Abstract

Increased population density along the world's coastlines is driven by the wide range of services provided by costal systems, such as transportation, fisheries, trade, and tourism. Despite offering fundamental services, coastal systems are exposed to a range of natural hazards such as riverine and/or coastal flooding, and erosion, thus robust risk analyses are needed to protect coastal ecosystems, infrastructure, and people dwelling in proximity to the shoreline. Numerical and multivariate statistical models have become crucial tools in coastal risk management to support robust risk analyses, and their combined use provides many benefits in the analysis of coastal hazards. This dissertation addresses two of the most pressing issues in coastal management, which are coastal dune erosion and compound flooding, by exploiting the advantages of combining multivariate statistical and numerical models. One of the shortcomings of numerical models is that they are often computationally expensive, thus their application is hindered when fast and accurate predictions are needed. Surrogate modelling, a process that simplifies complex numerical simulations through statistical modelling, can expedite predictions without significantly compromising accuracy. Here, surrogate modelling is successfully applied to predict dune erosion under stormy conditions, and water level variability along rivers caused by the interaction of oceanographic and fluvial variables, which is known as compound flooding. The latter is further investigated through the application of several multivariate statistical frameworks, assessing discrepancies between subjective model setups when data are limited. A sensitivity test is provided for two commonly used multivariate statistical models, highlighting the need for long overlapping records to attain robust estimates of the compound flooding hazard. The effect of data shortness is further explored by using extended data of flooding drivers via climate simulations. By shorting the data set, the uncertainty introduced by natural climate variability is demonstrated for each component of the multivariate modelling framework.

Notes

If this is your thesis or dissertation, and want to learn how to access it or for more information about readership statistics, contact us at STARS@ucf.edu.

Graduation Date

2021

Semester

Spring

Advisor

Wahl, Thomas

Degree

Doctor of Philosophy (Ph.D.)

College

College of Engineering and Computer Science

Department

Civil, Environmental, and Construction Engineering

Degree Program

Civil Engineering

Format

application/pdf

Identifier

CFE0008496

Language

English

Release Date

May 2022

Length of Campus-only Access

1 year

Access Status

Doctoral Dissertation (Campus-only Access)

Share

COinS