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L’ouragan Maria a frappé la Dominique en septembre 2017, et a occasionné des dégâts colossaux à ce petit état insulaire en développement (PEID), situé entre la Martinique et la Guadeloupe. Plusieurs milliers de personnes auraient été déplacés pendant cet événement climatique extrême tropical (ECET). La capacité qu’elles ont eue à y faire face tient compte de divers facteurs de vulnérabilité (ressources socioéconomiques, qualité des infrastructures, etc.). Une série d’entrevues ont été conduites sur place en 2019 et en 2020, dans le cadre du projet CliMiHealth, auprès de personnes ayant été déplacées lors de l’ouragan. Ce projet vise à mieux comprendre leurs expériences migratoires et sanitaires, dans un contexte de changements climatiques aux Antilles. Notre étude exploratoire s’intéresse quant à elle aux spécificités des migrations climatiques internes en contexte insulaire caribéen pendant un ouragan, à la lumière de la vulnérabilité des personnes déplacées par ce type d’événements. Les analyses des données qualitatives suggèrent que la migration en contexte d’ouragan serait marquée d’une trajectoire multi-étapes, proche du lieu de départ. Nous estimons que ce déplacement tient plus d’une mobilité que d’une migration, dont la multiplicité dépendrait des opportunités qui se présentent aux personnes grâce à leur réseau social notamment. Ce facteur jouerait un rôle crucial dans la réduction de leur vulnérabilité. En outre, les perceptions du changement climatique varieraient selon le niveau d’éducation des personnes. Plus les personnes auraient un niveau scolaire élevé, plus elles s’accorderaient avec le discours scientifique des changements climatiques. L’intensité de l’ouragan semble avoir marqué les personnes, événement pour lequel elles s’estiment impuissantes. La temporalité des besoins exprimés serait aussi liée au niveau d’éducation, et soulève des tensions quant au soutien des institutions pendant l’ouragan. Nous encourageons une présence institutionnelle plus accrue auprès des populations plus vulnérables, particulièrement à l’approche de la saison des ouragans. / Hurricane Maria struck Dominica in September 2017, causing colossal damage to the small island developing state (SIDS), located between Martinique and Guadeloupe. Several thousand people were reportedly displaced during this extreme tropical weather event (ETWE). Their ability to cope with it considers various vulnerability factors (socioeconomic resources, quality of infrastructure, etc.). A series of interviews were conducted on site in 2019 and 2020, as part of the CliMiHealth project, with people who were displaced by the hurricane. This project aims to better understand their migration and health experiences in the context of climate change in the Caribbean. Our exploratory study focuses on the specificities of climate-related migration in a Caribbean island context during a hurricane, considering the vulnerability of displaced persons to this type of event. The analyses of qualitative data suggest that migration in the context of a hurricane is marked by a multi-stage trajectory, close to the place of departure. We believe that this displacement is more akin to mobility than to migration, the multiplicity of which would depend on the opportunities presented to people through their social network. This factor would play a crucial role in reducing their vulnerability. In addition, perceptions of climate change would vary according to people's level of education. The higher the level of education, the more they would agree with the scientific discourse on climate change. The intensity of the hurricane seems to have left its mark on people, against which they feel powerless. The temporality of the needs expressed would also be linked to the level of education, and raises tensions regarding institutional support during the hurricane. We encourage a greater institutional presence with the most vulnerable populations, especially as the hurricane season approaches.

This dissertation approaches high resolution scatterometry from a new perspective. Three related general topics are addressed: high resolution σ^0 imaging, wind estimation from high resolution σ^0 images over the ocean, and high resolution wind estimation directly from the scatterometer measurements. Theories of each topic are developed, and previous approaches are generalized and formalized. Improved processing algorithms for these theories are developed, implemented for particular scatterometers, and analyzed. Specific results and contributions are noted below. The σ^0 imaging problem is approached as the inversion of a noisy aperture-filtered sampling operation-extending the current theory to deal explicitly with noise. A maximum aposteriori (MAP) reconstruction estimator is developed to regularize the problem and deal appropriately with noise. The method is applied to the SeaWinds scatterometer and the Advanced Scatterometer (ASCAT). The MAP approach produces high resolution σ^0 images without introducing the ad-hoc processing steps employed in previous methods. An ultra high resolution (UHR) wind product has been previously developed and shown to produce valuable high resolution information, but the theory has not been formalized. This dissertation develops the UHR sampling model and noise model, and explicitly states the implicit assumptions involved. Improved UHR wind retrieval methods are also developed. The developments in the σ^0 imaging problem are extended to deal with the nonlinearities involved in wind field estimation. A MAP wind field reconstruction estimator is developed and implemented for the SeaWinds scatterometer. MAP wind reconstruction produces a wind field estimate that is consistent with the conventional product, but with higher resolution. The MAP reconstruction estimates have a resolution similar to the UHR estimates, but with less noise. A hurricane wind model is applied to obtain an informative prior used in MAP estimation, which reduces noise and ameliorates ambiguity selection and rain contamination.

An electromagnetic model for predicting the microwave blackbody emission from the ocean surface under the forcing of strong surface winds in hurricanes is being developed. This ocean emissivity model will be incorporated into a larger radiative transfer model used to infer ocean surface wind speed and rain rate in hurricanes from remotely sensed radiometric brightness temperature. The model development is based on measurements obtained with the Stepped Frequency Microwave Radiometer (SFMR), which routinely flys on the National Oceanic and Atmospheric Administration's hurricane hunter aircraft. This thesis presents the methods used in the wind speed model development and validation results for wind speeds up to 70 m/sec. The ocean emissivity model relates changes in measured C-band radiometric brightness temperatures to physical changes in the ocean surface. These surface modifications are the result of the drag of surface winds that roughen the sea surface, produce waves, and create white caps and foam from the breaking waves. SFMR brightness temperature measurements from hurricane flights and independent measurements of surface wind speed are used to define empirical relationships between microwave brightness temperature and surface wind speed. The wind speed model employs statistical regression techniques to develop a physics-based ocean emissivity model dependent on geophysical parameters, such as wind speed and sea surface temperature, and observational parameters, such as electromagnetic frequency, electromagnetic polarization, and incidence angle.

The following presents a method for determining topographic elevation error for overland unstructured finite element meshes derived from bare earth LiDAR for use in a shallow water equations model. This thesis investigates the development of an optimal interpolation method to produce minimal error for a given element size. In hydrodynamic studies, it is vital to represent the floodplain as accurately as possible since terrain is a critical factor that influences water flow. An essential step in the development of a coastal inundation model is processing and resampling dense bare earth LiDAR to a DEM and ultimately to the mesh nodes; however, it is crucial that the correct DEM grid size and interpolation method be employed for an accurate representation of the terrain. The following research serves two purposes: 1) to assess the resolution and interpolation scheme of bare earth LiDAR data points in terms of its ability to describe the bare earth topography and its subsequent performance during relevant tide and storm surge simulations

Recent natural disasters have highlighted the need to evacuate people as quickly as possible. During hurricane Rita in 2005, people were stuck in queue buildups and large scale congestions, due to improper use of capacity, planning and inadequate response to vehicle breakdown, flooding and accidents. Every minute is precious in situation of such disaster scenarios. Understanding evacuation demand loading is an essential part of any evacuation planning. One of the factors often understood to effect evacuation, but not modeled has been the effect of a previous hurricane. This has also been termed as the 'Katrina Effect', where, due to the devastation caused by hurricane Katrina, large number of people decided to evacuate during Hurricane Rita, which hit Texas three weeks after Katrina hit Louisiana. An important aspect influencing the rate of evacuation loading is Evacuation Preparation Time also referred to as 'Mobilization time' in literature. A methodology to model the effect of a recent past hurricane on the mobilization times for evacuees in an evacuation has been presented utilizing simultaneous estimation techniques. The errors for the two simultaneously estimated models were significantly correlated, confirming the idea that a previous hurricane does significantly affect evacuation during a subsequent hurricane. The results show that the home ownership, number of individuals in the household, income levels, and level/risk of surge were significant in the model explaining the mobilization times for the households. Pet ownership and number of kids in the households, known to increase the mobilization times during isolated hurricanes, were not found to be significant in the model. Evacuation operations are marred by unexpected blockages, breakdown of vehicles and sudden flooding of transportation infrastructure. A fast and accurate simulation model to incorporate flexibility into the evacuation planning procedure is required to react to such situations. Presently evacuation guidelines are prepared by the local emergency management, by testing various scenarios utilizing micro-simulation, which is extremely time consuming and do not provide flexibility to evacuation plans. To gain computational speed there is a need to move away from the level of detail of a micro-simulation to more aggregated simulation models. The Cell Transmission Model which is a mesoscopic simulation model is considered, and compared with VISSIM a microscopic simulation model. It was observed that the Cell Transmission Model was significantly faster compared to VISSIM, and was found to be accurate. The Cell Transmission model has a nice linear structure, which is utilized to construct Linear Programming Problems to determine optimal strategies. Optimization models were developed to determine strategies for optimal scheduling of evacuation orders and optimal crossover locations for contraflow operations on freeways. A new strategy termed as 'Dynamic Crossovers Strategy' is proposed to alleviate congestion due to lane blockages (due to vehicle breakdowns, incidents etc.). This research finds that the strategy of implementing dynamic crossovers in the event of lane blockages does improve evacuation operations. The optimization model provides a framework within which optimal strategies are determined quickly, without the need to test multiple scenarios using simulation. Destination networks are the cause of the main bottlenecks for evacuation routes, such aspects of transportation networks are rarely studied as part of evacuation operations. This research studies destination networks from a macroscopic perspective. Various relationships between network level macroscopic variables (Average Flow, Average Density and Average speed) over the network were studied. Utilizing these relationships, a "Network Breathing Strategy" was proposed to improve dissipation of evacuating traffic into the destination networks. The network breathing strategy is a cyclic process of allowing vehicles to enter the network till the network reaches congestion, which is followed by closure of their entry into the network until the network reaches an acceptable state. After which entrance into the network is allowed again. The intuitive motivation behind this methodology is to ensure that the network does not remain in congested conditions. The term 'Network Breathing' was coined due to the analogy seen between this strategy to the process of breathing, where vehicles are inhaled by the network (vehicles allowed in) and dissipated by the network (vehicles are not allowed in). It is shown that the network breathing improves the dissipation of vehicle into the destination network. Evacuation operations can be divided into three main levels: at the origin (region at risk), routes and destination. This research encompasses all the three aspects and proposes a framework to assess the whole system in its entirety. At the Origin the demand dictates when to schedule evacuation orders, it also dictates the capacity required on different routes. These breakthroughs will provide a framework for a real time Decision Support System which will help emergency management official make decisions faster and on the fly.

The thesis examines post-event displacement of households in the year following Hurricane Harvey. Using data gathered from a three-page mail survey conducted approximately 1-year after the storm, this study examines two primary research objectives. First this thesis aims to identify variables that predict displacement or non-displacement after the disaster. Second, this study explores patterns in the destination and duration of displaced households following Hurricane Harvey. Logistic regression analyses were used to examine the extent to which household composition characteristics and level of damage sustained during Hurricane Harvey predicted post-disaster displacement. Next, independent sample t-tests and descriptive statistical analyses were used to identify patterns in the destination of post-event relocations. Research findings indicate in the overall binary logistic regression model that after Hurricane Harvey, being White, level of home damage, wind damage, and number of days a member of the household returned home post-Harvey increased the likelihood of a household being displaced. Analysis of the survey responses also indicated that many households made multiple moves following Hurricane Harvey and specifically, displaced households were more likely to stay with a friend or relative. Additionally, this study found that with each additional relocation, the duration of stay at each destination increased while the distance from their pre-disaster home decreased. This thesis advances understanding of what predicts household displacement after a disaster and offers new insights into where people go during the short-term and beginning of the long-term recovery phases.

Climate modeling and tropical cyclone forecasting are two significant is- sues that are continuously being improved upon for more accurate weather forecasting and preparedness. In this thesis, we have studied three climate models and formulated a new model with a view to determine the outgoing longwave radiation (OLR) budget at the top of the atmosphere (TOA) as ob- served by the National Oceanic and Atmospheric Administration’s (NOAA) satellite based Advanced Very High Resolution Radiometer (AVHRR). In 2006, Karnauskas proposed the African meridional OLR as an Atlantic hur- ricane predictor, the relation was further proven in 2016 by Karnauskas and Li. Here we have considered a similar study for all other tropical cyclone basins.

This dissertation studies a framework in support biomass wood pellet supply chain. The worldwide wood pellet market is growing at a phenomenal rate. However, the economic sustainment of this business depends on how well the producers manage the uncertainty associated with biomass yield and quality. In the first part of the dissertation, we propose a two-stage stochastic programming model that optimizes different critical decisions (e.g., harvesting, storage, transportation, quality inspection, and production decisions) of a biomass-to-pellet supply system under biomass yield and quality uncertainty to economically produce pellets while accounting for the different pellet standards set forward by the U.S. and European markets. The study develops a hybrid algorithm that combines Sample Average Approximation with an enhanced Progressive Hedging algorithm. We propose two parallelization schemes to efficiently speed up the convergence of the overall algorithm. We use Mississippi as a testing ground to visualize and validate the algorithms performance. Experimental results indicate that the biomass-to-pellet supply system is sensitive to the biomass quality parameters (e.g., ash and moisture contents). In the second part of the dissertation, we propose a bi-level mixed-integer linear programming model that captures important features such as the hurricane’s degree, quality of damaged timbers, price-related issues, optimizes different critical decisions (e.g., purchasing, storage, and transportation decisions) of a post-hurricane damaged timber management problem. Lack of efficient tools to manage the wood market interactions in the post-hurricane situation increases timber salvage loss drastically. The overall goal is to provide an efficient decision-making tool for planning and recovering damaged timber to maximize its monetary value and mitigate its negative ecological impacts. Due to the complexity associated with solving the proposed model, we developed two exact solution methods, namely, the enhanced Benders decomposition and the Benders-based branch-and-cut algorithms, to efficiently solve the model in a reasonable time-frame. We use 15 coastal counties in southeast Mississippi to visualize and validate the algorithms' performance. Key managerial insights are drawn on the sensitivity of a number of critical parameters, such as selling/purchasing prices offered by the landowners/mills, quality-level, and deterioration rate of the damaged timbers on their economic recovery following a natural catastrophe.

This thesis uses flooding driven by Hurricane Harvey in 2017 and a history of inundation in Houston, Texas to critique the systems of floodplain mapping through the National Flood Insurance Program (NFIP). The role of Geographic Information Systems becomes a subject of interest in the context of U.S governance and the role of property as a driving force in urban development. The shortcomings of existing systems of mitigation are examined through mappings that bring measures of risk, damage, and recovery into contrast with each other. Racial and economic inequality are integrated into the analysis through a deeper consideration of the NFIP as the main form of federal protection against losses. Seeing that the NFIP has not protected the true status quo of urban life, it is argued that public perceptions of risk are formed contrary to the logic of home insurance, leading to observable inequalities in preparation and recovery