Infrastructure Engineering - Research Publications
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ItemThe value of water in storage: Implications for operational policies(Curran Associate Inc., 2018-01-01)With desalination plants becoming an increasingly common feature of water supply systems for major cities, the options for managing water security are now markedly different to past times when the short-term response to low water availability essentially revolved around reducing usage. The operation of desalination plants and other components of diversified water supply systems now enable operators to increase availability, essentially by producing water. The operation of such systems clearly impacts operational costs but, more subtly, also impacts future augmentation decisions. This can have major cost implications as there is a trade-off between the costs of operating a water supply system and the probability and timing of future augmentations that leads to important differences in the economics of reliably supplying water. This paper first summarises an economic analysis framework in which to explore the interaction of short (operational) and long (capital investment) term decisions towards maintaining water security. It then explores the implications of different operation approaches in Melbourne’s water supply system, assuming a planned augmentation pathway under conditions of low water availability. We assume augmentation decisions are prompted by critically low water availability events, rather than long-term reliability analysis. We show that the majority of the variation in cost of maintaining a reliable water supply is associated with impacts of operational rules on likely capital investment and that this results in a strong interaction between short and long-term decision making. The outcome of this work has implications for both operational decision making and augmentation planning for urban water supply systems. These implications are relevant to any water supply system where a climate independent water supply source, such as desalination, can be accessed.
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ItemNo Preview AvailableEquifinality and process-based modelling(American Geophysical Union, 2018-11-26)Equifinality is understood as one of the fundamental difficulties in the study of open complex systems, including catchment hydrology. A review of the hydrologic literature reveals that the term equifinality has been widely used, but in many cases inconsistently and without coherent recognition of the various facets of equifinality, which can lead to ambiguity but also methodological fallacies. Therefore, in this study we first characterise the term equifinality within the context of hydrological modelling by reviewing the genesis of the concept of equifinality and then presenting a theoretical framework. During past decades, equifinality has mainly been studied as a subset of aleatory (arising due to randomness) uncertainty and for the assessment of model parameter uncertainty. Although the connection between parameter uncertainty and equifinality is undeniable, we argue there is more to equifinality than just aleatory parameter uncertainty. That is, the importance of equifinality and epistemic uncertainty (arising due to lack of knowledge) and their implications is overlooked in our current practice of model evaluation. Equifinality and epistemic uncertainty in studying, modelling, and evaluating hydrologic processes are treated as if they can be simply discussed in (or often reduced to) probabilistic terms (as for aleatory uncertainty). The deficiencies of this approach to conceptual rainfall-runoff modelling are demonstrated for selected Australian catchments by examination of parameter and internal flux distributions and interactions within SIMHYD. On this basis, we present a new approach that expands equifinality concept beyond model parameters to inform epistemic uncertainty. The new approach potentially facilitates the identification and development of more physically plausible models and model evaluation schemes particularly within the multiple working hypotheses framework, and is generalisable to other fields of environmental modelling as well.
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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)