Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/127299
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dc.contributor.advisorMaier, Holger-
dc.contributor.advisorWestra, Seth-
dc.contributor.advisorvan der Linden, Leon-
dc.contributor.authorMcPhail, Cameron-
dc.date.issued2020-
dc.identifier.urihttp://hdl.handle.net/2440/127299-
dc.description.abstractThe long-term planning of water and environmental systems presents major challenges to decision-makers, requiring them to make decisions despite a significant degree of uncertainty in the future state of the world. Frequently, decision-makers are operating at the level of deep uncertainty, which refers to when deterministic and stochastic processes are insufficient for representing the future state of the world, and the consideration of multiple plausible futures (scenarios) is required. Further complicating this, probabilities cannot be placed on the scenarios, and therefore traditional performance metrics such as reliability, vulnerability, resilience, or expected value do not apply. Rather, deep uncertainty requires robustness metrics, which aim to determine the level of system performance and how that performance varies across all scenarios. The specific aims of this research are (i) to introduce a unified framework for the calculation of a wide range of robustness metrics, enabling the robustness values and rankings obtained from different metrics to be compared in an objective fashion; (ii) to develop a deeper understanding of how different selections of scenarios can affect the absolute and relative robustness and rankings of decision alternatives of interest; and (iii) to create a generic guidance framework and software tool to assist with the identification of the most robust decision alternative for a given problem. For the first aim, this research presents a unifying framework for the calculation of robustness metrics, which assists with understanding how robustness metrics work, when they should be used, and why they sometimes disagree. The framework categorizes the suitability of metrics to a decision-maker based on the decision-context, the decision maker's preferred level of risk aversion, and the decision-maker's preference towards maximizing performance or minimizing variance. This research also introduces a conceptual framework describing when different robustness metrics are likely to agree and disagree. For the second aim, the research describes how scenarios are generally represented in model-based assessments, and develops a systematic, quantitative methodology for exploring the influence of different sets of scenarios on the absolute and relative robustness of different decision alternatives, which is then applied to the Lake Problem.Case study results show that despite different sets of scenarios causing a significant difference in robustness values, there is little difference in the corresponding rankings, and therefore similar decision outcomes will be reached regardless of how the scenarios are selected. It is also revealed that the impact of the scenarios on the robustness values is due to complex interactions with the system model and robustness metrics. For the third aim, the research considers the knowledge developed in the first two aims and builds a guidance framework for decision-makers on how to identify the most robust decision alternative for a given problem. The guidance caters to a variety of situations where the scenarios and/or robustness metrics are known or not known and also includes guidance on how to create a custom robustness metric for the problem at hand. An opensource software package is introduced, the RAPID package, to assist in the consistency and ease-of-use of implementing the guidance framework.en
dc.language.isoenen
dc.subjectdeep uncertaintyen
dc.subjectrobustnessen
dc.subjectscenariosen
dc.subjectwater resourcesen
dc.subjectdecision-makingen
dc.titleTowards a better understanding of scenarios and robustness for the long-term planning of water and environmental systemsen
dc.typeThesisen
dc.contributor.schoolSchool of Civil, Environmental and Mining Engineeringen
dc.provenanceThis electronic version is made publicly available by the University of Adelaide in accordance with its open access policy for student theses. Copyright in this thesis remains with the author. This thesis may incorporate third party material which has been used by the author pursuant to Fair Dealing exceptions. If you are the owner of any included third party copyright material you wish to be removed from this electronic version, please complete the take down form located at: http://www.adelaide.edu.au/legalsen
dc.description.dissertationThesis (Ph.D.) -- University of Adelaide, School of Civil, Environmental, and Mining Engineering, 2020en
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