Infrastructure Engineering - Research Publications
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ItemLearning from concurrent adaptive management in multiple catchments within a large environmental flows program in Australia(WILEY, 2020-05)Abstract Adaptive management is central to improving outcomes of environmental water delivery. The Australian Government's Murray−Darling Basin (MDB) Plan 2012 explicitly states that adaptive management should be applied in the planning, prioritisation and use of environmental water. A Long Term Intervention Monitoring (LTIM) program was established in 2014 to evaluate responses to environmental water delivery for seven Areas within the MDB, with evaluation also undertaken at the Basin scale. Adaptive management at the Area scale was assessed using two approaches: (a) through a reflective exercise undertaken by researchers, water managers and community members and (b) through an independent review and evaluation of the program, where relevant reports were reviewed and managers and researchers involved in the LTIM program were interviewed. Both assessment approaches revealed that the scale of management actions influenced the extent to which learnings were incorporated into subsequent actions. Although there were many examples where learnings within an Area had been used to adaptively manage subsequent environmental water deliveries within that Area, there was inconsistent documentation of the processes for incorporating learnings into decision making. Although this likely limited the sharing of learnings, there were also examples where learnings from one Area had influenced environmental water management in another, suggesting that sharing between concurrent projects can increase learning. The two assessments identified ways to improve and systematically document the adaptive management learnings. With improved processes to increase reflection, documentation and sharing of learnings across projects, there is an opportunity to improve management of environmental water and ecosystem outcomes.
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ItemA web-based interface to visualize and model spatio-temporal variability of stream water quality(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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ItemIntegrated modelling of spatio-temporal variability in stream water quality across victorian catchments(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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ItemUsing a data-driven approach to understand the interaction between catchment characteristics and water quality responses(River Basin Management Society, 2016)
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ItemThe impact of dams on floodplain geomorphology: are there any, should we care, and what should we do about it?(River Basin Management Society, 2014)