Determining the amount of resources needed, specifically food and water, following a hurricane is not a straightforward task. Through this research effort, an estimating tool was developed that takes into account key demographic and evacuation behavioral effects, as well as hurricane storm specifics to estimate the number of meals required for the first fourteen days following a hurricane making landfall in the State of Florida. The Excel based estimating tool was created using data collected from four hurricanes making landfall in Florida during 2004-2005. The underlying model used in the tool is a Regression Decision Tree with predictor variables including direct impact, poverty level, and hurricane impact score. The hurricane impact score is a hurricane classification system resulting from this research that includes hurricane category, intensity, wind field size, and landfall location. The direct path of a hurricane, a higher than average proportion of residents below the poverty level, and the hurricane impact score were all found to have an effect on the number of meals required during the first fourteen days following a hurricane making landfall in the State of Florida

The current cycle of climate change along with increases in hurricane activity, changing precipitation patterns, glacial melt, and other extremes of weather has led to interest and research into the global correlation or teleconnection between these events. Examination of historical climate records, proxies and observations is leading to formulation of hypotheses of climate dynamics with modeling and simulation being used to test these hypotheses as well as making projections. Ocean currents are believed to be an important factor in climate change with thermohaline circulation (THC) fluctuations being implicated in past cycles of abrupt change. Freshwater water discharge into high-latitude oceans attributed to changing precipitation patterns and glacial melt, particularly the North Atlantic, has also been associated with historical abrupt climate changes and is believed to have inhibited or shut down the THC overturning mechanism by diluting saline surface waters transported from the tropics. Here we analyze outputs of general circulation model (GCM) simulations parameterized by different levels of freshwater flux (no flux (control), 0.1 Sverdrup (Sv) and 1.0 Sv) with respect to tropical cyclone-like vortices (TCLVs) to determine any trend in simulated tropical storm frequency, duration, and location relative to flux level, as well as considering the applicability of using GCMs for tropical weather research. Increasing flux levels produced fewer storms and storm days, increased storm duration, a southerly and westerly shift (more pronounced for the 0.1 Sv level) in geographic distribution and increased activity near the African coast (more pronounced for the 1.0 Sv level). Storm intensities and tracks were not realistic compared to observational (real-life) values and is attributed to the GCM resolution not being fine enough to realistically simulate storm (microscale) dynamics.

Natural disasters, specifically hurricanes, can cause catastrophic loss of life and property. In recent years, the United States has endured significant losses due to a series of devastating hurricanes (e.g., Hurricanes Charley and Ivan in 2004, and Hurricanes Katrina and Wilma in 2005). Several Federal authorities report that there are weaknesses in the emergency and disaster planning and response models that are currently employed in practice, thus creating a need for better decision models in emergency situations. The current models not only lack fast communication with emergency responders and the public, but are also inadequate for advising the pre-positioning of supplies at emergency shelters before the storm's impact. The problem of emergency evacuation relief shelter planning during anticipated hurricane events is addressed in this research. The shelter planning problem is modeled as a joint location-allocation-inventory problem, where the number and location of shelter facilities must be identified. In addition, the evacuating citizens must be assigned to the designated shelter facilities, and the amount of emergency supply inventory to pre-position at each facility must be determined. The objective is to minimize total emergency evacuation costs, which is equal to the combined facility opening and preparation cost, evacuee transportation cost and emergency supply inventory cost. A review of the emergency evacuation planning literature reveals that this class of problems has not been largely addressed to date. First, the emergency evacuation relief sheltering problem is formulated under deterministic conditions as a mixed integer non-linear programming (MINLP) model. For three different evacuation scenarios, the proposed MINLP model yields a plan that identifies the locations of relief shelters for evacuees, the assignment of evacuees to those shelters and the amount of emergency supplies to stockpile in advance of an anticipated hurricane. The MINLP model is then used (with minor modifications) to explore the idea of equally distributing the evacuees across the open shelters. The results for the three different scenarios indicate that a balanced utilization of the open shelters is achieved with little increase in the total evacuation cost. Next, the MINLP is enhanced to consider the stochastic characteristics of both hurricane strength and projected trajectory, which can directly influence the storm's behavior. The hurricane's strength is based on its hurricane category according to the Saffir-Simpson Hurricane Scale. Its trajectory is represented as a Markov chain, where the storm's path is modeled as transitions among states (i.e., coordinate locations) within a spherical coordinate system. A specific hurricane that made landfall in the state of Florida is used as a test case for the model. Finally, the stochastic model is employed within a robust optimization strategy, where several probable hurricane behavioral scenarios are solved. Then, a single, robust evacuation sheltering plan that provides the best results, not only in terms of maximum deviation of total evacuation cost across the likely scenarios, but also in terms of maximum deviation of unmet evacuee demand at the shelter locations, is generated. The practical value of this robust plan is quite significant. This plan should accommodate unexpected changes in the behavior of an approaching storm to a reasonable degree with minimal negative impact to the total evacuation cost and the fulfillment of evacuee demand at the shelter locations. Most importantly, the re-allocation and re-mobilization of emergency personnel and supplies are not required, which can cause confusion and potentially increase the response time of responders to the hurricane emergency. The computational results show the promise of this research and usefulness of the proposed models. This work is an initial step in addressing the simultaneous identification of shelter locations, assignment of citizens to those shelters, and determination of a policy for stockpiling emergency supplies in advance of a hurricane. Both the location-allocation problem and the inventory problem have been extensively and individually studied by researchers as well as practitioners. However, this joint location-allocation-inventory problem is a difficult problem to solve, especially in the presence of stochastic storm behavior. The proposed models, even in the deterministic case, are a significant step beyond the current state-of-the-art in the area of emergency and disaster planning.

Tropical cyclone (TC) rapid intensity change negatively impacts forecast error. Many studies have investigated rapid intensification, but fewer explore rapid weakening, particularly with aircraft observations due to fewer weakening TCs being flown. This study assesses factors contributing to the rapid weakening of Hurricane Lorenzo (2019) and the comparatively slower weakening of Hurricane Florence (2018) using aircraft observations and satellite-based products to enhance understanding of processes related to TC weakening. Intrusion of environmental dry air into Lorenzo's core under persistent moderate vertical wind shear, in conjunction with quickly decreasing SSTs, largely contributed to the TC's rapid weakening. Conversely, SSTs were higher and decreased more slowly along Florence's track, and dry air did not reach the TC's core. Confirming these processes with both aircraft and satellite observations implies that satellite analysis in the absence of reconnaissance could detect these features to some extent which may support future operational forecasting.

No description available.

Natural and anthropogenic disturbances drive change in ecosystems, especially highly disturbed coastal systems, which are at the interface between the land and the sea and contain both aquatic and terrestrial ecosystems. This transitional zone is at the forefront of climate change. As sea level rises, disturbance regimes are expected to change. Simultaneously, the frequency and intensity of extreme storm events, such as hurricanes, may increase, along with increases in fire intensity and severity in certain regions. Historically, fire was a natural disturbance along the northern Gulf of Mexico where lightning frequency is high; however, today fire along the Gulf is often anthropogenic in origin (i.e., prescribed fire). As disturbance regimes change, the interaction between hurricanes and fire is likely to become increasingly prevalent, since increased production of dead debris from more intense hurricanes is likely to serve as additional fuel material for fires. Sea level rise may also act synergistically with the typical pulse disturbances coastal ecosystems face, including hurricanes and fire. This combination of acute and chronic stressors may prevent coastal ecosystems from recovering and returning to their pre-disturbance state if layered legacies of these events decrease ecosystem stability and resilience. The goal of this study was to investigate the effects of layered legacies of disturbances on community composition, species distributions, extent of coastal zones (e.g., salt marsh, fresh marsh, forest) and vegetation vigor in coastal communities over a 17-year period (2004 to 2021) in coastal Alabama to explore the resilience of coastal systems and their persistence in the face of sea level rise. A combination of ground-collected data from 2004, 2011 and 2021, and fine resolution satellite images taken every other year from 2006-2019 were analyzed. Disturbances altered community composition between 2004 and 2021, which coincided with expansion of salt marsh and fresh marsh species distributions at lower elevations, and declines in woody species in the scrub-shrub ecotone and forest at higher elevations. The scrub-shrub ecotone disappeared, and the forest began to deteriorate, while the extent of the fresh marsh increased. Additionally, vegetation vigor (as measured by the Normalized Difference Vegetation Index; NDVI) was calculated from moderate resolution Landsat images within one month prior to and following each extreme storm event from 2004-2020. NDVI decreased after some extreme storm events but increased after others, and there was an overall increase in NDVI over the last five years of the study period. This study was conducted at a critical time; coastal systems are facing an increasing amount of chronic stress from sea level rise, in addition to more immediate stress from pulse disturbances. Despite these stressors, coastal systems along the northern Gulf of Mexico appear to be more resilient than previously realized because upslope migration of species is evident. Extreme storm events and fires appear to contribute to, and even promote, the persistence of coastal wetlands in the face of sea level rise. However, persistence of coastal wetlands along the northern Gulf of Mexico coast may be prevented in areas dominated by upslope barriers to migration (i.e., current/future urban development and levees), such as in Louisiana.

Climate change is increasing the frequency and intensity of (extra-) tropical cyclones including hurricanes and winter storms worldwide. Waterborne diseases, resulting from flood-related impacts, affect public health and are of major concern for society. Previous research studies have highlighted a statistically significant linear correlation between waterborne diseases and climate variables, especially precipitation and temperature. However, to the best of our knowledge, no studies have explored nonlinear models (e.g., machine learning) to predict waterborne disease outbreaks in the aftermath of hurricanes and winter storms. Here, we aim at predicting waterborne disease counts as well as disease outbreaks using historic climate demographic, and public health data of Florida, U.S. that date back to 1992. For this, we first predicted diseases in aggregated coastal counties using multiple linear (MLR) and random forest regression (RFR) models. Then, we developed a binary random forest classifier (RFC) model to predict waterborne disease outbreaks (e.g., 0: no outbreak and 1: outbreak). Results of this study showed that the highest coefficient of determination (R2) for the MLR model was 0.65 for two aggregated county groups, namely St. Johns-Duval-Nassau and Sarasota-Charlotte-Lee. The RFR model showed the highest R2 of 0.69 for the county group Sarasota-Charlotte-Lee. The highest Root Mean Square Error (RMSE) was found for the county group Miami Dade-Broward- Palm Beach with a value of 15 and 16 people for both the MLR and RFR models. St. Johns-Duval-Nassau and Sarasota-Charlotte-Lee groups achieved the highest Kling-Gupta Efficiency (KGE) of 0.76 for the MLR model. Sarasota-Charlotte-Lee also performed the best in terms of KGE for the RFR model with a score of 0.69. On the other hand, the binary RFC model for Pinellas-Hillsborough-Manatee achieved a model's accuracy of 0.93 and f1-score of 0.48. We anticipate that the models' performance can substantially be improved with access to higher spatial resolution climate data as well as longer demographic and public health records. Nevertheless, we here provide a solid methodology that can inform local authorities about imminent public health impacts and mitigate negative effects on society, economy, and environment. / Master of Science / Climate change is increasing the frequency and intensity of tropical storms, which include hurricanes and winter storms worldwide. Extreme weather events have been shown to increase the risk of waterborne disease outbreaks (i.e. diseases that are transmitted by water), especially due to increased flooding. Previous studies showed a correlation between climate factors, such as precipitation and temperature, and waterborne diseases, but no concrete models have been developed to predict these outbreaks. Advanced prediction models can help predict where disease outbreaks are most likely to occur and can help in preparing for and mitigating the severity of these outbreaks to help save lives, protect the environment, and reduce the damage done to infrastructure. Our research focused on developing a model framework using climate and demographic data from coastal Florida counties dating back to 1992 to predict Salmonellosis, a common waterborne bacterial infection, after a hurricane event. We created two regression models, one a multiple linear regression (MLR) and the other a random forest regression (RFR) to predict the number of Salmonellosis cases. Additionally, we created a random forest classifier model (RFC) to predict whether an outbreak would occur. After running analyses for these three models on groups of three counties, we found that the MLR and RFR showed similar accuracies at predicting cases, with the MLR performing slightly better for most counties. For the Sarasota-Charlotte-Lee county group, the RFR performed the best. The RFC model performed the best with the highest accuracy of 93% for Pinellas-Hillsborough-Manatee. Future improvements can help make these models more reliable, such as using better and more data, along with adding more variables.

The tropical cyclone is a majestic, yet violent atmospheric weather system occurring over tropical waters. Their majesty evolves from the significant range of spatial scales they operate over: from the mesoscale, to the larger synoptic-scale. Their associated violent winds and seas, however, are often the cause of damage and destruction for settlements in their path. Between 10/11/07 and 16/11/07, tropical cyclone Sidr formed and intensified into a category 5 hurricane over the southeast tropical waters of the northern Indian Ocean. Sidr tracked west, then north, during the course of its life, and eventually made landfall on 15/11/07, as a category 4 cyclone near the settlement of Barguna, Bangladesh. The storm affected approximately 2.7 million people in Bangladesh, and of that number 4234 were killed. In this study, the dynamics of tropical cyclone Sidr are simulated using version 2.2.1 of Advanced Weather Research and Forecasting — a non-hydrostatic, two-way interactive, triply-nested-grid mesoscale model. Three experiments were developed examining model sensitivity to ocean-atmosphere interaction; initialisation time; and choice of convective parameterisation scheme. All experiments were verified against analysed synoptic data. The ocean-atmosphere experiment involved one simulation of a cold sea surface temperature, fixed at 10 °C; and simulated using a 15 km grid resolution. The initialisation experiment involved three simulations of different model start time: 108-, 72-, and 48-hours before landfall respectively. These were simulated using a 15 km grid resolution. The convective experiment consisted of four simulations, with three of these using a different implicit convective scheme. The three schemes used were, the Kain-Fritsch, Betts-Miller-Janjic, and Grell-Devenyi ensemble. The fourth case simulated convection explicitly. A nested domain of 5km grid spacing was used in the convective experiment, for high resolution modelling. In all experiments, the Eta-Ferrier microphysics scheme, and the Mellor-Yamada-Janjic planetary boundary layer scheme were used. As verified against available observations, the model showed considerable sensitivity in each of the experiments. The model was found to be well suited for combining ocean-atmosphere interactions: a cool sea surface caused cyclone Sidr to dissipate within 24 hours. The initialisation simulations indicated moderate model sensitivity to initialisation time: variations were found for both cyclone track and intensity. Of the three simulations, an initialisation time 108 hours prior to landfall, was found to most accurately represent cyclone Sidr’s track and intensity. Finally, the convective simulations showed that considerable differences were found in cyclone track, intensity, and structure, when using different convective schemes. The Kain-Fritsch scheme produced the most accurate cyclone track and structure, but the rainfall rate was spurious on the sub-grid-scale. The Betts-Miller-Janjic scheme resolved realistic rainfall on both domains, but cyclone intensity was poor. Of particular significance, was that explicit convection produced a similar result to the Grell-Devenyi ensemble for both model domain resolutions. Overall, the results suggest that the modelled cyclone is highly sensitive to changes in initial conditions. In particular, in the context of other studies, it appears that the combination of convective scheme, microphysics scheme, and boundary layer scheme, are most significant for accurate track and intensity prediction.

CHDS State/Local / This thesis focuses on the medical as part of the public health response to Hurricane Katrina, specific to the issues of the private, non-governmental health professional. A brief survey was completed by 39 state level Bioterrorism Hospital Coordinators. Information obtained highlights the issues of the inability to deploy these private health professionals. Traditional governmental mutual aid mechanisms do not cover private non-governmental health professionals for workers compensation and death benefits. A review of the potential deployment mechanisms provides insight to the challenges and complexity specific to private health professionals. The motivation for volunteerism highlights the importance of targeting volunteer activities to the motivation of the individual volunteer. Investigating the impact thwarting the private, nongovernmental health professionals may have on future planning and response activities reinforce the need to modify the structures currently in place. The National Response Plan stresses the importance of including private industry into emergency preparedness and response strategies. This thesis outlines a strategy to pilot a project working with an established state volunteer registry by providing mechanisms to federalize those pre-identified, pre-credentialed volunteer health professionals. Once completed, this pilot could be expanded to other states ensuring a solid mechanism to quickly and safely mobilize this critical response discipline. / Bioterrorism Hospital Coordinator, Michigan Department of Community Health

Aboveground plant community dynamics in the oligohaline marsh at Big Branch Marsh National Wildlife Refuge, Louisiana, USA, were assessed in response to nutrient loading (3 N x 3 P treatments) and disturbance (both planned lethal disturbance and stochastic tropical storm/hurricane disturbance). Sampling was conducted seasonally from April 2004 to September 2006. Spartina patens and Schoenoplectus americanus are co-dominant plant species in this marsh. Low N-loading additions resulted in increased S. patens cover. However, increased N loading did not result in a shift in plant community composition despite S. americanus consistently having higher leaf tissue N than S. patens. Our results indicate that S. americanus may be more resilient than S. patens to disturbances that do not increase marsh surface elevation. Hurricane Katrina deposited significant amounts of sediment into remaining plots (August 29, 2005). By 2006, this disturbance resulted in a significant increase in both species richness and S. patens cover.