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Title:Modeling of hurricane-storm surge occurrences and analysis of their financial implications
Author(s):Contento, Alessandro
Director of Research:Gardoni, Paolo
Doctoral Committee Chair(s):Gardoni, Paolo
Doctoral Committee Member(s):Valocchi, Albert; Dominguez, Francina; Rosowsky, David; Guerrier, Stéphane
Department / Program:Civil & Environmental Eng
Discipline:Civil Engineering
Degree Granting Institution:University of Illinois at Urbana-Champaign
Subject(s):Hurricane, storm surge, random field
Abstract:In the last decades, the devastating impact of hurricanes revealed the vulnerability of large areas of the U.S. East and Gulf coasts to this natural hazard. Additionally, the impact of hurricanes may be exacerbated by climate change and the population growth of coastal communities. A framework for the risk analysis for hurricanes needs to consider three fundamental aspects of hurricanes (wind, rainfall, and storm surge), should be able to account for the effects of climate change, and should adopt wind, rainfall, and storm surge models that are computationally efficient. Several wind and rainfall models in the literature can account for climate change effects and are computationally efficient. However, current models for storm surge that can account for the effects of climate change are generally computationally inefficient. A few models that are computationally efficient are empirical, and their functional forms lack physical meaning and are not based on the understanding of the underlying physical phenomena. Moreover, because of the specific formulation used to construct such models, it is generally not possible to incorporate both results from simulations and historical observations in the model calibration. Consequently, there is a need to define efficient models that can account for the effects of climate change while capturing the physics of the phenomena and can incorporate data from both simulations and historical observations. This dissertation develops a novel physics-based (or physics-inspired) probabilistic formulation as a valid alternative for storm surge prediction. Such a formulation is developed using the combination of two models, a logistic model and a random field. The two models provide two complementary pieces of information. The logistic model estimates the probability that a location is flooded. The random field model estimates the distribution of the storm surge depths, given that a location is wet. Being physics-based, the proposed probabilistic formulation has the advantages that account for the underlying physics of the phenomena, is computationally efficient, and overcomes some of the limitations of the available models on the model calibration and prediction. The formulation is computationally efficient because the models adopted in the formulation require a limited amount of data for calibration to provide initial predictions of storm surge height. Consequently, the proposed formulation is suitable for comparative analyses on the effects of climate change for different climate change scenarios since the calibrations of the models require only a limited amount of simulations for each scenario. Although performing the calibration with a limited amount of simulations generally affects the accuracy of the results, it allows us to develop a formulation that is extendable to regions that are neighbors of the one for which the models are calibrated by updating the models with a limited number of historical records and simulations. The random field model considers the spatial correlation among the storm surge at different locations and predicts storm surge at locations that are different from those of the observations used for the model calibration and not restricted to be alongshore. Additionally, both models (logistic and random field) are calibrated using a realistic set of storms. Two applications of the formulation show the possibility of using it to model both storm surge and inundation.
Issue Date:2020-12-01
Rights Information:Copyright 2020 Alessandro Contento
Date Available in IDEALS:2021-03-05
Date Deposited:2020-12

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