A Hurricane Specific Risk Assessment of the United States' Gulf Coast

A Hurricane Specific Risk Assessment of the United States’ Gulf Coast Counties

THESIS

Presented in Partial Fulfillment of the Requirements for the Degree Master of Science in the Graduate School of The Ohio State University

Caitlin Stripling, B.S.

Graduate Program in Atmospheric Sciences

The Ohio State University

2016

Master's Examination Committee:

Dr. Jay Hobgood, Advisor

Dr. Jialin Lin

Caitlin Stripling

2016

Abstract

Utilization and understanding of risk assessments in disastrous hurricane events aids in the preparation of and recovery from the event; an especially helpful tool to the residents of Gulf of Mexico coastal counties.

This hurricane specific analysis consists of variables that make counties along the gulf coast of the United States more vulnerable or resilient to damage from hurricanes and the hypothetical ability to recover in the aftermath. The analysis included social vulnerability based on the demographics of the county, physical data from ten different hurricanes, and the resilience opportunities available to the residents of these counties.

Analyses of the specified variables resulted in a correlation between high initial vulnerabilities and high hurricane hazards with respect to higher risks. However, a stronger correlation occurred between the resiliency of the county and the overall risk associated with each hurricane. Counties with larger overall vulnerabilities only received high risks if the county was not equipped with the necessary resiliency factors. Hancock

County, while having lower vulnerabilities than other counties and not always acquiring the largest hurricane parameters, was consistently the county with the highest risk in relevant hurricane cases due to its extremely low resiliency. This low resiliency was based on the county’s population of over 2 million and its deficiency in emergency services available to the community.

Dedication

Although he passed during my studies, my grandfather, Dr. Burnell Stripling, M.D., was always my biggest fan. He supported every decision I made no matter how big or how small. His interest in my life has far exceeded what I could have asked from him.

Because of how supportive he was, and how much I looked up to him, I would like to dedicate my work to him. I know I continue to make him proud.

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Acknowledgments

I would like to thank my advisor, Jay Hobgood, as well as Alvaro Montenegro and Jialin Lin for all their guidance and support on this thesis. Their knowledge of severe weather and computer functionality made this project possible.

I would also like to thank my parents, Burnie and Cathy Stripling, and my fiancé,

Jeremy Hamer, for supporting me through yet another journey. Whether you were there to offer advice, or just lend an ear, I couldn’t have taken this step without you backing me the entire way.

Vita

May 2009 ...... Ann Arbor Pioneer High School, Ann

Arbor, MI

2013...... B.S. Earth Science, Michigan State

University, East Lansing, MI

Sep. 2015 to May 2016 ...... Graduate Teaching Assistant, Department of

Geography, The Ohio State University

Fields of Study

Major Field: Atmospheric Sciences

Table of Contents

Abstract ...... ii

Dedication ...... iii

Acknowledgments...... iv

Vita ...... v

Table of Contents ...... vi

List of Tables ...... vii

List of Figures ...... viii

Chapter 1: Introduction ...... 1

Chapter 2: Literature Review ...... 5

Chapter 3: Scientific Objectives and Methodology ...... 17

Chapter 4: Results ...... 55

Chapter 5: Conclusion and Future Work ...... 190

References ...... 198

List of Tables

Table 1. Ratio Calculations and Rank Examples for Female Populations… ...... 48

Table 2. Hazard and Vulnerability Variables ...... 51

Table 3. Resiliency Variables ...... 52

Table 4. Initial Calculated Risks for all Counties in Study Area ...... 112

Table 5. Comparative Look at Initial and Overall Vulnerabilities, Resiliencies,… ...... 116

Table 6. Hurricane Charley Hazard Values and Vulnerabilities by County ...... 121

Table 7. Hurricane Ivan Hazard Values and Vulnerabilities by County ...... 126

Table 8. Hurricane Dennis Hazard Values and Vulnerabilities by County ...... 133

Table 9. Hurricane Katrina Hazard Values and Vulnerabilities by County ...... 142

Table 10. Hurricane Rita Hazard Values and Vulnerabilities by County ...... 148

Table 11. Hurricane Wilma Hazard Values and Vulnerabilities by County ...... 155

Table 12. Hurricane Gustav Hazard Values and Vulnerabilities by County ...... 158

Table 13. Hurricane Ike Hazard Values and Vulnerabilities by County (Coastal) ...... 165

Table 14. Hurricane Ike Hazard Values and Vulnerabilities by County (Inland) ...... 171

Table 15. Hurricane Ida Hazard Values and Vulnerabilities by County ...... 177

Table 16. Hurricane Isaac Hazard Values and Vulnerabilities by County ...... 184

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List of Figures

Figure 1: Atlantic Basin Track Error Trends in Tropical Cyclones...... 3

Figure 2. Atlantic Basin Intensity Error Trends in Tropical Cyclones ...... 4

Figure 3. Constant Variable Relationship for Decay Model ...... 50

Figure 4. Individual Gulf Coast County Layers in Project Area ...... 53

Figure 5. Individual Inland County Layers for Study of Hurricane Ike… ...... 54

Figure 6. Hurricane Risks of Gulf Coast Counties Associated with Hurricane Charley 120

Figure 7. Hurricane Risks of Gulf Coast Counties Associated with Hurricane Ivan ..... 125

Figure 8. Hurricane Risks of Gulf Coast Counties Associated with Hurricane Dennis . 132

Figure 9. Hurricane Risks of Gulf Coast Counties Associated with Hurricane Katrina . 141

Figure 10. Hurricane Risks of Gulf Coast Counties Associated with Hurricane Rita .... 147

Figure 11. Hurricane Risks of Gulf Coast Counties Associated with Hurricane Wilma 154

Figure 12. Hurricane Risks of Gulf Coast Counties Associated with Hurricane Gustav 157

Figure 13. Hurricane Risks of Gulf Coast Counties Associated with Hurricane Ike ..... 164

Figure 14. Hurricane Risks of Inland Counties along Hurricane Ike’s Track ...... 170

Figure 15. Hurricane Risks of Gulf Coast Counties Associated with Hurricane Ida ..... 176

Figure 16. Hurricane Risks of Gulf Coast Counties Associated with Hurricane Isaac .. 183

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Chapter 1

Introduction

With current weather and climate trends pointing toward greater differences between extremes we would be naïve to think that severe weather events will not also become more extreme. For much of the United States’ coastal areas hurricanes are a powerful threat to health, infrastructure, economics, and society. While we can accurately predict the track of a hurricane with our current methods and models (including aircraft reconnaissance, imagery from METEOSAT, GOES, and SLOSH models), forecasting the intensity of any given hurricane is quite inaccurate due to limitations in numerical modeling and related data assimilation (Rogers et al., 2013). Per the National Hurricane

Center (NHC), between 2000 and 2010 there has been a 57% decrease in errors found in

48-hour track forecasts (Figure 1) while errors in the 48-hour intensity forecasts have decreased by only 13% (Figure 2) (“Official error trends,” 2016). The intensity of the hurricane is dependent on the structure of the system and associated environmental factors, such as wind shear, vorticity, moisture, and sea surface temperature. If these factors adjust slightly along the track of the hurricane they will change the structure of the storm and cause previous forecasts to be erroneous.

Because of these inaccuracies, it is difficult to give the public expectations about the storm until it is closer to making landfall. Until meteorologists generate a model that is more precise in identifying areas most at risk during a hurricane, we must use data from past hurricanes to assess the risk each area has and try to lessen the impacts through mitigation.

Figure 1: Atlantic Basin Track Error Trends in Tropical Cyclones. (“Official error

trends,” 2016)

Figure 2: Atlantic Basin Intensity Error Trends in Tropical Cyclones. (“Official error

trends,” 2016)

Chapter 2

Literature Review

2.1 Hurricane Life Cycle

There are four basic stages that a hurricane system goes through before officially becoming classified as a hurricane. These stages are:

1. Tropical Wave

2. Tropical Depression

3. Tropical Storm/Cyclone

4. Hurricane

Many of the hurricanes that develop in the Atlantic Ocean begin as tropical waves. Tropical waves are characterized by areas of thunderstorms and unorganized wind circulation. The waves that form the Atlantic hurricanes typically form from African easterly waves, or areas of low pressure within the trade winds, that have developed due to the large temperature gradient between the Sahara Desert and the Gulf of Guinea

(Frank, 1987). As the tropical wave moves west, away from Africa, it undergoes wind convergence in the lower to middle troposphere associated with a mesoscale convective complex. With help from the Coriolis Effect, this complex generates rotating winds that organize into a column, or vortex, of rotating wind. Continued growth and resurgence of

the mesoscale convective vortex allows intensification of the wave into a tropical depression.

At this stage in development, a tropical depression is assigned a number that is dependent on the number of depressions that have formed during the hurricane season of a given year, in a given water basin. Tropical depressions are named for the area of low pressure in the center of the system. The low-pressure forms as the winds rotating about the system converge toward the center. Energy from the ocean, in the form of latent heat, is necessary to continue the intensification of the tropical depression. Typically, ocean surface temperatures over 80°F are necessary for continued growth, as they allow heat transfer from the ocean’s surface to the atmosphere through evaporation (“Hurricanes:

Science and Society,” 2015). This, among other factors occurring within the depression, causes the sea surface temperature to cool and for heat storage within water vapor droplets to occur. The energy given from the ocean to the tropical depression is used to continue thunderstorm formation and intensification until classification as a tropical storm, or tropical cyclone.

Once becoming a tropical storm, the system is given a name by the National

Hurricane Center. These names occur in alphabetical order, alternating between male and female names, and vary in terms of country of origin. Tropical storms do not always intensify into hurricanes. After developing sustained winds greater than 74 mph, the tropical storm is reclassified as a hurricane (“Hurricanes: Science and Society,” 2015).

Hurricanes are easily distinguishable from their circular cloud shape that consists of an eye, eyewall, and an outer region. The eye of a hurricane is the central-most point, has light winds, low pressure, and shrinking or growing of the diameter is a sign of 6

intensification or dissipation, respectively. The innermost ring of clouds surrounding the eye make up the eyewall. Unlike the eye, the eyewall contains the strongest winds of the hurricane, which weaken as you move outward from the center. This area is characterized by rising warm air from the ocean, adding energy to the system, and creating areas of great precipitation. Toward the outer edges of the hurricane are rain bands that are known to produce large amount of precipitation, strong winds, and tornadoes.

Dissipation of a hurricane occurs due to changes in sea surface temperature, moisture content, wind speed, wind direction, and elevation. Changes in the ocean temperature influence the energy transfer of a hurricane. Latent heat is transferred from the warm ocean to the system and when the hurricane moves into more northern latitudes cooler waters reduce the amount of evaporation occurring, therefore reducing the amount of latent heat being added to the system. The cool water leads to drier air directly above also increasing the decay of the hurricane. Vertical wind shears increase the rate at which the dry air is entrained into the system, thereby causing downdrafts within the eyewall and disrupting intensification. Sometimes these wind shears are strong enough to separate the cloud tops from their bases or cause the stacking of the low-pressure center within the eye to tilt and become less established. Landfall impacts the decay most quickly and once making landfall, it loses the energy that was provided by the ocean. The land’s surface cannot hold nearly as much energy as the ocean can and removing that source of energy causes dissipation. Additionally, elevations of landmasses cause the destruction of the established eye and eyewall due to the frictional forces found along a rough surface of the earth.

2.2 Risk and Risk Assessment

2.2.1 Insurance and Risk

Companies, agencies, and individuals all over the world calculate risk through mathematical methods of probability and scientific measures to determine the impacts of dangerous events to our health. Risk was first discovered through trial and error and the causal links between an activity and a devastating outcome. As our society has grown, it has become easier to use experimentation and modeling as a means of calculating these types of risk (Covello & Mumpower, 1985). Advancements made in technology have played a part in helping humans understand the different risks associated with different events while also encouraging us to consider different ways to reduce the effects of the problem. We know, from countless studies of hurricanes and the damage that they cause, that we must find ways to reduce the impacts on the health of our society. This can be done by reducing the exposure, creating organizations that provide aid, adjusting previous solutions to make them safer, or avoiding the risk all together (Covello &

Mumpower, 1985). In the case of hurricanes and other events that you cannot necessarily avoid, it is essential to the safety of all in that area to perform a risk assessment.

Calculating the risk of an event uses a variation on the following equation, depending upon the study being conducted:

푅푖푠푘 = 퐻푎푧푎푟푑 ∗ (푉푢푙푛푒푟푎푏푖푙푖푡푦 − 푅푒푠표푢푟푐푒푠)

(1) where Risk is the loss being assessed, Hazard is the event that is causing the harm,

Vulnerability is the measure of the likelihood of loss based on certain factors, and

Resources is the mitigation techniques available (Flanagan, Gregory, Hallisey, Heitgerd,

& Lewis, 2011).

Risk and risk assessments are most frequently heard relating to insurance providers. Whether you own a car or a house finding the appropriate coverage for your area allows you to benefit from knowing your investments are safe. Insurance can be defined as, “an agreement where, for a stipulated payment called the premium, one part agrees to pay to the other a defined amount upon the occurrence of a specific loss”

(Anderson & Brown, 2005). This is, of course, a simplified description of the agreement, which typically involves many factors influencing the type and frequency of these payments. Insurance providers set fair deductibles, monetary thresholds that the customer must pay before the company covers excess expenses, to protect themselves from the potential hazards that their customers might encounter while encouraging the customer to prevent losses that cause them to make claim payments (Anderson & Brown, 2005). The deductibles that actuaries set for their clients are based on an analysis of the clients’ accident history; if their clients have had several accidents in a recent timeframe, their insurance deductible will be higher than someone who has not had as many accidents.

The insurance coverage varies based on the situation. Insurance companies frequently cover events that are out of their customers’ control, such as “acts of God” or accidents that were no fault of the customer. Weather events including tornadoes, wild fires, flash flooding, and hurricanes are all classified as an act of God, where there was nothing that the customer could do to prevent the accident from occurring.

2.2.2 Hurricane Risk

One method of calculating risks associated with hurricanes involves a slightly modified version of equation (1), which contains more specific variables, as cited by

King (2010).

퐻푎푧푎푟푑 ∗ 푉푢푙푛푒푟푎푏푖푙푖푡푦 ∗ 퐸푙푒푚푒푛푡푠 푎푡 푅푖푠푘 푇표푡푎푙 푅푖푠푘 = 푅푒푠푖푙푖푒푛푐푒 ∗ 푀푖푡푖푔푎푡푖표푛

(2)

These variables can be grouped into two separate categories in terms of likeness: hazard, vulnerability, and elements at risk, and resilience and mitigation.

Hazards and Vulnerability are linked through a specific event, like hurricanes.

The hazard is the hurricane and the vulnerability is how much this hazard will impact an area. Hazards tend to cover the physical scope of impacts from the hurricane such as flooding, destruction caused by high winds, tornadoes, and storm surges. These hazards are important to acknowledge. For instance, while high winds would not have a large impact on an ocean wall or levee, a storm surge capable of reaching heights taller than these barriers would lead to damage or collapse. Vulnerability covers the more social impacts. Vulnerable areas tend to have more isolation (especially in the aftermath) and less awareness of hurricane risks (King, 2010). When homeowners have not had direct experience it makes it more difficult for the area to recover. Elements at Risk include some of the social aspects of vulnerability including demographic information (King,

2010). These elements include the people, homes, businesses, or any other quantifiable variable that is impacted by the hazard.

Resilience and Mitigation are both techniques used to prevent either the event itself or to lessen the impacts. In the case of hurricanes, properly educating the public about emergency protocol and evacuation procedures would go a long way to make the area more resilient and less vulnerable (King, 2010). Homeowners would be more aware of the types of building structures appropriate for coastal areas frequented by hurricanes and how to properly protect their homes. Following evacuation routes in a timely manner out of the most at risk areas would keep the homeowners safe and lessen the vulnerability of the people. Even with much of the population evacuating, peoples’ assets are still at risk. For homeowners who are insured, their insurance providers will cover some of the costs to repair or replace damaged goods. Being unable to afford this type of insurance puts a person at higher risk, making them more vulnerable and less resilient.

2.2.3 Risk Assessment

To complete this type of risk assessment, the social vulnerability of the area must be identified. Social vulnerability is defined as an “analytical tool for describing susceptibility to harm, powerlessness, and the marginality of both physical and social systems” (Burton, 2010). The best way to determine the social vulnerability requires the use of a social vulnerability index, or a combination of all variables for a given area that cause that area to be more or less vulnerable. Using an equation similar to equation (2) is most helpful for calculating hurricane risks. Use of an additional equation adds precision to the analysis through ranking each of the variables. This can be calculated using the following equation:

(푅푎푛푘 − 1) 푃푒푟푐푒푛푡푖푙푒 푅푎푛푘 = (푁 − 1)

(3) where N is the total number of data points and Rank is the location of a specific data point among all other data points (Flanagan, et al., 2011). To calculate the percentile rank, each individual variable must be ranked in order from highest to lowest, except in cases of income since higher values indicate less vulnerability (Flanagan, et al., 2011). For use in disaster management calculating the percentile rank by county or by tract compared to the entire state yields the most accurate results.

As implied by the previously stated definition, the vulnerability involves the comparisons and quantifications of different risk factors based on the relative impacts that a location could sustain in the event of a disaster. Once all the appropriate data is gathered a vulnerability index can be made.

2.2.4 Social Vulnerability Index Variables

In the context of this study, vulnerability relies on data pertaining to a county’s demographics, median household income, hurricane impacts, proximity of assets to the coast, evacuation, and mitigation. The collected data includes a mix of physical and social data. Physical vulnerability is based on hurricane sustained winds, gusts, surges, and rainfall data at time of landfall for five separate gulf coast hurricanes as well as evacuation routes and the number of relief organizations available on a county level.

Social vulnerability is based on population, gender, age, race, median household income, and asset proximity to coast.

2.2.4.1 Population

Population density may act to increase or decrease the vulnerability based on whether the area is more rural or urban. Rural areas, with less population density, are more prone to lower income and must rely upon local reparation services (Cutter, Boruff,

& Shirley, 2003). However, the lower population size would positively affect the evacuation times. In contrast, urban and suburban areas typically have more stable, better paying employment and access to many different services. While these factors decrease the vulnerability of the areas, the increased population causes clogging and chaos during evacuation procedures.

2.2.4.2 Gender

An area with a high population of women will be at a larger risk than an area with a higher population of men. While this is not entirely intuitive or accurate in some aspects, women are at a higher risk due to physical and social differences. Women naturally have less muscular anatomies and due to the composition of their bodies experience more difficulty when recovering from hazardous events (Cutter et al., 2003).

In a social context, working women tend to work in positions that make lower wages than men and have familial responsibilities or burdens that men do not always have, in an average heterosexual relationship where both parents are present. Making less money may mean that they cannot afford the insurance they need or the ability to repair anything that becomes destroyed (Cutter et al., 2003). Especially when these women have larger

families, they put a much greater effort into assisting their family than in protecting themselves (Cutter et al., 2003).

2.2.4.3 Age

The healthy human body reaches its physical peak sometime in ones 30’s, generally speaking (Robson, 2015). After this period, bodies slowly begin to deteriorate.

Therefore, ages much younger and much older than 30-years are at the highest risk during disasters. Young children and the elderly have weakened immune function and cannot tolerate recovery from illness or accidents as easily. Both age groups depend upon the middle-aged group for day to day care. Children and teens often do not begin working until out of school, so they cannot contribute financially in instances of disasters. If their daycare centers or high schools are affected by a hurricane they are relocated to their homes to be cared for by the middle-aged parents who sacrifice their paychecks to stay home (Cutter et al., 2003). Similarly, the elderly, if in a retirement home affected by a hurricane, must be relocated to the family’s home or to another location. If the elderly have decreased mobility, it puts the middle-aged caretakers in a difficult situation that adds strain to care for their family member (Cutter et al., 2003).

2.2.4.4 Race, Ethnicity, and Language

While not true for all races, those with a non-native background are most at risk.

Moving into a different country poses many challenges but none as great as language barriers. Speaking a non-native language puts these people in an isolated position (Cutter et al., 2003). They may not reach out during a time of crisis due to the inability to interact 14

in an understandable way nor would they understand emergency broadcasts without a translation. Being unable to prepare for a disaster and eventually suffering that disaster makes recovery that much harder. In communities with cultural barriers the post-disaster support requires the ability to communicate with outside resources, which may not be able to assist or may, unfortunately, have prejudices and refuse to assist (Cutter et al.,

2003).

2.2.4.5 Socioeconomic Status

Areas and individuals representative of higher socioeconomic statuses tend to be better prepared and more capable in the event of a disaster. Having a greater income allows for increased resilience, as it allows the purchase of insurance, stronger social connections, better medical care, and entitlement to the best services available (Cutter et al., 2003). Having that sense of security allows these groups of people to maintain their lifestyles without having to face the repercussions that those in lower classes might face.

Lower socioeconomic classes are susceptible to job losses, poor quality living spaces, and complete dependence on social services (Cutter et al., 2003). The lower class may experience higher rates of job loss due to the types of jobs that they work. For example, fast food chains hire many employees but most are part-time workers who do not enjoy the benefits of full-time employees. This also makes them more expendable. Without a high paying job, this class of people cannot be expected to purchase a high-quality home or the insurance to go with it. For some people in the lower class, dependence on social service income increases their risk post-disaster, as they must continue to rely upon the government for support (Cutter et al., 2003). 15

2.2.4.6 Housing and Proximity to Coast

Different types of housing respond differently in a disaster. It is important to consider these types when analyzing vulnerability. Houses, such as mobile homes, are inexpensive to own and make due to lower quality materials used (Flanagan et al., 2011).

Because of this, natural disasters take a much larger toll on mobile homes than they do their higher quality counterparts. While mobile homes may be more at risk, single family housing may face similar risks if not built to the area’s specifications. For example, there are four “at risk” zones along the gulf coast: Zone V, Coastal A Zone, Zone A, and Zone

X (“Recommended Residential Construction for Coastal Areas,” 2009). Zone V, Coastal

A Zone, and Zone A are areas closest to the coast where changes in sea level or wave height may impact the houses. New constructions in those zones are planned with potential impacts in mind by building what are called “open foundation” homes, or elevated homes. These houses are common among the coastal cities and may be identified by the piers, piles, or poles that support the main structure (“Floodplain

Management Regulation, Building Codes, and Standards,” N.d.). Some states’ counties have regulations in place keeping new housing from being built too close to the coastline.

Florida’s building construction workers must abide by the Coast Construction Control

Line (CCCL) both to protect future homeowners from damage but also to protect the coastlines from further erosion, that would put inland houses at risk (“The Coastal

Construction Control Line Permitting (CCCL),” 2016). Areas that have coastal guidelines such as these are at less risk because they eliminate the potentiality of unnecessary damage to homes built too close to the shoreline or built under the high-tide height.

Chapter 3

Scientific Objectives and Methodology

3.1 Introduction

The purpose of this risk assessment is to understand which areas bordering the

Gulf of Mexico are most susceptible to hurricane damage and to look at rectifying the shortcomings that each area possesses. To assess the risk potential for the states and counties in this study data regarding demographics, building codes, evacuation routes, relief organizations, and hurricanes was collected.

3.2 Data Selection

3.2.1 Demographic Information

Demographic information could be considered the backbone of the information for this assessment. The demographics of 139 coastal counties between Texas and the western coast of Florida were gathered from the 2010 United States Census, as well as an additional 62 inland counties following the track of Hurricane Ike. Demographic information is comprised of populations of the total county, female vs. male, age ranges

(including under 18 and over 18, which is separated further in 20-24, 25-34, 35-49, 50-

64, and over 65), “dependent” and “independent” age ranges, median household incomes,

and racial distributions (further separated into Hispanic/Latino, White, African American,

Asian, American Indian and Alaskan, Native Hawaiian and Pacific Islanders, other races, and those identified by two or more races) (“2010 Census Interactive Population Search,”

N.d.).

Median household income was used instead of median income for the state to demonstrate the economic status of the residents living within each county rather than for the state. This method presented a better understanding of the economics of the counties along the Gulf of Mexico and how much these households can contribute to any county services. The four demographic variables, non-white races, dependents, white races, and independents, are treated in a way such that one county does not receive vulnerability and resiliency values for each section. For instance, a county with a population of dependents that ranks within the top 50% of all counties will receive a vulnerability value for age.

Since the independent rank is expected to be in the lowest 50%, the county will not receive any resiliency points for the age variable. This applies, also, to the ranks of percentages of non-white and white races within each county. This is done to prevent unnecessary use of the same variable twice within the final risk equations.

The counties with higher populations of female residents will be included within the vulnerability subset due to the discussion from section 2.2.4.2, documenting the research of Cutter et. al, as well as its application in other vulnerability indices. Lisa

Rygel and her associates discussed the development of social vulnerability indices in her research associated with hurricane storm surges. She used gender as a basis for an area’s vulnerability and explained how women are at a disadvantage in disaster situations due to their higher chances of living in poverty when divorced or never married single parents,

working lower-level jobs that often disappear in disaster situations, and tend to put their own safety at risk when evacuating to save their young children or elderly family members (Rygel, O’Sullivan, & Yarnal, 2006). In another analysis done by Elaine

Enarson and her associates in their research of the correlations between gender and disasters, they argue that women and girls are faced with “limited constraints on their mobility, poverty, language and literacy barriers, insecure housing, limited or nonexistent land and inheritance rights, barriers to their fair access to new information technologies… and overt and covert constraints on their public presence and voice” (Enarson, Fothergill,

& Peek, 2007). Therefore, to convey this gender disaster inequality, female population ratios will be considered a vulnerability for the counties in this study.

As outlined in section 2.2.4, this information was primarily used to determine an initial social vulnerability index for use in analyzing risks posed by hurricanes today with similar specifications to the study hurricanes that occurred prior to the year 2013. This information provided a starting point from which to compare vulnerabilities. Areas may have had a predisposition to vulnerability to hurricanes and this information was used to determine which areas were already at risk.

3.2.2 Evacuation Routes

Some coastal states have clearly marked main highways that are always used in emergency circumstances while others may use different roads depending upon the hazard and where it is expected to occur. Being aware of the evacuation route out of the coastal areas during a hurricane ensures the safety of the residents living there. Most counties had specific highways that were to be used in evacuation circumstances and

linked to them via the county websites. In cases where specific roadways were predetermined, evacuation maps were analyzed and a sum of all roadways leading out of the county and into more inland areas was recorded. Unfortunately, not all counties had predetermined evacuation routes. Counties that did not designate routes before a catastrophic event had their evacuation route data based on the number of main highways, whether interstate or otherwise, leading out of the coastal county and toward more inland areas. This information was gathered from maps of the counties displayed on their websites or directly from Google’s Maps function, which has up-to-date information on locations and businesses across the United States, making it a vital, secondary tool for the data collection process. It is likely that these areas do not designate specific highways to prevent slowing evacuation times from unanticipated road construction or detouring.

While the roads specified in the study may not all be used in the event of a hurricane, they are all the available options for leaving the immediate area, effectively increasing the resilience of the counties.

3.2.3 Hurricane Relief

3.2.3.1 Organizations

Immediate action is required by different support groups before, during, and after a hurricane takes place. These groups include, but are certainly not limited to, FEMA,

American Red Cross, US Coast Guards, Army Corps, specific county Emergency

Management offices, and The Salvation Army. These organizations are responsible for educating the public, providing temporary shelter, assisting the injured, and returning the surrounding communities to a state as close as possible to before the storm. (“Disaster

Services,” N.d.) With the growing need for support to the coastal communities, especially after events such as Hurricane Katrina, public libraries have become more involved in community outreach before and after the storm (Jaeger et al., 2007). Their actions included creating and distributing preparedness guides, answering questions via phone call, providing shelter for the local residents, cooking meals, and working with relief organizations to clean up afterward (Jaeger et al., 2007). Due to increased efforts from public libraries along the gulf coast communities, several have been included in the relief organization data where applicable.

Data for hurricane relief organizations was collected using two different methods.

County webpages provided sponsored information and links directly to their webpages from any of the organizations that commonly assisted during hurricane events. If a county linked directly to public library branch, that was also gathered as an organization.

Google’s Maps function was also used to gather information on other local organizations or other library branches that were not represented on the county’s webpage. Regardless of whether an organization had several different locations within a county, only the organization was counted in the total sum.

3.2.3.2 Hospitals

Hospitals that reside within the counties in the study area have been factored into the resilience data. Areas with lower hospital to population ratios have the best opportunities for assisting those injured in the aftermath. Hospital information was gathered using Google’s Maps function. This allowed for a beneficial spatial representation of the hospitals located in and around the counties. It is common for

hospitals to have many different locations within a single county to accommodate larger populations. However, each individual site was not counted in the data, rather the hospital was counted. This data did not include doctors’ offices or urgent care.

3.2.3.3 Evacuation Shelters

Evacuation shelters are a beneficial resource in the event of tropical storms and

Category 1 or 2 hurricanes as they assist people living in homes that are in areas at more risk to potential flooding or damage from the weaker storms. Those people using the shelters in these situations are given an opportunity to evacuate their home temporarily and receive shelter and any type of assistance they may need. In the case of Category 3 or higher hurricanes, the shelters become unnecessary due to the very strong evacuation suggestions or requirements. In these circumstances, coastal hurricane shelters are no safer than staying in one’s home; neither is recommended over evacuating the county or state. Evacuation shelters are comprised of local public and private schools, churches, and other shelters that can house many people for a week’s time, giving the community plenty of time to assess and repair any damages that prevent them from returning to their homes (“MEMA Shelter Update,” 2012). Some areas along the coast have main locations that are available to the public at the time of a hurricane while other communities insist upon evacuation to a nearby, inland county and therefore do not provide these types of shelters.

For counties that use the evacuation shelters, information on the shelters was found on the county webpages, displayed as lists of schools, churches, and local safe houses for residents to report, based on where they lived within the county. Much like

evacuation roadways, the shelters were not always designated before a catastrophe. In these situations, Google’s Maps function was used to search for local school or hurricane shelters if the county’s webpage did not include that information. If this action did not yield results, the county was given a rank representative of their efforts in providing emergency shelters. Counties that did not participate in coastal evacuation shelters, including Texas, Florida, less inhabited counties in Louisiana, and more inland counties did not receive resilience credit for this parameter.

3.2.4 Hurricane Data

Data was collected on ten different hurricanes that made landfall along the gulf coast of the United States between 2004 and 2012. These hurricanes include Charley,

Ivan, Dennis, Katrina, Rita, Wilma, Gustav, Ike, Ida, and Isaac. All information was gathered from relevant NOAA post storm reports as close to the time of initial landfall as possible. Hurricane data consists of 10-minute averaged wind speed, 1-minute averaged maximum wind gusts, storm surge heights for counties along the coasts, and 24-hour rainfall totals for all 139 counties along the United States gulf coast. Due to many of the study hurricanes occurring before 2010, this assessment of the risk posed by the hurricanes should be viewed only as an indication of the risks posed by similar hurricanes occurring today.

3.2.4.1 Hurricane Charley

Charley made its initial landfall with the United States on the gulf coast side of the Florida peninsula during the late hours of August 13, 2004 (Pasch, Brown, & Blake,

2011). On August 4, 2004, a wave developed off the shores of west Africa with signs of low surface pressure. The system organized as it quickly moved westward across the

Atlantic Ocean and transformed into a tropical depression on August 9, 2004, just southeast of Barbados (Pasch et al., 2011). A deep layer high pressure system to the north caused the depression to move quickly to the west-northwest. The depression strengthened into a tropical storm with characteristically low vertical shear and highly developed upper-level outflow (Pasch et al., 2011). The system continued moving into the central Caribbean, gradually strengthening into a hurricane just before interacting with Jamaica. The system passed Jamaica to the south but turned northwestward and made landfall with Cuba as a Category 2 hurricane in the early hours of August 13, 2004

(Pasch et al., 2011). After crossing Cuba, the slightly weakened system interacted with an unusually strong mid-tropospheric trough that extended into the Gulf of Mexico from the eastern United States (Pasch et al., 2011). The system began tracking north- northeastwardly while rapidly intensifying in the gulf. Hurricane Charley reached a

Category 4 status with a small eye and confined diameter before making landfall in

Southwest Florida (Pasch et al., 2011). Charley continued to track northeastwardly, crossing the entire Floridian peninsula before returning to the Atlantic Ocean one day after landfall. Charley strengthened again and made landfall a second time in South

Carolina. The system finally merged with a frontal zone moving across the eastern United

States, into Massachusetts on August 15, 2004 (Pasch et al., 2011). Hurricane Charley was estimated to reach peak intensity upon landfall in Cayo Costa, Florida, at 130 knots

(Pasch et al., 2011). This measurement is based on flight-level data collected by air reconnaissance missions (Pasch et al., 2011). Surface measurements did not reflect this

intensity at the point of landfall. Of the few wind instruments that did not fail as Charley made its way across Florida, strong winds were continually reported as the system moved inland.

Hurricane Charley was responsible for 16 tornadoes in the United States, with nine of these occurring in Florida (Pasch et al., 2011). All the tornadoes were within the

F0 and F1 ratings (Pasch et al., 2011). Rainfall associated with Charley left parts of

Florida with 6-8 inch totals, while Cuba received 5 inches, and the Carolina’s received 5-

7 inches upon the second landfall (Pasch et al., 2011). Also associated with Hurricane

Charley were 3 to 7-foot storm surges across the southwestern coast of Florida, with maximum surges up to 7 feet occurring on Sanibel and Estero Islands (Pasch et al., 2011).

Charley was directly responsible for 15 deaths, 10 of which occurred in the

United States because of falling trees, car accidents, falling structures, and drowning

(Pasch et al., 2011). An additional 25 deaths occurring in the U.S. were indirectly caused by the hurricane (Pasch et al., 2011). The combined insured and uninsured losses in the

United States due to Hurricane Charley were estimated to be $15.113 billion; making

Charley the sixth most costly hurricane to ever affect the United States as of 2011 (Pasch et al., 2011).

3.2.4.2 Hurricane Ivan

Ivan devastated the southern coast of the United States on September 16, 2004, initially making landfall in Alabama (Stewart, 2011). Ivan strengthened as it moved its way across the Atlantic Ocean from the western coast of Africa, undergoing a period of rapid intensification and periods of weakening and strengthening until making landfall

(Stewart, 2011). By the time it struck the coast of Alabama it had decreased from a

Category 5 to a Category 3 hurricane. It was unable to maintain the energy it had acquired during its travel over the warm gulf, but was still capable of producing surface winds at 105 knots (120 miles-per-hour) (Stewart, 2011). Contact with land caused Ivan to decrease its intensity to a tropical depression as it traveled over northeast Alabama.

However, a leftover low continued to move southeastward back into the Atlantic where the warm waters spurred another period of strengthening as Ivan moved westward across southern Florida and the Gulf of Mexico, making a second landfall in southwestern

Louisiana. After 23 days and over 5600 miles, Ivan dissipated entirely (Stewart, 2011).

The effects and damages caused by Ivan were felt across the southeastern US.

Tornados occurred throughout the Mid-Atlantic States including Virginia, Georgia,

Florida, Pennsylvania, Alabama, South Carolina, Maryland, North Carolina, and West

Virginia (Stewart, 2011). This outbreak consisted of 117 tornadoes (Stewart, 2011). The areas of study (Florida and Alabama) shared 26 of these tornados, killing or injuring 25 residents (Stewart, 2011). Storm surges along the coast varied. From Destin, FL to

Baldwin County, Alabama, there were 10 to 15-foot storm surges while areas along the southwest coast of Florida saw storm surges as little as 3.5 feet (Stewart, 2011). High wave heights, storm surges, and 4-10 inches of rain caused major flooding.

3.2.4.3 Hurricane Dennis

Dennis made landfall between the westernmost edge of Florida and coastal

Alabama on July 10, 2005. A wave moving off Africa’s western coast encountered a broad area of low pressure and convection, causing the wave to organize into two

separate low-level systems (Beven, 2014). The western system lost its organization while moving into the Caribbean while the eastern system, having more time to develop over the Atlantic Ocean, moved westward and approached the Caribbean as a tropical depression (Beven, 2014). Before strengthening into a tropical storm, the system turned west-northwest, tracking toward Cuba. Two days after attaining tropical storm status, and shortly before landfall with Cuba, the system strengthened to hurricane status and rapidly intensified to a Category 4 hurricane (Beven, 2014). While moving over Cuba, the hurricane’s strength diminished to a Category 3 but returned to a Category 4 before striking west Cuba (Beven, 2014). Dennis weakened greatly on its second encounter with

Cuba before entering the Gulf of Mexico where it rapidly intensified and shifted to a north-northwestward track. After reaching its peak intensity, dry air entrainment from a system to the west weakened the hurricane to a Category 3 before making landfall

(Beven, 2014). The system continued its north-northwestward trajectory before weakening and moving northeastward over the Ohio Valley, eventually merging with a larger low-pressure system in Ontario, Canada (Beven, 2014).

The maximum sustained winds associated with Hurricane Dennis occurred on

July 8, 2005, near Punta Mangles Altos, Cuba, at 130 knots (Beven, 2014). It is likely that stronger winds and gusts occurred over Cuba, but malfunction and destruction of the anemometers prevented those measurements (Beven, 2014). Measurements of 1-minute average winds and gusts of 86 and 105 knots, respectively, were gathered from a tower at the Florida Coastal Monitoring Program (Beven, 2014). Hurricane force winds were localized around the eye and eyewall while tropical storm force winds extended to parts of southern Florida and the panhandle because of the expansive area that the hurricane’s

cyclonic envelope had covered. Florida’s western coast was affected by storm surges ranging from 6 to 9 feet, especially in areas around Santa Rosa Island, the central point of landfall (Beven, 2014). The panhandle experienced storm surges between 4 and 6 feet

(Beven, 2014). The heaviest rainfall documented due to Hurricane Dennis occurred in

Topes de Collantes, Cuba, with 27.67 inches (Beven, 2014). Widespread heavy rainfall occurred along Dennis’s track northward across the United States. Camden, Alabama received 12.8 inches while Monticello, Florida received 6.95 inches of rainfall (Beven,

2014). Dennis produced at least nine tornadoes in Florida and all ranged between F0 and

F1 intensities (Beven, 2014).

Hurricane Dennis was responsible for 42 deaths and indirectly responsible for 12

(Beven, 2014). Fifteen of the total deaths occurred in Florida and other parts of the

United States due to drowning, falling trees, electrocution, carbon monoxide poisoning, automobile accidents, and stress-induced natural deaths (Beven, 2014). Dennis caused

$2.23 billion in insured and uninsured damages (Beven, 2014).

3.2.4.4 Hurricane Katrina

Katrina’s interaction with the United States took place between August 23 and

August 30, 2005, initially making landfall in Florida, then gaining strength to affect

Louisiana and Mississippi (Knabb, Rhome, & Brown, 2011). Katrina formed from a dissipating tropical depression and a tropical wave in the western half of the Atlantic. The tropical depression became more organized as it sat over the Bahamas, gradually increasing energy uptake from the ocean as well as episodes of deep convection. An organized eye formed and Katrina, upon reaching hurricane status and after moving

westward beyond the Bahamas, made landfall with the southeast coast of Florida as a

Category 1 hurricane (Knabb et al., 2011). As Katrina traveled across the southern part of the Florida, it weakened slightly to a tropical storm, but then experienced redefinition of the eyewall and two periods of rapid intensification as it traveled west-northwest across the Gulf of Mexico (Knabb et al., 2011). During the intensification, Katrina roughly doubled in size and began its track northward, toward Louisiana and Mississippi, with help from a mid-level ridge system (Knabb et al., 2011). Some structural weakening occurred as Katrina moved across cooler northern gulf temperatures, began interactions with the coast, and partially formed a new eyewall. Katrina quickly degraded to a

Category 3 hurricane with surface wind maximums of 110 knots (127 mph) (Knabb et al.,

2011). Finally, on August 29, hurricane Katrina made landfall with the Pearl River, between the borders of Louisiana and Mississippi, and rapidly weakened as it progressed northward across the United States before being engulfed by a low-pressure system around the Great Lakes (Knabb et al., 2011).

The impact from the winds, as well as the storm surge, caused widespread destruction all along the coast and in surrounding areas. Storm surges, measured by high water marks on building varied from 5-28 feet in Louisiana and Mississippi. The wave heights and storm surges caused flooding of inland areas along the gulf, putting the levees in New Orleans, LA under strain and eventually leading to overflowing and breaches. The levees breaking enhanced the widespread flooding leaving areas of New

Orleans up to 20 feet under water. The rainfall associated with hurricane Katrina affected areas along the coast between Louisiana and southern Florida. Florida had 14.04 inches of rainfall in Miami-Dade County. Areas in Louisiana and Mississippi had anywhere

from 8-10 inches of rain. As the remnants of Katrina extended into the rest of the continental United States, areas such as Tennessee Valley were hit with 4-8 inches of rainfall. Katrina produced a total of 43 tornadoes as it moved across the area. The Florida

Keys reported one tornado while Alabama reported 11, Mississippi reported 11, and

Georgia reported 20.

While hurricane Katrina itself was not one of the strongest hurricanes that the

United States has encountered based on physical properties of the storm, such as maximum sustained wind speeds and rain accumulation, its size and the amount of human and property damage has put this hurricane at the top of the list for overall damages. Whether directly or indirectly impacted, there were 1,833 known fatalities across Louisiana, Mississippi, Alabama, and Florida. Many people were reported missing following the events of the hurricane and have remained missing, which could alter the total death toll. In addition to the number of fatalities caused by Katrina, homes, work places, high-rises, and entire neighborhoods were devastated by the storm surge and flooding. Katrina caused roughly $108 billion worth of damage and worsened the blight of New Orleans (“Hurricane Katrina Statistics Fast Facts,” 2015).

3.2.4.5 Hurricane Rita

Hurricane Rita began as a weather disruption that formed from a wave off west

Africa and a cold front. The cold front had extended southward into the Caribbean where it became stationary. The southernmost part of the trough lost formation while a nearby high pressure system carried the detached and disorganized portion of the trough to the west, toward Puerto Rico (Knabb, Brown, & Rhome, 2006). Upon reaching Puerto Rico,

the tropical wave merged with the remnants of the surface low producing thunderstorms in the surrounding area. The system organized due to limited vertical shear and strengthened to a tropical depression on September 18, 2005 (Knabb et al., 2006). The depression continued to organize as it moved west-northwestward over the Bahamas, becoming a tropical storm. Rita began a westward track, passing the Florida Keys, and entered the Gulf of Mexico where it immediately strengthened to a Category 3 hurricane

(Knabb et al., 2006). The warm southern gulf waters allowed Hurricane Rita to continue to strengthen, reaching its peak intensity of 155 knots as a Category 5 hurricane (Knabb et al., 2006). An upper level ridge located to the northeast of the system redirected Rita toward the northwest. The combination of cooler northern gulf waters and the formation of a second eyewall caused Rita to weaken to a Category 3 hurricane (Knabb et al.,

2006). It maintained this strength and intensity until making landfall on the border of

Texas and Louisiana on September 24, 2005 (Knabb et al., 2006). As the system followed the states border inland, it weakened to a tropical depression. A frontal system to the west pushed the depression northeastwardly where it lost its organized convection and was absorbed by a low-pressure system near the Great Lakes.

As a Category 3 hurricane making landfall, Hurricane Rita had estimated maximum sustained winds of 100 knots (Knabb et al., 2006). The highest measured surface wind speed near the point of landfall was 71 knots with gusts of 86 knots (Knabb et al., 2006). The large size of the hurricane produced expansive damage from storm surges, which extended toward parts of Louisiana. Unfortunately, the instruments used to measure storm surge experienced failures several hours before Hurricane Rita made landfall (Knabb et al., 2006). Unofficial estimates in Cameron Parish put the storm surge

at 15 feet, causing large amounts of damage to buildings and flooding up to 30 miles inland from the coast (Knabb et al., 2006). Portions of Louisiana to the east had surges of

8 to 12 feet while southeastern Louisiana received 4 to 7 feet (Knabb et al., 2006). The coast of Texas had 3- to 5-foot storm surges, which occurred prior to landfall (Knabb et al., 2006). Portions of eastern Texas, Louisiana, and Mississippi received 5 to 9 inches of rainfall and some local maxima up to 15 inches (Knabb et al., 2006). Rita produced 90 tornadoes within the United States, affecting Alabama, Mississippi, Louisiana, and

Arkansas (Knabb et al., 2006).

Hurricane Rita was directly responsible for nine deaths due to drowning, downed trees, and tornadoes (Knabb et al., 2006). An additional 55 deaths were indirectly associated with the hurricane, all of which were reported to have occurred in Texas

(Knabb et al., 2006). The damages caused by Hurricane Rita, both insured and uninsured, cost the United States $12.037 billion (Knabb et al., 2006).

3.2.4.6 Hurricane Wilma

Wilma developed from a large monsoon-like lower tropospheric circulation over the Caribbean Sea, which separated into eastern and western portions (Pasch, Blake,

Cobb III, & Roberts, 2006). The eastern portion of the low-pressure system merged with an extratropical cyclone while the western portion merged with tropical waves moving through the area and became more concentrated and organized around Jamaica. On

October 15, 2005, the system developed into a tropical depression (Pasch et al., 2006). A mid-level anticyclonic flow located to the northeast of the system slowly guided it westward and then southwestward for a couple days, all while slowly strengthening.

While moving with the anticyclonic flow and shifting its track west-northwestward,

Wilma strengthened from a depression to a tropical storm and finally to a hurricane within a day’s time. The next 24 hours, Wilma rapidly intensified to its peak intensity.

Hurricane Wilma had maximum sustained winds of 160 knots and an eye with a 2- nautical-mile diameter (Pasch et al., 2006). It maintained its Category 5 status until weakening to an intensity of 130 knots, where it remained until making landfall in the

United States (Pasch et al., 2006). A series of shortwave troughs moving across the Gulf of Mexico redirected Wilma toward the northeast, where it impacted the easternmost point of the Yucatan Peninsula, before moving into the gulf on October 23, 2005 (Pasch et al., 2006). The troughs located over the central United States continued to push

Hurricane Wilma toward the coast of Cape Romano, Florida, where it made landfall with maximum sustained winds of 105 knots (Pasch et al., 2006). Wilma traveled across the

Florida peninsula, returning to the Atlantic Ocean where it briefly intensified before merging with an extratropical system around Nova Scotia (Pasch et al., 2006).

Hurricane Wilma reached its estimated peak intensity with winds of 160 knots traveling across the northwest Caribbean (Pasch et al., 2006). By the time Wilma approached Florida, its surface wind intensity had decreased to 105 knots, though the highest measured maximum wind speed recorded in Florida was only 80 knots (Pasch et al., 2006). Most counties in the southwestern peninsula experienced Category 1 and 2 force winds. Storm surges associated with Hurricane Wilma varied between 4 and 15 feet

(Pasch et al., 2006). The Yucatan Peninsula doesn’t have official surge measurements but were estimated to have had up to 15-foot storm surges (Pasch et al., 2006). The southwest coast of Grand Bahama Island received 12-foot surges (Pasch et al., 2006). The Florida

Keys saw surges of 4 to 5 feet, although Marathon, Florida had a storm surge up to 9 feet

(Pasch et al., 2006). Uninhabited areas of southern Florida, south of landfall, reported 4- to 8-foot storm surges (Pasch et al., 2006). The maximum 24-hours rainfall total associated with Hurricane Wilma was 62.05 inches, occurring as the system slowly crossed the easternmost part of the Yucatan Peninsula (Pasch et al., 2006). Rainfall totals over Florida were not as large as those seen in the Caribbean due to how quickly the system moved across Florida. The highest recorded rainfall in Florida ranged between 3 to 7 inches, while some areas received even less (Pasch et al., 2006). Ten tornadoes formed across southern Florida due to the hurricane (Pasch et al., 2006). Three of these tornadoes occurred within counties associated with the study area.

Hurricane Wilma downed trees and powerlines, destroyed crop production, and caused extensive damage to buildings, windows, and roofs. Wilma also caused a significant loss of electricity in 98% of south Florida (Pasch et al., 2006). Estimated insured and uninsured losses due to damage caused by Hurricane Wilma was $21.007 billion, making Wilma the third costliest hurricane at the time (Pasch et al., 2006).

3.2.4.7 Hurricane Gustav

The movement of a tropical wave off the western coast of Africa spurred the development of the storm system that would later be called “Hurricane Gustav,” which made landfall in Louisiana on September 1, 2008. Organization of the system occurred steadily as it moved westward toward the southern Caribbean, eventually forming a tropical depression on August 25, just northeast of the Netherland Antilles (Beven &

Kimberlain, 2009). Rapid intensification occurred following the depression’s

development of a small inner core, causing the transition first to tropical storm and then hurricane status on August 26, southwest of Haiti (Beven & Kimberlain, 2009). A low and mid-level ridge developing over Florida influenced the westward movement of the system as it moved across the Caribbean, weakening in response to landfall and an off- balance eye. Following the movement of the ridge, Gustav began its track northwestward toward the Gulf of Mexico. The system intensified due to the warmer waters in the northern Caribbean, causing Gustav to reach peak intensity of 135 knots, as a Category 4 hurricane, just before making landfall with the eastern coast of Cuba (Beven &

Kimberlain, 2009). Landfall with Cuba weakened the system before it moved into the gulf. Before making landfall in Louisiana, the system continued to weaken in the Gulf of

Mexico due to upper level dry air entrainment and a trough to the west and the associated vertical wind shear that disrupted the organization of Hurricane Gustav (Beven &

Kimberlain, 2009). It’s movement from western inland Louisiana, northwestward to

Oklahoma led to the weakening of the system to a tropical depression. Finally, the system was absorbed into an extratropical front moving northeastward toward the Great Lakes in

Michigan.

Hurricane Gustav had maximum surface winds estimated to be 108 knots on

August 30, 2008, based on data gathered from a Stepped Frequency Microwave

Radiometer (SFMR) while measurements from a station in Pinar Del Rio reported peak winds of 135 knots and gusts of 184 knots (Beven & Kimberlain, 2009). The World

Meteorological Organization believes the Pinar Del Rio wind measurements to be made in error. Gustav produced 41 tornadoes, with 39 of them occurring within the five gulf coast states (Beven & Kimberlain, 2009). The strongest tornado, measuring an EF2,

touched down in Evangeline Parish, Louisiana (Beven & Kimberlain, 2009). Storm surges accompanying Hurricane Gustav were reported between 12 and 13 feet in southeastern Louisiana, along the border with Mississippi. Other southern coastal areas of

Louisiana saw 9 to 10 feet (Beven & Kimberlain, 2009). Additionally, rainfall totals measured at Larto Lake, Louisiana, reported a high of 21 inches (Beven & Kimberlain,

2009). The levees were topped due to these surges and rainfall totals but did not cause widespread flooding to surrounding areas.

Hurricane Gustav was directly responsible for 112 deaths, with only 11 deaths occurring in the United States, and indirectly responsible for an additional 41 (Beven &

Kimberlain, 2009). Deaths associated with the hurricane were caused by the EF2 tornadoes and drowning in rip currents. Gustav was also responsible for $2.15 billion in insured losses in the United States (Beven & Kimberlain, 2009). However, with the majority of the damage occurring in areas of poverty within Louisiana, uninsured losses were around $4.3 billion (Beven & Kimberlain, 2009).

3.2.4.8 Hurricane Ike

Ike impacted the United States September 13, 2008, where it initially made landfall in Texas (Berg, 2014). The impending hurricane was generated by a combination of a tropical wave with a low-pressure system off the coast of Africa. It was listed as a tropical depression on September 1 and strengthened to its peak intensity on September 4 as a Category 4 hurricane (Berg, 2014). Ike moved westward across the Caribbean

Islands, eventually traveling over Cuba. Cuba, with Pico Turquino as the country’s highest point at 6,578 feet, caused Ike’s intensity to diminish, as the high elevations

broke apart the inner structure (Berg, 2014). In the case of Ike, the inner core became compromised as the winds circulating the storm expanded outward. Due to a strong subtropical ridge, Ike was directed northwest from Cuba toward the gulf coast of Texas, where it made landfall on September 13 as a Category 1 hurricane (Berg, 2014). It quickly dissipated once reaching land, reducing to an extratropical storm as it moved through Arkansas and Missouri. Remnants of Ike included wind gusts in Ohio Valley before being absorbed into a low-pressure system in areas of Ontario and Quebec,

Canada (Berg, 2014).

On September 4, the day of its peak intensity, it is estimated that Ike had surface winds of 125 knots (144 mph) (Berg, 2014). By the time Ike had worked its way north across the Gulf of Mexico to Texas, an anemometer measurement from Crab Lake measured surface winds of 83 knots (96 mph) (Berg, 2014). The expansive nature of the winds surrounding hurricane Ike and the forcing of water movement associated with the winds produced measurable storm surges along the coast from Texas to Florida. Florida’s west coast had storm surges of 1-3 feet (Berg, 2014). Alabama, Mississippi, and the southeastern coast of Louisiana experienced surges of 3-6 feet (Berg, 2014). Moving west across the coast of Louisiana, storm surges dramatically increased to 5-13 feet (Berg,

2014). Texas was the center of impact for hurricane Ike and experienced storm surges of

15-20 feet (Berg, 2014). The lowest rainfall totals reported along the coast came from

Florida, with 5.98 inches. The highest rainfall total reported was in an area just north of

Houston, Texas, with 18.9 inches (Berg, 2014). Rainfall varied across the gulf coast and as remnants of Ike moved across the United States, large amounts of rain and flooding occurred between Missouri and the Indiana and Illinois area. Among the rainfall and

storms brought on by Ike, 29 tornadoes occurred (Berg, 2014). Florida had 2, Louisiana had 17, Texas had 1, and Arkansas had 9 (Berg, 2014). Florida had damage to coasts and reported tree branches falling. They took precaution and evacuated tourists from the area.

No deaths occurred. In Texas, Louisiana, and Arkansas, 21 deaths occurred directly and

64 occurred indirectly from hurricane Ike. (Berg, 2014) Due to the high storm surge, deaths in Texas were the outcome of flooding and entrapment in the debris. Ike cost the

United States about $29.5 billion worth of damage and put about 2.6 million people without power between Louisiana and Texas (Berg, 2014).

3.2.4.9 Hurricane Ida

Ida’s impacts were felt in the United States on November 5, 2009, hitting

Alabama and Florida initially (Avila & Cangialosi, 2010). The combination of a tropical wave with a low-level cyclone in the southern Caribbean Sea spurred the growth of hurricane Ida. Without moving its location much, deep convection and upper-level cyclone formation led to the tropical depression which formed in early November. There was no rapid intensification associated with Ida, but periods of slight weakening and regrowth occurred as Ida moved north, passing between Cuba and the Yucatan, reaching

America and the coasts of Alabama and Florida (Avila & Cangialosi, 2010). Strong wind shears, the large wind field of the storm, and cooler waters prevented Ida from strengthening to a hurricane before it made landfall as an extratropical cyclone on

November 10 (Avila & Cangialosi, 2010).

Ida’s passage through the Yucatan Channel was accompanied by the strongest surface winds at 90 knots (104 mph) (Avila & Cangialosi, 2010). By the time Ida made

landfall with the United States Gulf Coast, surface winds had drastically reduced to 25 knots (29 mph) (Avila & Cangialosi, 2010). Rainfall measurements ranged from 3-5 inches and storm surges were 3-5 feet (Avila & Cangialosi, 2010). Due to the reduction of intensity, Alabama and Florida were mostly affected by flash flooding associated with the rainfall. Flash floods caused roadways, schools, and other buildings to be closed briefly following the storm.

3.2.4.10 Hurricane Isaac

Isaac made its approach and landfall in Louisiana between the late evening and early morning hours of August 28 and 29, 2012, respectively (Berg, 2013). Isaac began as a tropical wave off the western coast of Africa that merged with a developing low pressure system. Deep convection and the development of a well-defined center allowed

Isaac to strengthen from a tropical depression to a tropical storm shortly before traveling westward across the Atlantic and into the Caribbean Sea. Isaac made landfall with Haiti and Cuba over the next couple days whose rough terrain prevented the storm from intensifying further (Berg, 2013). Isaac followed the northern coast of Cuba as it moved west-northwest, finally entering the Gulf of Mexico. As Isaac moved across the gulf, convection allowed the inner core to become well-developed and strengthened the storm to hurricane status. Hurricane Isaac was forced to slow and stall just off the coast of

Louisiana due to a mid-level ridge (Berg, 2013). Because of this delay, Isaac continuously affected surrounding areas until making its first of two landfalls over

Louisiana as a Category 1 hurricane (Berg, 2013).

Based on satellites, aircraft data, and ship reports, Isaacs’ strongest winds of 96 knots (110 mph) occurred on August 28 before making landfall with Louisiana (Berg,

2013). The highest land gust measuring 84 knots (97 mph) in St. Bernard Parish,

Louisiana (Berg, 2013). Storm surges measured from 1-17 feet along the coast. Laplace,

Louisiana had measurements of 1 to 3-foot storm surges while the Plaquemines Parish,

LA had 10-17 feet storm surges (Berg, 2013). Alabama’s highest storm surge was 4.5 feet with other areas ranging 1-3 feet. Storm surges along the Florida panhandle were 1-3 feet (Berg, 2013). Flooding due to heavy rainfall was severe in parts of Florida, including

Palm Beach County whose totals ranged from 13.02- 15.86 inches and caused a two-inch rise in levels of Lake Okeechobee (Berg, 2013). Alabama, Mississippi, and Louisiana had rainfall totals of at least 10 inches and experienced flash floods and river flooding. The highest measured totals were 13.99 inches, 22.20 inches, and 20.66 inches, respectively

(Berg, 2013). About 26 tornadoes formed in the United States as a direct result of hurricane (Berg, 2013). Three deaths occurred in Louisiana from a car flooding in an off- road ditch and a house being flooded from the storm surge. The damage done to homes and businesses in the United States have been estimated at $2.35 billion (Berg, 2013).

Damage in Florida was limited to downed trees and power lines from the sustained winds as well as moderate to severe flood damage to homes and roads. Louisiana experienced damage from the storm surge including damage to homes, cars, and roads. Isaac, occurring six years after Katrina, acted as a “test-round” for the federal levees system

Hurricane and Storm Damage Risk Reduction System (HSDRRS) (Berg, 2013). Areas within the system held up to the storm surges produced by the hurricane but there were about 59,000 homes outside of the system’s area which sustained extensive damage.

Mississippi and Alabama had about 6,000 homes with damage from the storm surge, freshwater flooding, and wind damage (Berg, 2013).

3.3 Methods

3.3.1 Excel Calculations

Demographic data including population totals, female populations, minority populations, median household income, and dependent age ranges were gathered for each county. Due to the variety in population totals for each county, the demographic data was converted from raw numbers to ratios compared to the total population using a simple equation. All population related variables (gender, race, age, etc.) shared the same equation format to create ratios of a specific part of the population relative to the rest of the county and to provide a better understanding of the percentage of the population that may be most vulnerable. For demonstrative purposes, an example showing the ratio of female residents compared to the total population was calculated using:

푇표푡푎푙 퐹푒푚푎푙푒 푃표푝푢푙푎푡푖표푛 푝푒푟 퐶표푢푛푡푦

푇표푡푎푙 푃표푝푢푙푎푡푖표푛 푝푒푟 퐶표푢푛푡푦

(4)

Use of these ratios was important for understanding the social structure of the counties.

For example, Bay County, Florida has a total population of 168,852 and a female population of 85,196, while Cameron Parish, Louisiana has a total population of 6,839 and a female population of 3,444. Despite the great difference in total populations, the ratio of females in each county is 50% (Table 1).

Hurricane data was gathered from NOAA Tropical Cyclone, Post Storm Reports,

NOAA Daily Summary Maps, and NNDC Daily Summaries. Numerical data was gathered of wind speeds, gusts, rainfall, and storm surges that occurred on the day of landfall. All values were recorded using the Imperial Measurement System. Wind speeds and gusts were converted from kilometers per hour to miles per hour (mph) due to inconsistencies in formatting among the reports, storm surge was recorded in feet (ft), and rainfall was recorded in inches (in). In situations where there was more than one measurement station in a given county, the largest data point was collected for the county. This method of collection ensured calculations were based on highest potential levels of risk. In instances of lack of data for a given county, specifically for inland counties lacking wind or gust data, a technique introduced by John Kaplan and Mark

DeMaria was used. From their paper, A Simple Empirical Model for Predicting the Decay of Tropical Cyclone Winds after Landfall, they discussed several specific derivations of the model dependent upon coastal proximities and regional variations that may impact the wind decay. For clarity and simplicity, the most basic derivation of the decay equation was utilized for this study. The equation

−∝푡 푉(푡) = 푉푏 + (푅푉표 − 푉푏)푒

(5) where V(t) is the wind speed at a specific time after landfall, Vb is the background wind speed that the hurricane decays to based on the observation that tropical cyclones decay to the same 1-minute surface wind speed, R is a constant reduction factor, Vo is the wind speed at t=0, α is a decay constant, and t is elapsed time after landfall (Kaplan &

DeMaria, 1995). This equation was solved with the following constant values of R=1,

-1 α=0.115 h , and Vb=27.0 kts, as denoted in Figure 3, a table containing the relationships between the constant variables that Kaplan and DeMaria established. The time variable changed depending upon when effects from the hurricane would impact a specific county after time of landfall. These values ranged from 1-9 hours. The use of this equation helped estimate the wind speeds and gusts that inland counties may have experienced when data was missing.

Information on building proximity to coast, disaster relief organizations, hospitals, evacuation routes, and evacuation shelters was gathered from state and county governmental websites and Google Maps where applicable. These variables were then adjusted into a ratio based on the number of available compared to the population.

Regarding shelters, some counties did not list their shelters until the event of an emergency. These counties were given the median rank, 69. This was done to credit those counties with that specific resiliency, using the median rank to represent the county’s efforts without giving them a rank that is too high or too low. Proximity to coast was not adjusted from a raw value into a ratio, but kept as a measurement of feet. Using the values for each county of the distance in which homes were built away from shore, each county was ranked from lowest value to highest value and given a numerical rank, with one being least resilient and 139 being most resilient. The upper limit for this ranking system was based on the number of counties that had a building proximity guideline. The remaining coastal counties without this guideline received a ranking of one. Inland counties did not have building codes that regulated how closely to shore they could be built, so those counties did not take this variable into consideration in the final equation.

The ranking system used in this study is described in more detail in upcoming paragraphs.

All relevant information was compiled into excel worksheets with each row representing the counties in the study area and each column representing the physical and social vulnerability and resilience variables. While there was no specific order of variables within the worksheet, keeping the vulnerability variables separate from the resilience variables proved helpful when calculations began. Equations were chosen to analyze the data to relate the variables that represented vulnerability to those that represented resilience. For vulnerability and hazard variables, the data was ranked from lowest to highest values per county and assigned a number from 1-139, one being the least vulnerable (Table 2). For resilience variables, the data was ranked from lowest to highest and given a numerical value from 1-139, one being the least resilient (Table 3).

After ranking the counties for a given variable, the percentile rank was calculated using equation (3). It is important to note that hazard and vulnerability variables received a percentile rank value closer to one if they put more risk on the county. Values closer to zero put the county at less risk. Similarly, for resilience variables, a percentile rank of one made the county more resilient while a value closer to zero made the county less resilient.

Following the percentile ranking, an initial risk was calculated based only on vulnerability and resilience variables. To calculate this risk, a modified equation (2) was used, where

푉푢푙푛푒푟푎푏푖푙푖푡푦 푅푖푠푘 = 푅푒푠푖푙푖푒푛푐푒

(6)

In this equation, Vulnerability represents the sum of all percentile ranked demographic variables and Resilience represents the sum of all percentile ranked resilience variables.

This equation has been modified from the original equation introduced by King to keep the calculations simplified and less prone to error. King’s Hazard variable is not included in this equation because it contains all of the hurricane elements, which do not need to be included when calculating a county’s initial risk. The Vulnerability variable is comprised of King’s Vulnerability and Elements at Risk variables while the Resilience variable combines the Resilience and Mitigation terms.

Once the initial risk value was calculated, the risks for each hurricane were calculated using a different modified version of equation (2), where

퐻푎푧푎푟푑 ∗ 푉푢푙푛푒푟푎푏푖푙푖푡푦 푅푖푠푘 = 푅푒푠푖푙푖푒푛푐푒

(7) and the new variable, Hazard represents the sum of all percentile ranked hurricane variables. The closer the value to zero, the lower the risk of damage from a hurricane for the area. Adding in the hurricane data caused the values for each county to vary based on whether they were affected and how strongly compared to the other counties. Areas that received higher percentile rankings for the hurricane impacts resulted in a higher overall vulnerability. The increased overall vulnerability caused an increase in overall risk calculated. These values varied dramatically between each of the hurricane cases. It is important to understand that the values for a specific hurricane are only representative of the data used in that case. Having a higher risk in one hurricane case is not indicative of that case having the most devastating hurricane. These values are created and to be

compared only with the other counties within a specific case. Using multiple Excel “IF” statements, the values calculated from the final risk calculation were assigned a numerical value between zero and seven, with zero indicating that the area was least at risk for each hurricane. This type of equation looks like:

“=IF (GN80=0,"0”, IF (GN80<2,"1”, IF (GN80<4,"2”, IF (GN80<6,"3”, IF

(GN80<8,"4”, IF (GN80<10,"5”, IF (GN80<12,"6”, IF

(GN80<14,"7","ERR"))))))))”

These numbers would later be associated with colors between the red and blue spectrum to visually represent risk.

3.3.2 ArcGIS Online

To create a visualization of the associated risks of the ten hurricanes ESRI’s

ArcGIS Webmap feature was utilized. A basic light gray earth canvas was selected as the base map. Five different county map layers were downloaded and added to the blank map of the United States. These maps contained county borders for Texas, Louisiana,

Mississippi, Alabama, and Florida (Figure 4). For the observation of Ike’s impacts to inland counties following its track, county maps for Texas, Oklahoma, Arkansas,

Missouri, Illinois, Indiana, Ohio, and Michigan were also downloaded (Figure 5). The states were formatted without any fill and with 1.5-pixel boundary lines. After laying the base layers of the maps, the “Find Existing Locations” analysis tool was used to select and create individual layers for each of the counties in this study. Unfortunately, these counties had to be separated individually so that they could be edited and colored

individually. With each county now contained within its own layer on the map, they were ready to be assigned their risk for hurricane damage.

Using the color scheme values from the Excel spreadsheet analysis, a color scheme was created. Initially there were going to be 14 different colors, but limitations in the program’s color availability made this impossible. Instead, numbers were paired from one to 14 (e.g. 1 & 2, 3 & 4, etc.), producing a total of seven different colors that would be used in the scheme. A value of zero did not receive a color, but instead remained gray.

County colors were entered separately for each of the ten study hurricanes on a gradient rainbow scale from red to blue. Red tones represented the most amount of risk while blue tones represented the least amount of risk.

County, State Total Pop. Female Pop. Ratio Rank County, State Total Pop. Female Pop. Ratio Rank

Cameron, TX 406220 210826 0.519 50 Harrison, MS 187105 94072 0.503 24 Willacy, TX 22134 10104 0.456 5 Jackson, MS 139668 70818 0.507 33 Kenedy, TX 416 204 0.490 12 Mobile, AL 412992 214618 0.520 52 Kleberg, TX 32061 15724 0.490 12 Baldwin, AL 182265 93069 0.511 38 Nueces, TX 340223 173185 0.509 37 Escambia, FL 297619 150500 0.506 31 San Patricio, TX 64804 32641 0.504 27 Santa Rosa, FL 151372 74950 0.495 16 Aransas, TX 23158 11673 0.504 27 Okaloosa, FL 180822 89979 0.498 19 Refugio, TX 7383 3714 0.503 24 Walton, FL 55043 26866 0.488 9 Calhoun, TX 21381 10534 0.493 14 Bay, FL 168852 85196 0.505 29 Matagorda, TX 36702 18339 0.500 20 Gulf, FL 15863 6379 0.402 1 Brazoria, TX 313166 154166 0.492 13 Franklin, FL 11549 4893 0.424 2 Galveston, TX 291309 147075 0.505 29 Wakulla, FL 30776 13784 0.448 4

48 Chambers, TX 35096 17435 0.497 17 Jefferson, FL 14761 7034 0.477 8

Jefferson, TX 252273 123327 0.489 10 Taylor, FL 22570 10016 0.444 3 Orange, TX 81837 41129 0.503 24 Dixie, FL 16422 7580 0.462 6 Cameron, LA 6839 3444 0.504 27 Levy, FL 40801 20723 0.508 34 Vermilion, LA 57999 29785 0.514 46 Citrus, FL 141236 72877 0.516 49 Iberia, LA 73240 37484 0.512 40 Hernando, FL 172778 90244 0.522 53 St. Mary, LA 54650 27658 0.506 31 Pasco, FL 464697 238811 0.514 46 … Continued

Table 1. Ratio Calculation and Rank Example for Female Populations in Select Counties.

Table 1. Continued.

County, State Total Pop. Female Pop. Ratio Rank County, State Total Pop. Female Pop. Ratio Rank

Terrebonne, LA 111860 56292 0.503 24 Pinellas, FL 916542 476533 0.520 52 Lafourche, LA 96318 48994 0.509 37 Hillsborough, FL 1229226 630142 0.513 41 Jefferson, LA 432552 222544 0.514 46 Manatee, FL 322833 166718 0.516 49 Plaquemines, LA 23042 11484 0.498 19 Sarasota, FL 379448 198407 0.523 54 St. Bernard, LA 35897 17735 0.494 15 Charlotte, FL 159978 82216 0.514 46 Orleans, LA 343829 177581 0.516 49 Lee, FL 618754 315154 0.509 37 St. Tammany, LA 233740 119767 0.512 40 Collier, FL 321520 163026 0.507 33 Hancock, MS 2967297 1526057 0.514 46 Monroe, FL 73090 34128 0.467 7

Figure 3. Constant Variable Relationships for Decay Model. (Kaplan & DeMaria, 1995)

Hazard and Vulnerability

Variables Hazard Vulnerability

Total Population x

Female x

Median Household Income x

Dependent (<18,>65) x

Non-White Single Race x

Hurricane Wind Speed x

Hurricane Wind Gust x

Hurricane Storm Surge x

Hurricane Rainfall x

Table 2. Hazard and vulnerability variables. Used in the dataset and assigned a rank from

1-139 for each county. A low rank (1) denotes a higher vulnerability.

Resilience Variables

Independent (18

White

Proximity to Water

Medical Aid (Hospitals)

Relief Organizations

Evacuation Routes

Evacuation Shelters

Table 3. Resilience variables. Used in the dataset and assigned a rank from 1-139 for

each county. A low rank (1) denotes a lower resilience.

Figure 4. Individual Gulf Coast County Layers in Project Area.

Figure 5. Individual Inland County Layers for Study of Hurricane Ike after Landfall.

Chapter 4

Results

4.1 Resilient and Vulnerable Counties

Before developing specific hurricane results, county information was gathered on those that were best and least prepared for disaster than others strictly based on the previously mentioned initial overall risk calculation. This initial overall risk was calculated from the demographic vulnerability variables divided by the resilience variables (Table 4).

The county that was least vulnerable to a disaster, meaning it had an appropriate amount of hospitals, evacuation routes and shelters compared to the county’s populations, was Cameron Parish, Louisiana. Cameron Parish has the third lowest population of all

139 counties in the coastal study, which is especially important when considering evacuation during a hurricane. Cameron Parish has the lowest initial risk of the study area at 0.10 units. The low risk value is partially due to population density. Cameron Parish has 6,839 residents, whom of which are 50% female, 37% dependent, and 4% minorities, making a median household income of $64,574 per year. The parish’s yearly income is the fifth highest of the study area and they have some of the best ratios of evacuation routes, evacuation shelters, and hospitals to population, allowing the people living there

plenty of opportunity to receive relief from hurricanes without meeting obstacles related to shortages in space or supplies. The top ten counties most resilient to disasters were

Cameron Parish, LA, Franklin County, FL, Monroe County, FL, Lafayette County, FL,

Holmes County, FL, Walton County, FL, Dixie County, FL, Liberty County, FL,

Wakulla County, FL, and Gilchrist County, FL. These counties are the most resilient in similar, but different ways. Cameron Parish, Louisiana is resilient due to having a small population of only 6,839 residents and low ratios of hospitals and organizations, evacuation routes, and evacuation shelters, of 1:3420, 1:977, and 1:1710, respectively.

These low ratios relative to the population, in conjunction with the high annual median household income, put this county in the best position for resistance and recovery after a hurricane. Alternately, the counties in Florida have populations ranging from 8365 residents in Liberty County to 73,090 residents in Monroe County. These counties have among the lowest populations of the coastal counties of Florida. Franklin County, with a total population of 11,549, has hospital and evacuation route ratios of 1:3,850 and

1:1,925, respectively, and does not offer evacuation shelters. Monroe County has a total population of 73,090, with hospital, evacuation route, and evacuation shelter ratios of

1:5,622, 1:73,090, and 1:8,121, respectively. Lafayette County, with a total population of

8,870, has hospital, evacuation route, and evacuation shelter ratios of 1:4,435, 1:4,435, and 1:554, respectively. Holmes County has a total population of 19,927, has hospital, evacuation route, and evacuation shelter ratios of 1:3,985 and 1:2,847, and 1:2,491, respectively. Walton County has a total population of 55,043, with hospital, evacuation route, and evacuation shelter ratios of 1:6,880, 1:3,058, and 1:11,009, respectively. Dixie

County, with a population of 16,422, has hospital, evacuation route, and evacuation 56

shelter ratios of 1:4,106, 1:3,284, and 1:5,474, respectively. Liberty County, with a population of 8,365, has hospital, evacuation route, and evacuation shelter ratios of

1:2,788, 1:4,183, and 1:8,365, respectively. Wakulla County, with a population of

30,776, has hospital and evacuation route ratios of 1:6,155, and 1:5,129, respectively, and does not offer evacuation shelters. Finally, Gilchrist County has a population of 16,939, hospital, evacuation route, and evacuation shelter ratios of 1:8,470, 1:8,470 and 1:5,646, respectively. From these numbers, lower populations and increased opportunity for safety, through the accessibility of hospitals, shelters, and evacuation routes relative to the population, makes these counties in Louisiana and Florida most resilient to disasters.

Cameron County, Texas, was calculated to have the highest vulnerability of all counties in this study. Unlike Cameron Parish, Cameron County has the thirteenth highest population among all other counties and has a median household income of $32,215 per year. Due to shortcomings in the median income compared to the population of the county, there has been a tremendous lack of services necessary to protect and assist the residents living there. The ten most at risk counties include Cameron County, TX, St.

Landry Parish, LA, Iberia Parish, LA, Hancock County, MS, Rapides Parish, LA,

Hidalgo County, TX, Marion County, FL, Pike County, MS, Polk County, FL, and

Clarke County, AL. The majority of these counties hold higher populations, ranging from

25,833 in Clarke County to 2,967,297 in Hancock County, but make low to average median household annual income between $29,357 and $44,522. The mix of high populations and low income has put these counties at higher risk due to lack of necessary resources during catastrophic events. As briefly mentioned, Cameron County has the highest vulnerability of the study area. The county houses 406,220 residents but has high 57

ratios of hospitals and evacuation routes of 1:50,778 and 1:67,703, respectively, and do not offer evacuation shelters. St. Landry Parish has a total population of 83,384. The ratios of hospitals, evacuations routes, and evacuation shelters per population are

1:10,423, 1:16, 677, and 1:83,384, respectively. Iberia Parish, with a population of

73,240 has hospital and evacuation route ratios of 1:18,310 and 1:7,324, respectively, and does not offer evacuation shelters. Hancock County has a total population of 2,967,297 and hospital, evacuation routes, and evacuation shelter ratios of 1:329,700, 1:989,099, and 1:1,483,649, respectively. Rapides Parish has a total population of 131,613 and hospital, evacuation routes, and evacuation shelter ratios of 1:10,124, 1:13,161, and

1:131,613, respectively. Hidalgo County has a total population of 774,769 and hospital, evacuation routes, and evacuation shelter ratios of 1:48,423, 1:387,385, and 1:64,564, respectively. Marion County has a total population of 331,298 and hospital and evacuation route ratios of 1:25,484, and 1:55,216, respectively. Marion County does offer evacuation shelters, but they are not announced until the time of emergency. Pike County has a total population of 40,404 and hospital, evacuation routes, and evacuation shelter ratios of 1:5,772, 1:8,081, and 1:40,404, respectively. Polk County has a total population of 602,095 and hospital, evacuation routes, and evacuation shelter ratios of 1:46,315,

1:100,349, and 1:13,684, respectively. Clarke County has a total population of 25,833 and hospital and evacuation route ratios of 1:5,167, 1:4,306, respectively, and does not offer evacuation shelters. These counties had fewer resilience opportunities compared to their populations, justifiably increasing their vulnerability.

4.2 Hurricane Impacts

After understanding areas of the least and most risk based on the demographics and emergency services offered by the given counties, the initial vulnerabilities, overall resiliencies, and hurricane hazards could be used to create an overall vulnerability and finally the overall risk of a county for each specific hurricane. The values obtained by these calculations are represented in Table 5, which will be referenced in the following hurricane cases.

4.2.1 Hurricane Charley

Figure 6 depicts the varying levels of risk for the counties in the study area during

Hurricane Charley. Red colors indicate high risk while blue colors indicate low risk.

Table 6 displays the data collected for the hurricane hazard variables and the vulnerability values that contributed to the final risk assessment. Hurricane Charley reached its peak intensity as a Category 4 hurricane just before making landfall along the southern gulf coast of Florida.

The county that endured the highest risk as a result of Charley was Marion

County, Florida, with a final risk value of 7.25 units. Marion County’s initial risk was

2.78 units, which was the seventh highest initial risk rating before including the hurricane parameters. The county has a population of 331,298, of which 52% are female, 45% are dependents, 19% are minorities, and 10.9% are Hispanic. The median household income of the county is $39,453. The rankings of the vulnerability variables are 123, 133, 133,

52, 90, and 95, respectively. Marion County is an inland county that does not benefit from additional resilience based on the building proximity to the coast. However, the 59

county does receive some resilience from its hospital and evacuation route to population ratios of 1:25,484 and 1:55,216. These ratios are among the lowest ranked for the two factors, with rankings of 23 and 16, respectively. Marion County does not have predetermined shelters, although they do open shelters in emergency situations, granting them a median rating of 69. The combination of high ranked vulnerabilities and low ranked resiliencies produces the high initial risk value that was calculated. Hurricane

Charley produced 21 mph winds, 24 mph gusts, no storm surge, and 0.78 in of rainfall.

These hazard values received rankings of 125, 122, 0, and 116, respectively. With the maximum rank of 139, Marion County had among the highest ranked impacts from the hurricane. The product of the sum of hazard variables with the sum of vulnerability variables increased the already at risk county to most at risk (Table 5) for Hurricane

Charley.

Polk County, Florida had the second highest risk associated with Charley, with a final risk rating of 6.76 units. This value increased from an initial risk calculation of 2.55 units. Similar to Marion County, Polk County also started with a relatively high initial risk, the ninth highest of the dataset. This high risk was based on a population of 602,095, of which 50.9% are female, 41.5% are dependents, 24.8% are minorities, and 17.7% are

Hispanic. Polk County has a median household income of $43,133. The rankings of the vulnerability variables for Polk County are 133, 93, 110, 76, 98, and 72. Polk is also an inland county, which does not obtain resiliencies from a building proximity to the coast distance. The resiliencies that were offered in Polk county included hospitals, evacuation routes, and evacuation shelters, with ratios to population of 1:46,315, 1:100,349, and

1:13,684, respectively. These had rankings of 14, 6, and 108, respectively. While Polk 60

county had lower ranked minority populations and yearly income, the majority of their vulnerability variables were average to high. Polk County also had a high rank of shelter ratios to population. Unfortunately, the remainder of the county’s resiliencies were among the lowest ranked. This overall high rank of the vulnerabilities with the overall low rank of the resiliencies gave Polk County its high initial risk calculation. Polk County recorded 47.18 mph winds, 62.14 mph gusts, no storm surge, and 0.22 in of rainfall from

Hurricane Charley. These variables received rankings of 131, 131, 0, and 107, respectively. Polk County also had highly ranked impacts from the hurricane and when these factors were integrated into the final equation, Polk County received the second highest overall risk from Hurricane Charley (Table 5).

Lee County, Florida received the third highest overall risk with a value of 5.49 units. Before including the hurricane hazard values to the final equation, Lee County had an initial risk value of 1.40 units, the 42nd highest initial risk. This initial risk value was based on a population of 618,754, of which 50.9% were female, 43% were dependents,

17% were minorities, 18% were Hispanic, and the county had a median household income of $47,439. These variables received rankings of 134, 92, 120, 40, 100, and 43,

Lee County is a coastal county with a building proximity regulated distance of 200 ft from the coast, with a rank of 127. The county also offers hospitals, evacuation routes and shelters, with ratios to population of 1:51,563, 1:61,875, and 1:21,336, respectively.

The hospital ratio has a ranking of 9, evacuation routes have a ranking of 12, and evacuation shelters have a ranking of 60, all of which are on the lower end of the spectrum in terms of resiliencies. The vulnerabilities varied from low to high more so than other counties in the study, however, the resiliencies were consistently low. In this 61

situation, the lower resiliencies played a big role in determining the initial risk. Factoring in the hurricane parameters for Lee County increases the final risk calculation. This is due to winds of 60.99 mph, gusts of 78.25 mph, a storm surge of 6.50 ft, and rainfall of

3.53 in. These are some of the highest ranked impacts from Hurricane Charley at 133,

134, 139, and 139, respectively. The addition of the high hurricane hazard parameters to the final calculation caused Lee County to have the third highest risk as a result of

Hurricane Charley (Table 5).

Pasco County, Florida was the fourth highest risk county for Hurricane Charley, with an overall risk of 4.22 units. This county had an initial risk of 1.64 units, the 27th highest value. Pasco County has a population of 225,886, with 51.4% of the population female, 41.9% dependents, 11.8% minorities, and 11.7% Hispanic, with a median household income of $43,888. The ranks of these variables are 131, 111, 117, 14, 91, and

66. While Pasco County is located on the coast, they do not have any guidelines for the construction of buildings near the coast. The hospital, evacuation route and shelter ratios for Pasco County are 1:92,939, 1:21,123, and 1:17,211, with ranks of 4, 34, and 104, respectively. The vulnerabilities for this county are mostly in the average to high rankings, while most the resiliencies are ranked low. The combination of these rankings for the vulnerability and resilience contribute to the initial risk value. Hurricane Charley left Pasco County with 24 mph winds, 31 mph gusts, no measured storm surge, and 0.13 in of rainfall. These were ranked 127, 124, 0, and 106, respectively. The addition of the hurricane parameters to the final risk equation did produce an increase in overall risk

(Table 5), though not as abrupt the previously mentioned counties, making Pasco County the county with the fourth highest risk. 62

Pinellas County, Florida was granted the fifth highest overall risk, with a value of

4.05 units. The initial risk for Pinellas County was 1.53 units, which was the 32nd highest initial risk value. The population of Pinellas County is 916,542, of which 52% are female,

39% are dependents, 17.9% are minorities, 8% are Hispanic, and have a median household income of $45,535. These variables have ranks of 136, 132, 72, 46, 85, and 52.

Pinellas County is a coastal county with a building proximity regulated distance of 200 ft from the coast, with a rank of 127. The county also has hospital, evacuation route, and evacuation shelter ratios to population of 1:76,379, 1:101,838, and 1:27,774, respectively.

The rankings of each resilience variable are 6, 5, and 56, respectively. The average to high ranked vulnerabilities in addition to the very low ranked resiliencies contributed to the initial risk value for Pinellas County. Hurricane Charley caused 31.07 mph winds,

36.82 mph gusts, no measured storm surge, and 0.43 in of rainfall in Pinellas County.

Each of these is ranked 129, 129, 0, and 110, respectively, and are among the higher values for this hurricane’s impacts. Due to the high ranked hurricane impacts, Pinellas

County was made fifth most at risk county after Hurricane Charley made landfall (Table

5).

The counties in Florida along the northwestern part of the gulf coast and the easternmost counties of Alabama received little risk associated with the hurricane due to being far west of the center of the eye and having less critical impacts from the system.

Counties to the west of Alabama were not affected by Hurricane Charley upon landfall.

4.2.2 Hurricane Ivan

Figure 7 demonstrates the varying levels of risk for the counties in the study area during Hurricane Ivan. Table 7 displays the data collected for the hurricane hazard variables and values that contributed to the final risk assessment. At the time of landfall,

Hurricane Ivan had weakened from a Category 5 to a Category 3 hurricane as the center of the storm contacted the coastal counties of Alabama and affecting counties in

Louisiana, Mississippi, Alabama, and Florida.

The county with the highest calculated risk because of Hurricane Ivan was

Hancock County, Mississippi, with a final risk of 10.90 units. Hancock county had the fourth highest initial risk value of 3.22 units. This high initial risk was based on the county’s population of 2,967,297 people, of which 51.4% are female, 38.3% are dependent, 40.9% are minorities, 2.7% are Hispanic, and the median household income is

$44,522. These vulnerability variables were ranked 138, 114, 60, 118, 35, and 59, respectively. Despite being a coastal county, Hancock County did not have any proximity guidelines for construction along the coast, so it did not receive any resiliency benefits from that parameter. It does, however, offer hospitals, evacuation routes, and evacuation shelters in the event of a hurricane. The ratios of services to population and their respective ranks are 1:329,700, ranked 2, 1:989,099, ranked 1, and 1:1,483,649, ranked

32. The combination of average to high values and ranks of demographic vulnerabilities with the low ranks of resiliencies resulted in the high initial risk rating. Hurricane Ivan produced winds of 33 mph, gusts of 61 mph, 4.76 ft storm surges, and 1.80 in of rainfall around the time of landfall. Relative to the rest of the counties affected by the hurricane these values were ranked 105, 124, 127, and 115 respectively. These are rated within the 64

top 25% highest measurements for each hurricane parameter. The impacts from

Hurricane Ivan resulted in a sharp increase from the initial risk to the final and highest risk (Table 5) associated with this specific hurricane.

Orleans Parish, Louisiana, had the second highest overall risk associated with

Hurricane Ivan, with a value of 8.39 units. This value is another sharp increase from the initial risk value of 2.45 units, the 11th highest initial risk value. The high initial risk value was based on the county’s population of 343,829, of which 51.7% are female, 32.2% are dependents, 67% are minorities, and 5.3% are Hispanic, with a median household income of $37,146. These variables are ranked 125, 124, 5, 138, 70, and 104, respectively.

Orleans Parish is a coastal county but does not have any specific guidelines for building proximity along the coast, therefore, it forfeits those resiliency points. The county offers hospitals, evacuation routes, and evacuation shelters in the event of an emergency. The ratio of hospitals to population is 1:34,383, with a rank of 16. The ratio of evacuation routes to population is 1:49,118, with a rank of 17. The ratio of shelters to population is

1:343,829, with a rank of 33. Orleans Parish has one of the lowest percentages of dependents, but the remainder of the demographic variables are among the highest. Their ratios of emergency services are among the lowest ranks, giving the county a low sum for their overall resilience. Hurricane Ivan produced winds of 47.20 mph, gusts of 55.20 mph, storm surges of 6.10 ft, and 0.08 in of rainfall. These effects are ranked 123, 121,

132, and 101 respectively. These hurricane impacts are among the highest values recorded for the storm. Therefore, the combination of high vulnerabilities, low resiliencies, and high hurricane impacts resulted in the second highest overall risk for

Hurricane Ivan (Table 5). 65

Clarke County, Alabama, was calculated to have the third highest overall risk associated with Hurricane Ivan, with a value of 6.97 units. This is an increase from its initial risk of 2.46 units, the 10th highest initial risk rating. The initial risk calculation was based on Clarke County’s population of 25,833, of which 52.7% are female, 40.9% are dependents, 45.5% are minorities, and 1.1% are Hispanic, with a median household income of $29,357. These values and percentages are ranked 48, 139, 101, 125, 7, and

136, respectively. Clarke County is an inland county and does not benefit from resiliency points from building proximities to the coast. Clarke County offers hospitals and evacuation routes, but does not offer evacuation shelters. The ratios of hospitals and routes to population are 1:5,167 and 1:4,306, with ranks of 107 and 102, respectively.

The lower percentage of Clarke County’s Hispanic population and its overall low population did not benefit the county or do much to reduce its initial risk, especially since the county has the highest population of females in the study area. The resiliencies are ranked higher than the previously mentioned counties for this hurricane, preventing the initial risk from increasing more. Hurricane Ivan caused winds of 63.46 mph, gusts of

81.91 mph, 6.02 in of rainfall, and no storm surges. These hurricane hazard values were ranked 132, 133, 129, and 0 respectively. While Clarke County is an inland county and did not receive hazard points for storm surge, the remainder of the hurricane hazards were ranked in the top 8% for Hurricane Ivan. The combination of moderately high and high vulnerabilities, high hurricane hazards, and moderately high resiliencies (Table 5) resulted in Clarke County becoming the third highest at risk area for Hurricane Ivan.

Pike County, Mississippi, received the fourth highest calculated overall risk in the case of Hurricane Ivan, with a value of 5.51 units. Pike County’s initial risk was 2.65 66

units, ranked 8th highest. The resultant initial risk was based on the county’s population of 40,404, of which 52.4% are female, 41.3% are dependents, 53.6% are minorities, and

1.2% are Hispanic, with a median household income of $34,841. These are ranked 68,

138, 107, 134, 9, and 117, respectively. Pike County is an inland county and does not receive resilience benefits from building proximities to water. However, the county does receive resiliency points from offering hospitals, evacuation routes, and evacuation shelters. The ratios and ranks of these parameters are 1:5,772 and ranked 92, 1:8,081 and ranked 66, and 1:40,404 and ranked 50, respectively. Pike County has a low total population and a small Hispanic population. Unfortunately, they also have among the highest percentages of females and minorities and a low median household income.

Because of the smaller population, their emergency service ratios are smaller, granting them average to high resiliency values. Hurricane Ivan produced winds of 24 mph, gusts of 31 mph, 0.03 in of rainfall, and no storm surge. These values are ranked 94, 97, 99, and 0 respectively. Pike County received moderately high hurricane hazard ranks and values. The combination of the high hazards, high overall vulnerabilities, and moderately high resiliencies (Table 5) resulted in the fourth highest overall risk associated with

Hurricane Ivan.

Monroe County, Alabama, resulted in the fifth highest risk associated with

Hurricane Ivan, with a value of 4.72 units. The initial calculated risk for the county was

1.70 units, the 25th highest. The resultant initial risk was based on the county’s population of 23,068, of which 51.8% are female, 41.0% are dependents, 44.9% are minorities, and

0.9% are Hispanic, with a median household income of $29,203. These are ranked 41,

128, 104, 124, 5, and 137, respectively. Monroe County is an inland county and does not 67

receive any benefits to resiliency for building proximities to water. Monroe offers hospitals, evacuation routes, and evacuation shelters, with ratios to population of 1:5,767,

1:4,614, and 1:11,534, respectively. These are also ranked 93, 93, and 113, respectively.

The vulnerabilities of Monroe County are generally high, not including the overall population and the Hispanic population. Also, because of the small population, the emergency service ratios were awarded with high resilience rankings. Hurricane Ivan was responsible for winds of 50.18 mph, gusts of 69.79 mph, 7.26 in of rainfall, and no storm surge. These variables received ranks of 126, 128, 132, and 0, respectively. The impacts from Hurricane Ivan were high for Monroe County. This, in combination with the high vulnerability and the high resilience, resulted in Monroe County receiving the fifth highest risk associated with Hurricane Ivan (Table 5).

Although the counties were not all affected with strong storm impacts, the initial risks and lack of reasonable resiliencies contributed to the high overall risks seen with the counties. As demonstrated in Table 6, several counties in Florida received higher winds, gusts, surges, or rainfall but did not appear in the top five most affected counties. This is likely due to the county’s income and services offered being more adequate than these counties. The southwestern coast of Florida and the coast of Texas were not at risk from

Hurricane Ivan.

4.2.3 Hurricane Dennis

Figure 8 depicts the levels of risk for the counties in the study area for Hurricane

Dennis. Table 8 displays the data collected for each of the hurricane hazard variables and the associated ranks and calculated vulnerability values. Hurricane Dennis reached its 68

peak intensity as a Category 4 hurricane over Cuba and weakened to a Category 3 hurricane before making landfall in the Florida panhandle.

Hancock County, Mississippi, was calculated to have the highest risk associated with the landfall of Hurricane Dennis, with an overall risk value of 8.87 units. As discussed in the case of Hurricane Ivan, the initial risk of 3.22 units was based on a combination of high population, high ranking demographic vulnerabilities, and too few emergency services available to a population of over 2 million. While the demographic and resilience factors do not change for this case, the hurricane hazard values and their ranks have changed. Hurricane Dennis caused 23 mph winds, 36 mph gusts, 1.66 ft storm surges, and 0.48 in of rainfall. Each of these variables is ranked 77, 87, 133, and 87, respectively. The recorded winds and gusts seem slower than expected, given Hancock

County’s overall risk, but all of the hurricane variables were ranked within the top 45%.

When the sum of the hazard vulnerability values was multiplied by the demographic vulnerabilities, it caused the overall vulnerability to increase while the resiliencies stayed the same (Table 5). The combination of the high vulnerabilities with the moderate hurricane hazards and the low resiliencies resulted in Hancock County being most at risk from Hurricane Dennis.

Orleans Parish, Louisiana, received the second highest overall risk associated with

Hurricane Dennis, with a value of 7.38 units. Also discussed in regard to Hurricane Ivan, the initial risk of 2.45 units was based on a combination of a high population, high demographic vulnerabilities, and a lack in emergency services available with respect to the population size. Dennis produced different hurricane impacts on the county than Ivan.

Hurricane Dennis produced winds of 39.13 mph, gusts of 48.33 mph, a storm surge of 69

0.08 ft, and rainfall totals of 0.08 in. These were ranked 117, 112, 131, and 60, respectively. Every hurricane variable except the rainfall was given a high ranking. The rainfall total was a more moderate rank due to how little the county received relative to all other counties. Because of the effects from Hurricane Dennis, Orleans Parish’s risk increased more than Hancock County’s (Table 5), but the resiliencies of Orleans Parish almost doubled those of Hancock County, making the overall risk second highest.

Pike County, Mississippi, received the third highest overall risk for Hurricane

Dennis with a value of 4.95 units. The initial risk value of 2.65 units, as discussed in the

Hurricane Ivan section, was based on the high demographic vulnerabilities of the small population and the insufficient amount of emergency services available to the county.

The hurricane impacts from Dennis were 23 mph winds, 30 mph gusts, no storm surge, and 1.42 in of rainfall, which were ranked 77, 73, 0, and 111. The wind and gusts impacts from Hurricane Dennis for this inland county were similar in intensity to those of

Hancock County. Pike County received triple the amount of rainfall, increasing the rank and vulnerability value of that hazard value compared with Hancock County. When the hurricane hazard values were added into the final risk equation, Pike County’s overall vulnerability increased slightly (Table 5). The higher resiliencies that Pike County offered, when compared with the top five counties at risk for Hurricane Dennis, slightly reduced the overall risk. It is with the combination of all hazard, vulnerability, and resilience variables that Pike County is made the county with the third highest risk due to

Hurricane Dennis.

Marion County, Florida, was calculated to have the fourth highest hurricane risk associated with Hurricane Dennis with a value of 4.81 units. The initial risk of the county 70

was 2.78 units, the seventh highest initial risk of the study area. The resultant initial risk, as discussed in the Hurricane Charley case, was based on the county’s demographic vulnerabilities being within the top 35%, not including the minority population which was ranked within the bottom 50%, of the study area and the lack of emergency services relative to population. In Marion County, Hurricane Dennis caused 13 mph winds, 31 mph gusts, no storm surge, and 1.61 in of rainfall. These hazards are ranked 48, 77, 0, and 117, respectively. The gusts and rainfall were among the highest ranked hazards for

Hurricane Dennis, but the wind speed was ranked lowly. These hurricane values were more moderate than those experienced by other counties, but still resulted in an increase in the county’s overall vulnerability (Table 5). Therefore, the moderate hurricane hazards, high demographic vulnerabilities, and low resiliencies caused Marion County to have the fourth highest risk associated with Hurricane Dennis.

Polk County, Florida received the fifth highest hurricane risk associated with

Hurricane Dennis, with an overall value of 4.69 units. This county had an initial risk of

2.55 units, the ninth highest initial risk of the study area, based on the moderate to high demographic vulnerabilities in combination with the overall low resiliencies, outlined in the Hurricane Charley case. While the hospital and evacuation route ratios are quite large and show that the county needs to put more work into these areas, the available shelters for Polk County with respect to population are ranked highly. Hurricane Dennis produced

18 mph winds, 29 mph gusts, no storm surge, and 1.85 in of rainfall in Polk County.

These hurricane hazards are ranked 69, 69, 0 and 119, respectively. These values are within the top 50% of values for Hurricane Dennis, meaning Polk County did receive some of the stronger impacts from the storm. The moderately high to high vulnerabilities, 71

moderately high hurricane hazards, and moderate resiliencies resulted in Polk County having the fifth highest risk associated with Hurricane Dennis (Table 5).

Aside from the counties listed above, areas with higher risk appear along the southwest coast of Florida, the Florida panhandle, coastal and inland Mississippi, and coastal and inland Louisiana. The coastal and inland counties of Texas were not put at risk during the landfall of Hurricane Dennis.

4.2.4 Hurricane Katrina

Figure 9 demonstrates the varying levels of risk for the counties in the study area during Hurricane Katrina. Table 9 displays the data collected for the hurricane hazard variables and the vulnerability values that contributed to the final risk assessment.

Katrina, at its strongest point was a Category 5 hurricane, but quickly weakened to a

Category 3 hurricane before making landfall in Louisiana.

Hancock County, Mississippi, was calculated to have the highest hurricane risk associated with Hurricane Katrina, with a final risk value of 11.81 units. As seen in cases with Ivan and Dennis, the resultant initial risk of 3.22 units was based on a combination of high population, high ranking demographic vulnerabilities, and too few emergency services available to the large population. While the demographic and resilience factors do not change for this case, the hurricane hazard values are different than in other cases.

Hurricane Katrina produced 49 mph winds, 85 mph gusts, 22 ft storm surges, and 7.3 in of rainfall in Hancock County. These impacts were ranked 115, 128, 138, and 129 respectively. With these hurricane impacts being among the highest values in the study area, it is no surprise that a county with a higher initial risk also has a high final risk. 72

After the inclusion of the hurricane hazards into the equation, the total amount of vulnerability tripled while the resilience did not change. The resultant high vulnerabilities with the low resiliencies (Table 5) caused Hancock County to have the highest overall risk associated with Hurricane Katrina.

Orleans Parish, Louisiana, received the second highest hurricane risk associated with Hurricane Katrina, with a value of 9.54 units. As seen with Hurricane Ivan, the initial risk value of 2.45 units was based on a combination of a high population, high demographic vulnerabilities, and a lack in emergency services available with respect to the population size. The effects of Hurricane Katrina on this county produced different final risk outcomes than in previous cases. In Orleans Parish, winds were 69 mph, gusts were 97.80 mph, storm surges were 11.8 ft, and rainfall totals were 12.49 in. These hurricane variables were ranked 135, 135, 133, and 139, respectively. Each of these variables ranked within the top 5% in the entire study area for Katrina. Therefore, the sharp increase in overall vulnerability after including the hurricane hazard values into the final equation comes as no surprise (Table 5). While these hurricane variables were stronger than those of Hancock County, Orleans Parish concluded with the second highest risk from Hurricane Katrina due to the slightly higher resiliencies.

St. Landry Parish, Louisiana, ended up having the third highest risk associated with Hurricane Katrina, with a risk value of 7.11 units. The initial risk value for St.

Landry was calculated to be 3.60 units, the second highest initial risk value among all counties. The resultant initial risk was based on the county’s population of 83,384, of which 52.2% are female, 40.9% are dependents, 44.1% are minorities, and 1.6% are

Hispanic, with a median household income of $35,503. These are ranked 94, 134, 102, 73

123, 15, and 113, respectively. St. Landry is an inland county and does not receive credit for having building proximity guidelines, however, resilience is earned through their offers of hospitals, evacuation routes, and evacuation shelters. The hospital ratio and rank are 1:10,423 and 60. The evacuation route ratio and rank are 1:16,677 and 42. The evacuation shelter ratio and rank is 1:83,384 and 41. The most vulnerably aspects of this county are their female and minority populations, while their most resilient aspect is the number of hospitals offered relative to population. Unfortunately, the mostly high vulnerabilities in combination with the low resiliencies resulted in such a high initial risk.

Hurricane Katrina caused 28.78 mph winds, 37.70 gusts, no storm surge, and 0.16 in of rainfall. Each of these variables has been ranked 91, 95, 0, and 90, respectively. The high vulnerabilities in combination with the moderately high hurricane variables and the low resiliencies contributed to the third highest risk outcome in association with Hurricane

Katrina.

Pike County, Mississippi, resulted in the fourth highest hurricane risk for

Hurricane Katrina, with a value of 6.99 units. As mentioned in the previous section about

Hurricane Ivan, the initial risk value of 2.65 units for the county was based on the high demographic vulnerabilities of the small population and the insufficient amount of emergency services available to the county. The hurricane impacts caused by Hurricane

Katrina contribute to the different final risks of this county compared to other counties.

Hurricane Katrina produced 54.15 mph winds, 73.08 mph gusts, no storm surge, and 6.76 in of rainfall. These hurricane hazards have rankings of 119, 120, 0, and 128, respectively. Pike County had a slightly higher resilience value than Orleans Parish and

Hancock County. Therefore, when the high hurricane variables were included in the final 74

equation with the high vulnerabilities, the slightly higher resiliencies kept the risk slightly lower (Table 5). This resulted in Pike County receiving the fourth highest risk value associated with Hurricane Katrina.

Iberia Parish, Louisiana, received the fifth highest hurricane risk for Hurricane

Katrina, with a value of 6.86 units. This initial risk for this county was calculated to be

3.25 units, which was ranked third highest of the data set. The resultant initial risk was based on the county’s population of 73,420, of which 51.2% are female, 39.3% are dependents, 37.8% are minorities, and 3.1% are Hispanic, with a median household income of $44,262. These are ranked 91, 101, 77, 111, 42, and 61, respectively. Iberia

Parish, while a coastal county, does not require buildings to be built at a specific distance to the coast. It is important to know that much of Louisiana’s southeastern coast is uninhabited or uninhabitable wetlands, making this type of building guideline unnecessary. Iberia Parish offers hospitals and evacuation routes in emergency situations, but, because of their location on the coastline, does not offer evacuation shelters. Instead they insist their communities evacuate to more inland counties when possible. The hospital ratio and rank are 1:18,310 and 38. The evacuation route ratio and rank are

1:7,324 and 70. The majority of the demographic vulnerabilities are in the moderate to high rankings while the resilience factors are dispersed in both the low and moderate ranks. It is think combination of vulnerabilities and resiliencies that produced the high initial risk. Hurricane Katrina was responsible for 31 mph winds, 39 mph gusts, and 1.12 in of rainfall. Unfortunately, Iberia did not have any accurate measurements of storm surge. Therefore, the rankings of these hurricane variables are 98, 97, 99, and 0, respectively. Iberia Parish had high vulnerabilities, moderate to low resiliencies, and 75

moderate to high hurricane impacts. The combination of these variables and impacts resulted in Iberia having the fifth highest risk associated with Hurricane Katrina.

The majority of the risk from Hurricane Katrina was localized in the central portions of Louisiana and coastal areas of Mississippi and Alabama. Three counties along the Florida panhandle received some effects while the remainder of the counties did not report severe impacts. Counties in Texas also reported no risk associated with Hurricane

Katrina.

4.2.5 Hurricane Rita

Figure 10 demonstrates the varying levels of risk for the counties in the study area during Hurricane Rita. Table 10 displays the data collected for the hurricane hazard variables and the vulnerability values that contributed to the final risk assessment. Rita reached its peak intensity as a Category 5 hurricane upon entering the Gulf of Mexico. By the time Rita made landfall, it had weakened to a Category 3 hurricane.

St. Landry Parish, Louisiana, was calculated to have the highest overall risk associated with Hurricane Rita, with a value of 10.19 units. As seen the case with

Hurricane Katrina, the initial risk value of 3.60 units was based on a combination of a high population of females and minorities in conjunction with other high demographic vulnerabilities, and a lack in emergency services available for the population. Unlike the

Katrina case, Hurricane Rita produced 46.03 mph winds, 70.2 mph gusts, and 9.85 in of rainfall. There was no storm surge for this inland county. These factors were ranked 129,

130, and 135, respectively. The inclusion of the hurricane parameters with the overall vulnerabilities for the county resulted in a jump from a vulnerability of 4.07 to 11.52 76

units (Table 5). Since the resiliencies do not change, the drastic increase in vulnerability resulted in St Landry acquiring the highest risk value for Hurricane Rita.

Rapides Parish, Louisiana, had the second highest overall risk associated with

Hurricane Rita, with a value of 7.83 units. The initial risk value for the county was calculated to be 2.89 units, the fifth highest initial risk value of the entire study area. This initial risk value was based on the county’s population of 131,613, of which 51.8% are female, 39.5% are dependents, 36.7% are minorities, and 2.6% are Hispanic, with a median household income of $40,930. These are ranked 102, 127, 80, 108, 34, and 85, respectively. Rapides Parish in an inland county, so building proximity guidelines do not apply. The resiliency factors offered by this county include hospitals, evacuation routes, and evacuation shelters. The ratios and ranks of these variables are 1:10,124 and 62,

1:13,161 and 47, and 1:131,613 and 35, respectively. It is due to the moderately high vulnerabilities and moderately low resiliencies that Rapides Parish has an initial risk value of 2.89 units. Hurricane Rita was responsible for 49.48 mph winds, 62.14 mph gusts, and 7.68 in of rainfall. This inland county did not have a storm surge. Each of these hurricane parameters were ranked 130, 123, and 124, respectively. The high hurricane hazard parameters, when included in the final equation and combined with the moderately high vulnerabilities and moderately low resiliencies, resulted in the second highest risk associated with Hurricane Rita.

Iberia Parish, Louisiana, received the third highest overall risk associated with

Hurricane Rita, with a value of 7.71 units. Also described in the case of Hurricane

Katrina, the initial risk value of 3.25 units was based on a combination of moderate to high vulnerabilities and low to moderate resiliencies. Unlike the Katrina scenario, 77

Hurricane Rita produced 21.86 mph winds, 59.84 mph gusts, 9.21 in of rainfall, and no storm surge was recorded. These variables were ranked at 78, 121, 131, and 0, respectively. All of the impacts from the hurricane that were given a rank ranged on the higher, more devastating side of the ranks. It was because of these high gusts and rainfall variables, combined with the moderately high demographic vulnerabilities and the moderately low resiliencies, that Iberia Parish was calculated to be the county with the third highest risk associated with Hurricane Rita.

Orleans Parish, Louisiana, had the fourth highest overall risk associated with

Hurricane Rita, with a value of 7.57 units. As seen in the case of Hurricane Ivan, the initial risk value of 2.45 units was based on a combination of a high population, high demographic vulnerabilities, and a lack in emergency services available with respect to the population size. Hurricane Rita caused 34.52 mph winds, 48.33 mph gusts, 6.5 ft storm surges, and 2.29 in of rainfall. These impacts had rankings of 104, 102, 129, and

96, respectively. Orleans Parish was assigned a high initial vulnerability based on the demographics of the county. It was also given a moderate resilience value based on the moderate amounts of emergency services with respect to the population. The addition of the moderate to high hurricane hazards into the final risk calculation resulted in an overall vulnerability over 7 units higher than the initial one (Table 5). This resulted in Orleans

Parish becoming the fourth highest county at risk of Hurricane Rita.

Jefferson County, Texas, received the fifth highest overall risk associated with

Hurricane Rita, with a value of 7.07 units. The county’s initial risk was 1.87 units, the fourteenth highest initial risk value of the study area. This initial risk value was based on the county’s population of 252,273, of which 48.9% are female, 36.6% are dependents, 78

47.8% are minorities, and 17% are Hispanic, with a median household income of

$41,147. These are ranked 116, 33, 32, 128, 97, and 83, respectively. Jefferson County is a coastal county but does not have any building proximity guidelines, so it does not benefit from the related resilience. It also does not offer evacuation shelters in the event of a hurricane, but rather insists its community evacuates to inland counties. Jefferson received a moderate to low amount of resilience from its hospital and evacuation route ratios. The hospital ratio and rank is 1:16,818 and 42. The evacuation route ratio and rank are 1:25,227 and 32. Jefferson has very little in terms of emergency services available to the county. It also has lower demographic vulnerabilities compared to other counties in the study. The combination of the vulnerability and resilience resulted in Jefferson

County’s initial risk. Hurricane Rita produced 80.55 mph winds, 104.72 mph gusts, 7.93 ft storm surges, and 5.37 in of rainfall. These hurricane factors were ranked 139, 138,

137, and 111. Jefferson County had the most hazard variables within the top three ranks of the Rita data set. This combination of exceptionally high hurricane hazards in conjunction with the low to moderately high vulnerabilities and moderately low resiliencies resulted in the five unit jump in overall risk due to impacts from Hurricane

Rita.

The areas of highest risk associated with Hurricane Rita occur in central

Louisiana, from the coast to the counties north-northwestward, and in the counties surrounding Houston, Texas. Mississippi, Alabama, and Florida did not report any impacts that could have been caused from Hurricane Rita.

4.2.6 Hurricane Wilma

Figure 11 demonstrates the varying levels of risk for the counties in the study area during Hurricane Wilma. Table 11 displays the data collected for the hurricane hazard variables and the vulnerability values that contributed to the final risk assessment.

Hurricane Wilma reached its peak intensity as a Category 5 hurricane just before interacting with the Yucatan Peninsula. After a period of weakening upon reaching the

Gulf of Mexico, Wilma strengthened into a Category 3 hurricane and made landfall in

Florida.

Marion County, Florida, was calculated to have the highest risk associated with

Hurricane Wilma, with an overall risk value of 7.49 units. As discussed in relation to

Hurricane Charley, the initial risk value for Marion County, 2.78 units, was based on several high demographic vulnerabilities and the overall low resiliencies. Hurricane

Wilma produced different hazards than seen in previous cases. Marion County received

26.47 mph winds, 39.13 mph gusts, and 1.32 in of rainfall. The impacts of Hurricane

Wilma were ranked 126, 125, and 124, respectively. Because this county is an inland county, it did not report storm surge values. The high initial vulnerability of the county was combined with the high hurricane impacts, causing a 6.50 unit jump in overall vulnerability (Table 5). The large increase in overall vulnerability, in combination with the relatively low resiliencies produced the highest overall risk associated with Hurricane

Wilma.

Polk County, Florida, received the second highest risk associated with Hurricane

Wilma, at a value of 7.29 units. The reasoning behind the initial risk value of 2.55 units was discussed in the previous Hurricane Charley case as being the result of moderate to 80

high demographic vulnerabilities and low resiliencies. Unlike previous cases, Hurricane

Wilma caused 35.67 mph winds, 46.03 mph gusts, and 7.34 in of rainfall. These hazards were ranked 131, 129, and 138, respectively. Polk County, being an inland county, was not affected by storm surges. Polk County’s initial vulnerability was less than Marion

County’s vulnerability (Table 5), but the addition of the hurricane hazards caused roughly the same 6.50 unit increase in overall vulnerability. This similar increase was paired with a slightly lower resiliency, resulting in the second highest risk associated with Hurricane

Wilma, merely 0.20 units less than the highest overall risk.

Pasco County, Florida, was given the third highest risk associated with Hurricane

Wilma, at 4.48 units. This county had an initial risk value of 1.64 units, which was based on the moderate to high demographic vulnerabilities (not including the low population of minorities) and the overall low resiliencies as discussed in the case of Hurricane Charley.

Hurricane Wilma impacted this county differently than Hurricane Charley, with 28.58 mph winds, 37.81 mph gusts, no reported storm surge and 2.1 in of rainfall. Each of these was ranked 127, 123, 0, and 129, respectively. The inclusion of the hurricane hazards increased the county’s overall vulnerability from 3.14 units to 8.57 units (Table 5). This overall vulnerability was roughly 1.40 units less than that from Polk County. Since Pasco

County had a higher overall resilience, the calculation for overall risk associated with

Hurricane Wilma was less than that for Marion and Polk Counties, becoming the third highest hurricane risk for this case.

Pinellas County, Florida, was calculated to have the fourth highest risk associated with Hurricane Wilma, at a value of 4.30 units. This final risk value is an increase from the initial risk of 1.53 units, which was based on several moderate to high vulnerabilities 81

and low resiliencies. The specifics of the vulnerabilities and resiliencies were discussed in the case with Hurricane Charley. Unlike Hurricane Charley, Hurricane Wilma produced 37.98 mph winds, 49.48 mph gusts, no reported storm surge, and 1.91 in of rainfall. The impacts from the hurricane were ranked 133, 131, 0, and 127, respectively.

Pinellas County had the same initial vulnerability as Pasco County (Table 5), and the higher hurricane hazards caused a greater increase in overall vulnerability for the county.

However, unlike Pasco County, Pinellas County had a higher resiliency value. The combination of the high overall vulnerability with the slightly higher overall resiliency resulted in Pinellas County having the fourth highest overall risk associated with

Hurricane Wilma.

Lee County, Florida, was the county with the fifth highest risk associated with

Hurricane Wilma, with a value of 4.12 units. This county had an initial risk rating of 1.40 units based upon moderately low to high vulnerabilities and low to moderate resiliencies, which were discussed in detail in the case of Hurricane Charley. While the demographic and emergency service information did not change from one case to the other, the hurricane impacts did change and influenced the final risk of this county. Hurricane

Wilma produced 62.14 mph winds, 79.40 mph gusts, no reported storm surge, and 5.44 in of rainfall. These hurricane parameters were given ranks of 138, 137, 0, and 134, respectively. Lee County had an overall vulnerability increase of 5.97 units (Table 5) after the high hurricane parameters were introduced into the final equation. This increase was only slightly lower than the increase seen in Marion and Polk Counties. Out of the top five most at risk counties, Lee County had the highest overall resiliency. It was the

combination of the large increase in overall vulnerability with the large resiliency value that made Lee County the fifth highest county at risk due to Hurricane Wilma.

Because of Hurricane Wilma’s track across the Florida Peninsula, the areas most at risk are located around Polk County and reduce in risk gradually in the counties that are farther away. No risk was reported for some northern Florida coastal and inland counties, Alabama, Mississippi, Louisiana, and Texas.

4.2.7 Hurricane Gustav

Figure 12 demonstrates the varying levels of risk for the counties in the study area during Hurricane Gustav. Table 12 displays the data collected for the hurricane hazard variables and the vulnerability values that contributed to the final risk assessment.

Hurricane Gustav reached its peak intensity upon entering the Gulf of Mexico as a

Category 4 hurricane. The travel over the cooler northern gulf waters caused the system to weaken to a Category 2 hurricane before making landfall in Louisiana.

Hancock County, Mississippi, received the highest overall risk associated with

Hurricane Gustav with a value of 11.76 units. The initial risk of 3.22 units was based on the high population and demographic vulnerabilities and the lack in resiliencies given the size of the population, which is discussed in more detail in the Hurricane Ivan case. In this county, Hurricane Gustave produced 54.09 mph winds, 66.75 mph gusts, 9.89 ft of storm surges, and 3.69 in of rainfall, which were ranked, 130, 125, 138, and 115, respectively. Each of these hurricane hazards are ranked among the highest values collected for Hurricane Gustav. These high values of hurricane impacts caused the overall vulnerability to increase by 8.17 units (Table 5). Due to the high population and 83

the lacking number of emergency services contributing to the low resiliency value of 0.96 units, Hancock County became the county with the highest risk associated with Hurricane

Gustav.

Orleans Parish, Louisiana, had the second highest overall risk associated with

Hurricane Gustav, with a value of 9.12 units. The initial risk of 2.45 units was based on the high population, high demographic vulnerabilities, and low resiliencies. This risk is discussed in more detail in the Hurricane Ivan case. Hurricane Gustav was responsible for

46 mph winds, 72 mph gusts, 10.35 ft of storm surges, and 5.89 in of rainfall. These variables were ranked 120, 128, 139, and 131, respectively. These hurricane effects were also among the highest values produced by Hurricane Gustav. This resulted in an increase in overall vulnerability by 9.61 units (Table 5). The county’s resiliency value of 1.44 units was not enough to keep the risk to a more manageable value. Therefore, the high hurricane hazards combined with the high vulnerabilities and low resiliencies caused

Orleans Parish to have the second highest risk associated with Hurricane Gustav.

St. Landry Parish, Louisiana, had the third highest overall risk associated with

Hurricane Gustav, calculated at 8.57 units. The county had a high initial risk of 3.30 units based on the high population of females and minorities, high demographic vulnerabilities, and low resiliencies due to inadequate numbers of emergency services. These statements are defined in more detail in the case of Hurricane Katrina. Hurricane Gustav caused

35.76 mph winds, 44.93 mph gusts, and 3.90 in of rainfall. These were ranked 112, 102, and 118, respectively. St. Landry is an inland county and storm surge was not measured.

Figure # shows that after including the hurricane parameters in the overall vulnerability calculation, the overall vulnerability increased by 5.62 units (Table 5). The outcome of 84

the overall vulnerability was the lowest among the five counties most at risk, but due to a low resiliency of 1.13 units, St. Landry Parish was calculated to be the county with the third highest risk associated with Hurricane Gustav.

Rapides Parish, Louisiana, received the fourth highest risk associated with

Hurricane Gustav of 7.79 units. It is due to the moderately high vulnerabilities and moderately low resiliencies that Rapides Parish has an initial risk value of 2.89 units, which is outlined more specifically in the Hurricane Rita case. Hurricane Gustave was responsible for 42.58 mph winds, 60.99 mph gusts, and 8.73 in of rainfall. Rapides Parish was not affected by storm surges. These hurricane parameters were ranked 115, 122, and

138, respectively. The risks associated with the hurricane are moderately high to high.

When the hurricane parameters were included in the overall vulnerability calculation, the overall vulnerability increased by 6.11 units (Table 5). It was the combination of the overall vulnerability increase and the low resiliency that resulted in Rapides Parish having the fourth highest risk associated with Hurricane Gustav.

Jefferson Parish, Louisiana, received the fifth highest overall risk associated with

Hurricane Gustav, with a risk value of 6.95 units. The initial risk of this county was lower than those within the five most at risk counties, with a value of 1.83 units. This initial risk was based on a population of 432,552, where 51.5% are female, 36.2% are dependents,

37.1% are minorities, and 12.4% are Hispanic, earning a median household income of

$48,261. These values are ranked 129, 116, 23, 110, 93 and 39, respectively. Jefferson

Parish is a county that is among the Louisiana wetlands and while it is along the coast, it does not have any official building proximity guidelines. Jefferson Parish offered emergency services in the way of hospitals, evacuation routes, and evacuation shelters. 85

The ratio of hospitals to population was 1:48,061 and was ranked 12. The ratio of evacuation routes to population was 1:36,046 and ranked 25. The shelter to population ratio was 1:216,276, with a rank of 34. This county had high populations in general and had high populations of females and minorities. The median income was among some of the largest values, which aided in decreasing the overall vulnerability of the demographic variables. Unfortunately, having a higher annual household income can be overlooked by the high ratios of emergency services. This indicates a low resiliency. The combination of the moderate to high demographic vulnerabilities and lower resiliencies produced the initial risk of 1.83. Hurricane Gustav produced 66.75 mph winds, 86.31 mph gusts, 4.82 ft of storm surges, and 5.02 in of rainfall in Jefferson Parish. These values were assigned ranks of 137, 136, 130, and 125, respectively. It was the combination of these high hurricane parameters with the more moderate vulnerabilities and lower resiliencies that caused Jefferson Parish to have the fifth highest risk associated with Hurricane Gustav.

The counties with the most risk associated with Hurricane Gustav seem to be localized between eastern Louisiana and western Mississippi. The hurricane impacts spread along the coast of Texas but some of the more inland counties did not report strong impacts from the storm. Inland Alabama and Florida were not affected by

Hurricane Gustav.

4.2.8 Hurricane Ike

The analysis of Hurricane Ike was based on both its effects on the coastal counties and the effects on the counties following its track inland. Figure 13 demonstrates the varying levels of risk for the coastal counties in the study area and Figure 14 shows the 86

risk levels for the inland counties along Ike’s track. Table 13 displays the data collected of hurricane hazard variables for coastal counties and the vulnerability values that contributed to the final risk assessment. Table 14 shows the hurricane hazard data for the inland counties along Ike’s track. Ike reached its maximum classification before making landfall with Cuba as a Category 4. However, by the time it made landfall in Galveston

County, Texas, it had weakened to a Category 2 hurricane. This section will discuss the top three most at risk counties along the coast before discussing some of the outliers along the Ike’s track inland.

Iberia Parish, Louisiana, was calculated as having the highest risk in a coastal county associated with Hurricane Ike, with a value of 8.32 units. As described in the cases of Hurricanes Katrina and Rita, the initial risk value of 3.25 units was based on a combination of moderate to high vulnerabilities and low to moderate resiliencies.

Hurricane Ike produced winds of 36 mph, gusts of 52 mph, no measurable storm surge, and 2.03 in of rainfall. These hurricane values were ranked 116, 118, 0, and 122. These are not the highest rankings for the Ike, but they are among the highest. It is because of these high hurricane values that Iberia’s overall vulnerabilities increased by 4.92 units

(Table 5). Unfortunately, this county had the lowest resiliencies of the top three most at risk counties for Hurricane Ike. The lack in resiliency was coupled with the newly calculated overall vulnerability and produced the highest risk value associated with

Hurricane Ike.

St. Landry Parish, Louisiana, received the second highest overall risk of the coastal counties in association with Hurricane Ike with a value of 7.37 units. Also seen the cases with Hurricanes Katrina and Rita, the initial risk value of 3.60 units was based 87

on a combination of a high population of females and minorities in conjunction with other high demographic vulnerabilities, and a lack in emergency services available for the population. St. Landry Parish received 26 mph winds, 43 mph gusts, and 1.18 in of rainfall. There was no storm surge for this inland county. These hurricane variables were given ranks of 80, 98, and 108, respectively. These values are moderately high compared with the rest of the study area. When the hurricane hazards were included into the overall vulnerability calculation, the overall vulnerability increased by 4.27 units (Table 5). The vulnerability did not increase by as much in St. Landry as it did in Iberia Parish. St.

Landry also had an overall resiliency 0.16 units higher than that of Iberia Parish. It was the combination of this increased vulnerability and the slightly higher resiliency that contributed to St. Landry Parish’s second highest hurricane risk value for Hurricane Ike.

Rapides Parish, Louisiana, received the third highest risk of the coastal counties associated with Hurricane Ike of 6.49 units. It is due to the moderately high vulnerabilities and moderately low resiliencies that Rapides Parish has an initial risk value of 2.89 units, which is outlined more specifically in the Hurricane Rita case.

Hurricane Ike was responsible for 29 mph winds, 45 mph gusts, and 1.97 in of rainfall.

Rapides Parish was not affected by storm surges. These hurricane parameters were ranked 87, 105, and 121, respectively. The risks associated with the hurricane are moderately high to high. When the hurricane parameters were included in the overall vulnerability calculation, the overall vulnerability increased 4.49 units (Table 5), which was a lower increase than Iberia Parish but a higher increase than St. Landry Parish. Of the top three counties, Rapides Parish had the highest overall resiliency. It was the

combination of the vulnerability increase and the higher resiliency that resulted in

Rapides Parish having the third highest risk associated with Hurricane Ike.

Made apparent by Figure 14 of Hurricane Ike’s track inland, the coastal regions were most highly impacted and among the most at risk from Ike, with gradual decreases as the system made its way inland, represented by the green and blue colored counties.

Interestingly, there were some counties between Illinois and Michigan that received higher than expected risks. A discussion about two of the 63 inland counties, St. Clair

County, Illinois, and Washtenaw County, Michigan, will look into what may have caused these counties to be more at risk than surrounding inland counties around the end of

Hurricane Ike’s track.

St. Clair County, Illinois, was calculated to have an overall risk value of 7.39 units, the third highest value of the counties inland along Ike’s track. The initial risk of this county was 3.95 units, which was the seventh highest initial risk value of the inland counties. This high initial risk was based upon the county’s population of 270,056, where

51.9% are female, 37.9% are dependents, 35.4% are minorities, and 3.3% are Hispanic, with a median household income of $50,578. These demographics are ranked 60, 63, 15,

62, 36, and 13, respectively. Being an inland county typically not affected by hurricanes, no information was collected on building proximity codes nor evacuation shelters.

However, information on hospitals and emergency organizations and evacuation routes were collected. There was a hospital and organization to population ratio of 1:90,019, which was given a rank of 3. The evacuation route ratio was 1:15,003 and had a rank of

8. St. Clair County offers a total of 3 different groups of hospitals and organizations as well as 18 major highways out of the county. Unfortunately, because of the size of the 89

population, the hospitals would be overwhelmed and the roadways would be unmoving in an emergency situation. The hurricane hazard information reported by St. Clair County includes 33 mph winds, 48 mph gusts, and 3.70 in of rainfall. These values were ranked

32, 32, and 55, respectively. These wind speeds, gusts, and rainfall are those that would be more frequently experienced in severe thunderstorm situations than hurricanes.

Relative to the rest of the counties, the winds and gusts are moderate while the rainfall is high. The combination of the demographic vulnerabilities, resiliencies, and hurricane hazards resulted in the unusually high risk associated with Hurricane Ike. Due to the location of this county, it is highly unlikely that an evacuation would occur because of a hurricane or that the emergency service opportunities would need to assist the entire population of the county. Therefore, the high overall risk in St. Clair County associated with Hurricane Ike is based more on the size of the population and the vulnerabilities that come with that than with damage done by the hurricane.

Washtenaw County, Michigan, was calculated to have an overall risk associated with Hurricane Ike of 3.32 units. This value is higher than expected for a county so far north. The initial risk of Washtenaw County was 2.67, which was the twelfth highest initial risk. This initial risk was based upon the county’s population of 344,791, where

50.7% are female, 31.0% are dependents, 25.5% are minorities, and 4.0% are Hispanic, with a median household income of $59,055. These values are ranked 61, 49, 2, 54, 39, and 5, respectively. Similar to St. Clair County, Washtenaw County did not have information regarding building proximity to coast guidelines nor evacuation shelters.

This county did offer hospitals and emergency organizations, as well as evacuation routes. The ratio of hospitals and organizations to population was 1:34,479, with a rank 90

of 5. The ratio of evacuation routes was 1:57,465, with a rank of 3. Aside from minority populations and household income, the vulnerabilities of Washtenaw County are among those that are most highly ranked. The resiliencies are astoundingly low, considering the population. The county has 10 different hospitals, not including those of the same branch located in a different part of the county, and offers 6 major highways out of the county.

The impacts recorded in association with Hurricane Ike’s inland track are 26 mph winds,

37 mph gusts, and 3.28 in of rainfall. These values are ranked 19, 9, and 52. respectively.

The maximum sustained winds and gusts are, once again, those commonly experienced in this area with severe thunderstorms. The rainfall is among the highest ranked in the inland counties. The demographic vulnerabilities, lacking resiliencies specific to hurricanes, and hurricane hazards contributed to the unusually high risk associated with

Hurricane Ike’s inland track. It’s highly unlikely that Washtenaw County would find itself in a situation where the communities within the county would need to seek shelter more inland in the event of a hurricane. The high risk associated with this county is based more on the vulnerabilities associated with a high population than those associated with

Hurricane Ike.

Matagorda County, Texas, and Vermilion Parish, Louisiana, were among the top five most at risk coastal counties for Hurricane Ike, with overall risks of 6.33 and 6.23 units, respectively. The majority of the risk associated with Hurricane Ike was centralized around Galveston and Harris Counties. Some coastal and inland counties in Louisiana also resulted in higher risks. The southernmost coast of Texas as well as Mississippi,

Alabama, and Florida did not report any risks associated with Hurricane Ike.

4.2.9 Hurricane Ida

Figure 15 demonstrates the varying levels of risk for the counties in the study area during Hurricane Ida. Table 15 displays the data collected for the hurricane hazard variables and the vulnerability values that contributed to the final risk assessment. At its greatest intensity, Ida was classified as a Category 2 hurricane as it made its way through the Yucatan Channel. By the time it made landfall in Alabama, Ida slowed and began transitioning into an extratropical cyclone.

Hancock County, Mississippi, received the highest overall risk associated with

Hurricane Ida with a value of 11.32 units. The initial risk of 3.22 units was based on the enormous population, the high demographic vulnerabilities that come along with a high population, and the lack in resiliencies given the size of the population, which is discussed in more detail in the Hurricane Ivan case. In this county, Hurricane Ida produced 25 mph winds, 44 mph gusts, 3.19 ft of storm surges, and 2.42 in of rainfall, which were ranked 105, 126, 136, and 122, respectively. These hurricane hazards are ranked among the moderately high and highest values collected for Hurricane Ida at time of landfall. The high hurricane hazards, when included in the overall vulnerability caused a 7.74 unit increased in overall vulnerability (Table 5). Due to the high population and the too few emergency services contributing to the low resiliency value of 0.96 units,

Hancock County was calculated to be the county most at risk in association with

Hurricane Ida.

Orleans Parish, Louisiana, had the second highest overall risk associated with

Hurricane Ida, receiving a value of 8.78 units. This county had an initial risk of 2.45 units, primarily based on the high population, high demographic vulnerabilities, and low 92

resiliencies. This risk is discussed in more detail in the Hurricane Ivan case. Hurricane

Ida was responsible for 36.80 mph winds, 44.90 mph gusts, 2.35 ft of storm surges, and

0.13 in of rainfall. These variables were ranked 132, 130, 133, and 104, respectively.

These hurricane effects were also among the highest for the study area with respect to

Ida, which caused an increase in overall vulnerability by 9.13 units (Table 5). Orleans

Parish was better fit with emergency services than Hancock County, with an overall resiliency of 1.44 units. Despite being better equipped, this resiliency was not enough to keep the risk to a more manageable value for the county. Therefore, the high hurricane hazards led to Orleans Parish having the second highest risk associated with Hurricane

Ida.

Clarke County, Alabama, was calculated to have the third highest overall risk associated with Hurricane Ida with a value of 6.80 units. The initial risk of 2.46 units was based on the low population, high demographic vulnerabilities, and the moderately high resiliencies that are introduced in the Hurricane Ivan case. Clarke County received 42.15 mph winds and gusts, and 2.84 in of rainfall. These were ranked 136, 122, and 126, respectively. Because Clarke County is an inland county, it was not affected by storm surge. The initial vulnerabilities for the county were increased by 6.94 units (Table 5) after the hurricane effects were included in the overall vulnerability. Clarke County had an overall resiliency of 1.60 units. The increase in vulnerability in combination with the highest resiliency of the five county most at risk contributed to Clarke County receiving the third highest overall risk.

St. Landry Parish, Louisiana, had the fourth highest overall risk associated with

Hurricane Ida, calculated at 5.47 units. The county had a high initial risk of 3.30 units 93

based on the high population of females and minorities, the overall high demographic vulnerabilities, and the low resiliencies due to inadequate available emergency services.

These statements are defined in more detail in the case of Hurricane Katrina. Hurricane

Ida caused 25.21 mph winds, 35.02 mph gusts, and no recorded rainfall. These were ranked 108, 104, and 1, respectively. St. Landry is an inland county and storm surge was not measured. With this county, having the second highest initial risk and an initial vulnerability of 4.07 units, it was no surprise that the moderately high hurricane effects caused a 2.12 unit increase in the overall vulnerability (Table 5). The outcome of the overall vulnerability was the lowest among the five counties most at risk, but due to a low resiliency of 1.13 units, St. Landry Parish was calculated to be the county with the fourth highest risk in the case of Hurricane Ida.

Pike County, Mississippi, received the fifth highest overall risk associated with

Hurricane Ida with a value of 5.26 units. The initial risk for Pike County was 2.65 units and, as discussed in the case of Hurricane Ivan, was based on a high demographic vulnerability of a county with a small population and the subpar resiliencies for this community. In Pike County, Hurricane Ida was responsible for 22 mph winds, 29 mph gusts, and 0.01 in of rainfall. Again, being an inland county, Pike did not have any measurable storm surge associated with this storm. Pike County had a high initial vulnerability value of 4.05 units (Table 5). When the hurricane variables were included into the overall vulnerability calculation, the overall vulnerability increased by 3.99 units and was the second lowest of the five counties most at risk. This value was slightly higher than what was calculated for St. Landry Parish, but because the overall resiliency

was 0.40 units higher than that of St. Landry, Pike County was ranked fifth highest county at risk associated with Hurricane Ida.

The greatest risks associated with Hurricane Ida occurred around Hancock and

Pike Counties. Lesser risks occurred in west and central Louisiana and the Florida panhandle. Texas and the counties of the Florida Peninsula did not report any impacts associated with Hurricane Ida.

4.2.10 Hurricane Isaac

Figure 16 demonstrates the varying levels of risk for the counties in the study area during Hurricane Isaac. Table 16 displays the data collected for the hurricane hazard variables and the vulnerability values that contributed to the final risk assessment. Isaac only reached a Category 1 classification due in part by its large size and disorganized eye, but still caused damage in areas surrounding Louisiana.

Hancock County, Mississippi, received the highest overall risk associated with

Hurricane Isaac with a value of 12.34 units. The initial risk of 3.22 units was based on the population of over 2 million residents, the high demographic vulnerabilities that come along with a high population, and the low resiliencies given the size of the population, which is discussed in more detail in the Hurricane Ivan section. In this county, Hurricane

Isaac produced 50.70 mph winds, 66.60 mph gusts, 9 ft of storm surges, and 18.3 in of rainfall, which were ranked 130, 130, 136, and 137, respectively. These hurricane hazards are ranked among the highest values of the study area collected for Hurricane Isaac. The high hurricane hazards, after being included in the overall vulnerability calculation, resulted in an increase from initial to overall vulnerability of 8.73 units, as seen in Table 95

5. Due to the high overall vulnerabilities and the low resiliency value of 0.96 units,

Hancock County was calculated to be the county most at risk in association with

Hurricane Isaac.

Orleans Parish, Louisiana, had the second highest overall risk associated with

Hurricane Isaac, receiving a value of 9.63 units. This county had an initial risk of 2.45 units, based on the high population, high demographic vulnerabilities, and low resiliencies. This risk is discussed in more detail in the Hurricane Ivan section. Hurricane

Isaac was responsible for 59.80 mph winds, 76 mph gusts, 11 ft of storm surges, and 20.7 in of rainfall. These variables were ranked 137, 135, 137, and 138, respectively. These hurricane effects were among the highest for the study area in the case of Hurricane

Isaac. Orleans Parish had an overall resiliency of 1.44 units (Table 5), which was overshadowed by the drastic increase in overall vulnerability by 10.36 units. It was due to these high hurricane hazards and the resultant high overall vulnerability that Orleans

Parish had the second highest risk associated with Hurricane Isaac.

Jefferson Parish, Louisiana, received the third highest overall risk associated with

Hurricane Isaac, with a risk value of 7.03 units. The initial risk of this county was based on high populations in general and had high populations of females and minorities as well as high ratios of emergency services, indicative of a low resiliency. Hurricane Isaac produced 66.4 mph winds, 84.8 mph gusts, 8.5 ft of storm surge, and 4.39 in of rainfall, which were ranked 139, 139, 135, and 121, respectively. The winds and gusts in

Jefferson Parish were the highest recorded for Isaac, while the storm surge and rainfall were among the highest recorded values. These high hurricane values in combination with the moderate to high vulnerabilities produced an increase of 8.02 units in the overall 96

vulnerability of the county (Table 5). With a low resilience value of 1.54 divided into the overall vulnerability, it is no surprise that Jefferson Parish was calculated to have the third highest risk associated with Hurricane Isaac.

St. Landry Parish, Louisiana, had the fourth highest overall risk associated with

Hurricane Isaac, calculated at 6.20 units. The county had a high initial risk of 3.30 units based on the high population of females and minorities, the overall high demographic vulnerabilities, and low resiliencies, which are defined in more detail in the case of

Hurricane Katrina. Hurricane Isaac caused 40.85 mph winds, 49.28 mph gusts, and no recorded rainfall. These were ranked 124, 116, and 1, respectively. St. Landry is an inland county and storm surge was not measured. St. Landry had the second highest initial risk and an initial vulnerability of 4.07 units, shown in Table 5. Once the high hurricane hazards were integrated into the overall vulnerability, the overall vulnerability increased by 2.94 units. The outcome of the overall vulnerability was, again, the lowest among the five counties most at risk, but the low resiliency value of 1.13 units resulted in

St. Landry Parish being the fourth highest county at risk in the case of Hurricane Isaac.

Pike County, Mississippi, received the fifth highest overall risk associated with

Hurricane Isaac with a value of 6.03 units. The initial risk for Pike County was 2.65 units and, as discussed in the case of Hurricane Ivan, was based on a high demographic vulnerability of a county with a small population and the insufficient resiliencies for this community. In Pike County, Hurricane Isaac caused 25 mph winds, 44 mph gusts, and

6.69 in of rainfall. Being an inland county, Pike did not have any measurable storm surge associated with this storm. Pike County’s initial vulnerability was 4.05 units, as demonstrated in Table 5. When the hurricane variables were combined into the overall 97

vulnerability calculation, the overall vulnerability increased by 5.17 units. The overall vulnerability was the second lowest of the five counties most at risk. After dividing the overall vulnerability by the overall resiliency, a value of 1.53 units, Pike County became the county with the fifth highest risk associated with Hurricane Isaac.

The risks associated with Hurricane Isaac occurred with areas between central

Louisiana and western Alabama experiencing the highest risks. The Florida panhandle and western Louisiana received low risks. Texas and the counties along the Florida

Peninsula reported no impacts associated with Hurricane Isaac and, therefore, received no risk from this hurricane.

4.3 Non-Linear Risk Analysis

After discovering that several counties repeatedly showed up in the counties most at risk for multiple hurricane cases, another analysis was completed to correct the initial linearization of the outcome. This was done through a “z-score-” type analysis, using the highest value for each variable (rather than using the mean in a typical z-score analysis), to more appropriately designate the distribution of all other counties’ variables in relationship to the one with the highest and lowest risks for each vulnerability and resiliency term. This means, for example, that the county with the highest population receives a vulnerability value of “1” and all other counties use the value of highest population as the denominator in a ratio to calculate their own vulnerability value. So, instead of receiving a vulnerability value based on a ranking system that increases and decreases by the same value, this new method redistributes the values to more precisely depict the differing levels of vulnerability across the dataset. The new analysis was 98

completed only for the coastal counties dataset and did not include the inland counties following the track of Hurricane Ike.

4.3.1 Initial Risk Differences

After the new analysis, much of the new initial risk calculations varied from their original calculations by one to three units. However, there were some counties that received substantial increases to their initial risks. The top ten counties with the highest initial county risks were Polk County, FL, Iberia Parish, LA, Matagorda County, TX, St.

Landry Parish, LA, Rapides Parish, LA, Newton County, TX, Wharton County, TX,

Evangeline Parish, LA, Avoyelles Parish, LA, and Pike County, MS.

Polk County received the highest initial risk with a value of 31.14 units. This value is a substantial increase from the original study’s calculation of 2.55 units. This new initial risk calculation was based on a slight decrease in the county’s vulnerability units and a great decrease in the resiliency units. Polk County’s new vulnerability value is

2.94 units, down from 3.47 units, and the new resiliency value is 0.09 units, down from

1.36 units.

Iberia Parish received the second highest initial risk with a value of 17.47 units.

This value has increased from the original calculation of 3.25 units. The new initial risk calculation is based on decreases in both the vulnerability and resiliency values. Iberia

Parish’s new vulnerability calculation is 2.79 units, down from 3.16 units. The new resiliency value is 0.16 units, down from 0.97 units.

Matagorda County received the third highest initial risk with a value of 15.37 units. This value is increased from the original calculation of 1.82 units. In this case, the 99

vulnerability value increased slightly while the resiliency value decreased substantially.

The new vulnerability calculation yielded a value of 3.06 units, increased from 2.71 units, while the resiliency value was 0.20 units, down from 1.49 units.

St. Landry Parish received the fourth highest initial risk with a value of 12.16 units. This new value is increased from the original risk calculation of 3.60 units. The parish’s vulnerability and resiliency values both decreased with the new calculation. The new vulnerability value for St. Landry Parish is 3.04 units, down from 4.07 units. The new resiliency value is 0.25 units, down from 1.13 units.

Rapides Parish received the fifth highest initial risk after the new calculation with a value of 11.02 units. The original calculation produced an initial risk value of 2.89 units. The increase in initial risk from the original study to the new study was based on a decrease in the new calculation vulnerability and a drastic decrease in the new resiliency calculation. The new vulnerability is 2.84 units, down from 3.60 units, while the new resiliency is 0.26 units, down from 1.25 units.

Newton County received the sixth highest initial risk of 10.33 units, which is increased from the original calculation of 0.97 units. Wharton County received the seventh highest initial risk with a value of 9.59 units. This is increased from the original calculation of 1.61 units. Evangeline Parish received the eighth highest initial risk of 9.29 units, which is increased from its original calculation of 1.71 units. Avoyelles Parish received the ninth highest initial risk calculation with a value of 8.48 units. This new initial risk calculation is increased from the original calculation of 1.82 units. Finally,

Pike County received the tenth highest initial risk calculation with a value of 6.97 units which has increased from the original calculation of 2.65 units. 100

Immediately, it is apparent that the new risk calculations provide values across a much greater spread than the original calculations provided. Based on the information in section 4.1, the original study produced a list of ten counties that received the highest initial risks based only on the demographic vulnerabilities and the mitigation techniques that played into their resiliencies. These values did not exceed the maximum initial risk of 3.97 units produced by Cameron County, Texas. Most of the new initial risk calculations are also between 0 and 3.97 units, but 15.8% of the calculations exceed the original study's maximum. Additionally, five of the counties that appeared in the original study’s assessment of the highest initial risks also appeared in the new calculated top ten highest initial risks. The original top ten counties included Cameron County, TX, St.

Landry Parish, LA, Iberia Parish, LA, Hancock County, MS, Rapides Parish, LA,

Hidalgo County, TX, Marion County, FL, Pike County, MS, Polk County, FL, and

Clarke County, AL.

To get a better understanding of how the new calculations accurately or inaccurately impact the overall risks associated with hurricanes the original values will be compared with the new values in the case of Hurricane Charley and Hurricane Katrina.

These cases had counties that continually overlapped with other hurricane events and will be used to describe how the new calculations influence those counties that were repetitive throughout the study.

4.3.2 Hurricane Charley Risk Assessment

As previously discussed in section 4.2.1, Hurricane Charley produced the highest overall risks in Marion County, FL, Polk County, FL, Lee County, FL, Pasco County, FL, 101

and Pinellas County, FL, based on a number of high and low vulnerability and resiliency variables. The new overall risk calculation produced a top five that included Polk County,

FL, De Soto County, FL, Hardee County, FL, Lee County, FL, and Charlotte County, FL.

Polk County, Florida, was the county with the highest risk associated with

Hurricane Charley, with a value of 31.47 units. This county had an initial risk rating of

31.14 units based upon an initial vulnerability value of 2.94 units and an extremely low resiliency value of 0.09 units. Instead of receiving a rank for each variable to denote vulnerability or resiliency, a z-score calculation was completed to give each county a value for each variable based on how much it deviates from the maximum amount. Polk

County, with a population of 602,095, received a total population z-score value of 0.147 units. This shows that Polk County’s population makes up about 15% of the 4,092,459- maximum value for total population. The female to male ratio for the county received a z- score value of 0.966 units, as it has among the highest percentages of female populations.

The dependent ratio was given a z-score value of 0.791 units. The household income was given a z-score of 0.507 units, Hispanic populations were given a value of 0.185 units, and the non-white race populations were given a value of 0.348 units. Resiliency values were given for hospital, evacuation route, and evacuation shelter ratios of 0.052 units,

0.002 units, and 0.040 units, respectively. Hurricane Charley produced 47.18 mph winds,

62.14 mph gusts, no reported storm surge, and 0.22 in of rainfall. These hurricane parameters were assessed and given z-scores of 0.526 units, 0.423 units, 0 units, and

0.062 units, respectively.

De Soto County, Florida, was the county with the second highest risk associated with Hurricane Charley, with a value of 11.28 units. This county had an initial risk rating 102

of 4.95 units based upon an initial vulnerability value of 3.01 units and a low resiliency value of 0.61 units. De Soto County, with a population of 34,862, received a total population z-score value of 0.009 units. This shows that De Soto County’s population makes up about 0.9% of the 4,092,459-maximum value for total population. The female to male ratio for the county received a z-score value of 0.824 units. The dependent ratio was given a z-score value of 0.768 units. The household income was given a z-score of

0.625 units, Hispanic populations were given a value of 0.313 units, and the non-white race populations were given a value of 0.473 units. Resiliency values were given for hospital, evacuation route, and evacuation shelter ratios of 0.553 units, 0.024 units, and

0.032 units, respectively. Hurricane Charley produced 76.86 mph winds, 103.57 mph gusts, no reported storm surge, and 2.53 in of rainfall. These hurricane parameters were assessed and given z-scores of 0.856 units, 0.705 units, 0 units, and 0.717 units, respectively.

Hardee County, Florida, was the county with the third highest risk associated with

Hurricane Charley, with a value of 10.07 units. This county had an initial risk rating of

4.91 units based upon an initial vulnerability value of 3.09 units and a low resiliency value of 0.63 units. Hardee County, with a population of 27,731, received a total population z-score value of 0.007 units. This shows that Hardee County’s population makes up about 0.7% of the 4,092,459-maximum value for total population. The female to male ratio for the county received a z-score value of 0.880 units. The dependent ratio was given a z-score value of 0.773 units. The household income was given a z-score of

0.589 units, Hispanic populations were given a value of 0.448 units, and the non-white race populations were given a value of 0.390 units. Resiliency values were given for 103

hospital, evacuation route, and evacuation shelter ratios of 0.521 units, 0.008 units, and

0.100 units, respectively. Hurricane Charley produced 76.86 mph winds, 147 mph gusts, no reported storm surge, and 0.68 in of rainfall. These hurricane parameters were assessed and given z-scores of 0.856 units, 1 unit, 0 units, and 0.193 units, respectively.

Lee County, Florida, was the county with the fourth highest risk associated with

Hurricane Charley, with a value of 7.75 units. This county had an initial risk rating of

2.41 units based upon an initial vulnerability value of 2.59 units and a resiliency value of

1.07 units. Lee County, with a population of 618,754, received a total population z-score value of 0.151 units. This shows that Lee County’s population makes up about 15% of the 4,092,459-maximum value for total population. The female to male ratio for the county received a z-score value of 0.966 units, as it has among the highest percentages of female populations. The dependent ratio was given a z-score value of 0.819 units. The household income was given a z-score of 0.461 units, Hispanic populations were given a value of 0.191 units, and the non-white races were given a value of 0 units. Resiliency values were given for hospital, evacuation route, and evacuation shelter ratios of 0.047 units, 0.003 units, and 0.026 units, respectively. Resilience was also awarded to Lee

County based on its white populations and coastal proximity guidelines, giving it z-scores of 0.864 units and 0.133 units, respectively. Hurricane Charley produced 60.99 mph winds, 78.25 mph gusts, 6.5 feet of storm surge, and 3.53 in of rainfall. These hurricane parameters were assessed and given z-scores of 0.679 units, 0.532 units, 1 unit, and 1 unit, respectively.

Charlotte County, Florida, was the county with the fifth highest risk associated with Hurricane Charley, with a value of 5.01 units. This county had an initial risk rating 104

of 1.82 units based upon an initial vulnerability value of 2.49 units and a resiliency value of 1.37 units. Charlotte County, with a population of 159,978, received a total population z-score value of 0.039 units. This shows that Charlotte County’s population makes up about 3.9% of the 4,092,459-maximum value for total population. The female to male ratio for the county received a z-score value of 0.975 units, as it has among the highest percentages of female populations. The dependent ratio was given a z-score value of

0.922 units. The household income was given a z-score of 0.493 units, Hispanic populations were given a value of 0.060 units, and the non-white race populations were given a value of 0 units. Resiliency values were given for hospital, evacuation route, and evacuation shelter ratios of 0.135 units, 0.013 units, and 0.048 units, respectively.

Additional resiliency was awarded to Charlotte County for its white population and proximity guidelines as 0.937 units and 0.233 units, respectively. Hurricane Charley produced 89.76 mph winds, 111.63 mph gusts, no reported storm surge, and 3.5 in of rainfall. These hurricane parameters were assessed and given z-scores of 1 unit, 0.759 units, 0 units, and 0.992 units, respectively.

Few counties overlap in the original assessment and the new assessment of the hurricane risk associated with Hurricane Charley. These counties were Polk and Lee.

While both counties’ final risks increased, Polk County saw an increase from 6.76 units to 31.47 units. This increase was so substantial due to the major decrease in the resiliency parameters in the new assessment. The county had among the fewest mitigation services

(hospitals, evacuation routes and shelters) which played a large role in the final calculation. This information seems to more accurately portray the risk of this county based on how its vulnerability and resiliency parameters measured against the other 105

counties along the coast. Concern for this method lies in three of the top five most at risk counties being inland counties. This result isn’t expected, but due to shortcomings in data collection in those coastal counties, especially hurricane data, it is understandable.

Despite some of the inland counties receiving the majority of the risk, the spread of the risk is more localized around the southern counties that received the highest hurricane impacts, leading to the understanding that the new analysis may, in fact, be more realistic than the original assessment.

4.3.3 Hurricane Katrina Risk Assessment

Based on the discussion in section 4.2.4, Hurricane Katrina produced the highest overall risks in Hancock County, MS, Orleans Parish, LA, St. Landry Parish, LA, Pike

County, MS, and Iberia Parish, LA, based on several different combinations of high and low vulnerability and resiliency variables. The new overall risk calculation produced a top five that included Washington Parish, LA, Iberia Parish, LA, Pike County, MS,

Hancock County, MS, and Clarke County, AL.

Washington Parish, Louisiana, was the county with the highest risk associated with Hurricane Katrina, with a value of 15.19 units. This county had an initial risk rating of 6.73 units based upon an initial vulnerability value of 2.90 units and a low resiliency value of 0.43 units. Instead of receiving a rank for each variable to denote vulnerability or resiliency, a z-score calculation was completed to give each county a value for each variable based on how much it deviates from the maximum amount. Washington Parish, with a population of 47,168, received a total population z-score value of 0.012 units. This shows that Washington Parish’s population makes up about 1.2% of the 4,092,459- 106

maximum value for total population. The female to male ratio for the county received a z- score value of 0.957 units, as it has among the highest percentages of female populations.

The dependent ratio was given a z-score value of 0.755 units. The household income was given a z-score of 0.688 units, Hispanic populations were given a value of 0.020 units, and the non-white race populations were given a value of 0.466 units. Resiliency values were given for hospital, evacuation route, and evacuation shelter ratios of 0.408 units,

0.022 units, and 0 units, respectively. Hurricane Katrina produced 65.33 mph winds,

92.07 mph gusts, no reported storm surge, and 9.22 in of rainfall. These hurricane parameters were assessed and given z-scores of 0.778 units, 0.741 units, 0 units, and

0.738 units, respectively.

Iberia Parish, Louisiana, was the county with the second highest risk associated with Hurricane Katrina, with a value of 13.49 units. This county had an initial risk rating of 17.47 units based upon an initial vulnerability value of 2.79 units and an extremely low resiliency value of 0.16 units. Iberia Parish, with a population of 73,240, received a total population z-score value of 0.018 units. This shows that Iberia Parish’s population makes up about 1.8% of the 4,092,459-maximum value for total population. The female to male ratio for the county received a z-score value of 0.971 units, as it has among the highest percentages of female populations. The dependent ratio was given a z-score value of 0.748 units. The household income was given a z-score of 0.494 units, Hispanic populations were given a value of 0.033 units, and the non-white race populations were given a value of 0.530 units. Resiliency values were given for hospital, evacuation route, and evacuation shelter ratios of 0.131 units, 0.028 units, and 0 units, respectively.

Hurricane Katrina produced 31 mph winds, 39 mph gusts, no reported storm surge, and 107

1.12 in of rainfall. These hurricane parameters were assessed and given z-scores of 0.369 units, 0.313 units, 0 units, and 0.090 units, respectively.

Pike County, Mississippi, was the county with the third highest risk associated with Hurricane Katrina, with a value of 12.36 units. This county had an initial risk rating of 6.97 units based upon an initial vulnerability value of 3.18 units and a low resiliency value of 0.46 units. Pike County, with a population of 40,404, received a total population z-score value of 0.010 units. This shows that Pike County’s population makes up about

1.0% of the 4,092,459-maximum value for total population. The female to male ratio for the county received a z-score value of 0.994 units, as it has among the highest percentages of female populations. The dependent ratio was given a z-score value of

0.787 units. The household income was given a z-score of 0.628 units, Hispanic populations were given a value of 0.013 units, and the non-white race populations were given a value of 0.752 units. Resiliency values were given for hospital, evacuation route, and evacuation shelter ratios of 0.417 units, 0.026 units, and 0.014 units, respectively.

Hurricane Katrina produced 54.15 mph winds, 73.08 mph gusts, no reported storm surge, and 6.76 in of rainfall. These hurricane parameters were assessed and given z-scores of

0.645 units, 0.588 units, 0 units, and 0.541 units, respectively.

Hancock County, Mississippi, was the county with the fourth highest risk associated with Hurricane Katrina, with a value of 8.76 units. This county had an initial risk rating of 3.25 units based upon an initial vulnerability value of 2.79 units and a low resiliency value of 0.86 units. Hancock County, with a population of 2,967.297, received a total population z-score value of 0.725 units. This shows that Hancock County’s population makes up about 72.5% of the 4,092,459-maximum value for total population. 108

The female to male ratio for the county received a z-score value of 0.976 units, as it has among the highest percentages of female populations. The dependent ratio was given a z- score value of 0 units. The household income was given a z-score of 0.491 units,

Hispanic populations were given a value of 0.029 units, and the non-white race populations were given a value of 0.573 units. Resiliency values were given for hospital, evacuation route, and evacuation shelter ratios of 0.007 units, 0.0002 units, and 0.0004 units, respectively. Additional resiliency was awarded to Hancock County for its high percentage of independents, producing a z-score value of 0.852 units. Hurricane Katrina produced 49 mph winds, 85 mph gusts, a 22-foot storm surge, and 7.3 in of rainfall.

These hurricane parameters were assessed and given z-scores of 0.583 units, 0.684 units,

0.846 units, and 0.584 units, respectively.

Clarke County, Alabama, was the county with the fifth highest risk associated with Hurricane Katrina, with a value of 8.49 units. This county had an initial risk rating of 6.18 units based upon an initial vulnerability value of 3.18 units and a low resiliency value of 0.51 units. Clarke County, with a population of 25,833, received a total population z-score value of 0.006 units. This shows that Clarke County’s population makes up about 0.6% of the 4,092,459-maximum value for total population. The female to male ratio for the county received a z-score value of 1 unit, meaning that Clarke

County has the highest percentage of female populations in the dataset. The dependent ratio was given a z-score value of 0.779 units. The household income was given a z-score of 0.745 units, Hispanic populations were given a value of 0.011 units, and the non-white race populations were given a value of 0.638 units. Resiliency values were given for hospital, evacuation route, and evacuation shelter ratios of 0.466 units, 0.048 units, and 0 109

units, respectively. Hurricane Katrina produced 61.41 mph winds, 76.83 mph gusts, no reported storm surge, and 0.31 in of rainfall. These hurricane parameters were assessed and given z-scores of 0.731 units, 0.618 units, 0 units, and 0.025 units, respectively.

Again, a few counties overlapped in the original assessment and the new assessment of the hurricane risk associated with Hurricane Katrina. These counties were

Iberia Parish, Pike County, and Hancock County. Iberia Parish and Pike County saw increases in their overall risks between the two studies while Hancock County’s overall risk decreased from 11.81 units to 8.76 units. This decrease was based on the 4 unit decrease in overall vulnerability and 0.10 unit decrease in the resiliency parameters. It seems that the demographic resiliency of white populations aids most in Hancock

County’s resiliency calculation. It is unlikely that the white populations play such a major role in the resiliency of the county. The astoundingly small z-scores for the shelter, evacuation route, and hospital variables are likely to play a more dominant role in a true overall risk calculation. It is because of these variables that the resiliency remains low and that Hancock County received a high overall risk in the new overall risk assessment.

Of the top five most at risk counties, three of them are inland. We now know that many of the more inland counties of Louisiana and the surrounding area received major flooding directly and indirectly because of Hurricane Katrina. Unfortunately, it is impossible to say if another hurricane of Katrina’s magnitude would produce the same results due to the causes of the flooding in this specific case. Despite some of the inland counties receiving most the risk, the spread of the risk is more localized around the southeastern counties of Louisiana that received the highest hurricane impacts. This, once

110

again, points to the use of the new analysis being more realistic than the original assessment in calculating overall hurricane risks along the gulf coast.

111

County, State Initial Overall Risk County, State Initial Overall Risk County, State Initial Overall Risk

Baldwin, AL 1.00 Washington, FL 0.38 Pearl River, MS 1.27

Clarke, AL 2.46 Acadia, LA 1.49 Pike, MS 2.65

Covington, AL 1.24 Ascension, LA 0.94 Stone, MS 0.42

Escambia, AL 0.85 Assumption, LA 1.05 Walthall, MS 1.83

Geneva, AL 1.06 Avoyelles, LA 1.82 Wilkinson, MS 0.74

Mobile, AL 1.21 Beauregard, LA 1.26 Aransas, TX 0.91

Monroe, AL 1.70 Calcasieu, LA 1.34 Austin, TX 0.92

Washington, AL 1.00 Cameron, LA 0.10 Bee, TX 0.43

112 Bay, FL 0.65 East Baton Rouge, LA 2.10 Brazoria, TX 1.30

Calhoun, FL 0.36 East Feliciana, LA 0.52 Brooks, TX 0.75

Charlotte, FL 0.91 Evangeline, LA 1.71 Calhoun, TX 0.94

Citrus, FL 1.41 Iberia, LA 3.25 Cameron, TX 3.97

Collier, FL 0.99 Iberville, LA 0.76 Chambers, TX 0.44

DeSoto, FL 1.18 Jefferson Davis, LA 1.17 Colorado, TX 1.18 … Continued

Table 4. Initial Calculated Risks for all Counties in Study Area; Arranged Alphabetically by State and County.

112

Table 4. Continued.

County, State Initial Overall Risk County, State Initial Overall Risk County, State Initial Overall Risk

Dixie, FL 0.30 Jefferson, LA 1.83 DeWitt, TX 0.86

Escambia, FL 0.86 Lafayette, LA 1.22 Duval, TX 0.75

Franklin, FL 0.26 Lafourche, LA 0.89 Fayette, TX 0.71

Gadsden, FL 1.56 Livingston, LA 0.49 Fort Bend, TX 1.80

Gilchrist, FL 0.32 Orleans, LA 2.45 Galveston, TX 1.30

Glades, FL 0.81 Plaquemines, LA 0.58 Goliad, TX 0.53

Gulf, FL 0.33 Pointe Coupee, LA 1.33 Harris, TX 1.61

113

Hardee, FL 1.09 Rapides, LA 2.89 Hidalgo, TX 2.81

Hernando, FL 1.40 Sabine, LA 1.43 Jackson, TX 0.82

Hillsborough, FL 1.48 St. Bernard, LA 0.68 Jasper, TX 1.46

Holmes, FL 0.28 St. Charles, LA 0.89 Jefferson, TX 1.87

Jackson, FL 0.67 St. Helena, LA 0.96 Jim Hogg, TX 0.76

Jefferson, FL 0.65 St. James, LA 0.86 Jim Wells, TX 1.21

Lafayette, FL 0.28 St. John the Baptist, LA 1.07 Kenedy, TX 0.47 … Continued

113

Table 4. Continued.

County, State Initial Overall Risk County, State Initial Overall Risk County, State Initial Overall Risk

Lee, FL 1.40 St. Landry, LA 3.60 Kleburg, TX 0.62

Leon, FL 1.50 St. Martin, LA 1.48 Lavaca, TX 0.96

Levy, FL 1.02 St. Mary, LA 1.72 Liberty, TX 0.83

Liberty, FL 0.30 St. Tammany, LA 0.99 Live Oak, TX 0.44

Madison, FL 0.62 Tangipahoa, LA 1.24 Matagorda, TX 1.82

Manatee, FL 1.26 Terrebonne, LA 0.95 Newton, TX 0.97

Marion, FL 2.78 Vermilion, LA 1.78 Nueces, TX 1.06

114

Monroe, FL 0.27 Vernon, LA 0.64 Orange, TX 1.17

Okaloosa, FL 0.48 Washington, LA 1.83 Refugio, TX 1.03

Pasco, FL 1.64 West Baton Rouge, LA 0.60 San Patricio, TX 1.43

Pinellas, FL 1.53 West Feliciana, LA 0.33 Starr, TX 1.63

Polk, FL 2.55 Amite, MS 1.47 Tyler, TX 0.73

Santa Rosa, FL 0.35 George, MS 0.49 Victoria, TX 1.69

Sarasota, FL 1.21 Hancock, MS 3.22 Waller, TX 0.83 … Continued

114

Table 4. Continued.

County, State Initial Overall Risk County, State Initial Overall Risk County, State Initial Overall Risk

Sumter, FL 1.13 Harrison, MS 1.19 Washington, TX 1.20

Suwannee, FL 0.94 Jackson, MS 1.16 Webb, TX 2.05

Taylor, FL 0.43 Lamar, MS 0.87 Wharton, TX 1.61

Wakulla, FL 0.32 Marion, MS 1.76 Willacy, TX 0.62

Walton, FL 0.30

115

Initial Overall Initial Overall Overall Final Risk Hurricane County, State Risk Vulnerabilities Resiliencies Vulnerabilities Evaluation

Marion, FL 2.78 3.85 1.38 10.04 7.25

Polk, FL 2.55 3.47 1.36 9.21 6.76

Charley Lee, FL 1.40 3.07 2.20 12.04 5.49

Pasco, FL 1.64 3.14 1.91 8.07 4.22

Pinellas, FL 1.53 3.14 2.05 8.30 4.05

Hancock, MS 3.22 3.08 0.96 10.42 10.90

Orleans, LA 2.45 3.53 1.44 12.10 8.39

116 Ivan Clarke, AL 2.46 3.94 1.60 11.17 6.97

Pike, MS 2.65 4.05 1.53 8.42 5.51

Monroe, AL 1.70 3.83 2.25 10.64 4.72 … Continued

Table 5. Comparative Look at Initial and Overall Vulnerabilities, Resiliencies, and Risks by Hurricane.

116

Table 5. Continued.

Initial Overall Initial Overall Overall Final Risk Hurricane County, State Risk Vulnerabilities Resiliencies Vulnerabilities Evaluation

Hancock, MS 3.22 3.08 0.96 8.48 8.87

Orleans, LA 2.45 3.53 1.44 10.64 7.38

Dennis Pike, MS 2.65 4.05 1.53 7.57 4.95

Marion, FL 2.78 3.85 1.38 6.66 4.81

Polk, FL 2.55 3.47 1.36 6.39 4.69

Hancock, MS 3.22 3.08 0.96 11.29 11.81

117 Orleans, LA 2.45 3.53 1.44 13.76 9.54

Katrina St. Landry, LA 3.60 4.07 1.13 8.04 7.11

Pike, MS 2.65 4.05 1.53 10.68 6.99

Iberia, LA 3.25 3.16 0.97 6.66 6.86

St. Landry, LA 3.60 4.07 1.13 11.52 10.19

Rapides, LA 2.89 3.60 1.25 9.76 7.83

Rita Iberia, LA 3.25 3.16 0.97 7.49 7.71

Orleans, LA 2.45 3.53 1.44 10.92 7.57

Jefferson, TX 1.87 2.58 1.38 9.74 7.07 … Continued 117

Table 5. Continued.

Initial Overall Initial Overall Overall Final Risk Hurricane County, State Risk Vulnerabilities Resiliencies Vulnerabilities Evaluation

Marion, FL 2.78 3.85 1.38 10.37 7.49

Polk, FL 2.55 3.47 1.36 9.94 7.29

Wilma Pasco, FL 1.64 3.14 1.91 8.57 4.48

Pinellas, FL 1.53 3.14 2.05 8.82 4.30

Lee, FL 1.40 3.07 2.20 9.04 4.12

Hancock, MS 3.22 3.08 0.96 11.25 11.76

118 Orleans, LA 2.45 3.53 1.44 13.14 9.12

Gustav St. Landry, LA 3.60 4.07 1.13 9.69 8.57

Rapides, LA 2.89 3.60 1.25 9.71 7.79

Jefferson, LA 1.83 2.83 1.54 10.73 6.95

Iberia, LA 3.25 3.16 0.97 8.08 8.32

St. Landry, LA 3.60 4.07 1.13 8.34 7.37

Ike (Coast) Rapides, LA 2.89 3.60 1.25 8.09 6.49

Matagorda, TX 1.82 2.71 1.49 9.41 6.33

Vermilion, LA 1.78 2.78 1.56 9.71 6.23

118 … Continued

Table 5. Continued.

Initial Overall Initial Overall Overall Final Risk Hurricane County, State Risk Vulnerabilities Resiliencies Vulnerabilities Evaluation

Hancock, MS 3.22 3.08 0.96 10.82 11.32

Orleans, LA 2.45 3.53 1.44 12.66 8.78

Ida Clarke, AL 2.46 3.94 1.60 10.88 6.80

St. Landry, LA 3.60 4.07 1.13 6.19 5.47

Pike, MS 2.65 4.05 1.53 8.04 5.26

Hancock, MS 3.22 3.08 0.96 11.81 12.34

119 Orleans, LA 2.45 3.53 1.44 13.89 9.63

Isaac Jefferson, LA 1.83 2.83 1.54 10.85 7.03

St. Landry, LA 3.60 4.07 1.13 7.01 6.20

Pike, MS 2.65 4.05 1.53 9.22 6.03

119

Figure 6. Hurricane Risks of Gulf Coast Counties Associated with Hurricane Charley at

Time of Landfall.

120

Wind Wind Gusts Gust Storm Surge Rainfall County, State Speed Rank Rank Rank Rainfall (in) Rank Vuln. (mph) Vuln. Surge (ft) Vuln. Vuln. (mph)

Bay, FL 15.00 117 0.8406 16.00 98 0.7029 0.00 1 0.0000 0.33 108 0.7754

Calhoun, FL 10.00 102 0.7319 16.00 98 0.7029 0.00 1 0.0000 0.00 1 0.0000

Charlotte, FL 89.76 137 0.9855 111.63 138 0.9928 0.00 1 0.0000 3.50 138 0.9928

Citrus, FL 15.00 117 0.8406 31.00 124 0.8913 0.00 1 0.0000 0.45 111 0.7971

Collier, FL 43.73 130 0.9348 84.00 135 0.9710 3.00 137 0.9855 1.75 130 0.9348

Covington, AL 14.00 115 0.8261 16.00 98 0.7029 0.00 1 0.0000 0.00 1 0.0000

DeSoto, FL 76.86 135 0.9710 103.57 137 0.9855 0.00 1 0.0000 2.53 137 0.9855

121 Dixie, FL 9.00 98 0.7029 18.00 118 0.8478 0.00 1 0.0000 1.77 132 0.9493

Escambia, FL 18.00 123 0.8841 19.00 119 0.8551 0.00 1 0.0000 0.00 1 0.0000

Franklin, FL 10.00 102 0.7319 16.00 98 0.7029 0.00 1 0.0000 1.36 127 0.9130

Gadsden, FL 10.00 102 0.7319 16.00 98 0.7029 0.00 1 0.0000 0.85 119 0.8551

Geneva, AL 11.00 108 0.7754 16.00 98 0.7029 0.00 1 0.0000 0.00 1 0.0000 … Continued

Table 6. Hurricane Charley Hazard Values and Vulnerabilities by County.

121

Table 6. Continued.

Wind Wind Gusts Gust Storm Surge Rainfall County, State Speed Rank Rank Rank Rainfall (in) Rank Vuln. (mph) Vuln. Surge (ft) Vuln. Vuln. (mph)

Gilchrist, FL 15.00 117 0.8406 23.00 121 0.8696 0.00 1 0.0000 1.60 129 0.9275

Glades, FL 76.86 136 0.9783 94.24 136 0.9783 0.00 1 0.0000 1.75 130 0.9348

Gulf, FL 10.00 102 0.7319 16.00 98 0.7029 0.00 1 0.0000 0.39 109 0.7826

Hardee, FL 76.86 134 0.9638 147.00 139 1.0000 0.00 1 0.0000 0.68 115 0.8261

Hernando, FL 15.00 117 0.8406 31.00 124 0.8913 0.00 1 0.0000 1.10 123 0.8841

Hillsborough, FL 24.00 127 0.9130 62.14 131 0.9420 0.00 1 0.0000 0.95 120 0.8623

122 Holmes, FL 13.00 111 0.7971 16.00 98 0.7029 0.00 1 0.0000 0.00 1 0.0000

Jackson, FL 10.00 102 0.7319 16.00 98 0.7029 0.00 1 0.0000 0.06 105 0.7536

Jefferson, FL 11.00 108 0.7754 16.00 98 0.7029 0.00 1 0.0000 0.79 117 0.8406

Lafayette, FL 9.00 98 0.7029 16.00 98 0.7029 0.00 1 0.0000 1.34 126 0.9058

Lee, FL 60.99 133 0.9565 78.25 134 0.9638 6.50 139 1.0000 3.53 139 1.0000

Leon, FL 13.00 111 0.7971 16.00 98 0.7029 0.00 1 0.0000 0.50 113 0.8116

Levy, FL 22.00 126 0.9058 31.00 124 0.8913 0.00 1 0.0000 2.00 134 0.9638

Liberty, FL 10.00 102 0.7319 16.00 98 0.7029 0.00 1 0.0000 1.15 125 0.8986 … Continued

122

Table 6. Continued.

Wind Wind Gusts Gust Storm Surge Rainfall County, State Speed Rank Rank Rank Rainfall (in) Rank Vuln. (mph) Vuln. Surge (ft) Vuln. Vuln. (mph)

Madison, FL 9.00 98 0.7029 16.00 98 0.7029 0.00 1 0.0000 1.95 133 0.9565

Manatee, FL 89.76 137 0.9855 62.14 131 0.9420 0.00 1 0.0000 2.29 136 0.9783

Marion, FL 21.00 125 0.8986 24.00 122 0.8768 0.00 1 0.0000 0.78 116 0.8333

Monroe, FL 48.00 132 0.9493 60.00 130 0.9348 6.00 138 0.9928 1.04 121 0.8696

Okaloosa, FL 13.00 111 0.7971 16.00 98 0.7029 0.00 1 0.0000 1.14 124 0.8913

Pasco, FL 24.00 127 0.9130 31.00 124 0.8913 0.00 1 0.0000 0.13 106 0.7609

123 Pinellas, FL 31.07 129 0.9275 36.82 129 0.9275 0.00 1 0.0000 0.43 110 0.7899

Polk, FL 47.18 131 0.9420 62.14 131 0.9420 0.00 1 0.0000 0.22 107 0.7681

Santa Rosa, FL 18.00 123 0.8841 19.00 119 0.8551 0.00 1 0.0000 0.00 1 0.0000

Sarasota, FL 89.76 137 0.9855 35.67 128 0.9203 0.00 1 0.0000 2.00 135 0.9710

Sumter, FL 17.00 122 0.8768 30.00 123 0.8841 0.00 1 0.0000 0.48 112 0.8043

Suwannee, FL 9.00 98 0.7029 16.00 98 0.7029 0.00 1 0.0000 1.40 128 0.9203

Taylor, FL 11.00 108 0.7754 16.00 98 0.7029 0.00 1 0.0000 0.79 117 0.8406

Wakulla, FL 13.00 111 0.7971 16.00 98 0.7029 0.00 1 0.0000 1.08 122 0.8768 … Continued

123

Table 6. Continued.

Wind Wind Gusts Gust Storm Surge Rainfall County, State Speed Rank Rank Rank Rainfall (in) Rank Vuln. (mph) Vuln. Surge (ft) Vuln. Vuln. (mph)

Walton, FL 15.00 117 0.8406 16.00 98 0.7029 0.00 1 0.0000 0.60 114 0.8188

Washington, FL 14.00 115 0.8261 16.00 98 0.7029 0.00 1 0.0000 0.00 1 0.0000

124

Figure 7. Hurricane Risks of Gulf Coast Counties Associated with Hurricane Ivan at

Time of Landfall.

125

Wind Wind Gusts Gust Storm Surge Rainfall County, State Speed Rank Rank Rank Rainfall (in) Rank Vuln. (mph) Vuln. Surge (ft) Vuln. Vuln. (mph)

Acadia, LA 18.09 84 0.6014 23.43 87 0.6232 0.00 1 0.0000 0.00 1 0.0000

Amite, MS 33.94 106 0.7609 38.05 104 0.7464 0.00 1 0.0000 0.00 1 0.0000

Ascension, LA 12.70 72 0.5145 23.03 84 0.6014 0.00 1 0.0000 0.00 1 0.0000

Assumption, LA 34.75 109 0.7826 22.54 76 0.5435 0.00 1 0.0000 0.00 1 0.0000

Avoyelles, LA 19.05 87 0.6232 23.03 84 0.6014 0.00 1 0.0000 0.00 1 0.0000

Baldwin, AL 67.90 134 0.9638 88.60 135 0.9710 8.81 137 0.9855 10.16 139 1.0000

Bay, FL 74.24 135 0.9710 82.26 134 0.9638 4.94 128 0.9203 4.41 125 0.8986

126 Beauregard, LA 11.00 69 0.4928 17.00 66 0.4710 0.00 1 0.0000 0.00 1 0.0000

Calcasieu, LA 11.00 69 0.4928 17.00 66 0.4710 0.00 1 0.0000 0.00 1 0.0000

Calhoun, FL 45.83 122 0.8768 49.02 116 0.8333 0.00 1 0.0000 2.00 116 0.8333

Cameron, LA 10.00 67 0.4783 39.10 106 0.7609 0.00 1 0.0000 0.00 1 0.0000

Clarke, AL 63.46 132 0.9493 81.91 133 0.9565 0.00 1 0.0000 6.02 129 0.9275 … Continued

Table 7. Hurricane Ivan Hazard Values and Vulnerabilities by County.

126

Table 7. Continued.

Wind Wind Gusts Gust Storm Surge Rainfall County, State Speed Rank Rank Rank Rainfall (in) Rank Vuln. (mph) Vuln. Surge (ft) Vuln. Vuln. (mph)

Covington, AL 47.66 124 0.8913 65.14 126 0.9058 0.00 1 0.0000 9.96 138 0.9928 East Baton Rouge, 25.00 95 0.6812 22.00 69 0.4928 0.00 1 0.0000 0.00 1 0.0000 LA East Feliciana, LA 25.22 96 0.6884 22.00 69 0.4928 0.00 1 0.0000 0.00 1 0.0000

Escambia, AL 53.00 128 0.9203 75.00 132 0.9493 0.00 1 0.0000 8.26 134 0.9638

Escambia, FL 87.50 139 1.0000 107.00 139 1.0000 12.92 139 1.0000 8.00 133 0.9565

Evangeline, LA 19.05 87 0.6232 23.82 90 0.6449 0.00 1 0.0000 0.00 1 0.0000

127 Franklin, FL 38.89 119 0.8551 40.90 108 0.7754 5.04 130 0.9348 2.22 121 0.8696

Gadsden, FL 34.50 108 0.7754 35.78 102 0.7319 0.00 1 0.0000 2.00 116 0.8333

Galveston, TX 0.00 1 0.0000 0.00 1 0.0000 1.60 118 0.8478 0.00 1 0.0000

Geneva, AL 36.00 115 0.8261 51.00 117 0.8406 0.00 1 0.0000 5.62 128 0.9203

George, MS 35.80 114 0.8188 52.35 118 0.8478 0.00 1 0.0000 2.90 123 0.8841

Gulf, FL 50.70 127 0.9130 54.72 120 0.8623 5.00 129 0.9275 2.90 123 0.8841

Hancock, MS 33.00 105 0.7536 61.00 124 0.8913 4.76 127 0.9130 1.80 115 0.8261

Harrison, MS 48.30 125 0.8986 71.30 130 0.9348 4.07 125 0.8986 2.49 122 0.8768 … Continued

127

Table 7. Continued.

Wind Wind Gusts Gust Storm Surge Rainfall County, State Speed Rank Rank Rank Rainfall (in) Rank Vuln. (mph) Vuln. Surge (ft) Vuln. Vuln. (mph)

Holmes, FL 56.82 130 0.9348 61.89 125 0.8986 0.00 1 0.0000 5.43 127 0.9130

Iberia, LA 15.00 74 0.5290 22.00 69 0.4928 0.00 1 0.0000 0.00 1 0.0000

Iberville, LA 19.05 87 0.6232 23.03 84 0.6014 0.00 1 0.0000 0.00 1 0.0000

Jackson, FL 41.96 120 0.8623 44.50 111 0.7971 0.00 1 0.0000 2.20 120 0.8623

Jackson, MS 42.60 121 0.8696 58.70 123 0.8841 0.00 1 0.0000 2.10 119 0.8551 Jefferson Davis, 17.00 78 0.5580 23.00 80 0.5725 0.00 1 0.0000 0.00 1 0.0000 LA

128 Jefferson, FL 30.76 101 0.7246 31.40 98 0.7029 4.00 123 0.8841 0.61 108 0.7754

Jefferson, LA 34.50 107 0.7681 47.20 114 0.8188 2.80 120 0.8623 0.00 1 0.0000

Lafayette, LA 17.00 78 0.5580 22.00 69 0.4928 0.00 1 0.0000 0.00 1 0.0000

Lafourche, LA 9.00 66 0.4710 22.00 69 0.4928 2.51 119 0.8551 0.95 113 0.8116

Lamar, MS 29.00 98 0.7029 46.00 112 0.8043 0.00 1 0.0000 0.84 110 0.7899

Leon, FL 31.74 103 0.7391 32.54 99 0.7101 0.00 1 0.0000 0.90 111 0.7971

Liberty, FL 36.44 116 0.8333 38.05 103 0.7391 0.00 1 0.0000 2.00 116 0.8333

Livingston, LA 14.25 73 0.5217 23.46 88 0.6304 0.00 1 0.0000 0.00 1 0.0000 … Continued

128

Table 7. Continued.

Wind Wind Gusts Gust Storm Surge Rainfall County, State Speed Rank Rank Rank Rainfall (in) Rank Vuln. (mph) Vuln. Surge (ft) Vuln. Vuln. (mph)

Marion, MS 29.00 98 0.7029 46.00 112 0.8043 0.00 1 0.0000 0.32 105 0.7536

Mobile, AL 58.70 131 0.9420 74.80 131 0.9420 8.00 135 0.9710 9.90 137 0.9855

Monroe, AL 50.18 126 0.9058 69.79 128 0.9203 0.00 1 0.0000 7.26 132 0.9493

Okaloosa, FL 75.07 136 0.9783 89.80 137 0.9855 6.12 133 0.9565 8.40 135 0.9710

Orleans, LA 47.20 123 0.8841 55.20 121 0.8696 6.10 132 0.9493 0.08 101 0.7246

Pearl River, MS 31.00 102 0.7319 53.50 119 0.8551 0.00 1 0.0000 0.57 107 0.7681

29 Pike, MS 24.00 94 0.6739 31.00 97 0.6957 0.00 1 0.0000 0.03 99 0.7101

Plaquemines, LA 35.70 111 0.7971 48.30 115 0.8261 8.72 136 0.9783 0.14 102 0.7319 Pointe Coupee, 19.92 90 0.6449 23.46 88 0.6304 0.00 1 0.0000 0.00 1 0.0000 LA Rapides, LA 12.00 71 0.5072 21.00 68 0.4855 0.00 1 0.0000 0.00 1 0.0000

Sabine, LA 17.00 78 0.5580 23.00 80 0.5725 0.00 1 0.0000 0.00 1 0.0000

Santa Rosa, FL 80.93 138 0.9928 98.31 138 0.9928 9.66 138 0.9928 6.50 131 0.9420

St. Bernard, LA 0.00 1 0.0000 0.00 1 0.0000 7.56 134 0.9638 0.15 103 0.7391

St. Charles, LA 16.87 77 0.5507 24.19 93 0.6667 0.00 1 0.0000 0.00 1 0.0000 … Continued

129

Table 7. Continued.

Wind Wind Gusts Gust Storm Surge Rainfall County, State Speed Rank Rank Rank Rainfall (in) Rank Vuln. (mph) Vuln. Surge (ft) Vuln. Vuln. (mph)

St. Helena, LA 15.64 75 0.5362 23.84 91 0.6522 0.00 1 0.0000 0.00 1 0.0000

St. James, LA 10.96 68 0.4855 22.54 76 0.5435 0.00 1 0.0000 0.00 1 0.0000 St. John the 17.97 83 0.5942 24.49 95 0.6812 0.00 1 0.0000 0.00 1 0.0000 Baptist, LA St. Landry, LA 18.09 84 0.6014 22.54 76 0.5435 0.00 1 0.0000 0.00 1 0.0000

St. Martin, LA 18.09 84 0.6014 22.54 76 0.5435 0.00 1 0.0000 0.00 1 0.0000

St. Mary, LA 17.00 78 0.5580 22.00 69 0.4928 0.00 1 0.0000 0.00 1 0.0000

130 St. Tammany, LA 36.80 117 0.8406 42.60 110 0.7899 3.72 122 0.8768 0.26 104 0.7464

Stone, MS 38.65 118 0.8478 58.65 122 0.8768 0.00 1 0.0000 1.00 114 0.8188

Tangipahoa, LA 23.00 93 0.6667 35.00 101 0.7246 0.00 1 0.0000 0.00 1 0.0000

Taylor, FL 29.99 100 0.7174 30.50 96 0.6884 4.00 123 0.8841 0.61 108 0.7754

Terrebonne, LA 35.70 111 0.7971 22.00 69 0.4928 3.38 121 0.8696 0.47 106 0.7609

Vermilion, LA 17.00 78 0.5580 23.00 80 0.5725 0.00 1 0.0000 0.00 1 0.0000

Vernon, LA 16.00 76 0.5435 23.00 80 0.5725 0.00 1 0.0000 0.00 1 0.0000

Wakulla, FL 32.96 104 0.7464 33.97 100 0.7174 4.50 126 0.9058 0.90 111 0.7971 … Continued

130

Table 7. Continued.

Wind Wind Gusts Gust Storm Surge Rainfall County, State Speed Rank Rank Rank Rainfall (in) Rank Vuln. (mph) Vuln. Surge (ft) Vuln. Vuln. (mph)

Walthall, MS 26.50 97 0.6957 38.50 105 0.7536 0.00 1 0.0000 0.03 99 0.7101

Walton, FL 80.00 137 0.9855 89.00 136 0.9783 5.39 131 0.9420 8.92 136 0.9783

Washington, AL 55.26 129 0.9275 69.61 127 0.9130 0.00 1 0.0000 6.02 129 0.9275

Washington, FL 64.54 133 0.9565 70.91 129 0.9275 0.00 1 0.0000 4.41 125 0.8986

Washington, LA 35.74 113 0.8116 40.91 108 0.7754 0.00 1 0.0000 0.00 1 0.0000 West Baton 20.69 91 0.6522 23.84 91 0.6522 0.00 1 0.0000 0.00 1 0.0000 Rouge, LA

131 West Feliciana, 21.37 92 0.6594 24.19 93 0.6667 0.00 1 0.0000 0.00 1 0.0000

LA Wilkinson, MS 34.79 110 0.7899 39.39 107 0.7681 0.00 1 0.0000 0.00 1 0.0000

131

Figure 8. Hurricane Risks of Gulf Coast Counties Associated with Hurricane Dennis at

Time of Landfall.

132

Storm Wind Speed Wind Gust Surge Rainfall Rainfall County, State Rank Gusts (mph) Rank Surge Rank Rank (mph) Vuln. Vuln. Vuln. (in) Vuln. (ft)

Acadia, LA 18.50 74 0.5290 21.33 54 0.3841 0.00 1 0.0000 0.00 1 0.0000

Amite, MS 16.30 60 0.4275 23.43 66 0.4710 0.00 1 0.0000 1.42 111 0.7971

Ascension, LA 12.70 46 0.3261 30.79 76 0.5435 0.00 1 0.0000 0.00 1 0.0000

Assumption, LA 18.09 72 0.5145 18.09 47 0.3333 0.00 1 0.0000 0.15 71 0.5072

Avoyelles, LA 18.26 73 0.5217 22.23 60 0.4275 0.00 1 0.0000 0.00 1 0.0000

Baldwin, AL 97.82 137 0.9855 49.48 119 0.8551 0.00 1 0.0000 3.02 127 0.9130

Bay, FL 58.69 132 0.9493 72.50 131 0.9420 5.72 138 0.9928 2.29 124 0.8913

133 Beauregard, LA 14.00 49 0.3478 19.00 48 0.3406 0.00 1 0.0000 0.00 1 0.0000

Calcasieu, LA 17.00 63 0.4493 24.00 68 0.4855 0.00 1 0.0000 0.01 54 0.3841

Calhoun, FL 52.18 127 0.9130 63.15 127 0.9130 0.00 1 0.0000 7.01 138 0.9928

Cameron, LA 8.00 42 0.2971 0.00 1 0.0000 0.00 1 0.0000 0.02 55 0.3913

Charlotte, FL 25.00 87 0.6232 33.00 84 0.6014 0.00 1 0.0000 0.60 92 0.6594 … Continued

Table 8. Hurricane Dennis Hazard Values and Vulnerabilities by County.

133

Table 8. Continued.

Storm Wind Speed Wind Gust Surge Rainfall Rainfall County, State Rank Gusts (mph) Rank Surge Rank Rank (mph) Vuln. Vuln. Vuln. (in) Vuln. (ft)

Citrus, FL 37.00 105 0.7536 79.00 134 0.9638 0.00 1 0.0000 1.93 121 0.8696

Clarke, AL 97.82 137 0.9855 0.00 1 0.0000 0.00 1 0.0000 1.25 106 0.7609

Collier, FL 28.00 89 0.6377 37.00 91 0.6522 0.00 1 0.0000 0.98 100 0.7174

Covington, AL 77.16 134 0.9638 49.48 119 0.8551 0.00 1 0.0000 2.28 123 0.8841

DeSoto, FL 30.57 93 0.6667 36.81 89 0.6377 0.00 1 0.0000 0.22 75 0.5362

Dixie, FL 18.00 69 0.4928 31.00 77 0.5507 0.00 1 0.0000 2.70 125 0.8986

134 East Baton 23.00 77 0.5507 33.00 84 0.6014 0.00 1 0.0000 0.11 64 0.4565 Rouge, LA East Feliciana, 23.43 80 0.5725 32.35 82 0.5870 0.00 1 0.0000 0.47 85 0.6087 LA Escambia, AL 90.13 136 0.9783 0.00 1 0.0000 0.00 1 0.0000 0.41 81 0.5797

Escambia, FL 34.52 98 0.7029 79.40 137 0.9855 0.00 1 0.0000 5.80 136 0.9783

Evangeline, LA 14.00 49 0.3478 22.00 57 0.4058 0.00 1 0.0000 0.00 1 0.0000

Franklin, FL 47.18 126 0.9058 64.44 129 0.9275 6.94 139 1.0000 3.80 128 0.9203

Gadsden, FL 43.04 124 0.8913 56.75 125 0.8986 0.00 1 0.0000 4.09 132 0.9493

Geneva, AL 37.98 111 0.7971 50.63 121 0.8696 0.00 1 0.0000 1.03 102 0.7319 … Continued

134

Table 8. Continued.

Storm Wind Speed Wind Gust Surge Rainfall Rainfall County, State Rank Gusts (mph) Rank Surge Rank Rank (mph) Vuln. Vuln. Vuln. (in) Vuln. (ft)

George, MS 24.33 86 0.6159 37.81 93 0.6667 0.00 1 0.0000 1.27 108 0.7754

Gilchrist, FL 34.00 97 0.6957 43.00 98 0.7029 0.00 1 0.0000 1.22 104 0.7464

Glades, FL 25.22 88 0.6304 32.35 82 0.5870 0.00 1 0.0000 0.60 92 0.6594

Gulf, FL 58.69 132 0.9493 72.50 131 0.9420 0.00 1 0.0000 3.80 128 0.9203

Hancock, MS 23.00 77 0.5507 36.00 87 0.6232 1.66 133 0.9565 0.48 87 0.6232

Hardee, FL 30.57 93 0.6667 36.81 89 0.6377 0.00 1 0.0000 0.55 90 0.6449

135 Harrison, MS 29.92 92 0.6594 46.03 109 0.7826 2.21 134 0.9638 0.93 99 0.7101

Hernando, FL 37.00 105 0.7536 79.00 134 0.9638 0.00 1 0.0000 1.88 120 0.8623

Hillsborough, FL 37.00 105 0.7536 46.00 105 0.7536 0.00 1 0.0000 0.53 89 0.6377

Holmes, FL 52.18 127 0.9130 63.15 127 0.9130 0.00 1 0.0000 0.86 96 0.6884

Iberia, LA 19.21 75 0.5362 22.75 62 0.4420 0.00 1 0.0000 0.02 55 0.3913

Iberville, LA 14.29 52 0.3696 22.23 60 0.4275 0.00 1 0.0000 0.11 64 0.4565

Jackson, FL 28.00 89 0.6377 43.00 98 0.7029 0.00 1 0.0000 1.50 113 0.8116

Jackson, MS 24.00 84 0.6014 39.13 96 0.6884 2.50 135 0.9710 1.34 110 0.7899 … Continued

135

Table 8. Continued.

Storm Wind Speed Wind Gust Surge Rainfall Rainfall County, State Rank Gusts (mph) Rank Surge Rank Rank (mph) Vuln. Vuln. Vuln. (in) Vuln. (ft) Jefferson Davis, 16.30 60 0.4275 19.87 50 0.3551 0.00 1 0.0000 0.00 1 0.0000 LA Jefferson, FL 36.79 102 0.7319 45.77 103 0.7391 0.00 1 0.0000 3.89 130 0.9348

Jefferson, LA 39.13 117 0.8406 48.33 112 0.8043 0.00 1 0.0000 0.11 64 0.4565

Lafayette, FL 35.74 100 0.7174 45.77 103 0.7391 0.00 1 0.0000 1.52 115 0.8261

Lafayette, LA 16.00 59 0.4203 21.00 52 0.3696 0.00 1 0.0000 0.00 1 0.0000

Lafourche, LA 9.00 43 0.3043 31.77 81 0.5797 0.00 1 0.0000 0.30 77 0.5507

136 Lamar, MS 24.00 84 0.6014 36.00 87 0.6232 0.00 1 0.0000 0.67 94 0.6739

Lee, FL 17.00 63 0.4493 37.00 91 0.6522 0.00 1 0.0000 0.09 63 0.4493

Leon, FL 37.98 111 0.7971 48.06 110 0.7899 0.00 1 0.0000 4.09 132 0.9493

Levy, FL 18.00 69 0.4928 31.00 77 0.5507 0.00 1 0.0000 1.56 116 0.8333

Liberty, FL 44.99 125 0.8986 60.37 126 0.9058 0.00 1 0.0000 7.01 138 0.9928

Livingston, LA 14.25 51 0.3623 30.38 75 0.5362 0.00 1 0.0000 0.34 80 0.5725

Madison, FL 36.81 103 0.7391 48.06 110 0.7899 0.00 1 0.0000 1.30 109 0.7826

Manatee, FL 31.00 95 0.6812 38.00 94 0.6739 0.00 1 0.0000 1.22 104 0.7464 … Continued

136

Table 8. Continued.

Storm Wind Speed Wind Gust Surge Rainfall Rainfall County, State Rank Gusts (mph) Rank Surge Rank Rank (mph) Vuln. Vuln. Vuln. (in) Vuln. (ft)

Marion, FL 13.00 48 0.3406 31.00 77 0.5507 0.00 1 0.0000 1.61 117 0.8406

Marion, MS 23.82 83 0.5942 29.38 71 0.5072 0.00 1 0.0000 0.03 58 0.4130

Mobile, AL 36.82 104 0.7464 48.33 112 0.8043 2.76 136 0.9783 2.21 122 0.8768

Monroe, AL 83.27 135 0.9710 0.00 1 0.0000 0.00 1 0.0000 4.41 135 0.9710

Monroe, FL 43.00 123 0.8841 53.00 124 0.8913 0.00 1 0.0000 0.44 82 0.5870

Okaloosa, FL 55.00 129 0.9275 83.00 138 0.9928 0.00 1 0.0000 2.88 126 0.9058

137 Orleans, LA 39.13 117 0.8406 48.33 112 0.8043 0.08 131 0.9420 0.08 60 0.4275

Pasco, FL 37.00 105 0.7536 79.00 134 0.9638 0.00 1 0.0000 1.50 113 0.8116

Pearl River, MS 23.43 80 0.5725 35.02 86 0.6159 0.00 1 0.0000 0.57 91 0.6522

Pike, MS 23.00 77 0.5507 30.00 73 0.5217 0.00 1 0.0000 1.42 111 0.7971

Pinellas, FL 37.00 105 0.7536 46.00 105 0.7536 0.00 1 0.0000 1.12 103 0.7391

Plaquemines, LA 37.98 111 0.7971 43.73 100 0.7174 1.29 132 0.9493 0.11 64 0.4565 Pointe Coupee, 15.67 58 0.4130 22.75 62 0.4420 0.00 1 0.0000 0.11 64 0.4565 LA Polk, FL 18.00 69 0.4928 29.00 69 0.4928 0.00 1 0.0000 1.85 119 0.8551 … Continued

137

Table 8. Continued.

Storm Wind Speed Wind Gust Surge Rainfall Rainfall County, State Rank Gusts (mph) Rank Surge Rank Rank (mph) Vuln. Vuln. Vuln. (in) Vuln. (ft)

Rapides, LA 15.00 53 0.3768 22.00 57 0.4058 0.00 1 0.0000 0.00 1 0.0000

Sabine, LA 15.00 53 0.3768 19.00 48 0.3406 0.00 1 0.0000 0.00 1 0.0000

Santa Rosa, FL 98.97 139 1.0000 120.83 139 1.0000 3.50 137 0.9855 5.80 136 0.9783

Sarasota, FL 31.00 95 0.6812 38.00 94 0.6739 0.00 1 0.0000 0.48 87 0.6232

St. Bernard, LA 37.98 111 0.7971 41.43 97 0.6957 0.00 1 0.0000 0.08 60 0.4275

St. Charles, LA 39.13 117 0.8406 48.33 112 0.8043 0.00 1 0.0000 0.30 77 0.5507

138 St. Helena, LA 15.64 57 0.4058 30.01 74 0.5290 0.00 1 0.0000 0.08 60 0.4275

St. James, LA 10.96 44 0.3116 31.25 80 0.5725 0.00 1 0.0000 0.00 1 0.0000 St. John the 39.13 117 0.8406 48.33 112 0.8043 0.00 1 0.0000 0.30 77 0.5507 Baptist, LA St. Landry, LA 17.19 66 0.4710 21.65 55 0.3913 0.00 1 0.0000 0.00 1 0.0000

St. Martin, LA 12.74 47 0.3333 21.65 55 0.3913 0.00 1 0.0000 0.15 71 0.5072

St. Mary, LA 11.00 45 0.3188 21.00 52 0.3696 0.00 1 0.0000 0.03 58 0.4130 St. Tammany, 39.13 117 0.8406 48.33 112 0.8043 0.00 1 0.0000 0.18 74 0.5290 LA Stone, MS 29.60 91 0.6522 43.96 102 0.7319 0.00 1 0.0000 0.90 98 0.7029 … Continued

138

Table 8. Continued.

Storm Wind Speed Wind Gust Surge Rainfall Rainfall County, State Rank Gusts (mph) Rank Surge Rank Rank (mph) Vuln. Vuln. Vuln. (in) Vuln. (ft)

Sumter, FL 22.00 76 0.5435 29.00 69 0.4928 0.00 1 0.0000 1.70 118 0.8478

Suwannee, FL 34.79 99 0.7101 43.74 101 0.7246 0.00 1 0.0000 1.26 107 0.7681

Tangipahoa, LA 39.13 117 0.8406 48.33 112 0.8043 0.00 1 0.0000 1.00 101 0.7246

Taylor, FL 38.00 115 0.8261 50.63 121 0.8696 0.00 1 0.0000 3.89 130 0.9348

Terrebonne, LA 17.00 63 0.4493 17.00 46 0.3261 0.00 1 0.0000 0.15 71 0.5072

Vermilion, LA 17.47 67 0.4783 20.64 51 0.3623 0.00 1 0.0000 0.02 55 0.3913

139 Vernon, LA 15.00 53 0.3768 22.00 57 0.4058 0.00 1 0.0000 0.00 1 0.0000

Wakulla, FL 38.00 115 0.8261 50.63 121 0.8696 0.00 1 0.0000 4.09 132 0.9493

Walthall, MS 23.43 80 0.5725 29.67 72 0.5145 0.00 1 0.0000 0.83 95 0.6812

Walton, FL 56.39 131 0.9420 74.00 133 0.9565 0.00 1 0.0000 0.45 83 0.5942

Washington, AL 35.75 101 0.7246 46.01 107 0.7681 0.00 1 0.0000 0.24 76 0.5435

Washington, FL 55.25 130 0.9348 67.56 130 0.9348 0.00 1 0.0000 0.86 96 0.6884

Washington, LA 37.81 110 0.7899 46.01 107 0.7681 0.00 1 0.0000 0.45 83 0.5942 West Baton 16.90 62 0.4420 23.21 65 0.4638 0.00 1 0.0000 0.11 64 0.4565 Rouge, LA … Continued

139

Table 8. Continued.

Storm Wind Speed Wind Gust Surge Rainfall Rainfall County, State Rank Gusts (mph) Rank Surge Rank Rank (mph) Vuln. Vuln. Vuln. (in) Vuln. (ft) West Feliciana, 18.00 68 0.4855 23.62 67 0.4783 0.00 1 0.0000 0.13 70 0.5000 LA Wilkinson, MS 15.00 53 0.3768 23.00 64 0.4565 0.00 1 0.0000 0.47 85 0.6087

140

Figure 9. Hurricane Risks of Gulf Coast Counties Associated with Hurricane Katrina at

Time of Landfall.

141

Wind Wind Gusts Gust Storm Surge Rainfall Rainfall County, State Speed Rank Rank Rank Rank Vulv. (mph) Vuln. Surge (ft) Vuln. (in) Vuln. (mph)

Acadia, LA 18.09 84 0.6014 23.43 82 0.5870 0.00 1 0.0000 0.00 1 0.0000

Amite, MS 57.45 123 0.8841 78.70 124 0.8913 0.00 1 0.0000 2.40 113 0.8116

Ascension, LA 66.73 134 0.9638 81.03 125 0.8986 0.00 1 0.0000 1.89 107 0.7681

Assumption, LA 48.39 113 0.8116 64.44 110 0.7899 0.00 1 0.0000 2.30 110 0.7899

Avoyelles, LA 28.59 90 0.6449 36.53 93 0.6667 0.00 1 0.0000 0.00 1 0.0000

Baldwin, AL 65.60 132 0.9493 82.90 126 0.9058 5.81 130 0.9348 2.50 115 0.8261

Beauregard, LA 19.00 85 0.6087 32.00 87 0.6232 0.00 1 0.0000 0.00 1 0.0000

142 Calcasieu, LA 20.00 89 0.6377 29.00 85 0.6087 0.00 1 0.0000 0.04 89 0.6377

Cameron, LA 17.00 81 0.5797 23.00 80 0.5725 0.00 1 0.0000 0.02 85 0.6087

Clarke, AL 61.41 128 0.9203 76.83 122 0.8768 0.00 1 0.0000 0.31 92 0.6594

Covington, AL 51.37 118 0.8478 62.29 109 0.7826 0.00 1 0.0000 1.55 102 0.7319 East Baton 34.00 103 0.7391 49.00 102 0.7319 0.00 1 0.0000 2.24 109 0.7826 Rouge, LA … Continued

Table 9. Hurricane Katrina Hazard Values and Vulnerabilities by County.

142

Table 9. Continued.

Wind Wind Gusts Gust Storm Surge Rainfall Rainfall County, State Speed Rank Rank Rank Rank Vulv. (mph) Vuln. Surge (ft) Vuln. (in) Vuln. (mph) East Feliciana, 33.24 102 0.7319 46.61 100 0.7174 0.00 1 0.0000 1.74 105 0.7536 LA Escambia, AL 57.67 124 0.8913 71.41 119 0.8551 0.00 1 0.0000 0.36 94 0.6739

Escambia, FL 56.40 121 0.8696 71.30 118 0.8478 5.37 128 0.9203 2.71 116 0.8333

Evangeline, LA 19.00 85 0.6087 32.00 87 0.6232 0.00 1 0.0000 0.00 1 0.0000

Geneva, AL 48.72 114 0.8188 58.46 107 0.7681 0.00 1 0.0000 0.02 85 0.6087

George, MS 37.85 105 0.7536 86.65 131 0.9420 0.00 1 0.0000 5.91 126 0.9058

143 Hancock, MS 49.00 115 0.8261 85.00 128 0.9203 22.00 138 0.9928 7.30 129 0.9275

Harrison, MS 59.80 126 0.9058 122.00 138 0.9928 26.00 139 1.0000 8.69 132 0.9493

Hillsborough, FL 0.00 1 0.0000 0.00 1 0.0000 0.00 1 0.0000 6.00 127 0.9130

Iberia, LA 31.00 98 0.7029 39.00 97 0.6957 0.00 1 0.0000 1.12 99 0.7101

Iberville, LA 30.18 96 0.6884 36.53 93 0.6667 0.00 1 0.0000 4.40 123 0.8841

Jackson, MS 43.70 110 0.7899 124.30 139 1.0000 16.10 137 0.9855 7.30 129 0.9275 Jefferson Davis, 19.87 88 0.6304 28.78 84 0.6014 0.00 1 0.0000 0.00 1 0.0000 LA Jefferson, LA 33.00 101 0.7246 47.00 101 0.7246 12.00 134 0.9638 11.10 136 0.9783 … Continued

143

Table 9. Continued.

Wind Wind Gusts Gust Storm Surge Rainfall Rainfall County, State Speed Rank Rank Rank Rank Vulv. (mph) Vuln. Surge (ft) Vuln. (in) Vuln. (mph)

Lafayette, LA 29.00 92 0.6594 39.00 97 0.6957 0.00 1 0.0000 0.21 91 0.6522

Lafourche, LA 77.00 138 0.9928 95.00 134 0.9638 8.00 131 0.9420 3.94 121 0.8696

Lamar, MS 32.00 100 0.7174 49.00 102 0.7319 0.00 1 0.0000 3.49 119 0.8551

Livingston, LA 62.41 130 0.9348 75.16 121 0.8696 0.00 1 0.0000 2.32 112 0.8043

Marion, MS 34.00 103 0.7391 49.00 102 0.7319 0.00 1 0.0000 1.04 98 0.7029

Mobile, AL 65.60 132 0.9493 82.90 126 0.9058 11.45 132 0.9493 3.80 120 0.8623

144 Monroe, AL 54.34 120 0.8623 66.59 112 0.8043 0.00 1 0.0000 1.39 100 0.7174

Okaloosa, FL 38.00 106 0.7609 52.90 105 0.7536 4.52 127 0.9130 1.77 106 0.7609

Orleans, LA 69.00 135 0.9710 97.80 135 0.9710 11.80 133 0.9565 12.49 139 1.0000

Pearl River, MS 40.50 107 0.7681 67.00 113 0.8116 0.00 1 0.0000 3.05 118 0.8478

Pike, MS 54.15 119 0.8551 73.08 120 0.8623 0.00 1 0.0000 6.76 128 0.9203

Plaquemines, LA 84.00 139 1.0000 107.00 137 0.9855 14.14 135 0.9710 11.10 136 0.9783 Pointe Coupee, 29.83 95 0.6812 35.50 92 0.6594 0.00 1 0.0000 1.60 103 0.7391 LA Rapides, LA 17.00 81 0.5797 33.00 89 0.6377 0.00 1 0.0000 0.51 95 0.6812 … Continued

144

Table 9. Continued.

Wind Wind Gusts Gust Storm Surge Rainfall Rainfall County, State Speed Rank Rank Rank Rank Vulv. (mph) Vuln. Surge (ft) Vuln. (in) Vuln. (mph)

Sabine, LA 19.00 85 0.6087 29.00 85 0.6087 0.00 1 0.0000 0.00 1 0.0000

Santa Rosa, FL 56.40 121 0.8696 69.00 115 0.8261 5.37 128 0.9203 2.71 116 0.8333

St. Bernard, LA 0.00 1 0.0000 0.00 1 0.0000 15.50 136 0.9783 11.10 136 0.9783

St. Charles, LA 44.00 111 0.7971 65.26 111 0.7971 0.00 1 0.0000 4.50 124 0.8913

St. Helena, LA 58.56 125 0.8986 69.93 117 0.8406 0.00 1 0.0000 2.40 113 0.8116

St. James, LA 71.57 137 0.9855 87.61 132 0.9493 0.00 1 0.0000 1.89 107 0.7681

145 St. John the 42.15 109 0.7826 61.11 108 0.7754 0.00 1 0.0000 4.50 124 0.8913 Baptist, LA St. Landry, LA 28.78 91 0.6522 37.70 95 0.6812 0.00 1 0.0000 0.16 90 0.6449

St. Martin, LA 30.57 97 0.6957 37.70 95 0.6812 0.00 1 0.0000 2.30 110 0.7899

St. Mary, LA 31.00 98 0.7029 39.00 97 0.6957 0.00 1 0.0000 0.03 88 0.6304 St. Tammany, 70.00 136 0.9783 100.00 136 0.9783 0.00 1 0.0000 10.01 135 0.9710 LA Stone, MS 45.90 112 0.8043 85.50 130 0.9348 0.00 1 0.0000 8.69 132 0.9493

Tangipahoa, LA 40.51 108 0.7754 57.40 106 0.7609 0.00 1 0.0000 8.51 131 0.9420

Terrebonne, LA 51.00 116 0.8333 69.00 115 0.8261 0.00 1 0.0000 3.94 121 0.8696 … Continued

145

Table 9. Continued.

Wind Wind Gusts Gust Storm Surge Rainfall Rainfall County, State Speed Rank Rank Rank Rank Vulv. (mph) Vuln. Surge (ft) Vuln. (in) Vuln. (mph)

Vermilion, LA 17.00 81 0.5797 23.00 80 0.5725 0.00 1 0.0000 0.02 85 0.6087

Vernon, LA 15.00 80 0.5725 25.00 83 0.5942 0.00 1 0.0000 0.00 1 0.0000

Walthall, MS 51.20 117 0.8406 68.08 114 0.8188 0.00 1 0.0000 0.90 97 0.6957

Washington, AL 61.41 128 0.9203 76.83 122 0.8768 0.00 1 0.0000 0.31 92 0.6594

Washington, LA 65.33 131 0.9420 92.07 133 0.9565 0.00 1 0.0000 9.22 134 0.9638 West Baton 29.53 94 0.6739 34.58 91 0.6522 0.00 1 0.0000 1.40 101 0.7246 Rouge, LA

146 West Feliciana, 29.25 93 0.6667 33.75 90 0.6449 0.00 1 0.0000 1.60 103 0.7391 LA Wilkinson, MS 61.16 127 0.9130 85.00 129 0.9275 0.00 1 0.0000 0.63 96 0.6884

146

Figure 10. Hurricane Risks of Gulf Coast Counties Associated with Hurricane Rita at

Time of Landfall.

147

Wind Wind Gusts Gust Storm Surge Rainfall Rainfall County, State Speed Rank Rank Rank Rank Vuln. (mph) Vuln. Surge (ft) Vuln. (in) Vuln. (mph)

Acadia, LA 54.09 133 0.9565 73.65 132 0.9493 0.00 1 0.0000 8.33 128 0.9203

Aransas, TX 17.00 69 0.4928 24.00 67 0.4783 0.00 1 0.0000 0.00 1 0.0000

Ascension, LA 40.29 117 0.8406 51.27 110 0.7899 0.00 1 0.0000 10.11 136 0.9783

Assumption, LA 41.91 120 0.8623 54.22 112 0.8043 0.00 1 0.0000 4.91 104 0.7464

Austin, TX 33.00 98 0.7029 46.00 97 0.6957 0.00 1 0.0000 0.00 1 0.0000

Avoyelles, LA 43.96 125 0.8986 65.51 128 0.9203 0.00 1 0.0000 16.00 139 1.0000

Beauregard, LA 35.67 109 0.7826 62.14 123 0.8841 0.00 1 0.0000 13.61 138 0.9928

148 Bee, TX 18.09 76 0.5435 24.33 73 0.5217 0.00 1 0.0000 0.00 1 0.0000

Brazoria, TX 32.22 96 0.6884 43.73 92 0.6594 0.00 1 0.0000 0.49 91 0.6522

Brooks, TX 15.00 66 0.4710 19.00 65 0.4638 0.00 1 0.0000 0.00 1 0.0000

Calcasieu, LA 66.00 137 0.9855 95.51 137 0.9855 0.00 1 0.0000 9.16 129 0.9275

Calhoun, TX 26.00 82 0.5870 34.00 78 0.5580 0.00 1 0.0000 0.00 1 0.0000 … Continued

Table 10. Hurricane Rita Hazard Values and Vulnerabilities by County.

148

Table 10. Continued.

Wind Wind Gusts Gust Storm Surge Rainfall Rainfall County, State Speed Rank Rank Rank Rank Vuln. (mph) Vuln. Surge (ft) Vuln. (in) Vuln. (mph)

Cameron, LA 72.50 138 0.9928 108.17 139 1.0000 8.11 138 0.9928 9.16 129 0.9275

Cameron, TX 16.00 67 0.4783 0.00 1 0.0000 0.00 1 0.0000 0.00 1 0.0000

Chambers, TX 44.88 126 0.9058 62.14 123 0.8841 0.00 1 0.0000 2.35 97 0.6957

Colorado, TX 30.57 91 0.6522 42.15 87 0.6232 0.00 1 0.0000 0.00 1 0.0000

DeWitt, TX 31.77 94 0.6739 46.07 99 0.7101 0.00 1 0.0000 0.00 1 0.0000

Duval, TX 13.00 61 0.4348 25.22 76 0.5435 0.00 1 0.0000 0.00 1 0.0000

149 East Baton 41.43 118 0.8478 58.69 119 0.8551 0.00 1 0.0000 9.30 133 0.9565

Rouge, LA East Feliciana, 39.86 114 0.8188 55.25 115 0.8261 0.00 1 0.0000 5.78 115 0.8261 LA Evangeline, LA 26.47 83 0.5942 48.33 102 0.7319 0.00 1 0.0000 7.93 125 0.8986

Fayette, TX 25.00 81 0.5797 41.00 84 0.6014 0.00 1 0.0000 0.00 1 0.0000

Fort Bend, TX 37.98 112 0.8043 50.63 108 0.7754 0.00 1 0.0000 0.64 93 0.6667

Galveston, TX 44.88 126 0.9058 62.14 123 0.8841 4.58 127 0.9130 0.49 91 0.6522

Goliad, TX 39.86 114 0.8188 63.46 127 0.9130 0.00 1 0.0000 0.00 1 0.0000

Harris, TX 44.88 126 0.9058 60.99 122 0.8768 0.00 1 0.0000 2.35 97 0.6957 … Continued

149

Table 10. Continued.

Wind Wind Gusts Gust Storm Surge Rainfall Rainfall County, State Speed Rank Rank Rank Rank Vuln. (mph) Vuln. Surge (ft) Vuln. (in) Vuln. (mph)

Hidalgo, TX 14.00 63 0.4493 18.00 63 0.4493 0.00 1 0.0000 0.00 1 0.0000

Iberia, LA 21.86 78 0.5580 59.84 121 0.8696 0.00 1 0.0000 9.21 131 0.9420

Iberville, LA 40.28 116 0.8333 57.54 116 0.8333 0.00 1 0.0000 5.00 107 0.7681

Jackson, TX 32.35 97 0.6957 48.39 104 0.7464 0.00 1 0.0000 0.00 1 0.0000

Jasper, TX 29.00 88 0.6304 41.00 84 0.6014 0.00 1 0.0000 8.13 126 0.9058 Jefferson Davis, 43.73 122 0.8768 71.35 131 0.9420 0.00 1 0.0000 9.75 134 0.9638 LA

150 Jefferson, LA 34.52 104 0.7464 42.58 88 0.6304 7.34 135 0.9710 5.21 108 0.7754

Jefferson, TX 80.55 139 1.0000 104.72 138 0.9928 7.93 137 0.9855 5.37 111 0.7971

Jim Hogg, TX 14.00 63 0.4493 19.00 65 0.4638 0.00 1 0.0000 0.00 1 0.0000

Jim Wells, TX 17.00 69 0.4928 25.00 75 0.5362 0.00 1 0.0000 0.00 1 0.0000

Kenedy, TX 17.00 69 0.4928 24.00 67 0.4783 0.00 1 0.0000 0.00 1 0.0000

Kleburg, TX 17.00 69 0.4928 24.00 67 0.4783 0.00 1 0.0000 0.00 1 0.0000

Lafayette, LA 50.63 132 0.9493 58.69 119 0.8551 0.00 1 0.0000 6.24 118 0.8478

Lafourche, LA 43.73 122 0.8768 57.54 116 0.8333 5.00 128 0.9203 4.95 105 0.7536 … Continued

150

Table 10. Continued.

Wind Wind Gusts Gust Storm Surge Rainfall Rainfall County, State Speed Rank Rank Rank Rank Vuln. (mph) Vuln. Surge (ft) Vuln. (in) Vuln. (mph)

Lavaca, TX 30.18 90 0.6449 40.51 83 0.5942 0.00 1 0.0000 0.00 1 0.0000

Liberty, TX 33.00 98 0.7029 48.00 101 0.7246 0.00 1 0.0000 6.24 118 0.8478

Live Oak, TX 19.05 77 0.5507 24.62 74 0.5290 0.00 1 0.0000 0.00 1 0.0000

Livingston, LA 28.77 87 0.6232 40.28 82 0.5870 0.00 1 0.0000 6.94 120 0.8623

Matagorda, TX 33.00 98 0.7029 51.00 109 0.7826 4.53 126 0.9058 0.00 1 0.0000

Newton, TX 61.40 135 0.9710 81.92 135 0.9710 0.00 1 0.0000 7.60 122 0.8768

151 Nueces, TX 17.00 69 0.4928 24.00 67 0.4783 2.98 125 0.8986 0.00 1 0.0000

Orange, TX 65.59 136 0.9783 88.61 136 0.9783 0.00 1 0.0000 5.37 111 0.7971

Orleans, LA 34.52 104 0.7464 48.33 102 0.7319 6.50 129 0.9275 2.29 96 0.6884

Plaquemines, LA 34.52 104 0.7464 42.58 88 0.6304 7.34 135 0.9710 5.21 108 0.7754 Pointe Coupee, 38.84 113 0.8116 54.22 112 0.8043 0.00 1 0.0000 5.98 117 0.8406 LA Rapides, LA 49.48 130 0.9348 62.14 123 0.8841 0.00 1 0.0000 7.68 124 0.8913

Refugio, TX 17.00 69 0.4928 24.00 67 0.4783 0.00 1 0.0000 0.00 1 0.0000

Sabine, LA 32.00 95 0.6812 49.00 106 0.7609 0.00 1 0.0000 3.70 100 0.7174 … Continued

151

Table 10. Continued.

Wind Wind Gusts Gust Storm Surge Rainfall Rainfall County, State Speed Rank Rank Rank Rank Vuln. (mph) Vuln. Surge (ft) Vuln. (in) Vuln. (mph)

San Patricio, TX 17.00 69 0.4928 24.00 67 0.4783 0.00 1 0.0000 0.00 1 0.0000

St. Bernard, LA 34.52 104 0.7464 42.58 88 0.6304 0.00 1 0.0000 5.21 108 0.7754

St. Charles, LA 28.41 85 0.6087 37.55 80 0.5725 6.50 129 0.9275 4.95 105 0.7536

St. Helena, LA 28.58 86 0.6159 38.84 81 0.5797 0.00 1 0.0000 1.60 95 0.6812

St. James, LA 41.91 120 0.8623 54.22 112 0.8043 0.00 1 0.0000 7.60 122 0.8768 St. John the 28.25 84 0.6014 36.41 79 0.5652 6.50 129 0.9275 12.42 137 0.9855 Baptist, LA

152 St. Landry, LA 46.03 129 0.9275 70.20 130 0.9348 0.00 1 0.0000 9.85 135 0.9710

St. Martin, LA 22.42 80 0.5725 41.91 86 0.6159 0.00 1 0.0000 8.13 126 0.9058

St. Mary, LA 21.86 78 0.5580 43.73 92 0.6594 11.95 139 1.0000 4.00 102 0.7319 St. Tammany, 34.52 104 0.7464 43.73 92 0.6594 6.50 129 0.9275 0.66 94 0.6739 LA Starr, TX 14.00 63 0.4493 18.00 63 0.4493 0.00 1 0.0000 0.00 1 0.0000

Tangipahoa, LA 33.37 102 0.7319 44.88 96 0.6884 6.50 129 0.9275 5.65 114 0.8188

Terrebonne, LA 43.73 122 0.8768 57.54 116 0.8333 7.10 134 0.9638 3.90 101 0.7246

Tyler, TX 49.48 130 0.9348 75.95 134 0.9638 0.00 1 0.0000 9.25 132 0.9493 … Continued

152

Table 10. Continued.

Wind Wind Gusts Gust Storm Surge Rainfall Rainfall County, State Speed Rank Rank Rank Rank Vuln. (mph) Vuln. Surge (ft) Vuln. (in) Vuln. (mph)

Vermilion, LA 54.09 133 0.9565 73.65 132 0.9493 0.00 1 0.0000 7.39 121 0.8696

Vernon, LA 29.92 89 0.6377 49.48 107 0.7681 0.00 1 0.0000 4.60 103 0.7391

Victoria, TX 41.43 118 0.8478 67.90 129 0.9275 0.00 1 0.0000 0.00 1 0.0000

Waller, TX 33.00 98 0.7029 46.00 97 0.6957 0.00 1 0.0000 0.15 90 0.6449

Washington, LA 33.37 102 0.7319 47.18 100 0.7174 0.00 1 0.0000 2.61 99 0.7101

Washington, TX 31.00 92 0.6594 43.00 91 0.6522 0.00 1 0.0000 0.04 89 0.6377

153 Webb, TX 13.00 61 0.4348 25.41 77 0.5507 0.00 1 0.0000 0.00 1 0.0000

West Baton 37.55 111 0.7971 51.27 110 0.7899 0.00 1 0.0000 5.91 116 0.8333 Rouge, LA West Feliciana, 36.41 110 0.7899 48.63 105 0.7536 0.00 1 0.0000 5.39 113 0.8116 LA Wharton, TX 31.00 92 0.6594 44.00 95 0.6812 0.00 1 0.0000 0.00 1 0.0000

Willacy, TX 16.00 67 0.4783 0.00 1 0.0000 0.00 1 0.0000 0.00 1 0.0000

153

Figure 11. Hurricane Risks of Gulf Coast Counties Associated with Hurricane Wilma at

Time of Landfall.

154

Wind Wind Gusts Gust Storm Surge Rainfall Rainfall County, State Speed Rank Rank Rank Rank Vuln. (mph) Vuln. Surge (ft) Vuln. (in) Vuln. (mph)

Charlotte, FL 51.79 137 0.9855 70.20 136 0.9783 0.00 1 0.0000 6.00 135 0.9710

Citrus, FL 28.77 129 0.9275 39.13 125 0.8986 0.00 1 0.0000 1.00 123 0.8841

Collier, FL 40.28 135 0.9710 80.55 138 0.9928 7.00 139 1.0000 6.15 137 0.9855

DeSoto, FL 39.13 134 0.9638 64.44 134 0.9638 0.00 1 0.0000 4.60 133 0.9565

Glades, FL 49.10 136 0.9783 65.51 135 0.9710 0.00 1 0.0000 6.08 136 0.9783

Hardee, FL 23.02 123 0.8841 52.94 133 0.9565 0.00 1 0.0000 1.40 125 0.8986

Hernando, FL 28.77 129 0.9275 39.13 125 0.8986 0.00 1 0.0000 1.80 126 0.9058

155 Hillsborough, FL 24.17 124 0.8913 42.58 128 0.9203 0.00 1 0.0000 2.55 130 0.9348

Lee, FL 62.14 138 0.9928 79.40 137 0.9855 0.00 1 0.0000 5.44 134 0.9638

Manatee, FL 24.17 124 0.8913 49.48 131 0.9420 0.00 1 0.0000 3.60 131 0.9420

Marion, FL 26.47 126 0.9058 39.13 125 0.8986 0.00 1 0.0000 1.32 124 0.8913

Monroe, FL 71.35 139 1.0000 108.17 139 1.0000 6.43 138 0.9928 2.02 128 0.9203 … Continued

Table 11. Hurricane Wilma Hazard Values and Vulnerabilities by County.

155

Table 11. Continued.

Wind Wind Gusts Gust Storm Surge Rainfall Rainfall County, State Speed Rank Rank Rank Rank Vuln. (mph) Vuln. Surge (ft) Vuln. (in) Vuln. (mph)

Pasco, FL 28.58 127 0.9130 37.81 123 0.8841 0.00 1 0.0000 2.10 129 0.9275

Pinellas, FL 37.98 133 0.9565 49.48 131 0.9420 0.00 1 0.0000 1.91 127 0.9130

Polk, FL 35.67 131 0.9420 46.03 129 0.9275 0.00 1 0.0000 7.34 138 0.9928

Sarasota, FL 35.67 131 0.9420 48.33 130 0.9348 0.00 1 0.0000 7.45 139 1.0000

Sumter, FL 28.58 127 0.9130 37.81 123 0.8841 0.00 1 0.0000 4.06 132 0.9493

156

Figure 12. Hurricane Risks of Gulf Coast Counties Associated with Hurricane Gustav at

Time of Landfall.

157

Wind Wind Gusts Gust Storm Surge Rainfall Rainfall County, State Speed Rank Rank Rank Rank Vuln. (mph) Vuln. Surge (ft) Vuln. (in) Vuln. (mph)

Acadia, LA 42.58 115 0.8261 58.86 120 0.8623 0.00 1 0.0000 0.16 88 0.6304

Amite, MS 32.22 100 0.7174 54.09 113 0.8116 0.00 1 0.0000 0.01 78 0.5580

Aransas, TX 15.00 67 0.4783 17.00 69 0.4928 0.00 1 0.0000 0.00 1 0.0000

Ascension, LA 62.35 136 0.9783 70.63 127 0.9130 0.00 1 0.0000 3.75 116 0.8333

Assumption, LA 60.99 133 0.9565 90.91 137 0.9855 0.00 1 0.0000 1.13 102 0.7319

Avoyelles, LA 34.81 110 0.7899 42.99 97 0.6957 0.00 1 0.0000 4.99 124 0.8913

Baldwin, AL 29.92 96 0.6884 46.03 104 0.7464 0.00 1 0.0000 4.28 122 0.8768

158 Beauregard, LA 26.47 86 0.6159 42.58 95 0.6812 0.00 1 0.0000 3.62 114 0.8188

Bee, TX 16.00 71 0.5072 24.00 78 0.5580 0.00 1 0.0000 0.18 91 0.6522

Brazoria, TX 15.00 67 0.4783 22.00 73 0.5217 0.00 1 0.0000 0.00 1 0.0000

Brooks, TX 9.00 63 0.4493 0.00 1 0.0000 0.00 1 0.0000 0.00 1 0.0000

Calcasieu, LA 33.37 106 0.7609 46.03 104 0.7464 1.17 125 0.8986 1.45 106 0.7609 … Continued

Table 12. Hurricane Gustav Hazard Values and Vulnerabilities by County.

158

Table 12. Continued.

Wind Wind Gusts Gust Storm Surge Rainfall Rainfall County, State Speed Rank Rank Rank Rank Vuln. (mph) Vuln. Surge (ft) Vuln. (in) Vuln. (mph)

Calhoun, TX 17.00 72 0.5145 22.00 74 0.5290 0.00 1 0.0000 0.01 78 0.5580

Cameron, LA 49.00 123 0.8841 72.00 128 0.9203 0.00 1 0.0000 0.00 1 0.0000

Cameron, TX 15.00 67 0.4783 0.00 1 0.0000 0.00 1 0.0000 0.12 84 0.6014

Chambers, TX 29.92 96 0.6884 39.13 90 0.6449 0.00 1 0.0000 0.00 1 0.0000 East Baton 60.99 133 0.9565 90.91 137 0.9855 0.00 1 0.0000 7.28 136 0.9783 Rouge, LA East Feliciana, 54.01 129 0.9275 77.78 134 0.9638 0.00 1 0.0000 6.41 132 0.9493 LA

159 Evangeline, LA 40.89 114 0.8188 42.99 99 0.7101 0.00 1 0.0000 3.90 118 0.8478

Fayette, TX 15.00 67 0.4783 23.00 76 0.5435 0.00 1 0.0000 0.00 1 0.0000

Fort Bend, TX 22.00 83 0.5942 30.00 82 0.5870 0.00 1 0.0000 0.12 84 0.6014

Galveston, TX 34.52 108 0.7754 44.88 100 0.7174 0.87 124 0.8913 0.00 1 0.0000

George, MS 46.70 121 0.8696 64.06 123 0.8841 0.00 1 0.0000 4.01 120 0.8623

Hancock, MS 54.09 130 0.9348 66.75 125 0.8986 9.89 138 0.9928 3.69 115 0.8261

Harris, TX 26.00 85 0.6087 30.00 82 0.5870 0.00 1 0.0000 0.12 84 0.6014

Harrison, MS 51.79 126 0.9058 73.65 131 0.9420 7.30 134 0.9638 3.09 111 0.7971 … Continued

159

Table 12. Continued.

Wind Wind Gusts Gust Storm Surge Rainfall Rainfall County, State Speed Rank Rank Rank Rank Vuln. (mph) Vuln. Surge (ft) Vuln. (in) Vuln. (mph)

Hidalgo, TX 8.00 61 0.4348 0.00 1 0.0000 0.00 1 0.0000 0.03 82 0.5870

Iberia, LA 27.62 92 0.6594 41.43 93 0.6667 0.00 1 0.0000 0.03 82 0.5870

Iberville, LA 27.49 90 0.6449 38.47 89 0.6377 0.00 1 0.0000 1.24 103 0.7391

Jackson, MS 31.07 99 0.7101 46.03 104 0.7464 5.69 132 0.9493 4.61 123 0.8841

Jasper, TX 22.00 83 0.5942 34.00 85 0.6087 0.00 1 0.0000 2.15 108 0.7754 Jefferson Davis, 42.58 117 0.8406 58.87 121 0.8696 0.00 1 0.0000 1.41 105 0.7536 LA

160 Jefferson, LA 66.75 137 0.9855 86.31 136 0.9783 4.82 130 0.9348 5.02 125 0.8986

Jefferson, TX 29.92 96 0.6884 39.13 90 0.6449 1.90 126 0.9058 0.24 93 0.6667

Jim Hogg, TX 9.00 63 0.4493 0.00 1 0.0000 0.00 1 0.0000 0.00 1 0.0000

Jim Wells, TX 11.00 65 0.4638 20.00 71 0.5072 0.00 1 0.0000 0.01 78 0.5580

Kenedy, TX 17.00 72 0.5145 0.00 1 0.0000 0.00 1 0.0000 0.00 1 0.0000

Kleburg, TX 14.00 66 0.4710 18.00 70 0.5000 0.00 1 0.0000 0.01 78 0.5580

Lafayette, LA 51.79 126 0.9058 77.10 133 0.9565 0.00 1 0.0000 6.62 134 0.9638

Lafourche, LA 49.48 125 0.8986 56.39 116 0.8333 7.83 135 0.9710 5.02 125 0.8986 … Continued

160

Table 12. Continued.

Wind Wind Gusts Gust Storm Surge Rainfall Rainfall County, State Speed Rank Rank Rank Rank Vuln. (mph) Vuln. Surge (ft) Vuln. (in) Vuln. (mph)

Lamar, MS 33.37 106 0.7609 47.18 107 0.7681 0.00 1 0.0000 6.42 133 0.9565

Livingston, LA 58.51 131 0.9420 65.89 124 0.8913 0.00 1 0.0000 4.13 121 0.8696

Marion, MS 32.80 104 0.7464 50.64 111 0.7971 0.00 1 0.0000 0.62 99 0.7101

Matagorda, TX 18.00 78 0.5580 24.00 78 0.5580 0.00 1 0.0000 0.00 1 0.0000

Mobile, AL 36.82 113 0.8116 50.63 110 0.7899 3.50 128 0.9203 3.01 110 0.7899

Nueces, TX 17.00 72 0.5145 21.00 72 0.5145 0.00 1 0.0000 0.23 92 0.6594

161 Orange, TX 18.00 78 0.5580 31.00 84 0.6014 0.00 1 0.0000 0.00 1 0.0000

Orleans, LA 46.00 120 0.8623 72.00 128 0.9203 10.35 139 1.0000 5.89 131 0.9420

Pearl River, MS 43.73 118 0.8478 56.97 118 0.8478 0.00 1 0.0000 0.16 88 0.6304

Pike, MS 32.22 100 0.7174 54.09 113 0.8116 0.00 1 0.0000 8.92 139 1.0000

Plaquemines, LA 44.88 119 0.8551 70.19 126 0.9058 8.30 136 0.9783 5.02 125 0.8986 Pointe Coupee, 27.44 89 0.6377 37.22 88 0.6304 0.00 1 0.0000 0.17 90 0.6449 LA Rapides, LA 42.58 115 0.8261 60.99 122 0.8768 0.00 1 0.0000 8.73 138 0.9928

Sabine, LA 34.81 110 0.7899 42.99 97 0.6957 0.00 1 0.0000 1.45 106 0.7609 … Continued

161

Table 12. Continued.

Wind Wind Gusts Gust Storm Surge Rainfall Rainfall County, State Speed Rank Rank Rank Rank Vuln. (mph) Vuln. Surge (ft) Vuln. (in) Vuln. (mph)

San Patricio, TX 17.00 72 0.5145 21.00 73 0.5217 0.00 1 0.0000 0.00 1 0.0000

St. Bernard, LA 59.84 132 0.9493 78.25 135 0.9710 9.53 137 0.9855 5.02 125 0.8986

St. Charles, LA 52.94 128 0.9203 75.95 132 0.9493 4.68 129 0.9275 7.34 137 0.9855

St. Helena, LA 46.89 122 0.8768 57.90 119 0.8551 0.00 1 0.0000 5.43 130 0.9348

St. James, LA 83.00 138 0.9928 0.00 1 0.0000 0.00 1 0.0000 0.91 101 0.7246 St. John the 83.00 138 0.9928 0.00 1 0.0000 0.00 1 0.0000 2.40 109 0.7826 Baptist, LA

162 St. Landry, LA 35.77 112 0.8043 44.93 102 0.7319 0.00 1 0.0000 3.90 118 0.8478

St. Martin, LA 27.55 91 0.6522 39.86 92 0.6594 0.00 1 0.0000 1.24 103 0.7391

St. Mary, LA 27.62 92 0.6594 41.43 93 0.6667 3.39 127 0.9130 0.47 97 0.6957 St. Tammany, 28.77 95 0.6812 56.39 116 0.8333 5.00 131 0.9420 6.97 135 0.9710 LA Stone, MS 19.56 81 0.5797 48.33 108 0.7754 0.00 1 0.0000 3.48 113 0.8116

Tangipahoa, LA 21.86 82 0.5870 42.58 95 0.6812 0.00 1 0.0000 0.15 87 0.6232

Terrebonne, LA 61.00 135 0.9710 91.00 139 1.0000 6.74 133 0.9565 5.02 125 0.8986

Vermilion, LA 49.00 123 0.8841 72.00 128 0.9203 0.00 1 0.0000 0.31 94 0.6739 … Continued

162

Table 12. Continued.

Wind Wind Gusts Gust Storm Surge Rainfall Rainfall County, State Speed Rank Rank Rank Rank Vuln. (mph) Vuln. Surge (ft) Vuln. (in) Vuln. (mph)

Vernon, LA 32.22 100 0.7174 44.88 100 0.7174 0.00 1 0.0000 3.85 117 0.8406

Victoria, TX 17.00 72 0.5145 23.00 76 0.5435 0.00 1 0.0000 0.00 1 0.0000

Walthall, MS 32.80 104 0.7464 50.64 111 0.7971 0.00 1 0.0000 0.62 99 0.7101

Washington, AL 34.80 109 0.7826 45.77 103 0.7391 0.00 1 0.0000 3.10 112 0.8043

Washington, LA 28.41 94 0.6739 50.35 109 0.7826 0.00 1 0.0000 0.32 95 0.6812

Washington, TX 18.00 78 0.5580 26.00 81 0.5797 0.00 1 0.0000 0.00 1 0.0000

163 Webb, TX 8.00 61 0.4348 0.00 1 0.0000 0.00 1 0.0000 0.00 1 0.0000

West Baton 27.39 88 0.6304 36.11 87 0.6232 0.00 1 0.0000 0.38 96 0.6884 Rouge, LA West Feliciana, 27.35 87 0.6232 35.12 86 0.6159 0.00 1 0.0000 0.53 98 0.7029 LA Wharton, TX 17.00 72 0.5145 24.00 78 0.5580 0.00 1 0.0000 0.00 1 0.0000

Wilkinson, MS 32.22 100 0.7174 54.09 113 0.8116 0.00 1 0.0000 0.00 1 0.0000

163

Figure 13. Hurricane Risks of Gulf Coast Counties Associated with Hurricane Ike at

Time of Landfall.

164

Wind Wind Gusts Gust Storm Surge Rainfall Rainfall County, State Speed Rank Rank Rank Rank Vuln. (mph) Vuln. Surge (ft) Vuln. (in) Vuln. (mph)

Acadia, LA 33.00 109 0.7826 49.00 111 0.7971 0.00 1 0.0000 1.69 118 0.8478

Ascension, LA 29.45 96 0.6884 41.85 94 0.6739 0.00 1 0.0000 0.39 97 0.6957

Assumption, LA 29.90 97 0.6957 43.70 101 0.7246 0.00 1 0.0000 1.54 114 0.8188

Austin, TX 45.34 128 0.9203 55.12 125 0.8986 0.00 1 0.0000 1.27 109 0.7826

Avoyelles, LA 29.00 87 0.6232 45.00 105 0.7536 0.00 1 0.0000 0.39 97 0.6957

Beauregard, LA 33.00 109 0.7826 52.00 118 0.8478 0.00 1 0.0000 1.58 116 0.8333

Brazoria, TX 42.60 126 0.9058 64.40 130 0.9348 6.25 133 0.9565 9.31 138 0.9928

165 Calcasieu, LA 41.00 124 0.8913 48.00 109 0.7826 0.00 1 0.0000 2.35 123 0.8841

Calhoun, TX 28.80 86 0.6159 36.80 82 0.5870 3.86 131 0.9420 0.77 104 0.7464

Cameron, LA 70.00 137 0.9855 86.00 135 0.9710 9.80 135 0.9710 2.63 126 0.9058

Chambers, TX 52.90 132 0.9493 66.70 132 0.9493 17.00 139 1.0000 6.00 133 0.9565

Colorado, TX 38.05 120 0.8623 53.49 122 0.8768 0.00 1 0.0000 0.00 1 0.0000 … Continued

Table 13. Hurricane Ike Hazard Values and Vulnerabilities by County (Coastal).

165

Table 13. Continued.

Wind Wind Gusts Gust Storm Surge Rainfall Rainfall County, State Speed Rank Rank Rank Rank Vuln. (mph) Vuln. Surge (ft) Vuln. (in) Vuln. (mph)

DeWitt, TX 35.74 115 0.8261 47.58 108 0.7754 0.00 1 0.0000 0.00 1 0.0000 East Baton 33.00 109 0.7826 48.00 109 0.7826 0.00 1 0.0000 0.41 99 0.7101 Rouge, LA East Feliciana, 29.00 87 0.6232 42.50 95 0.6812 0.00 1 0.0000 0.69 103 0.7391 LA Evangeline, LA 26.00 80 0.5725 43.00 98 0.7029 0.00 1 0.0000 4.33 130 0.9348

Fayette, TX 41.57 125 0.8986 49.34 115 0.8261 0.00 1 0.0000 0.00 1 0.0000

Fort Bend, TX 40.91 123 0.8841 60.34 128 0.9203 0.00 1 0.0000 5.91 132 0.9493

166 Galveston, TX 46.00 129 0.9275 65.60 131 0.9420 11.17 137 0.9855 7.81 136 0.9783

Goliad, TX 28.43 84 0.6014 34.79 80 0.5725 0.00 1 0.0000 0.00 1 0.0000

Harris, TX 50.09 131 0.9420 62.39 129 0.9275 0.00 1 0.0000 10.92 139 1.0000

Iberia, LA 36.00 116 0.8333 52.00 118 0.8478 0.00 1 0.0000 2.03 122 0.8768

Iberville, LA 33.00 109 0.7826 49.00 111 0.7971 0.00 1 0.0000 0.60 102 0.7319

Jackson, TX 36.81 117 0.8406 50.09 116 0.8333 0.00 1 0.0000 0.36 96 0.6884

Jasper, TX 61.16 134 0.9638 81.03 134 0.9638 0.00 1 0.0000 2.40 124 0.8913 Jefferson Davis, 33.00 109 0.7826 49.00 111 0.7971 0.00 1 0.0000 1.42 112 0.8043 LA … Continued

166

Table 13. Continued.

Wind Wind Gusts Gust Storm Surge Rainfall Rainfall County, State Speed Rank Rank Rank Rank Vuln. (mph) Vuln. Surge (ft) Vuln. (in) Vuln. (mph)

Jefferson, LA 30.00 101 0.7246 40.00 85 0.6087 12.54 138 0.9928 5.45 131 0.9420

Jefferson, TX 70.00 137 0.9855 95.00 138 0.9928 0.00 1 0.0000 9.21 137 0.9855

Lafayette, LA 33.00 109 0.7826 49.00 111 0.7971 0.00 1 0.0000 1.96 120 0.8623

Lafourche, LA 23.00 79 0.5652 26.00 78 0.5580 0.00 1 0.0000 0.03 88 0.6304

Lavaca, TX 36.85 118 0.8478 50.61 117 0.8406 0.00 1 0.0000 0.00 1 0.0000

Liberty, TX 65.33 135 0.9710 87.61 136 0.9783 0.00 1 0.0000 7.01 135 0.9710

167 Livingston, LA 29.00 87 0.6232 40.00 85 0.6087 0.00 1 0.0000 0.55 101 0.7246

Matagorda, TX 38.00 119 0.8551 52.90 121 0.8696 6.00 132 0.9493 1.40 111 0.7971

Newton, TX 65.33 135 0.9710 87.61 137 0.9855 0.00 1 0.0000 2.40 124 0.8913

Orange, TX 70.00 137 0.9855 95.00 138 0.9928 0.00 1 0.0000 6.69 134 0.9638

Orleans, LA 28.00 80 0.5725 40.00 85 0.6087 0.00 1 0.0000 1.05 106 0.7609

Plaquemines, LA 30.00 101 0.7246 40.00 85 0.6087 0.00 1 0.0000 0.00 1 0.0000 Pointe Coupee, 26.00 80 0.5725 43.00 98 0.7029 0.00 1 0.0000 0.07 91 0.6522 LA Rapides, LA 29.00 87 0.6232 45.00 105 0.7536 0.00 1 0.0000 1.97 121 0.8696 … Continued

167

Table 13. Continued.

Wind Wind Gusts Gust Storm Surge Rainfall Rainfall County, State Speed Rank Rank Rank Rank Vuln. (mph) Vuln. Surge (ft) Vuln. (in) Vuln. (mph)

Sabine, LA 32.00 108 0.7754 47.00 107 0.7681 0.00 1 0.0000 1.34 110 0.7899

St. Bernard, LA 30.00 101 0.7246 40.00 85 0.6087 0.00 1 0.0000 0.00 1 0.0000

St. Charles, LA 30.00 101 0.7246 40.82 93 0.6667 0.00 1 0.0000 1.05 106 0.7609

St. Helena, LA 29.00 87 0.6232 42.50 95 0.6812 0.00 1 0.0000 0.15 92 0.6594

St. James, LA 30.00 101 0.7246 26.11 79 0.5652 0.00 1 0.0000 0.25 94 0.6739 St. John the 30.00 101 0.7246 39.32 84 0.6014 0.00 1 0.0000 0.00 1 0.0000 Baptist, LA

168 St. Landry, LA 26.00 80 0.5725 43.00 98 0.7029 0.00 1 0.0000 1.18 108 0.7754

St. Martin, LA 29.90 97 0.6957 43.70 101 0.7246 0.00 1 0.0000 1.54 114 0.8188

St. Mary, LA 29.90 97 0.6957 43.70 101 0.7246 6.75 134 0.9638 1.46 113 0.8116 St. Tammany, 22.00 78 0.5580 37.00 83 0.5942 0.00 1 0.0000 0.01 87 0.6232 LA Tangipahoa, LA 29.00 87 0.6232 40.00 85 0.6087 0.00 1 0.0000 0.17 93 0.6667

Terrebonne, LA 29.90 97 0.6957 43.70 101 0.7246 0.00 1 0.0000 0.48 100 0.7174

Tyler, TX 57.45 133 0.9565 75.16 133 0.9565 0.00 1 0.0000 4.24 129 0.9275

Vermilion, LA 40.00 122 0.8768 55.00 124 0.8913 9.91 136 0.9783 0.82 105 0.7536 … Continued

168

Table 13. Continued.

Wind Wind Gusts Gust Storm Surge Rainfall Rainfall County, State Speed Rank Rank Rank Rank Vuln. (mph) Vuln. Surge (ft) Vuln. (in) Vuln. (mph)

Vernon, LA 31.00 107 0.7681 54.00 123 0.8841 0.00 1 0.0000 1.60 117 0.8406

Victoria, TX 28.60 85 0.6087 35.74 81 0.5797 0.00 1 0.0000 0.00 1 0.0000

Waller, TX 47.58 130 0.9348 58.54 127 0.9130 0.00 1 0.0000 3.96 128 0.9203

Washington, LA 29.00 87 0.6232 40.00 85 0.6087 0.00 1 0.0000 0.05 90 0.6449

Washington, TX 43.35 127 0.9130 52.06 120 0.8623 0.00 1 0.0000 2.75 127 0.9130 West Baton 29.00 87 0.6232 40.00 85 0.6087 0.00 1 0.0000 0.04 89 0.6377 Rouge, LA

169 West Feliciana, 29.00 87 0.6232 42.50 95 0.6812 0.00 1 0.0000 0.33 95 0.6812

LA Wharton, TX 39.39 121 0.8696 56.72 126 0.9058 0.00 1 0.0000 1.95 119 0.8551

169

Figure 14. Hurricane Risks of Inland Counties along Hurricane Ike’s Track.

170

Wind Storm Wind Gusts Gust Surge Rainfall Rainfall County, State Speed Rank Rank Surge Rank Rank Vuln. (mph) Vuln. Vuln. (in) Vuln. (mph) (ft)

Anderson, TX 26.00 19 0.2903 39.00 15 0.2258 0.00 0 0.0000 3.96 58 0.9194

Baxter, AR 23.00 10 0.1452 43.00 21 0.3226 0.00 0 0.0000 1.85 31 0.4839

Bond, IL 0.00 1 0.0000 0.00 1 0.0000 0.00 0 0.0000 0.53 12 0.1774

Boone, AR 28.00 22 0.3387 51.00 34 0.5323 0.00 0 0.0000 2.98 48 0.7581

Camp, TX 15.00 2 0.0161 29.00 2 0.0161 0.00 0 0.0000 0.00 1 0.0000

Carroll, IN 0.00 1 0.0000 0.00 1 0.0000 0.00 0 0.0000 0.73 16 0.2419

Cass, IN 34.00 36 0.5645 44.00 24 0.3710 0.00 0 0.0000 0.89 18 0.2742

171 Chambers, TX 52.90 43 0.6774 66.70 41 0.6452 17.00 63 1.0000 6.00 60 0.9516

Cherokee, TX 57.54 45 0.7097 0.00 1 0.0000 0.00 0 0.0000 0.27 7 0.0968

Clinton, IL 37.00 38 0.5968 49.00 33 0.5161 0.00 0 0.0000 0.67 15 0.2258

Coles, IL 38.00 41 0.6452 59.00 40 0.6290 0.00 0 0.0000 1.11 22 0.3387

Dekalb, IN 25.00 15 0.2258 36.00 8 0.1129 0.00 0 0.0000 1.51 26 0.4032 … Continued

Table 14. Hurricane Ike Hazard Values and Vulnerabilities by County (Inland).

171

Table 14. Continued.

Wind Storm Wind Gusts Gust Surge Rainfall Rainfall County, State Speed Rank Rank Surge Rank Rank Vuln. (mph) Vuln. Vuln. (in) Vuln. (mph) (ft)

Dent, MO 19.00 6 0.0806 0.00 1 0.0000 0.00 0 0.0000 3.76 56 0.8871

Douglas, IL 0.00 1 0.0000 0.00 1 0.0000 0.00 0 0.0000 1.77 30 0.4677

Edgar, IL 23.00 10 0.1452 37.00 9 0.1290 0.00 0 0.0000 0.00 1 0.0000

Effingham, IL 37.00 38 0.5968 51.00 34 0.5323 0.00 0 0.0000 0.40 11 0.1613

Fayette, IL 0.00 1 0.0000 0.00 1 0.0000 0.00 0 0.0000 0.59 13 0.1935

Fountain, IN 57.54 45 0.7097 0.00 1 0.0000 0.00 0 0.0000 0.02 3 0.0323

172 Franklin, AR 23.00 10 0.1452 43.00 21 0.3226 0.00 0 0.0000 1.58 28 0.4355

Harris, TX 56.00 44 0.6935 74.00 42 0.6613 0.00 0 0.0000 10.92 63 1.0000

Hillsdale, MI 18.00 5 0.0645 30.00 3 0.0323 0.00 0 0.0000 3.92 57 0.9032

Houston, TX 29.00 25 0.3871 37.00 9 0.1290 0.00 0 0.0000 0.34 10 0.1452

Howell, MO 37.00 38 0.5968 53.00 36 0.5645 0.00 0 0.0000 2.72 47 0.7419

Iron, MO 46.03 42 0.6613 0.00 1 0.0000 0.00 0 0.0000 1.05 21 0.3226

Jefferson, MO 26.00 19 0.2903 41.00 19 0.2903 0.00 0 0.0000 3.64 54 0.8548

Johnson, AR 16.00 3 0.0323 34.00 6 0.0806 0.00 0 0.0000 3.24 51 0.8065 … Continued

172

Table 14. Continued.

Wind Storm Wind Gusts Gust Surge Rainfall Rainfall County, State Speed Rank Rank Surge Rank Rank Vuln. (mph) Vuln. Vuln. (in) Vuln. (mph) (ft)

Kosciusko, IN 22.00 9 0.1290 31.00 4 0.0484 0.00 0 0.0000 1.56 27 0.4194

Lenawee, MI 25.00 15 0.2258 41.00 19 0.2903 0.00 0 0.0000 3.45 53 0.8387

Logan, AR 16.00 3 0.0323 34.00 6 0.0806 0.00 0 0.0000 1.72 29 0.4516

Marion, AR 36.00 37 0.5806 53.00 36 0.5645 0.00 0 0.0000 1.85 31 0.4839

McCurtain, OK 28.00 22 0.3387 38.00 12 0.1774 0.00 0 0.0000 2.15 37 0.5806

Miami, IN 0.00 1 0.0000 0.00 1 0.0000 0.00 0 0.0000 0.74 17 0.2581

173 Monroe, IL 0.00 1 0.0000 0.00 1 0.0000 0.00 0 0.0000 0.30 9 0.1290

Montgomery, TX 97.82 47 0.7419 0.00 1 0.0000 0.00 0 0.0000 9.94 62 0.9839

Newton, AR 32.00 30 0.4677 46.00 28 0.4355 0.00 0 0.0000 3.02 49 0.7742

Noble, IN 0.00 1 0.0000 0.00 1 0.0000 0.00 0 0.0000 1.93 34 0.5323

Ozark, MO 0.00 1 0.0000 0.00 1 0.0000 0.00 0 0.0000 2.26 38 0.5968

Polk, AR 25.00 15 0.2258 38.00 12 0.1774 0.00 0 0.0000 4.60 59 0.9355

Red River, TX 21.00 7 0.0968 32.00 5 0.0645 0.00 0 0.0000 2.44 40 0.6290

Reynolds, MO 0.00 1 0.0000 0.00 1 0.0000 0.00 0 0.0000 2.40 39 0.6129 … Continued

173

Table 14. Continued.

Wind Storm Wind Gusts Gust Surge Rainfall Rainfall County, State Speed Rank Rank Surge Rank Rank Vuln. (mph) Vuln. Vuln. (in) Vuln. (mph) (ft)

San Jacinto, TX 33.00 32 0.5000 57.00 38 0.5968 0.00 0 0.0000 2.60 43 0.6774

Scott, AR 21.00 7 0.0968 38.00 12 0.1774 0.00 0 0.0000 2.65 44 0.6935

Searcy, AR 32.00 30 0.4677 46.00 28 0.4355 0.00 0 0.0000 2.12 36 0.5645

Shannon, MO 0.00 1 0.0000 0.00 1 0.0000 0.00 0 0.0000 1.98 35 0.5484

Shelby, IL 0.00 1 0.0000 0.00 1 0.0000 0.00 0 0.0000 1.92 33 0.5161

Smith, TX 30.00 26 0.4032 46.00 28 0.4355 0.00 0 0.0000 2.69 46 0.7258

174 St. Clair, IL 33.00 32 0.5000 48.00 32 0.5000 0.00 0 0.0000 3.70 55 0.8710

St. Francois, MO 23.00 10 0.1452 39.00 15 0.2258 0.00 0 0.0000 1.25 23 0.3548 Ste. Genevieve, 23.00 10 0.1452 39.00 15 0.2258 0.00 0 0.0000 1.41 25 0.3871 MO Steuben, IN 0.00 1 0.0000 0.00 1 0.0000 0.00 0 0.0000 2.68 45 0.7097

Texas, MO 33.00 32 0.5000 44.00 24 0.3710 0.00 0 0.0000 7.42 61 0.9677

Tippecanoe, IN 31.00 29 0.4516 43.00 21 0.3226 0.00 0 0.0000 0.98 20 0.3065

Titus, TX 28.00 22 0.3387 45.00 27 0.4194 0.00 0 0.0000 0.29 8 0.1129

Trinity, TX 33.00 32 0.5000 57.00 38 0.5968 0.00 0 0.0000 2.59 42 0.6613 … Continued

174

Table 14. Continued.

Wind Storm Wind Gusts Gust Surge Rainfall Rainfall County, State Speed Rank Rank Surge Rank Rank Vuln. (mph) Vuln. Vuln. (in) Vuln. (mph) (ft)

Upshur, TX 25.00 15 0.2258 39.00 15 0.2258 0.00 0 0.0000 0.14 5 0.0645

Vermilion, IL 30.00 26 0.4032 46.00 28 0.4355 0.00 0 0.0000 2.53 41 0.6452

Vermillion, IN 0.00 1 0.0000 0.00 1 0.0000 0.00 0 0.0000 0.66 14 0.2097

Wabash, IN 0.00 1 0.0000 0.00 1 0.0000 0.00 0 0.0000 0.89 18 0.2742

Warren, IN 0.00 1 0.0000 0.00 1 0.0000 0.00 0 0.0000 0.06 4 0.0484

Washington, MO 0.00 1 0.0000 0.00 1 0.0000 0.00 0 0.0000 3.08 50 0.7903

175 Washtenaw, MI 26.00 19 0.2903 37.00 9 0.1290 0.00 0 0.0000 3.28 52 0.8226

Whitley, IN 0.00 1 0.0000 0.00 1 0.0000 0.00 0 0.0000 0.19 6 0.0806

Williams, OH 30.00 26 0.4032 44.00 24 0.3710 0.00 0 0.0000 1.30 24 0.3710

175

Figure 15. Hurricane Risks of Gulf Coast Counties Associated with Hurricane Ida at

Time of Landfall.

176

Wind Wind Gusts Gust Storm Surge Rainfall Rainfall County, State Speed Rank Rank Rank Rank Vuln. (mph) Vuln. Surge (ft) Vuln. (in) Vuln. (mph)

Acadia, LA 0.00 1 0.0000 32.35 97 0.6957 0.00 1 0.0000 0.00 1 0.0000

Amite, MS 22.54 97 0.6957 26.11 79 0.5652 0.00 1 0.0000 0.04 98 0.7029

Ascension, LA 0.00 1 0.0000 37.33 108 0.7754 0.00 1 0.0000 0.00 1 0.0000

Assumption, LA 23.43 102 0.7319 33.69 100 0.7174 0.00 1 0.0000 0.00 1 0.0000

Avoyelles, LA 25.41 111 0.7971 34.15 101 0.7246 0.00 1 0.0000 0.00 1 0.0000

Baldwin, AL 44.00 137 0.9855 44.00 126 0.9058 0.00 1 0.0000 6.75 138 0.9928

Bay, FL 26.50 117 0.8406 47.20 137 0.9855 2.64 134 0.9638 2.11 119 0.8551

177 Beauregard, LA 14.00 80 0.5725 20.00 71 0.5072 0.00 1 0.0000 0.15 105 0.7536

Calcasieu, LA 18.00 86 0.6159 26.00 77 0.5507 0.00 1 0.0000 0.21 106 0.7609

Calhoun, FL 0.00 1 0.0000 0.00 1 0.0000 0.00 1 0.0000 1.90 118 0.8478

Clarke, AL 42.15 136 0.9783 42.15 122 0.8768 0.00 1 0.0000 2.84 126 0.9058

Covington, AL 23.00 99 0.7101 39.00 114 0.8188 0.00 1 0.0000 2.65 123 0.8841 … Continued

Table 15. Hurricane Ida Hazard Values and Vulnerabilities by County.

177

Table 15. Continued.

Wind Wind Gusts Gust Storm Surge Rainfall Rainfall County, State Speed Rank Rank Rank Rank Vuln. (mph) Vuln. Surge (ft) Vuln. (in) Vuln. (mph) East Baton Rouge, 26.00 113 0.8116 39.00 114 0.8188 0.00 1 0.0000 0.00 1 0.0000 LA East Feliciana, LA 26.11 115 0.8261 37.70 110 0.7899 0.00 1 0.0000 0.00 1 0.0000

Escambia, AL 40.51 135 0.9710 40.51 120 0.8623 0.00 1 0.0000 5.50 137 0.9855

Escambia, FL 31.00 125 0.8986 44.00 126 0.9058 0.00 1 0.0000 6.75 138 0.9928

Evangeline, LA 15.00 82 0.5870 22.00 74 0.5290 0.00 1 0.0000 0.00 1 0.0000

Franklin, FL 32.20 131 0.9420 40.30 119 0.8551 3.11 135 0.9710 3.15 127 0.9130

178 Gadsden, FL 31.13 128 0.9203 37.57 109 0.7826 0.00 1 0.0000 1.22 114 0.8188

Geneva, AL 22.00 92 0.6594 31.00 92 0.6594 0.00 1 0.0000 2.25 120 0.8623

George, MS 28.60 122 0.8768 42.96 124 0.8913 0.00 1 0.0000 3.34 129 0.9275

Gulf, FL 0.00 1 0.0000 0.00 1 0.0000 0.00 1 0.0000 3.92 134 0.9638

Hancock, MS 25.00 105 0.7536 44.00 126 0.9058 3.19 136 0.9783 2.42 122 0.8768

Harrison, MS 31.10 126 0.9058 48.30 138 0.9928 0.00 1 0.0000 3.24 128 0.9203

Holmes, FL 0.00 1 0.0000 43.05 125 0.8986 0.00 1 0.0000 0.06 102 0.7319

Iberville, LA 0.00 1 0.0000 28.59 85 0.6087 0.00 1 0.0000 0.00 1 0.0000 … Continued

178

Table 15. Continued.

Wind Wind Gusts Gust Storm Surge Rainfall Rainfall County, State Speed Rank Rank Rank Rank Vuln. (mph) Vuln. Surge (ft) Vuln. (in) Vuln. (mph)

Jackson, FL 20.00 90 0.6449 24.00 76 0.5435 0.00 1 0.0000 1.53 117 0.8406

Jackson, MS 28.80 123 0.8841 44.90 130 0.9348 3.59 137 0.9855 3.42 131 0.9420 Jefferson Davis, 15.41 84 0.6014 21.65 73 0.5217 0.00 1 0.0000 0.00 1 0.0000 LA Jefferson, FL 19.60 88 0.6304 26.50 80 0.5725 0.00 1 0.0000 1.42 115 0.8261

Jefferson, LA 37.00 133 0.9565 0.00 1 0.0000 1.57 129 0.9275 0.00 1 0.0000

Lafayette, LA 25.00 105 0.7536 36.00 106 0.7609 0.00 1 0.0000 0.00 1 0.0000

179 Lafourche, LA 26.00 113 0.8116 40.00 118 0.8478 1.76 130 0.9348 0.00 1 0.0000

Lamar, MS 24.00 103 0.7391 39.00 114 0.8188 0.00 1 0.0000 0.83 111 0.7971

Leon, FL 22.00 92 0.6594 31.00 92 0.6594 0.00 1 0.0000 0.38 108 0.7754

Liberty, FL 31.64 129 0.9275 38.86 113 0.8116 0.00 1 0.0000 1.16 112 0.8043

Livingston, LA 0.00 1 0.0000 36.21 107 0.7681 0.00 1 0.0000 0.01 96 0.6884

Marion, MS 23.03 101 0.7246 28.59 85 0.6087 0.00 1 0.0000 0.64 109 0.7826

Mobile, AL 28.00 121 0.8696 47.00 136 0.9783 0.00 1 0.0000 4.98 136 0.9783

Monroe, AL 39.04 134 0.9638 39.04 117 0.8406 0.00 1 0.0000 3.37 130 0.9348 … Continued

179

Table 15. Continued.

Wind Wind Gusts Gust Storm Surge Rainfall Rainfall County, State Speed Rank Rank Rank Rank Vuln. (mph) Vuln. Surge (ft) Vuln. (in) Vuln. (mph)

Okaloosa, FL 27.60 119 0.8551 46.00 134 0.9638 0.00 1 0.0000 4.12 135 0.9710

Orleans, LA 36.80 132 0.9493 44.90 130 0.9348 2.35 133 0.9565 0.13 104 0.7464

Pearl River, MS 25.22 108 0.7754 42.15 122 0.8768 0.00 1 0.0000 2.25 120 0.8623

Pike, MS 22.00 92 0.6594 29.00 89 0.6377 0.00 1 0.0000 0.01 96 0.6884

Plaquemines, LA 59.80 139 1.0000 73.60 139 1.0000 6.53 139 1.0000 1.16 112 0.8043

Pointe Coupee, LA 0.00 1 0.0000 28.42 84 0.6014 0.00 1 0.0000 0.00 1 0.0000

180 Rapides, LA 15.00 82 0.5870 22.00 74 0.5290 0.00 1 0.0000 0.00 1 0.0000

Sabine, LA 14.00 80 0.5725 21.00 72 0.5145 0.00 1 0.0000 0.00 1 0.0000

Santa Rosa, FL 25.00 105 0.7536 41.00 121 0.8696 0.00 1 0.0000 3.74 132 0.9493

St. Bernard, LA 45.00 138 0.9928 0.00 1 0.0000 5.62 138 0.9928 0.00 1 0.0000

St. Charles, LA 0.00 1 0.0000 34.32 102 0.7319 0.00 1 0.0000 0.05 99 0.7101

St. Helena, LA 0.00 1 0.0000 35.21 105 0.7536 0.00 1 0.0000 0.05 99 0.7101

St. James, LA 26.11 115 0.8261 38.59 112 0.8043 0.00 1 0.0000 0.00 1 0.0000 St. John the 24.00 103 0.7391 33.52 99 0.7101 0.00 1 0.0000 0.00 1 0.0000 Baptist, LA … Continued

180

Table 15. Continued.

Wind Wind Gusts Gust Storm Surge Rainfall Rainfall County, State Speed Rank Rank Rank Rank Vuln. (mph) Vuln. Surge (ft) Vuln. (in) Vuln. (mph)

St. Landry, LA 25.22 108 0.7754 35.02 104 0.7464 0.00 1 0.0000 0.00 1 0.0000

St. Martin, LA 0.00 1 0.0000 28.78 88 0.6304 0.00 1 0.0000 0.00 1 0.0000

St. Mary, LA 18.00 86 0.6159 29.00 89 0.6377 0.00 1 0.0000 0.00 1 0.0000

St. Tammany, LA 25.30 110 0.7899 32.20 96 0.6884 1.90 131 0.9420 0.78 110 0.7899

Stone, MS 30.65 124 0.8913 45.99 133 0.9565 0.00 1 0.0000 3.80 133 0.9565

Tangipahoa, LA 21.00 91 0.6522 31.00 92 0.6594 0.00 1 0.0000 0.00 1 0.0000

181 Taylor, FL 19.60 88 0.6304 26.50 80 0.5725 0.00 1 0.0000 1.42 115 0.8261

Terrebonne, LA 23.00 99 0.7101 34.50 103 0.7391 0.00 1 0.0000 0.00 1 0.0000

Vermilion, LA 22.00 92 0.6594 33.00 98 0.7029 0.00 1 0.0000 0.00 1 0.0000

Vernon, LA 16.00 85 0.6087 29.00 89 0.6377 0.00 1 0.0000 0.00 1 0.0000

Wakulla, FL 31.10 126 0.9058 38.00 111 0.7971 2.00 132 0.9493 0.36 107 0.7681

Walthall, MS 22.54 97 0.6957 28.78 87 0.6232 0.00 1 0.0000 0.00 1 0.0000

Walton, FL 32.00 130 0.9348 46.00 134 0.9638 0.00 1 0.0000 2.67 124 0.8913

Washington, AL 27.89 120 0.8623 44.83 129 0.9275 0.00 1 0.0000 2.70 125 0.8986 … Continued

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Table 15. Continued.

Wind Wind Gusts Gust Storm Surge Rainfall Rainfall County, State Speed Rank Rank Rank Rank Vuln. (mph) Vuln. Surge (ft) Vuln. (in) Vuln. (mph)

Washington, FL 26.55 118 0.8478 45.01 132 0.9493 0.00 1 0.0000 0.06 102 0.7319

Washington, LA 25.48 112 0.8043 31.64 95 0.6812 0.00 1 0.0000 0.00 1 0.0000 West Baton Rouge, 0.00 1 0.0000 28.26 83 0.5942 0.00 1 0.0000 0.00 1 0.0000 LA West Feliciana, LA 0.00 1 0.0000 28.13 82 0.5870 0.00 1 0.0000 0.00 1 0.0000

Wilkinson, MS 22.00 92 0.6594 26.00 77 0.5507 0.00 1 0.0000 0.05 99 0.7101

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Figure 16. Hurricane Risks of Gulf Coast Counties Associated with Hurricane Isaac at

Time of Landfall.

183

Wind Wind Gusts Gust Storm Surge Rainfall Rainfall County, State Speed Rank Rank Rank Rank Vuln. (mph) Vuln. Surge (ft) Vuln. (in) Vuln. (mph)

Acadia, LA 35.02 114 0.8188 46.61 107 0.7681 0.00 1 0.0000 0.00 1 0.0000

Amite, MS 23.50 86 0.6159 42.00 97 0.6957 0.00 1 0.0000 0.22 101 0.7246

Ascension, LA 25.00 89 0.6377 47.00 110 0.7899 0.00 1 0.0000 4.22 118 0.8478

Assumption, LA 33.00 108 0.7754 43.00 98 0.7029 0.00 1 0.0000 4.29 119 0.8551

Avoyelles, LA 32.56 107 0.7681 46.86 108 0.7754 0.00 1 0.0000 0.00 1 0.0000

Baldwin, AL 37.00 118 0.8478 48.00 114 0.8188 2.81 127 0.9130 4.34 120 0.8623

Bay, FL 25.00 89 0.6377 40.00 89 0.6377 0.00 1 0.0000 0.00 1 0.0000

184 Beauregard, LA 22.00 81 0.5797 31.00 80 0.5725 0.00 1 0.0000 0.00 1 0.0000

Calcasieu, LA 31.00 103 0.7391 43.00 98 0.7029 0.00 1 0.0000 0.00 1 0.0000

Calhoun, FL 15.00 71 0.5072 23.00 71 0.5072 0.00 1 0.0000 0.00 1 0.0000

Clarke, AL 34.95 113 0.8116 43.69 101 0.7246 0.00 1 0.0000 0.49 105 0.7536

Covington, AL 15.00 71 0.5072 26.00 76 0.5435 0.00 1 0.0000 0.02 95 0.6812 … Continued

Table 16. Hurricane Isaac Hazard Values and Vulnerabilities by County.

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Table 16. Continued.

Wind Wind Gusts Gust Storm Surge Rainfall Rainfall County, State Speed Rank Rank Rank Rank Vuln. (mph) Vuln. Surge (ft) Vuln. (in) Vuln. (mph) East Baton 38.00 119 0.8551 54.00 121 0.8696 0.00 1 0.0000 10.26 132 0.9493 Rouge, LA East Feliciana, 36.81 117 0.8406 51.07 119 0.8551 0.00 1 0.0000 1.12 110 0.7899 LA Escambia, AL 33.31 109 0.7826 40.26 94 0.6739 0.00 1 0.0000 0.90 106 0.7609

Escambia, FL 25.80 97 0.6957 46.90 109 0.7826 2.65 126 0.9058 0.00 1 0.0000

Evangeline, LA 31.00 103 0.7391 44.00 102 0.7319 0.00 1 0.0000 0.00 1 0.0000

Franklin, FL 22.00 81 0.5797 31.00 80 0.5725 0.00 1 0.0000 0.00 1 0.0000

185 Gadsden, FL 17.00 74 0.5290 24.00 74 0.5290 0.00 1 0.0000 0.00 1 0.0000

Geneva, AL 17.00 74 0.5290 23.00 71 0.5072 0.00 1 0.0000 0.01 94 0.6739

George, MS 47.58 128 0.9203 61.32 128 0.9203 0.00 1 0.0000 7.19 127 0.9130

Hancock, MS 50.70 130 0.9348 66.60 130 0.9348 9.00 136 0.9783 18.30 137 0.9855

Harrison, MS 44.90 126 0.9058 56.40 122 0.8768 6.50 132 0.9493 17.70 136 0.9783

Holmes, FL 25.00 89 0.6377 40.00 89 0.6377 0.00 1 0.0000 0.00 1 0.0000

Iberia, LA 0.00 1 0.0000 0.00 1 0.0000 0.00 1 0.0000 1.10 108 0.7754

Iberville, LA 20.25 78 0.5580 26.95 77 0.5507 0.00 1 0.0000 0.09 97 0.6957 … Continued

185

Table 16. Continued.

Wind Wind Gusts Gust Storm Surge Rainfall Rainfall County, State Speed Rank Rank Rank Rank Vuln. (mph) Vuln. Surge (ft) Vuln. (in) Vuln. (mph)

Jackson, FL 15.00 71 0.5072 23.00 71 0.5072 0.00 1 0.0000 0.00 1 0.0000

Jackson, MS 52.90 134 0.9638 70.20 134 0.9638 6.50 132 0.9493 22.20 139 1.0000 Jefferson Davis, 31.00 103 0.7391 46.00 106 0.7609 0.00 1 0.0000 0.00 1 0.0000 LA Jefferson, LA 66.40 139 1.0000 84.80 139 1.0000 8.50 135 0.9710 4.39 121 0.8696

Lafayette, LA 34.00 110 0.7899 52.00 120 0.8623 0.00 1 0.0000 0.00 1 0.0000

Lafourche, LA 57.50 136 0.9783 77.10 136 0.9783 4.00 130 0.9348 10.90 133 0.9565

186 Lamar, MS 22.00 81 0.5797 38.00 86 0.6159 0.00 1 0.0000 3.74 117 0.8406

Leon, FL 17.00 74 0.5290 37.00 84 0.6014 0.00 1 0.0000 0.00 1 0.0000

Liberty, FL 17.00 74 0.5290 24.00 74 0.5290 0.00 1 0.0000 0.00 1 0.0000

Livingston, LA 25.00 89 0.6377 47.00 110 0.7899 0.00 1 0.0000 0.93 107 0.7681

Marion, MS 23.50 86 0.6159 41.00 95 0.6812 0.00 1 0.0000 2.49 114 0.8188

Mobile, AL 36.20 116 0.8333 47.90 113 0.8116 4.27 131 0.9420 4.82 124 0.8913

Monroe, AL 32.02 106 0.7609 37.53 85 0.6087 0.00 1 0.0000 0.15 99 0.7101

Okaloosa, FL 28.20 101 0.7246 35.30 83 0.5942 3.41 129 0.9275 0.00 1 0.0000 … Continued

186

Table 16. Continued.

Wind Wind Gusts Gust Storm Surge Rainfall Rainfall County, State Speed Rank Rank Rank Rank Vuln. (mph) Vuln. Surge (ft) Vuln. (in) Vuln. (mph)

Orleans, LA 59.80 137 0.9855 76.00 135 0.9710 11.00 137 0.9855 20.70 138 0.9928

Pearl River, MS 45.83 127 0.9130 58.46 124 0.8913 0.00 1 0.0000 2.86 116 0.8333

Pike, MS 25.00 89 0.6377 44.00 102 0.7319 0.00 1 0.0000 6.69 126 0.9058 Plaquemines, 54.10 135 0.9710 84.00 138 0.9928 12.00 139 1.0000 9.40 131 0.9420 LA Pointe Coupee, 39.00 120 0.8623 59.00 125 0.8986 0.00 1 0.0000 2.10 112 0.8043 LA Rapides, LA 30.00 102 0.7319 45.00 104 0.7464 0.00 1 0.0000 0.00 1 0.0000

187 Sabine, LA 23.00 85 0.6087 32.00 82 0.5870 0.00 1 0.0000 0.25 102 0.7319

Santa Rosa, FL 27.00 99 0.7101 39.80 88 0.6304 2.50 125 0.8986 0.00 1 0.0000

St. Bernard, LA 63.90 138 0.9928 78.70 137 0.9855 11.10 138 0.9928 11.00 134 0.9638

St. Charles, LA 51.23 131 0.9420 66.81 131 0.9420 0.00 1 0.0000 9.27 130 0.9348

St. Helena, LA 25.00 89 0.6377 47.00 110 0.7899 0.00 1 0.0000 0.00 1 0.0000

St. James, LA 51.23 131 0.9420 66.81 131 0.9420 0.00 1 0.0000 0.08 96 0.6884 St. John the 51.23 131 0.9420 66.81 131 0.9420 0.00 1 0.0000 7.86 128 0.9203 Baptist, LA St. Landry, LA 40.85 124 0.8913 49.28 116 0.8333 0.00 1 0.0000 0.00 1 0.0000 … Continued

187

Table 16. Continued.

Wind Wind Gusts Gust Storm Surge Rainfall Rainfall County, State Speed Rank Rank Rank Rank Vuln. (mph) Vuln. Surge (ft) Vuln. (in) Vuln. (mph)

St. Martin, LA 21.65 80 0.5725 29.67 78 0.5580 0.00 1 0.0000 0.09 97 0.6957

St. Mary, LA 21.00 79 0.5652 30.00 79 0.5652 0.00 1 0.0000 1.10 108 0.7754 St. Tammany, 39.10 123 0.8841 57.50 123 0.8841 7.00 134 0.9638 11.90 135 0.9710 LA Stone, MS 41.22 125 0.8986 50.36 117 0.8406 0.00 1 0.0000 4.42 122 0.8768

Tangipahoa, LA 34.00 110 0.7899 51.00 118 0.8478 0.00 1 0.0000 1.26 111 0.7971

Terrebonne, LA 49.50 129 0.9275 65.60 129 0.9275 3.00 128 0.9203 8.30 129 0.9275

188 Vermilion, LA 36.00 115 0.8261 49.00 115 0.8261 0.00 1 0.0000 0.00 1 0.0000

Vernon, LA 28.00 100 0.7174 39.00 87 0.6232 0.00 1 0.0000 0.28 103 0.7391

Walthall, MS 23.50 86 0.6159 41.00 95 0.6812 0.00 1 0.0000 2.49 114 0.8188

Walton, FL 26.00 98 0.7029 40.00 89 0.6377 0.00 1 0.0000 0.00 1 0.0000

Washington, AL 34.31 112 0.8043 43.61 100 0.7174 0.00 1 0.0000 0.32 104 0.7464

Washington, FL 25.00 89 0.6377 40.00 89 0.6377 0.00 1 0.0000 0.00 1 0.0000

Washington, LA 25.00 89 0.6377 45.00 104 0.7464 0.00 1 0.0000 5.37 125 0.8986 West Baton 39.00 120 0.8623 59.00 125 0.8986 0.00 1 0.0000 0.20 100 0.7174 Rouge, LA … Continued

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Table 16. Continued.

Wind Wind Gusts Gust Storm Surge Rainfall Rainfall County, State Speed Rank Rank Rank Rank Vuln. (mph) Vuln. Surge (ft) Vuln. (in) Vuln. (mph) West Feliciana, 39.00 120 0.8623 59.00 125 0.8986 0.00 1 0.0000 2.10 112 0.8043 LA Wilkinson, MS 22.00 81 0.5797 40.00 89 0.6377 0.00 1 0.0000 4.52 123 0.8841

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Chapter 5

Conclusions and Future Work

5.1 Conclusion

Preparation before any disastrous event, like a hurricane, is important for ensuring the safety of all involved parties. United States counties on the coast of the Gulf of

Mexico have already begun making the areas more resilient to hurricane damage by creating relief organizations and emergency management offices, building hospitals, designating evacuation routes, opening evacuation shelters, setting building codes on structures closer to shore, and working as a community to recover from such events.

In this study, data was collected regarding the demographics, relief opportunities, and damage taken by hurricanes Charley, Ivan, Dennis, Katrina, Rita, Wilma, Gustav,

Ike, Ida, and Isaac in several counties. This data was then analyzed and assigned different numerical values depending upon ranks and, later, the amount of risk associated with each county. The resultant risks were then converted from a numerical value in a spreadsheet to a color value on a map to visually represent the areas most at risk in each hurricane case. In each hurricane scenario, the counties with the highest combination of demographic vulnerabilities and hurricane impacts with respect to lower overall resiliencies were calculated as having the highest hurricane risk. There were, however, several counties that continuously received high risks associated with hurricanes despite

190

being affected less than other counties in the study. Upon further analysis, it is clear that these counties have up to a 3-unit difference in their initial vulnerabilities and resiliencies, which becomes amplified by hazards posed by hurricanes that impact the areas in close proximity to these counties. The counties that appeared in the top five most at risk more than twice were Marion County, Florida, Polk County, Florida, Hancock

County, Mississippi, Orleans Parish, Louisiana, Pike County, Mississippi, St. Landry

Parish, Louisiana, Iberia Parish, Louisiana, and Rapides Parish, Louisiana.

Marion County appeared in the counties most at risk for hurricanes Charley,

Dennis, and Wilma. This county had an initial vulnerability of 3.85 units and a resiliency of 1.38 units. Once the sum of the hurricane parameters percentile ranks was multiplied by the initial vulnerability, the final vulnerabilities increased to 10.04 units for Charley,

6.66 units for Dennis, and 10.37 units for Wilma.

Polk County also appeared within the counties most at risk for hurricanes

Charley, Dennis, and Wilma. This county had an initial vulnerability of 3.47 units and a resiliency of 1.36 units. After applying the hurricane hazards to the equation, the overall vulnerabilities became 9.21 units for Charley, 6.39 units for Dennis, and 9.94 units for

Wilma.

Hancock County was among the highest risk counties for hurricanes Ivan, Dennis,

Katrina, Gustav, Ida, and Isaac. This county had an initial vulnerability of 3.08 units and a resiliency of 0.96 units. When the hurricane parameters were applied to the final equation, the overall vulnerabilities became 10.42 units for Ivan, 8.48 units for Dennis,

11.29 units for Katrina, 11.25 units for Gustav, 10.82 units for Ida, and 11.81 units for

Isaac. 191

Orleans Parish appeared in the counties most at risk for the most hurricane cases, including hurricanes Ivan, Dennis, Katrina, Rita, Gustav, Ida, and Isaac. This county had an initial vulnerability of 3.53 units and a resiliency of 1.44 units. Once the hurricane parameters were included in the final equation, the overall vulnerabilities became 12.10 units for Ivan, 10.64 units for Dennis, 13.76 units for Katrina, 10.92 units for Rita, 13.14 units for Gustav, 12.66 units for Ida, and 13.89 units for Isaac.

Pike County was among the highest risk counties for hurricane Ivan, Dennis,

Katrina, Ida, and Isaac. This county had an initial vulnerability of 4.05 units and a resiliency of 1.53 units. The product of the initial vulnerabilities with the respective hurricane hazards resulted in increases of overall vulnerabilities to 8.42 units for Ivan,

7.57 units for Dennis, 10.68 units for Katrina, 8.04 units for Ida, and 9.22 units for Isaac.

St. Landry Parish appeared in the counties most at risk for hurricanes Katrina,

Rita, Gustav, Ike, Ida, and Isaac. This county had an initial vulnerability of 4.07 units and a resiliency of 1.13 units. After applying the hurricane hazards to the final equation, the overall vulnerabilities became 8.04 units for Katrina, 11.52 units for Rita, 9.69 units for

Gustav, 8.34 units for Ike, 6.19 units for Ida, and 7.01 units for Isaac.

Iberia Parish was among the highest risk counties for hurricanes Katrina, Rita, and

Ike. This county had an initial vulnerability of 3.16 units and a resiliency of 0.97 units.

When the hurricane parameters were factored into the final vulnerability equation, these vulnerability values increased to 6.66 units for Katrina, 7.49 units for Rita, and 8.08 units for Ike.

Rapides Parish had the one of the highest risks associated with hurricanes Rita,

Gustav, and Ike. This county had an initial vulnerability of 3.60 units and a resiliency of 192

1.25 units. Once the hurricane parameters were included in the final vulnerability equation, the overall vulnerabilities became 9.76 units for Rita, 9.71 units for Gustav, and

8.09 units for Ike.

The results of this study showed a connection between the counties with the lowest resiliency values and the highest risks associated with each hurricane case. In each of the counties mentioned in previous paragraphs, the resiliency values are below 1.53 units. The highest resiliency value of the study area was 4.78 units, occurring in Walton

County, Florida. Many the counties, 119 out of 139, had resiliency values higher than those counties that occurred most frequently in the ten different hurricane cases. While it is true that the initial and final vulnerabilities played their roles in the final risk calculation, these values varied significantly depending upon what sorts of hazards the hurricanes posed on the counties. The resiliency variable did not change throughout the course of the study and was the overall determinate factor in the final calculation of risk.

For example, in the case of Hurricane Charley, both Hernando County, Florida and

Manatee County, Florida received overall risks of 3.65 units. Hernando County had an initial vulnerability of 3.39 units and a resiliency of 2.43 units. Manatee County had an initial vulnerability of 3.41 units and a resiliency of 2.71 units. Both counties had initial vulnerabilities within 0.02 units of each other. Their resiliencies, however, had a 0.28- unit difference. After factoring the hurricane hazards into the final equation, the overall vulnerabilities became 8.87 units for Hernando County and 9.90 units for Manatee

County. One would expect Manatee County, having the greatest overall vulnerability and only a slight difference in resiliency between the two counties, to receive the highest risk

193

value. However, it is because of this slightly higher resiliency value that the overall risk of the county ends up the same as Hernando County.

Despite the differences in results county by county and case by case, the study produced a numerical representation of the risk potential posed on each county, without the threat of a hurricane, which would benefit the communities within each coastal county in preparation and mitigation. Knowing where your county ranks in certain aspects compared to surrounding coastal counties would allow local and state governments to instate guidelines or codes in areas that may be lacking. For instance,

Hancock County, Mississippi, has over 2-million residents, but only nine hospital and relief organizations. In the event of a hurricane, each of these places would need to accommodate a maximum of 329,700 people. It is unlikely that all residents would find themselves needing medical care or other assistance, but it would still be difficult to fit half as many people into these facilities if there was ever a need for it. Having a more up- to-date and more specific analysis of the initial social risks posed on each county would benefit the preparation and mitigation techniques for future hurricanes, much like the construction of the HSDRRS after the failure of the levees during Hurricane Katrina.

5.2 Future Work

This study involved straightforward collection of data points and utilization of equations to analyze the data in an appropriate way. However, shortcomings occurred both in collecting the data and the analysis that could be improved upon in future work.

Collection of hurricane data created complications in this study. The post-storm reports and daily summaries for stations across the coast occasionally lacked records of 194

the hurricane parameters. This was especially true in instances where stations lost connection with their instruments due to strong winds and heavy rains. In instances where this occurred, the peak wind speeds and gusts were not recorded before the instrument failed, so the highest measurement up until the time of failure was used in this analysis.

Also, some coastal counties do not have buoys or ships to measure the storm surges associated with hurricanes. In this case, they use high water marks on sides of buildings to determine the height. While this method is appropriate, discrepancies during the surveying process may increase variability and potential for error in storm surge data.

Additionally, the use of the number of tornadoes produced in each hurricane case in each county where this data exists would improve the hurricane hazard calculations. Having more reliable sources of hurricane data would have assisted in making the risk analyses as accurate as possible.

Availability of information on evacuation procedures for each county varied depending upon the county. Some counties had their shelters and routes clearly displayed on their websites while others displayed only one or the other. Often, the evacuation routes were given by each county. Counties did not have clear evacuation shelters listed and would not provide their residents with access to this information until the days and hours leading up to the hurricane, during a public announcement of the open shelters within specific areas. Similarly, the proximity to shore restrictions were not consistent on a state-wide basis. Counties participating in these guidelines had different measurements that they followed while other counties either did not participate or did not publicly display the necessary information. This could be partly due to the variability of the coastal environments between the states. For example, much of the Louisiana coast is 195

uninhabited, or scarcely inhabited, wetlands. Due to the proximity of the wetlands to the coast, the populations in these areas have no choice but to build farther away from shore.

A rating system regarding the types of coastal environments contained within each county might provide more insight into why counties do or do not use this type of building code. Having more reliable and in depth access to these factors would have made the analysis more precise.

In terms of the variables themselves, this study could benefit from a weighting system. Each vulnerability, hazard, and resilience variable was given the same weight. Of course, each variable would not have the same weight associated with them in real circumstances. Unfortunately, no basis for a weight system was used due to the inability to create a fair scale. Creating a method for weighting these variables would help further predict the vulnerability. In addition to this weighting system, a method of non-linear analysis would improve the data as well. The work performed after the original study in accordance with the z-score calculations proved that the original one-point ranking system was not sufficient for this type of research because it did not accurately portray the vulnerabilities and resiliencies that each variable for each county presented. In future work, this type of method should be taken into consideration to produce the most accurate results.

Additionally, having access to the types of infrastructure in each county would make this hurricane analysis even more specific, especially regarding electrical, sewage, and other related systems. This information was considered, but costs a significant amount of money to obtain. States and counties are hesitant about giving this information out due questionable uses, resulting in a charge of several hundreds of dollars to obtain. 196

Having that funding would allow for the study of specific roadways, building structures within specific areas, sewer, and electric systems and how these play a role in the way communities can recover after a hurricane.

197

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