Hydrology is a field fraught with uncertainty. Uncertainty comes from both our inability to perfectly know the true nature of constant system components of hydrologic systems (e.g. hydraulic conductivity, geologic structure, etc.) and our inability to perfectly predict the behavior of variable system components (e.g. future precipitation, future streamflow, etc.). Hydrologic literature has increasingly recognized that within the bounds of uncertainty, many acceptable hydrologic models exist and differ in their predictions. Modeling applications that recognize this uncertainty have become more practical as a result of increasing computing power and improved software. Given a set of model predictions, the applied hydrologist or water resource manager is faced with an important question: in light of this uncertainty, how do I make the best decision? Many decision making criteria are valid for use in water resources, however, decision making criteria are subjective in their nature and require input from the decision maker about their values and outlook. Decision making criteria can range from optimistic to pessimistic, and can be probabilistic or non-probabilistic. This dissertation explores the importance of hydrologic uncertainty and the stance of the decision maker in selecting an appropriate decision making criterion. The dissertation comprises four manuscripts. The first manuscript presents an analysis of uncertainty arising from choice of groundwater sampling method. The study analyzes how three sampling methods compare across a range of analytes and well constructions. The second manuscript presents an analysis of the risk that a wellfield will not be able to meet water demands. A Monte-Carlo model is used to evaluate how uncertainty arising from variable groundwater recharge in an alluvial aquifer translates to total wellfield risk. The third manuscript reviews multi-model methods used to support decision making and makes an argument that non-probabilistic decision making methods deserve a larger role in hydrologic studies. A groundwater recharge example is presented that compares the performance of model selection, model averaging, probabilistic, and non-probabilistic decision making methods when used for decision making. The final manuscript presents the Discrimination Inference to Reduce Expected Cost Technique (DIRECT). DIRECT is a MATLAB® based computer code that optimizes project design under uncertainty using an expected utility decision criterion. Examples are presented for remediation system design and groundwater pumping.
| Identifer | oai:union.ndltd.org:arizona.edu/oai:arizona.openrepository.com:10150/621871 |
| Date | January 2016 |
| Creators | Bayley, Timothy West, Bayley, Timothy West |
| Contributors | Ferre, Paul A. "Ty", Ferre, Paul A. "Ty", Winter, Larry, Meixner, Thomas, Papuga, Shirley A. |
| Publisher | The University of Arizona. |
| Source Sets | University of Arizona |
| Language | en_US |
| Detected Language | English |
| Type | text, Electronic Dissertation |
| Rights | Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author. |
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