This study utilized content analysis of newspaper articles in the month following Hurricane Ike's landfall to evaluate the presence of agenda setting and framing. Three national newspapers were analyzed to determine the existence and order of news frames. The results indicate that Semetko and Valkenburg's (2000) news frames changed in order of importance in this study. The order of news frames varied among the three national newspapers. The newspaper with mostly human interest frames was determined to be more sensational than the other two, more serious newspapers with predominantly responsibility frames. This study then compared the five ordered frames to previous framing research on Hurricane Katrina. The two hurricanes differed greatly in amount of news coverage and varied slightly in the order of the news frames. An evaluation of news coverage of major U.S. events occurring in the month after Hurricane Ike was conducted, with results indicating that news attention of the hurricane was hindered by other major national events.

Hurricanes cause enormous loss to life and property worldwide. Predicting the damage caused by hurricane and figuring out what factors are responsible for the damage are important. This study utilizes multiple linear regression models to predict a hurricane – induced Texas Windstorm Insurance Association (TWIA) payout or TWIA payout ratio using independent variables that could affect the hurricane intensity, including distance from the coastline, distance from the hurricane track, distance from the landfall center of Hurricane Ike, proportion in floodplain zone (100 year, 500 year, 100-500 year), building area, proportion in island, number of buildings per parcel, and building age. The methodology of this study includes Pearson’s correlation and multiple linear regressions. First, Pearson’s correlation is used to examine whether there are any significant correlations between the dependent and independent variables. For TWIA payout, three independent variables, distance from the coastline, distance from the landfall center, and building area, are correlated to the TWIA payout at the 0.01 level. Distance from the coastline and distance from the landfall center have negative relations with the TWIA payout. The variable, building area, has a positive relation with the TWIA payout. Moreover, the improvement value is correlated to the TWIA payout at the 0.05 level. For TWIA payout ratio, distance from the coastline is correlated to the TWIA payout ratio at the level of 0.01 and distance from the landfall center is correlated to the TWIA payout ratio at the 0.05 level. These two variables have negative relations to the TWIA payout ratio. Multiple linear regressions are applied to predict the TWIA payout and payout ratio. A regression model with an Adjusted R Square of 0.264 is presented to predict the TWIA payout. This model could explain 26.4 percent of the variability in TWIA payout using the variables, distance from coastline and building area. A regression model with an Adjusted R Square of 0.121 is presented to predict the TWIA payout ratio.

Coastal marshes and wetlands are vital natural resources that offer habitats for plants and animals, serve as ecological filtration for soil and water pollutants, and act as protection for coastlines. Fishing, both commercial and sport, has a large economic impact in the study area – the Gulf Coast between Galveston Bay, TX and Oak Grove, LA. The objective of this research was to determine the contribution of Hurricane Ike storm surge sedimentation to long-term marsh aggradation in Texas and Louisiana coastal marshes. The research hypothesized that Hurricane Ike’s storm surge deposit would be equal to decades and possibly even a century’s worth of the average annual non-storm sedimentation. A quantitative field study was performed. The storm surge deposit was examined in a series of 15 transects covering approximately 180 km east of Hurricane Ike’s landfall. Nine of the 15 transects were re-surveyed a year after the initial measurement to assess preservation of the deposit. The results demonstrate that Hurricane Ike contributed between 10 to 135 years’ worth of sediment to coastal marshes along the coasts of Texas and Louisiana, and the sediment deposits have been preserved for over two years.

Recurring extreme events of nature challenge disaster-prone settlements in complex ways. Devastating property damages are one of the tests of survival for such settlements in both economic and social terms. It also provides unique opportunities to rethink the environment cleared by massive natural disasters. However, rebuilding for long-term resiliency is one of the least investigated areas, particularly when employing tacit knowledge in the sustainable recovery process. This study examines the post-disaster rebuilding process in spatial terms for Bolivar Peninsula in the aftermath of Hurricane Ike. It further investigates the nexus between connectivity among open space networks to various levels of surge damage among Bolivar spontaneous settlements. The study uses syntactical methods to measure axial connectivity of the Bolivar Peninsula access grid and one-way Analysis of Variance to interpret the way connectivity varies along the no damage to destroyed damage scale. In addition, the permeability rubric analyzes the elevation characteristics of houses that demonstrated higher probabilities of survival through a logistic regression. The conclusions are based on two basic premises. Local knowledge demands an indefinite time to be adapted and mobilized because of the increasing intensity of natural disasters. In addition, the high frequency of disaster events significantly challenges the versatility of local coping and survival strategies. The results reveal that the connectivity of the access grid has an inversely proportional relationship with various damage levels, particularly for no damage and destroyed. Furthermore, out of a number of resiliency characteristics listed in the literature, only ground elevation and ground enclosure demonstrated probability significances for survival. Potentially, the results of this research could support three significant outcomes pertaining to sustainable disaster recovery: preserving place character, social justice among affected groups, and promoting rapid recovery.

Following growing public awareness of the danger from hurricanes and tremendous demands for analysis of loss, many researchers have conducted studies to develop hurricane damage analysis methods. Although researchers have identified the significant indicators, there currently is no comprehensive research for identifying the relationship among the vulnerabilities, natural disasters, and economic losses associated with individual buildings. To address this lack of research, this study will identify vulnerabilities and hurricane indicators, develop metrics to measure the influence of economic losses from hurricanes, and visualize the spatial distribution of vulnerability to evaluate overall hurricane damage. This paper has utilized the Geographic Information System (GIS) to facilitate collecting and managing data, and has combined vulnerability factors to assess the financial losses suffered by Texas coastal counties. A multiple linear regression method has been applied to develop hurricane economic damage predicting models. To reflect the pecuniary loss, insured loss payment was used as the dependent variable to predict the actual financial damage and ratio. Geographical vulnerability indicators, built environment vulnerability indicators, and hurricane indicators were all used as independent variables. Accordingly, the models and findings may possibly provide vital references for government agencies, emergency planners, and insurance companies hoping to predict hurricane damage.

Over the 1951-2009 time period, 47% of all tropical systems in the Atlantic Basin transitioned to post-tropical storms. These storms are capable of producing hurricaneforce winds, torrential, flooding rains and storm surge that floods coastal areas. This study adds to previous climatological work by completing a case-study of Hurricane Ike (2008) and examining how teleconnections such as the El Niño Southern Oscillation (ENSO), the Madden-Julian Oscillation (MJO), the Atlantic Multidecadal Oscillation (AMO) and the Pacific Decadal Oscillation (PDO) contribute to the strength of a transitioning post-tropical storm. T-tests performed show strong statistical relationships between an increase (decrease) in post-tropical storm frequency and warm PDO – La Niña (cold PDO – La Niña), cold PDO – ENSO neutral (warm PDO – ENSO neutral), and warm (cold) AMO conditions. Moreover, nearly significant results were found for the same increase (decrease) and La Niña seasons since (pre) 1980 and for cold (warm) PDO conditions. Modeling the MJO suggests that increased (decreased) relative humidity associated with the wet (dry) phase could increase (decrease) precipitation output from the storm and decrease (increase) forward speed of the storm, decreasing (increasing) wind speeds observed at the surface.

Hurricanes have been considered one of the most costly disasters in United State, which lead to both economic loss and human fatalities. Therefore, understanding the characteristics of those who evacuated and of those who did not evacuate have been principal focus of some previous researches related to hurricane evacuation behavior. This research presents two sets of decision-making models for analyzing hurricane evacuation behavior, using two statistical methods: standard logistic model and mixed logistic model.The receipt of evacuation order, elevation, expenditure, the presence of children and elderly people, ownership of a house, and receipt of hurricane warning are found to be extremely important in evacuation decision making. When the mixed logistic model is applied, the rate of concern about hurricane threat is assumed to be random according to normal distribution. Mixed logistic models which account for the heterogeneity of household responses are found to perform better than standard logistic model.

Annually, tropical cyclones do tremendous damage and are agents of long-term coastal change. To test this idea of different tropical cyclones delivering consistent coastal change, a landform with such evolution is needed. One such landform is a spit. What contributions do tropical cyclones give toward the evolution of a spit, and do tropical cyclones give the same kinds of impacts? To determine if tropical cyclones have similar impacts, shoreline and volumetric change from four storms impacting Bolivar Peninsula are considered. Being a southwest-trended spit at a length of 33.5 kilometers, storm impacts are measured in the form of one dimensional shoreline and two dimensional volumetric change. These impacts are abstracted into shoreline change and volumetric change patterns. These patterns are identified and compared for differences between each storm and similarity among all storms. Results indicate that shoreline accretionary zones vary alongshore. Results from Hurricane Ike indicate an accretionary zone ten kilometers from the distal end. Shoreline change patterns for Hurricane Rita show an unstable accretionary zone at four kilometers from the distal end. Results for Tropical Storm Fay indicate an unstable accretionary zone that begins at the distal end and continues to the middle of the spit. In terms of similarity for shoreline change, all patterns from storms demonstrated erosion near Rollover Fish Pass. One dimensional volumetric change patterns were entirely erosive for Hurricanes Rita and Ike, and Tropical Storm Fay had by small zones of accretion near the distal portion of the spit. Tropical Storm Josephine demonstrated an accretion zone between the middle and distal portion of the spit. Results from two dimensional volumetric change patterns suggest a threshold for inland penetration. Tropical Storm Fay showed a ten to twenty meter wide pattern of erosion around five kilometers from the distal end and near the proximal end of the spit, and Hurricane Rita demonstrated a twenty meter wide pattern of erosion near the distal end. Hurricane Ike had erosive penetration of up to 200 meters around fifteen kilometers from the distal end. Results suggest that certain storms reinforce the standard spit growth model, and others work against it.

This ethnographic analysis of the social and physical effects of Hurricane Ike in Galveston, Texas and the consequent recovery that emerged afterward is based on 20 months of field research conducted immediately before and after the storm’s landfall. The introductory chapter locates the ethnographer just prior to the hurricane as he prepared for an unexpected evacuation. It then presents the conceptual framework for a multi-sited ethnography of “disaster culture” and introduces analytic keywords of “vulnerability,” “resilience,” “dreamworlds,” and “catastrophe.” It concludes by discussing a set of historical and contemporary socio-economic conditions in Galveston. This provides a frame of reference of both the social formations of storm experiences and the public recovery dynamics that attended with Ike’s aftermath that are discussed throughout the text. This is further supplemented with an explanation of Ike’s flooding and the geographic distribution of storm damage. Chapter two begins with an ethnographic vignette of the first townhall meeting held in Galveston after Ike. This introduces several recurrent topics of concern that were formative of disaster-culture dynamics. It then provides a literature review of the anthropology of disaster before segueing into a presentation of storm narratives. It ends with an analysis that further elaborates on the formative dynamics of Galvestonian disaster culture. Chapter three provides an analysis of the public deliberations that emerged over long-term redevelopment initiatives; particularly, the advocacy practices of a faith-based consortium; advocacy on behalf of restoring the University of Texas Medical Branch; the public Long Term Recovery Committee, and a FEMA buyout program that benefited higher income property owners on the western end of the island. The fourth chapter provides an extended case study concerning the rebuilding of 569 units of public housing that were subsequently destroyed after the hurricane. The rebuilding of public housing became the most vitriolic public issue during the course of fieldwork. The concluding chapter invokes the concepts of “dreamworlds” and “catastrophe” used by historian and philosopher Walter Benjamin to show the processual dynamics between the initial hopes for collectively strengthening Galveston through federally funded redevelopment and the increasingly negative assessments of the city’s long-term urban fortunes. / text

In September 2008 Hurricane Ike hit the Texas Gulf Coast with a force stronger than the category 2 storm at which it was rated. With a 3.8 m (12.5 ft) storm surge, the agricultural industry in the area was devastated. The goal of this research was to determine the length of time required to reduce the salt levels brought by the storm surge to near pre-hurricane levels. To do this, four sets of samples were taken across two years and analyzed for salinity using the saturated paste extract method. The initial salt levels in November 2008 had an electrical conductivity (ECe) of the inundated soils as high as 26.7 dS/m. Fifty-four percent of the soils sampled in the 0-15 cm horizons and 9% in the 15-30 cm horizons of the edge area had an ECe >= 4 dS/m. In the surge area 79% of the soils sampled in the 0-15 cm horizons and 30% in the 15-30 cm horizons had an ECe >= 4 dS/m. In April 2009, 38% of the soils sampled in the 0-15 cm horizons and 13% in the 15-30 cm horizons of the edge area had an ECe >= 4 dS/m. In the surge area 71% of the soils sampled in the 0-15 cm horizons and 39% in the 15-30 cm horizons had an ECe >= 4 dS/m. By December 2009, none of the soils sampled in the edge area had an ECe >= 4 dS/m. In the surge area 21% of the soils sampled in the 0-15 cm horizons and 33% in the 15-30 cm horizons had an ECe >= 4 dS/m. By October 2010, all soils sampled had leached sufficient salts to be classified as non-saline to very slightly saline soils. Utilizing the November 2008 data set, 28 random samples were selected for exchangeable Na percent (ESP) in order to develop the ESP-SAR (Na adsorption ratio) predictive equation, ESP= 1.19(SAR)^0.82. The SAR-ESP relationship is statistically significant (95% confidence level), with a correlation coefficient of 0.964 (df=26).