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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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    The role of floods and droughts on riverine ecosystems under a changing climate
    Parasiewicz, P ; King, EL ; Webb, JA ; Piniewski, M ; Comoglio, C ; Wolter, C ; Buijse, AD ; Bjerklie, D ; Vezza, P ; Melcher, A ; Suska, K (Wiley, 2019-12-01)
    Floods and droughts are key driving forces shaping aquatic ecosystems. Climate change may alter key attributes of these events and consequently health and distribution of aquatic species. Improved knowledge of biological responses to different types of floods and droughts in rivers should allow the better prediction of the ecological consequences of climate change‐induced flow alterations. This review highlights that in unmodified ecosystems, the intensity and direction of biological impacts of floods and droughts vary, but the overall consequence is an increase in biological diversity and ecosystem health. To predict the impact of climate change, metrics that allow the quantitative linking of physical disturbance attributes to the directions and intensities of biological impacts are needed. The link between habitat change and the character of biological response is provided by the frequency of occurrence of the river wave characteristic—that is the event's predictability. The severity of impacts of floods is largely related to the river wave amplitude (flood magnitude), while the impact of droughts is related to river wavelength (drought duration).
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    Not just a migration problem: Metapopulations, habitat shifts, and gene flow are also important for fishway science and management
    Wilkes, MA ; Webb, JA ; Pompeu, PS ; Silva, LGM ; Vowles, AS ; Baker, CF ; Franklin, P ; Link, O ; Habit, E ; Kemp, PS (John Wiley & Sons Ltd., 2019-12-01)
    Worldwide, fishways are increasingly criticized for failing to meet conservation goals. We argue that this is largely due to the dominance of diadromous species of the Northern Hemisphere (e.g., Salmonidae) in the research that underpins the concepts and methods of fishway science and management. With highly diverse life histories, swimming abilities and spatial ecologies, most freshwater fish species do not conform to the stereotype imposed by this framework. This is leading to a global proliferation of fishways that are often unsuitable for native species. The vast majority of fish populations do not undertake extensive migrations between clearly separated critical habitats, yet the movement of individuals and the genetic information they carry is critically important for population viability. We briefly review some of the latest advances in spatial ecological modelling for dendritic networks to better define what it means to achieve effective fish passage at a barrier. Through a combination of critical habitat assessment and the modelling of metapopulations, climate change-driven habitat shifts, and adaptive gene flow, we recommend a conceptual and methodological framework for fishway target-setting and monitoring suitable for a wide range of species. In the process, we raise a number of issues that should contribute to the ongoing debate about fish passage research and the design and monitoring of fishways.
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    Commonwealth Environmental Water Office Long Term Intervention Monitoring Project Goulburn River Selected Area Scientific Report 2018–19
    Webb, J ; Guo, D ; Baker, B ; Casanelia, S ; Grace, M ; Greet, J ; Kellar, C ; Koster, W ; Lovell, D ; McMahon, D ; Morris, K ; Myers, J ; Pettrigrove, V ; Vietz, G (University of Melbourne Commercial, 2019)
    This Scientific Report is a companion volume to the Summary Report for the Goulburn River Long Term Intervention Monitoring (LTIM) Project Webb et al. 2020). The two documents complement each other and overlap very little.
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    Environmental Flow Assessment Methods and Applications
    Williams, JG ; Moyle, PB ; Webb, JA ; Kondolf, GM (John Wiley & Sons, 2019)
    The following chapters assess standard and emerging methods, how they should be tested, and how they should (or should not) be applied. The book concludes with practical recommendations for implementing environmental flow assessment.
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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)
    Abstract 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.