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    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 ( 2019-07-23)
    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.
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    A multi-model approach to assessing the impacts of catchment characteristics on spatial water quality in the Great Barrier Reef catchments
    Liu, S ; Ryu, D ; Webb, JA ; Lintern, A ; Guo, D ; Waters, D ; Western, AW (ELSEVIER SCI LTD, 2021-11-01)
    Water quality monitoring programs often collect large amounts of data with limited attention given to the assessment of the dominant drivers of spatial and temporal water quality variations at the catchment scale. This study uses a multi-model approach: a) to identify the influential catchment characteristics affecting spatial variability in water quality; and b) to predict spatial variability in water quality more reliably and robustly. Tropical catchments in the Great Barrier Reef (GBR) area, Australia, were used as a case study. We developed statistical models using 58 catchment characteristics to predict the spatial variability in water quality in 32 GBR catchments. An exhaustive search method coupled with multi-model inference approaches were used to identify important catchment characteristics and predict the spatial variation in water quality across catchments. Bootstrapping and cross-validation approaches were used to assess the uncertainty in identified important factors and robustness of multi-model structure, respectively. The results indicate that water quality variables were generally most influenced by the natural characteristics of catchments (e.g., soil type and annual rainfall), while anthropogenic characteristics (i.e., land use) also showed significant influence on dissolved nutrient species (e.g., NOX, NH4 and FRP). The multi-model structures developed in this work were able to predict average event-mean concentration well, with Nash-Sutcliffe coefficient ranging from 0.68 to 0.96. This work provides data-driven evidence for catchment managers, which can help them develop effective water quality management strategies.
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    Synthesizing the impacts of baseflow contribution on concentration-discharge (C-Q) relationships across Australia using a Bayesian hierarchical model
    Guo, D ; Minaudo, C ; Lintern, A ; Bende-Michl, U ; Liu, S ; Zhang, K ; Duvert, C (COPERNICUS GESELLSCHAFT MBH, 2022-01-03)
    Abstract. Understanding concentration–discharge (C–Q) relationships can inform catchment solute and particulate export processes. Previous studies have shown that the extent to which baseflow contributes to streamflow can affect C–Q relationships in some catchments. However, the current understanding on the effects of baseflow contribution in shaping the C–Q patterns is largely derived from temperate catchments. As such, we still lack quantitative understanding of these effects across a wide range of climates (e.g. arid, tropical and subtropical). The study aims to assess how baseflow contributions, as defined by the median and the range of daily baseflow indices within individual catchments (BFI_m and BFI_range, respectively), influence C–Q slopes across 157 catchments in Australia spanning five climate zones. This study focuses on six water quality variables: electrical conductivity (EC), total phosphorus (TP), soluble reactive phosphorus (SRP), total suspended solids (TSS), the sum of nitrate and nitrite (NOx) and total nitrogen (TN). The impact of baseflow contributions is explored with a novel Bayesian hierarchical model. For sediments and nutrient species (TSS, NOx, TN and TP), we generally see largely positive C–Q slopes, which suggest a dominance of mobilization export patterns. Further, for TSS, NOx and TP we see stronger mobilization (steeper positive C–Q slopes) in catchments with higher values in both the BFI_m and BFI_range, as these two metrics are positively correlated for most catchments. The enhanced mobilization in catchments with higher BFI_m or BFI_range is likely due to the more variable flow pathways that occur in catchments with higher baseflow contributions. These variable flow pathways can lead to higher concentration gradients between low flows and high flows, where the former is generally dominated by groundwater/slow subsurface flow while the latter by surface water sources, respectively. This result highlights the crucial role of flow pathways in determining catchment exports of solutes and particulates. Our study also demonstrates the need for further studies on how the temporal variations of flow regimes and baseflow contributions influence flow pathways and the potential impacts of these flow pathways on catchment C–Q relationships.
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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)
    Globally, many rivers are experiencing declining water quality, for example, with altered levels of sediments, salts, and nutrients. Effective water quality management requires a sound understanding of how and why water quality differs across space, both within and between river catchments. Land cover, land use, land management, atmospheric deposition, geology and soil type, climate, topography, and catchment hydrology are the key features of a catchment that affect: (1) the amount of suspended sediment, nutrient, and salt concentrations in catchments (i.e., the source), (2) the mobilization ,and (3) the delivery of these constituents to receiving waters. There are, however, complexities in the relationship between landscape characteristics and stream water quality. The strength of this relationship can be influenced by the distance and spatial arrangement of constituent sources within the catchment, cross correlations between landscape characteristics, and seasonality. A knowledge gap that should be addressed in future studies is that of interactions and cross correlations between landscape characteristics. There is currently limited understanding of how the relationships between landscape characteristics and water quality responses can shift based on the other characteristics of the catchment. Understanding the many forces driving stream water quality and the complexities and interactions in these forces is necessary for the development of successful water quality management strategies. This knowledge could be used to develop predictive models, which would aid in forecasting of riverine water quality. WIREs Water 2018, 5:e1260. doi: 10.1002/wat2.1260 This article is categorized under: Science of Water > Hydrological Processes Science of Water > Water Quality
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    A Bayesian approach to understanding the key factors influencing temporal variability in stream water quality - a case study in the Great Barrier Reef catchments
    Liu, S ; Ryu, D ; Webb, JA ; Lintern, A ; Guo, D ; Waters, D ; Western, AW (COPERNICUS GESELLSCHAFT MBH, 2021-05-20)
    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.
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    Predicting quantiles of water quality from catchment characteristics
    Guo, D ; Liu, S ; Singh, D ; Western, AW (WILEY, 2021-01)
    Abstract Water quality is often highly variable both in space and time, which poses challenges for modelling the more extreme concentrations. This study developed an alternative approach to predicting water quality quantiles at individual locations. We focused on river water quality data that were collected over 25 years, at 102 catchments across the State of Victoria, Australia. We analysed and modelled spatial patterns of the 10th, 25th, 50th, 75th and 90th percentiles of the concentrations of sediments, nutrients and salt, with six common constituents: total suspended solids (TSS), total phosphorus (TP), filterable reactive phosphorus (FRP), total Kjeldahl nitrogen (TKN), nitrate‐nitrite (NOx), and electrical conductivity (EC). To predict the spatial variation of each quantile for each constituent, we developed statistical regression models and exhaustively searched through 50 catchment characteristics to identify the best set of predictors for that quantile. The models predict the spatial variation in individual quantiles of TSS, TKN and EC well (66%–96% spatial variation explained), while those for TP, FRP and NOx have lower performance (37%–73% spatial variation explained). The most common factors that influence the spatial variations of the different constituents and quantiles are: annual temperature, percentage of cropping land area in catchment and channel slope. The statistical models developed can be used to predict how low‐ and high‐concentration quantiles change with landscape characteristics, and thus provide a useful tool for catchment managers to inform planning and policy making with changing climate and land use conditions.
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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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    The influence of climate on water chemistry states and dynamics in rivers across Australia
    Lintern, A ; Liu, S ; Minaudo, C ; Dupas, R ; Guo, D ; Zhang, K ; Bende-Michl, U ; Duvert, C (WILEY, 2021-12)
    Abstract For effective water quality management and policy development, spatial variability in the mean concentrations and dynamics of riverine water quality needs to be understood. Using water chemistry (calcium, electrical conductivity, nitrate‐nitrite, soluble reactive phosphorus, total nitrogen, total phosphorus and total suspended solids) data for up to 578 locations across the Australian continent, we assessed the impact of climate zones (arid, Mediterranean, temperate, subtropical, tropical) on (i) inter‐annual mean concentration and (ii) water chemistry dynamics as represented by constituent export regimes (ratio of the coefficients of variation of concentration and discharge) and export patterns (slope of the concentration‐discharge relationship). We found that inter‐annual mean concentrations vary significantly by climate zones and that spatial variability in water chemistry generally exceeds temporal variability. However, export regimes and patterns are generally consistent across climate zones. This suggests that intrinsic properties of individual constituents rather than catchment properties determine export regimes and patterns. The spatially consistent water chemistry dynamics highlights the potential to predict riverine water quality across the Australian continent, which can support national riverine water quality management and policy development.
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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.