales, and explain a reasonable proportion of spatio-temporal variation in the different water quality constituents. The extension and adaption of these models is currently underway to create an operational tool to forecast stream water quality responses to potential land use and climatic changes.
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    Key Factors Affecting Temporal Variability in Stream Water Quality
    Guo, D ; Lintern, A ; Webb, JA ; Ryu, D ; Liu, S ; Bende-Michl, U ; Leahy, P ; Wilson, P ; Western, AW (AMER GEOPHYSICAL UNION, 2019-01-01)
    Understanding the factors that influence temporal variability in water quality is critical for designing water quality management strategies. In this study, we explore the key factors that affect temporal variability in stream water quality across multiple catchments using a Bayesian hierarchical model. We apply this model to a case study data set consisting of monthly water quality measurements obtained over a 20‐year period from 102 water quality monitoring sites in the state of Victoria (Southeast Australia). We investigate six water quality constituents: total suspended solids, total phosphorus, filterable reactive phosphorus, total Kjeldahl nitrogen, nitrate‐nitrite (NOx), and electrical conductivity. We find that same‐day streamflow has the greatest effect on water quality variability for all constituents. Additional important predictors include soil moisture, antecedent streamflow, vegetation cover, and water temperature. Overall, the models do not explain a large proportion of temporal variation in water quality, with Nash‐Sutcliffe coefficients lower than 0.49. However, when considering performance on a site‐by‐site basis, we see high model performance in some locations, with Nash‐Sutcliffe coefficients of up to 0.8 for NOx and electrical conductivity. The effect of the temporal predictors on water quality varies between sites, which should be explored further for potential spatial patterns in future studies. There is also potential for further extension of these temporal variability models into a predictive spatiotemporal model of riverine constituent concentrations, which will be a useful tool to inform decision making for catchment water quality management.
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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)