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dc.contributor.advisorMcCuen, Richard H.en_US
dc.contributor.authorGilroy, Kristin Leighen_US
dc.date.accessioned2011-07-06T05:53:46Z
dc.date.available2011-07-06T05:53:46Z
dc.date.issued2011en_US
dc.identifier.urihttp://hdl.handle.net/1903/11531
dc.description.abstractClimate change and urbanization are nonstationary factors that influence hydrologic data, which results in the concept of multinonstationarity in hydrologic data. Methods to deal with important aspects of multinonstationarity do not exist. Currently, a statistical method to detect multinonstationarity in a hydrologic time series is needed. Likewise, flood mitigation methods, such as infrastructure designs and the national flood insurance policy, are based on the assumption of stationarity and, therefore, may not provide expected levels of protection in a nonstationary environment. The goal of this study was to provide a method to detect and model multinonstationarity in hydrologic data, as well as to assess the change in risk associated with multinonstationarity. A statistical test was developed to identify multiple change points within a time series, which is necessary to achieve optimum modeling accuracy for hydrologic data in a nonstationary environment. A procedure was developed to incorporate multinonstationarity into the existing flood frequency analysis method based on two nonstationary factors: urbanization and climate change. Finally, a flood risk assessment was conducted in which the risks as well as the performance of a flood mitigation system were compared for stationary and multinonstationary environments. The results showed that the incorporation of multinonstationarity into the current flood frequency analysis creates a noticeable difference in the magnitude of floods for the same return period as well as the associated risk. Based on the developed method, engineers and policy makers can begin to analyze the hydrologic and risk sensitivity of communities to nonstationarity. If the sensitivities of the system are understood, the factors, such as urbanization and emissions rates that influence climate change, can potentially be controlled to mitigate the consequences. Therefore, while many uncertainties exist in regards to the future conditions of these nonstationary factors, through methods such as those proposed in this study, the range of possibilities will be better understood and lead to more informed decisions to mitigate future risks.en_US
dc.titleTHE DETECTION AND MODELING OF MULTINONSTATIONARITY FOR ACCURATE ASSESSMENT OF LONG-TERM HYDROLOGIC RISKen_US
dc.typeDissertationen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.contributor.departmentCivil Engineeringen_US
dc.subject.pqcontrolledCivil Engineeringen_US
dc.subject.pqcontrolledWater Resource Managementen_US
dc.subject.pquncontrolledclimate changeen_US
dc.subject.pquncontrolledflood frequencyen_US
dc.subject.pquncontrollednonstationarityen_US
dc.subject.pquncontrolledrisk assessmenten_US
dc.subject.pquncontrolledurbanizationen_US


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