Different sources of uncertainties have been inevitably induced into the environmental modeling due to different reasons such as the variability in the future climate state, incomplete
knowledge and complexity of the nature system, and randomness in the system properties. These uncertainties make the model predictions inherently uncertain, and uncertainty becomes an
important obstacle in environmental modeling. This dissertation presents a general framework for purpose of uncertainty quantification and it provides quantitative measures for relative
importance of different uncertain factors to model outputs. The framework includes two parts: uncertainty analysis which implements variance decomposition technique to decompose and quantify
different types of input uncertainty sources (i.e., scenario, model and parametric uncertainties); global sensitivity analysis which develops a new set of variance-based global sensitivity
indices for measuring importance of model parameters with considering multiple future climate scenarios and plausible models. To demonstrate the usage and compatibility of the uncertainty
quantification framework with different types of models, it was applied into two distinct cases: a synthetic groundwater reactive transport case and a barrier island morphological case. In
the groundwater case, a Bayesian network integrated groundwater reactive transport model was built and studied for a synthetic case. Different uncertainty sources are described as uncertain
nodes in the Bayesian network. All the nodes are characterized by multiple states, representing their uncertainty, in the form of continuous or discrete probability distributions that are
propagated to the model endpoint, which is the spatial distribution of contaminant concentrations. In the barrier island case, a new Barrier Island Profile (BIP) model which simulates the
barrier island cross-section morphological evolution was developed and studied. For a series of barrier island cross-sections derived from the characteristics of Santa Rosa Island, Florida,
BIP was used to evaluate their responses to random storm events and five potential accelerated rates of sea-level rise projected over a century. Monte Carlo simulation is used to decompose
and quantify the predictive uncertainties for uncertainty analysis of both cases. In the global sensitivity analysis, besides quasi-Monte Carlo simulation, sparse grid collocation method was
also implemented to estimate the global sensitivity index to save the computational cost in the groundwater case. The study of BIP model demonstrates that BIP is capable of simulating
realistic patterns of barrier island profile evolution over the span of a century using relatively simple representations of time- and space-averaged processes with consideration of
uncertainty of future climate impacts. The results of uncertainty quantification for both cases demonstrate different types of model input uncertainty sources and the relative importance of
model parameters can be quantified using the developed uncertainty quantification framework. And the global sensitivity indices may vary substantially between different models and scenarios.
Not considering the model and scenario uncertainties, may result biased identification of important model parameters. The framework will be very useful for environmental modelers to
prioritize different uncertainties and optimize expanse of limited resources to more efficiently decrease predictive uncertainty. Although only two applications are demonstrated, this
uncertainty quantification framework is mathematically general and it can be applied to a wider range of hydrologic and environmental problems. / A Dissertation submitted to the Department of Scientific Computing in partial fulfillment of the requirements for the degree of Doctor of Philosophy. / Fall Semester, 2014. / November 5, 2014. / Barrier Island Modeling, Coastal Modeling, Groundwater Reactive Transport Modeling, Multiple Scenarios and Models, Sensitivity Analysis, Uncertainty Analysis / Includes bibliographical references. / Ming Ye, Professor Directing Dissertation; Anke Meyer-Baese, Committee Member; Tomasz Plewa, Committee Member; Dennis Slice, Committee Member.
| Identifer | oai:union.ndltd.org:fsu.edu/oai:fsu.digital.flvc.org:fsu_252821 |
| Contributors | Dai, Heng (authoraut), Ye, Ming (professor directing dissertation), Kish, Stephen A. (university representative), Meyer-Baese, Anke (committee member), Plewa, Tomasz (committee member), Slice, Dennis E. (committee member), Florida State University (degree granting institution), College of Arts and Sciences (degree granting college), Department of Scientific Computing (degree granting department) |
| Publisher | Florida State University, Florida State University |
| Source Sets | Florida State University |
| Language | English, English |
| Detected Language | English |
| Type | Text, text |
| Format | 1 online resource (166 pages), computer, application/pdf |
| Rights | This Item is protected by copyright and/or related rights. You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s). The copyright in theses and dissertations completed at Florida State University is held by the students who author them. |
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