DOCSLIB.ORG

Improvement of Hurricane Risk Perceptions: Re-Analysis of a Hurricane Damage Index and Development of Spatial Damage Assessments

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Improvement of Hurricane Risk Perceptions: Re-Analysis of a Hurricane Damage Index and Development of Spatial Damage Assessments Improvement of hurricane risk perceptions: re-analysis of a hurricane damage index and development of spatial damage assessments Sandra N. Maina Academic Affiliation, Fall 2011: Senior, Florida Institute of Technology SOARS® Summer 2011 Science Research Mentor: James Done, Cindy Bruyere Writing and Communication Mentor: Cody Phillips ABSTRACT Implementation of potential damage assessment tools in hurricane-prone areas is an essential aspect of communicating accurate hurricane risk information. Several indices currently exist (e.g., Saffir-Simpson Hurricane Wind Scale) that effectively communicate storm strength; however, they are unable to quantify potential damage. The recently developed Maina Hurricane Index (MHI) addressed this deficiency and used a hurricane‟s central minimum pressure and radius of hurricane-force winds to create damage forecasts. The MHI was developed by analyzing data from 13 historical storms – a small sample size limited by the availability of storm size data. This work revisited the MHI using a larger data set, and results showed that central pressure is the best indicator of potential damage. By creating a single damage value per storm, the MHI aids in the decision-making of government and private agencies such as emergency managers, yet it does not convey storm impacts on individuals within a community. In response, this study also developed a methodology to generate spatial damage assessments that illustrate the relative damage distribution across a storm‟s path. A swath of maximum winds was first generated using a parametric wind field model, then translated to damage using a function based on wind speed and a measure of the directional change in wind. Through the ability to forecast both net damage of land falling storms and the distribution of damage across the storm‟s path, individual perceptions of hurricane risk can be unified to make way for safer preparation and evacuation decisions. The Significant Opportunities in Atmospheric Research and Science (SOARS) Program is managed by the University Corporation for Atmospheric Research (UCAR) with support from participating universities. SOARS is funded by the National Science Foundation, the National Oceanic and Atmospheric Administration (NOAA) Climate Program Office, the NOAA Oceans and Human Health Initiative, the Center for Multi-Scale Modeling of Atmospheric Processes at Colorado State University, and the Cooperative Institute for Research in Environmental Sciences. 1. Introduction During hurricane season, there is a need for effectively communicating storm risk and expected damage to the public. Forecasts of total potential damage are useful for groups such as insurance companies, emergency managers, and state governments. However, most of the general public is not interested in understanding and interpreting the meteorology and probabilities of storm damage. Instead, individuals are interested in the personal storm impact to their home and family. Unfortunately, there is no current system of quantifying damage for the individual. Therefore, the purpose of this study is to create a personal damage index via a spatial map of potential damage distribution. Such information can be used in hurricane preparation and evacuation decisions made by individuals A study conducted by Zhang et al. (2007) found that many people perform their own evaluation of hurricane risk when making evacuation decisions, a demonstration of the need for effective communication of hurricane forecasts and risk. Through a survey of 120 people affected by Hurricane Rita on the Texas Gulf Coast, Zhang et al. (2007) investigated perception of hurricane risk, evacuation decisions, and use of hurricane forecasts. It was shown that most people made preparation and/or evacuation decisions based on televised broadcasts of hurricane forecasts from the National Hurricane Center (NHC). Those who received information from the television were aware of the evolution of the hurricane and its relevant threat. In the United States, the NHC is the main source of the hurricane forecasts, where the Saffir-Simpson Hurricane Wind Scale (SSHWS) is used to convey a storm‟s forecast strength to the public. Zhang et al. (2007) showed that the public generally understands the information the SSHWS provides; however, the recognition and understanding of hurricane-related risks such as storm surge and flooding varies among individuals. As a result, people create their own risk perceptions resulting in self-made evacuation decisions, despite federal orders. Previous work by Maina (2010) concentrated on effective communication of hurricane risk to federal groups, for example, by initially questioning the accuracy of the SSHWS and ultimately developing a damage potential index, the Maina Hurricane Index (MHI). While the SSWHS uses one-minute sustained wind speeds to determine potential storm damage, the MHI uses a storm‟s minimum central pressure and radius of hurricane-force winds. The index also incorporates the number of housing units within an affected area to predict a storm‟s monetary damage anywhere along the coastal continental United States. One of the limitations of the MHI, however, was the lack of storm size data. Fortunately, a new data source has been discovered that resolves this problem, increasing the data set to 31 storms. As a result, the first portion of this study involves the validity of the MHI by re-evaluating the index using the new data set. The MHI addresses the need of disseminating accurate information to groups such as state governments but overlooks the public interest of personal impacts. Using the methods of hurricane wind field modeling presented by Vickery et al 2009, the approach used to develop the MHI can be adapted in the development of a spatial map of potential damage distribution across a storm. There is a three-step process outlined by Vickery et al. to create a hurricane wind field model. This process and its expansion to create the spatial map will be detailed later in the paper as the second portion of our study. 2. Re-Analysis of the MHI 2.1. Previous Findings Development of the MHI set the foundation for communicating hurricane risk through quantification of damage from land-falling storms. Maina (2010) acknowledged the importance of central minimum pressure and recognized the need to account for storm size resulting in MHI -1 1/2 = 10(Pmin/ Pmin0) + 1/2(Rh/Rh0) , where Rh is the radius of hurricane-force winds and Pmin0 and Rh0 are reference values (taken as 900hPa and 150km, respectively). Existing indices, such as the SSHWS, provide effective ways to communicate a storm‟s strength, but do not accurately measure a hurricane‟s potential damage. In fact, the MHI is shown to explain a larger portion of the variance in total economic damage for 13 recent US landfalling storms compared to the SSHWS and five other existing indices (e.g. Kantha 2006; Owens and Holland 2009; Smith 2006). Future work, described by Maina (2010), included addressing and resolving the limitations that arose in the development of the MHI. Factors such as storm surge, bathymetric topography, and infrastructure of structures were neglected due to their complex nature. Our re- analysis of the MHI continues not to address these issues. However, we are able resolve the small sample size by increasing the number of storms from to 31. The insufficient amount of information on storm size has been compromised through a new best track data set – the Tropical Cyclone Extended Best Track Data Set (TCEBT data set). 2.2. Tropical Cyclone Extended Best Track Data Set The extended best track data set recently became available online, providing information about all tropical depressions, storms, and hurricanes for the years 1988 – 2008. The data for storms in the Atlantic can be found at ftp://rammftp.cira.colostate.edu/demaria/ebtrk/ebtrk 19_atlc.txt. The following are the data used in the re-analysis of the MHI: date, time (UTC), latitude, longitude, central minimum pressure (hPa), radius of maximum winds (n.mi), and the radius of 34kt, 50kt, and 64kt (18 ms-1 , 26 ms-1 , and 33 ms-1 , respectively) winds to the NE, SE, NW, and SW. Out of the 100+ storms in the data set through the year 2005, 30 storms made landfall. Hurricane Camille, a landfalling storm in 1969, was also added to our data set because of its inclusion in the development of the MHI. Using the TCEBT data set, the relevance of the following hurricane parameters was examined: pressure, size, translation speed, and duration of winds. The size of the storms was calculated by averaging the radius from the four storm quadrants. The translation speed involved calculations with the latitude and longitude, and duration of winds is the radius of hurricane- force winds divided by translation speed. All the data needed in the re-analysis is shown in Table 1. Following the methodology of Maina (2010), the damage data for each storm was again adapted from Pielke et al. (2008) where the total economic damage of storms was normalized using the Collins/Lowe methodology. This involved adjusting hurricane losses with respect to changes in inflation, wealth, and number of housing units. The total damage was also divided by the average number of housing units affected by all storms since 1900 (861,287) to account for spatial variability. 2.3. Results Using the data provided in Table 1, we assessed the statistical significance between the four hurricane parameters and total economic damage. Using the Pearson‟s r test, we calculated the correlation of determination at a confidence interval of 95%. Regression plots (Figures 1 though 4) of each parameter against the damage, in billions, showed improved yet similar statistical results produced by Maina (2010). There was a strong statistical significance between central minimum pressure and damage, as indicated by the coefficient of determination (r2) of 0.410. Again, the radii of tropical storm force winds (18 ms-1 and 26 ms-1) were not statistically relevant, establishing the radius of hurricane-force winds (33 ms-1) is best indicator of storm size.
Load more

Recommended publications
  • Tropical Cyclone Report for Hurricane Ivan
    Tropical Cyclone Report Hurricane Ivan 2-24 September 2004 Stacy R. Stewart National Hurricane Center 16 December 2004 Updated 27 May 2005 to revise damage estimate Updated 11 August 2011 to revise damage estimate Ivan was a classical, long-lived Cape Verde hurricane that reached Category 5 strength three times on the Saffir-Simpson Hurricane Scale (SSHS). It was also the strongest hurricane on record that far south east of the Lesser Antilles. Ivan caused considerable damage and loss of life as it passed through the Caribbean Sea. a. Synoptic History Ivan developed from a large tropical wave that moved off the west coast of Africa on 31 August. Although the wave was accompanied by a surface pressure system and an impressive upper-level outflow pattern, associated convection was limited and not well organized. However, by early on 1 September, convective banding began to develop around the low-level center and Dvorak satellite classifications were initiated later that day. Favorable upper-level outflow and low shear environment was conducive for the formation of vigorous deep convection to develop and persist near the center, and it is estimated that a tropical depression formed around 1800 UTC 2 September. Figure 1 depicts the “best track” of the tropical cyclone’s path. The wind and pressure histories are shown in Figs. 2a and 3a, respectively. Table 1 is a listing of the best track positions and intensities. Despite a relatively low latitude (9.7o N), development continued and it is estimated that the cyclone became Tropical Storm Ivan just 12 h later at 0600 UTC 3 September.
    [Show full text]
  • Comparison of Destructive Wind Forces of Hurricane Irma with Other Hurricanes Impacting NASA Kennedy Space Center, 2004-2017
    Comparison of Destructive Wind Forces of Hurricane Irma with Other Hurricanes Impacting NASA Kennedy Space Center, 2004 - 2017 Presenter: Mrs. Kathy Rice Authors KSC Weather: Dr. Lisa Huddleston Ms. Launa Maier Dr. Kristin Smith Mrs. Kathy Rice NWS Melbourne: Mr. David Sharp NOT EXPORT CONTROLLED This document has been reviewed by the KSC Export Control Office and it has been determined that it does not meet the criteria for control under the International Traffic in Arms Regulations (ITAR) or Export Administration Regulations (EAR). Reference EDR Log #: 4657, NASA KSC Export Control Office, (321) 867-9209 1 Hurricanes Impacting KSC • In September 2017, Hurricane Irma produced sustained hurricane force winds resulting in facility damage at Kennedy Space Center (KSC). • In 2004, 2005, and 2016, hurricanes Charley, Frances, Jeanne, Wilma, and Matthew also caused damage at KSC. • Destructive energies from sustained wind speed were calculated to compare these hurricanes. • Emphasis is placed on persistent horizontal wind force rather than convective pulses. • Result: Although Hurricane Matthew (2016) provided the highest observed wind speed and greatest kinetic energy, the destructive force was greater from Hurricane Irma. 2 Powell & Reinhold’s Article 2007 • Purpose: “Broaden the scientific debate on how best to describe a hurricane’s destructive potential” • Names the following as poor indicators of a hurricane’s destructive potential • Intensity (Max Sustained Surface Winds): Provides a measure to compare storms, but does not measure destructive potential since it does not account for storm size. • The Saffir-Simpson scale: Useful for communicating risk to individuals and communities, but is only a measure of max sustained winds, again, not accounting for storm size.
    [Show full text]
  • The Flooding of Hurricane Ivan: How Far Ahead Can We Predict?
    P1.7 THE FLOODING OF HURRICANE IVAN: HOW FAR AHEAD CAN WE PREDICT? Michael P. Erb*, Douglas K. Miller National Environmental Modeling and Analysis Center University of North Carolina – Asheville, Asheville, North Carolina 1. INTRODUCTION will be modeled using the Weather Research and Forecasting (WRF) Model. Naturally, since On September 17th 2004, Hurricane Ivan (by this is only a case study, the results found here then a tropical depression) began making its will not answer the question in a general sense. way along the western edge of North Carolina, But it is hoped, at the very least, that they should threatening to bring a deluge of rainfall to give interested parties a better understanding of regions surrounding the Appalachian Mountains. the problem at hand. Many of these places had been flooded by Another reason for this research is that it Hurricane Frances only two weeks before and, ties in with the goals of the Renaissance still not fully recovered, feared a repeat of those Computing Institute (RENCI), an organization events. All indicators pointed to Ivan being on based in North Carolina which is dedicated to the same scale as Frances, and forecasters solving complex, multidisciplinary problems that called for heavy rain and yet more flooding. People there, understandably, prepared for the worst. (Figures 1 and 2 show the tracks of Hurricanes Ivan and Francis.) What actually happened over the next few days turned out to be on a somewhat smaller scale. The rain did come, but not to the extent forecasters had predicted. In places where Frances had dumped 20 or more inches of rain – Mount Mitchell, NC, for instance – Ivan brought 15 or less.
    [Show full text]
  • Tropical Cyclone Intensity
    Hurricane Life Cycle and Hazards John Cangialosi and Robbie Berg National Hurricane Center National Hurricane Conference 26 March 2012 Image courtesy of NASA/Goddard Space Flight Center Scientific Visualization Studio What is a Tropical Cyclone? • A relatively large and long‐lasting low pressure system – Can be dozens to hundreds of miles wide, and last for days • No fronts attached • Forms over tropical or subtropical oceans • Produces organized thunderstorm activity • Has a closed surface wind circulation around a well‐defined center • Classified by maximum sustained surface wind speed – Tropical depression: < 39 mph – Tropical storm: 39‐73 mph – Hurricane: 74 mph or greater • Major hurricane: 111 mph or greater Is This a Tropical Cyclone? Closed surface circulation? Organized thunderstorm activity? Tropical Depression #5 (later Ernesto) Advisory #1 issued based on aircraft data The Extremes: Tropical vs. Extratropical Cyclones Hurricane Katrina (2005) Superstorm Blizzard of March 1993 Tropical Cyclones Occur Over Tropical and Subtropical Waters Across the Globe Tropical cyclones tracks between 1985 and 2005 Atlantic Basin Tropical Cyclones Since 1851 Annual Climatology of Atlantic Hurricanes Climatological Areas of Origin and Tracks June: On average about 1 storm every other year. Most June storms form in the northwest Caribbean Sea or Gulf of Mexico. July: On average about 1 storm every year . Areas of possible development spreads east and covers the western Atlantic, Caribbean, and Gulf of Mexico. Climatological Areas of Origin and Tracks August: Activity usually increases in August. On average about 2‐3 storms form in August. The Cape Verde season begins. September: The climatological peak of the season. Storms can form nearly anywhere in the basin.
    [Show full text]
  • HURRICANE FRANCES CHARACTERISTICS and STORM TIDE EVALUATION (((DDDRRRAAAFFFTTT)))
    HURRICANE FRANCES CHARACTERISTICS and STORM TIDE EVALUATION (((DDDRRRAAAFFFTTT))) By Robert Wang and Michael Manausa Sponsored by Florida Department of Environmental Protection, Bureau of Beaches and Coastal Systems Submitted by Beaches and Shores Resource Center Institute of Science and Public Affairs Florida State University May 2005 Table of Contents Page I. Synoptic History 1 II. Storm Tide Records 4 III. Storm Tide Evaluation 5 IV. Storm Tide Return Period 10 V. Reference 11 i List of Figures Figure Description Page 1 Hurricane Frances Track, 21 August – 6 September 2004 1 2 Hurricane Frances Track passing over the Florida Coast 2 3 Eye of Hurricane Frances before Landfall 2 4 Surface Wind Fields Associated with Hurricane Frances at Landfall 3 5 Best Track Pressure and Wind Speed for Hurricane Frances, 1 - 6 September, 2004 4 6 Peak Surge Levels along the Atlantic coast for Hurricane Frances 6 7 Peak Surge Level in the Brevard County area for Hurricane Frances 7 8 Peak Surge Level in the Indian River County area for Hurricane Frances 8 9 Peak Surge Level in the Hurricane Frances Landfall areas 9 10 Hurricane Frances Storm Tide Return Period 10 ii I. Synoptic History Hurricane Frances developed from a tropical wave from the coast of Africa on 21 August, 2004, and gradually became organized into a tropical depression near 0000UTC 25 August. Figure 1 illustrates the track of Hurricane Frances. The depression moved westward and intensified, becoming a tropical storm later on 25 August. Frances turned west-northwestward on 26 August as it became a hurricane with winds near 80 mph.
    [Show full text]
  • Hurricane Summary Data CY2004 CY2005 Charley Dennis Frances Katrina Ivan Rita Jeanne Wilma
    Florida Office of Insurance Regulation Hurricane Summary Data CY2004 CY2005 Charley Dennis Frances Katrina Ivan Rita Jeanne Wilma Pensacola, FL October, 2004 -- Sunrise over Pensacola Bay a month after August, 2006 Hurricane Ivan. Photo by Bill Koplitz/FEMA Photo This document summarizes those insurance claims and related information as reported to the Florida Office of Insurance Regulation (Office) by the insurance entities affected. While the Office analyzes the data submissions for completeness and reasonability, the data has not been formally audited or verified. This is the final report for the 2004 and 2005 hurricane seasons and represents the latest information reported by affected insurers. Note: The total gross probable losses estimate has declined from the previous reports. This is largely due to insurers revising estimates as more claims were paid out over time and insurers revising allocation of losses among the Gulf States for hurricanes Rita and Katrina. Combined CY2004 & CY2005 Summary Data Page 3 of 74 Summary Data by Event and CY Estimated Gross Claims Total Loss Claim Payments Event Data As Of Probable Loss Reported Claims Made Charley 31-Dec-05 $10,158,404,847 474,771 17,679 $9,056,703,918 Frances 31-Dec-05 $7,952,635,936 541,589 14,105 $7,707,516,393 Ivan 31-Dec-05 $3,314,847,829 207,718 8,104 $3,205,437,734 Jeanne 31-Dec-05 $3,634,646,243 427,633 8,951 $3,513,823,790 CY2004 Total $25,060,534,855 1,651,711 48,839 $23,483,481,835 Dennis 31-Dec-05 $297,399,182 52,934 920 $269,807,639 Katrina 30-Apr-06 $853,000,053 122,798 3,153 $725,223,536 Rita 30-Apr-06 $25,242,545 4,375 167 $19,447,845 Wilma 30-Apr-06 $9,659,383,823 975,717 18,853 $8,848,516,509 CY2005 Total $10,835,025,603 1,155,824 23,093 $9,862,995,529 Overall Totals $35,895,560,458 2,807,535 71,932 $33,346,477,364 This information is compiled from data submitted by each reporting entity.
    [Show full text]
  • The Spatial Distribution of Meteorological Impacts Associated with Inland-Moving Tropical Cyclones
    The Spatial Distribution of Meteorological Impacts Associated with Inland-Moving Tropical Cyclones Margaret Mae Kovach A thesis submitted to the faculty of the University of North Carolina at Chapel Hill in Partial fulfillment of the requirements for the degree of Master of Arts in the Department of Geography Chapel Hill 2011 Approved by: Advisor: Charles E. Konrad, II Reader: Brian K. Eder Reader Erika K. Wise © 2011 Margaret Mae Kovach ALL RIGHTS RESERVED ii ABSTRACT Margaret Mae Kovach: The Spatial Distribution of Meteorological Impacts Associated with Inland-Moving Tropical Cyclones (Under the direction of Charles E. Konrad II) The southeastern United States is routinely hit by tropical cyclones. As tropical cyclones track inland and dissipate, their inland impacts can be substantial. Typically, these impacts occur due to any combination of the tropical cyclones heavy precipitation, high winds, or tornadoes. This study will examine the meteorological impacts of 31 inland- moving tropical cyclones from 1985 to 2008. The spatial distribution of meteorological impacts is plotted relative to the track (e.g. left vs. right quadrant) and location (forward vs. rear quadrant) of the cyclone center. Various tropical cyclone attributes, including size, strength, and speed of movement are related to the occurrence of different impacts and their location relative to the cyclone track. Results indicate a distinct variation in the spatial patterns of tornado, high wind and flash flood impacts, particularly when comparing tropical cyclones of different
    [Show full text]
  • Fatalities Due to Hurricane Katrina's Impacts In
    FATALITIES DUE TO HURRICANE KATRINA’S IMPACTS IN LOUISIANA A Dissertation Submitted to the Faculty of the Louisiana State University and Agricultural and Mechanical College in partial fulfillment of the requirements for the degree of Doctor of Philosophy in The Department of Geography and Anthropology by Ezra Boyd B.A., The University of Chicago, 1999 M.A., The University of New Orleans, 2003 August 2011 © Copyright 2011 Ezra Clay-Kelly Boyd All Rights Reserved ii Dedication This dissertation is dedicated to the people of southeast Louisiana who lost their homes and family members to the 2005 flood disasters. iii Acknowledgements I would like to express my sincerest appreciation for the support and assistance from the many people who helped make this dissertation possible. My committee members, Dr. Barry Keim, Dr. Marc Levitan, Dr. Ivor van Heerden, Dr. Craig Colten, and Dr. Troy Blanchard, all provided extremely helpful advice, insights, encouragement, and companionship. I would like to particularly acknowledge the personal sacrifices that Dr. van Heerden has had to endure because of his dedicated pursuit of the true underlying causes of this horrible disaster. During a time of tragic loss and overwhelming sorrow, Dr. Louis Cataldie and Frances Kosak, of the Louisiana State Medical Examiner’s Office, embraced my research goals, allowed me into their world, and shared their data and experiences with me. Their openness and support made this research possible. A number of my classmates and colleagues have provided invaluable advice and data. These people include DeWitt Braud, Hampton Peele, Ahmet Binselam, Stephanie Pedro, and many others. Also, I want to thank the many LSU students who assisted in the field surveys.
    [Show full text]
  • U.S. Billion-Dollar Weather & Climate Disasters 1980-2021
    U.S. Billion-Dollar Weather & Climate Disasters 1980-2021 https://www.ncdc.noaa.gov/billions/ The U.S. has sustained 298 weather and climate disasters since 1980 in which overall damages/costs reached or exceeded $1 billion. Values in parentheses represent the 2021 Consumer Price Index cost adjusted value (if different than original value). The total cost of these 298 events exceeds $1.975 trillion. Drought Flooding Freeze Severe Storm Tropical Cyclone Wildfire Winter Storm 2021 Western Drought and Heatwave - June 2021: Western drought expands and intensifies across many western states. A historic heat wave developed for many days across the Pacific Northwest shattering numerous all-time high temperature records across the region. This prolonged heat dome was maximized over the states of Oregon and Washington and also extended well into Canada. These extreme temperatures impacted several major cities and millions of people. For example, Portland reached a high of 116 degrees F while Seattle reached 108 degrees F. The count for heat-related fatalities is still preliminary and will likely rise further. This combined drought and heat is rapidly drying out vegetation across the West, impacting agriculture and contributing to increased Western wildfire potential and severity. Total Estimated Costs: TBD; 138 Deaths Louisiana Flooding and Central Severe Weather - May 2021: Torrential rainfall from thunderstorms across coastal Texas and Louisiana caused widespread flooding and resulted in hundreds of water rescues. Baton Rouge and Lake Charles experienced flood damage to thousands of homes, vehicles and businesses, as more than 12 inches of rain fell. Lake Charles also continues to recover from the widespread damage caused by Hurricanes Laura and Delta less than 9 months before this flood event.
    [Show full text]
  • 2021 Hurricane Preparedness Guide
    St. Johns County Emergency Management HURRICANE PREPAREDNESS GUIDE OFFICIAL GUIDE FOR ST. JOHNS COUNTY HURRICANE PREPAREDNESS GUIDE contents Contents 2 History of Hurricanes in St. Johns County 3 Weather - Thunderstorms, Tornadoes, Tornado EF-Scale . 4 - Tropical Storms, Storm Surge, Tides, Waves, Freshwater . 5 - Hurricanes, Saffir-Simpson Hurricane Wind Scale . 6 Preparedness - Disaster Planning . 7 - Business Planning . 8 - Being Prepared, Know Your Zone, Disaster Kit, Document Checklist . 9 - Flood Insurance . 10 - Being Informed, NOAA Weather Radio, Alert. St Johns, Local Radio and Television Stations . 11 - A Message from the City of St. Augustine and the St. Augustine Beach Police Department . 12 - Sheltering Options . 13 - Transportation . 14 - St. Johns County Evacuation Assistance Registration Form . 15-16 Before the Storm - Preparing your Home, Manufactured Home, Mobile Home and Boat . 17 - St. Johns County Sandbag Operations . 18 Evacuation and Sheltering - Mandatory Evacuation, Curfew, Vehicle Traffic, Travel . 19 - Designated Evacuation Routes for St. Johns County . 20 - FDOT Emergency Shoulder Use, What If I Don’t Evacuate . 21 - Sheltering During a Disaster, General Shelter Supply Kit . 22 - General Shelters and Locations . 23 - Pet Friendly Shelters, Pet Friendly Shelter Supply Kit, Pets Allowed at Shelters . 24 - Special Medical Needs Shelters, Special Medical Needs Shelter Supply Kit . 25 Recovery - ReEntry, After the Storm, Mosquitoes after a Hurricane . 26 - St. Johns County Debris Operations . 27 - Communicating During an Emergency, Beaches Energy Services . 28 - Florida Power & Light / FPL . 29 - Jacksonville Electric Authority / JEA . 30 St. Johns County Hurricane Evacuation Zones Map 31 2 | St. Johns County Emergency Management Hurricane Preparedness Guide | 3 St. Johns County Hurricane Impacts St. Johns County residents have experienced many storms throughout history.
    [Show full text]
  • Ten-Year Retrospective of the 2004 and 2005 Atlantic Hurricane Seasons Part 1: the 2004 Season
    TEN-YEAR RETROSPECTIVE OF THE 2004 AND 2005 ATLANTIC HURRICANE SEASONS PART 1: THE 2004 SEASON The 2004 Atlantic hurricane season kicked off on July 31, when the first named storm formed off the coast of the southeastern United States. It was to be the start of two back-to-back seasons that would bring hurricanes to the forefront of discussions in the media, the scientific community, and especially in the insurance/reinsurance industry. Many of the changes to underwriting practice, insurance and reinsurance contract wording, and catastrophe modeling as a result of the 2004 and 2005 hurricane seasons are still in practice today. This paper will revisit 2004 and 2005, exploring the underlying meteorological conditions that led to these two hurricane seasons and the impacts to the insurance and reinsurance industry, including changes to underwriting practices, claims adjusting practices, insurance and reinsurance contract wording and the Florida Hurricane Cat Fund. In addition, responses from Rating Agency and Catastrophe Model vendors will also be explored. Part I discusses the 2004 hurricane season and the immediate impacts of that season. Part II, published at a later date, will focus on the 2005 hurricane season and the cumulative impacts on the industry from the combined seasons. 1 F-1 | MONTAGE OF CHARLEY, FRANCES, IVAN AND JEANNE (Source: CIMSS) ATLANTIC HURRICANE SEASON SUMMARY The 2004 and 2005 hurricane seasons in the North Atlantic Basin were impactful to both residential and commercial property owners, the oil and gas industries, and the (re)insurance industry at large. In many ways these seasons reshaped our understanding of the hurricane threat and our approach to risk management.
    [Show full text]
  • Economic Impact of Hurricanes Harvey & Irma
    Economic impact of Hurricanes Harvey & Irma Nathaniel Karp / Marcial Nava / Boyd Nash-Stacey / Filip Blazheski 18 September 2017 Gross losses could reach $111bn for Texas and $52bn for Florida However, the net economic impact will be modest after reconstruction is completed Overdevelopment and fast population growth in low-lying coastal areas increase disaster potential The biggest challenge for Houston is to maintain its long-term attractiveness Economic effects Gross losses from natural disasters can be divided in lost value of capital stock and loss of short-term investment flows. Net losses take into account the gains from reconstruction activity. In terms of value-added, the destruction of capital stock -housing, commercial property, infrastructure and inventories- by itself does not reduce the level of current production or income. However, it does reduce wealth, the value of services of capital stock and tax revenues – mainly property taxes. This in turn causes a reduction in income, consumption, trade and investment, which reduces the level of economic activity, particularly over the short term. Meanwhile, the immediate reduction in short-term investment flows reduces the level of economic activity through lower hours worked, income, sales, output, tax revenues and trade. Moreover, since the direct effects also have second round effects across different sectors, the total negative impact is augmented. The negative impact tends to be large at first, but as conditions normalize, economic activity is positively affected as long as insurance and assistance funds are made available. In any case, the affected regions will face the challenge of maintaining its economic attractiveness over the longer-run, as devastating weather events may force people to move out and deter both future investment and migration, thereby reducing economic potential.
    [Show full text]