2012 annual report - FloridaDisaster.org

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FLORIDA HURRICANE LOSS MITIGATION
PROGRAM
2012 ANNUAL REPORT
January 1, 2013
Prepared by
Florida Division of Emergency Management
Rick Scott
Governor
Bryan W. Koon
Director
Table of Contents
Executive Summary
Page 2
Introduction
Page 4
Program Activities and Accounting
Page 6
January 1, 2012 – June 30, 2012
Program Activities and Accounting
Page 13
July 1, 2012 – December 31, 2012
Observations and Recommendation
Page 19
Appendix A
Page 20
Tallahassee Community College 2010-2011 Annual Report
Mobile Home Tie-Down Program
Appendix B
Page 24
Florida International University Annual Report
Hurricane Loss Mitigation Program – Annual Report 2012
Page 1
EXECUTIVE SUMMARY
This document satisfies subsection 215.559 (7) F.S., requirements to provide a report accounting
for activities undertaken through the Hurricane Loss Mitigation Program. The time period
covered by this report is January 1, 2012 through December 31, 2012. Therefore, the reporting
period includes the second half of State Fiscal Year 2012, and the first half of State Fiscal Year
2013.
Based on Section 215.559 (1) F. S., the Hurricane Loss Mitigation Program is established in the
Division of Emergency Management. The Division receives an annual appropriation of $10
million from the investment income of the Florida Hurricane Catastrophe Fund authorized under
the Florida General Appropriation Act and Section 215.555 (7) (c) F. S. The Public Shelter
Retrofit Program, Tallahassee Community College’s Mobile Home Tie Down Program, Florida
International University’s Hurricane Research Program and the Building Code Compliance and
Mitigation program account for $7,425,000.00 or seventy-four percent of the $10 million
appropriation. The remaining $2,575,000.00 is used to fund wind mitigation retrofits, outreach
programs to Florida Homeowners and local governments, statutorily created Advisory Council
and funding for mitigation staff’s salary and benefits. In compliance with the appropriation
language for State Fiscal Years 2012 and 2013 theses funds were distributed as required.
The Shelter Retrofit Program and Tallahassee Community College’s Mobile Home Tie Down
Program have separate reporting requirements as stated in section 252.385 F.S., and section
215.559 (2) (a) F. S., respectively. A separate report from Florida International University is
also required. A copy of TCC’s and FIU’s report is attached.
Program Activities - January 1, 2012 – June 30, 2012
A. Residential Construction Mitigation Program (RCMP) - The RCMP was created by
the Division within the Bureau of Mitigation and manages the remaining $2,575,000
from the original appropriation of $10 million. RCMP concentrates on wind mitigation
retrofits and outreach programs to Florida homeowners and local government officials
and its respective staff.
A Notice of Funding Availability (NOFA) was issued in May 2011 in anticipation of the
State Fiscal (SFY) 2012 $10 million appropriation as required by Section 215.555 (7) (c)
F. S. A review panel appointed by the Division selected appropriate eligible projects.
Based on this evaluation process the Division contracted with fourteen (14) grant
recipients to conduct wind mitigation retrofits to homes in the Cities of Tamarac, Delray
Beach, North Lauderdale, Jacksonville, Town of Davie and in the Counties of Manatee,
Hardee, St. Lucie, Escambia, Santa Rosa, Pinellas and Broward. The total contract award
amount for wind mitigation retrofits was $1,672,200.41. At the conclusion of State Fiscal
Year 2012, a total of $1,432,503.79 or eighty-six percent of the remaining $2,575,000.00
was expended for wind mitigation projects.
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The Division also issued a Request For Proposals in May 2011 for outreach projects. The
submitted proposals were competitively evaluated. The Division awarded contracts to all
five entities that submitted a total of seven proposals to conduct outreach programs to
Florida homeowners and local governments. These organizations include Florida State
University, Florida Home Builders Association, Florida’s Foundation, Florida
Association of Counties and the City of North Lauderdale.
The outreach programs include retrofit education to homeowners, local government
officials and staff through building code training, online residential retrofit materials, and
training of contractors for retrofit installation procedures. The total contract award
amount for outreach projects was $740,240.39. A total of $740,240.39 was awarded for
outreach projects and at the conclusion of State Fiscal Year 2012, a total amount of
$687 ,939.30 was expended. Therefore, ninety-three percent of the total award amount
was utilized for public outreach projects.
B. Florida International University (FIU)- As required by Chapter 215.559 (3), F. S., FIU
was allocated $700,000 from the annual appropriation to the Division for the Hurricane
Loss Mitigation Program. Five major efforts were completed by the International
Hurricane Research Center team for State Fiscal Year 2012. The subject areas were: a)
Development of Hurricane Resilient Composite Structural Insulated Wall Systems for
Residential Buildings; b) Computational Evaluation of Wind Load on Residential
Buildings with Regular and Complex Roof Shapes; c)Estimation of Surface Roughness
Using Airborne LiDAR Data; d) Reducing Losses From Extreme Hydro-meteorological
Events, Insights From a Survey of Florida Households; and e) Education and Outreach
Programs to Convey the Benefits of Various Hurricane Loss Mitigation Devises and
Techniques. The total funds expended by FIU for hurricane research as of June 30, 2012
is $597,920.66. Therefore, 85 percent of the $700,000 allocated amount was expended.
Program Activities - July 1, 2012 – December 31, 2012
A. Residential Construction Mitigation Program (RCMP) - The RCMP concentrates on
wind mitigation retrofits and outreach programs to Florida homeowners and local
government officials and its staff. Request For Proposals were issued in May 2012 to
solicit proposals for wind mitigation retrofits and implementation of various outreach
programs. An evaluation committee was appointed by the Division to evaluate the
submitted proposals and a total of fourteen projects were awarded to local governments,
state agencies, and non-profit organizations. The Division awarded ten contracts, in the
amount of $1,404,317.00, for wind mitigation retrofits and four contracts, in the amount
of $372,572.00, for outreach. The ten contracts awarded for wind mitigation retrofits
include: City of Cocoa, City of Melbourne, Rebuild Northwest Florida, Bay County,
Town of Davie, City of Tamarac, Hardee County, Community Action Program
Committee, St. Lucie County, and Town of Century. The four entities awarded contracts
for outreach programs are; Florida’s Foundation, Florida Home Builders Association,
Florida Funding Agency, and the Florida Association of Counties.
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B. Florida International University (FIU) - As required by Chapter 215.559 (3), F. S., FIU
was allocated $700,000 from the annual appropriation to the Division for the Hurricane
Loss Mitigation Program. The Division is currently working with FIU to identify
projects for use of the SFY 2013 funds.
INTRODUCTION
In the aftermath of Hurricane Andrew, the Florida Legislature created a series of programs to
stabilize the economy and insurance industry. These programs consist of the following:
A. Citizens Property Insurance Corporation (formed from a merger of the Florida
Windstorm Underwriting Association and the Florida Residential Property and Casualty
Joint Underwriting Association), the state insurance plan for residents unable to obtain a
conventional homeowners insurance policy;
B. The Florida Hurricane Catastrophe Fund, section 215.555 F.S., a re-insurance fund
established to limit insurance exposure after a storm; and
C. The Bill Williams Residential Safety and Preparedness Act, which in 1999 created the
Hurricane Loss Mitigation Program, section 215.559 F. S., with an annual appropriation
of $10 million.
Based on Section 215.559 (1) F. S., the Hurricane Loss Mitigation Program is established in the
Division of Emergency Management. The Division receives an annual appropriation of
$10 million from the investment income of the Florida Hurricane Catastrophe Fund authorized
under the Florida General Appropriation Act and Section 215.555 (7) (c) F. S. The purpose of
the $10 million annual appropriation is to provide funding to local governments, State agencies,
public and private educational institutions, and nonprofit organizations to support programs that
improve hurricane preparedness, reduce potential losses in the event of a hurricane, and to
provide research and education on how to reduce hurricane losses.
The funds are also to be used for programs that will assist the public in determining the
appropriateness of particular upgrades to structures and in the financing of such upgrades, or to
protect local infrastructure from potential damage from a hurricane. Section 215.559 F.S.,
establishes minimum funding levels for specific program areas and creates an Advisory Council
to make recommendations on developing programs.
Specific Program Areas and Funding Levels
A. Shelter Retrofits - According to Section 215.559 (2) (a) F. S., $3 million of the annual
$10 million appropriation for the Hurricane Loss Mitigation Program is directed to
retrofit existing public facilities to enable them to be used as public shelters. An annual
report of the state’s shelter retrofit program, entitled the Shelter Retrofit Report, is
prepared annually and separately submitted to the Governor and the Legislature pursuant
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to section 252.385 F.S. The remaining $7 million of the $10 million appropriation is
allocated according to different subsections in Section 215.559, F. S., as described below.
B. Tallahassee Community College (TCC) - As required by section 215.559 (2) (a) F. S.,
TCC is given an annual allocation of $2.8 million or 40 percent of the remaining $7
million. The funds are administered by TCC and are to be used to mitigate future losses
for mobile homes, and to provide tie-downs to mobile home in communities throughout
the State of Florida. Please see Appendix A for TCC’s 2011-2012 Annual Report.
C. Florida International University (FIU) - As required by Chapter 215.559 (3), F. S., FIU
is allocated $700,000, or 10 percent of the remaining $7 million. The funds are
administered by FIU and dedicated to hurricane research at the Type I Center of the State
University System to support hurricane loss reduction devices and techniques. Please see
Appendix B for FIU’s Annual report.
D. Building Code Compliance and Mitigation - The State of Florida General
Appropriations Act for SFY 2012 and SFY 2013 provide for a special allocation of
$925,000 from the Hurricane Loss Mitigation Program to fund the Building Code
Compliance and Mitigation program pursuant to Section 553.841, F. S. The purpose of
this allocation is to provide training and continuing education classes for Florida
contractors. This training is provided through the Florida Building Commission.
E. Residential Construction Mitigation Program (RCMP) - The RCMP was created by
the Division within the Bureau of Mitigation and manages the remaining $2,575,000
from the original appropriation of $10 million. In consultation with an Advisory Council
created under section 215.559 (4) F. S., the remaining funds are used by the RCMP for
funding residential wind mitigation retrofit projects and public outreach programs to
Florida homeowners and local governments.
1. Wind Mitigation Retrofit Program - RCMP’s residential wind mitigation
retrofit projects are accomplished in partnership with local governments and nonprofit organizations to fund inspections, wind mitigation retrofits and the
construction or modification of building components designed to increase a
structure’s ability to withstand hurricane-force winds.
2. Outreach Programs - RCMP’s outreach programs are designed to educate and
inform Florida homeowners and local government officials and their staff
regarding ways to minimize the impact of hurricane force winds on their property.
The program develops and utilizes Division owned and approved material
regarding wind mitigation retrofit techniques and training for local governments.
This material includes public service announcements, community workshops,
brochures, training seminars, hands-on demonstrations, and webinars. The RCMP
program continues to consider and review additional outreach approaches and
development to improve program content and delivery. Constant interaction with
grant recipients, homeowners and program contractors help to identify areas for
improvement. The Division awards contracts through a competitive process to
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have eligible entities deliver and develop material designed to meet the needs of
Florida homeowners and local government officials.
ANNUAL REPORT REQUIREMENTS
Subsection 215.559 (7), F.S., requires the Division on January 1st of each year to provide the
Speaker of the House of Representatives, the President of the Senate, and the Majority and
Minority Leaders of the House of Representatives and the Senate with an annual report on
programs implemented under the Hurricane Loss Mitigation Program. The time period covered
by this report is January 1, 2012 through December 31, 2012. Therefore, the reporting period
includes the second half of State Fiscal Year 2012, and the first half of State Fiscal Year 2013.
According to the referenced section above, the annual report must include: 1) a full report of
program activities, an accounting of program activities, and an evaluation of these activities.
Specific Program Activities and Accounting - January 1, 2012 – June 30, 2012
A. Shelter Retrofits - An annual report of the state’s shelter retrofit program, entitled the
Shelter Retrofit Report, is prepared annually and separately submitted to the Governor
and the Legislature pursuant to section 252.385 F.S.
B. Tallahassee Community College - At the conclusion of State Fiscal Year 2012, TCC
provided tie-Downs and retrofitted over 1,770 manufactured homes in thirteen mobile
home communities within the following eight counties: Hillsborough, Pinellas, Polk,
Highlands, Martin, St. Lucie, Suwannee and Sarasota. A full report of these program
actives are provided in Appendix D, The 2011-2012, TCC, Annual Report for the Mobile
Home Tie Down Program.
C. Florida International University - At the conclusion of State Fiscal Year 2012 FIU
expended $597,920.66 for hurricane research. Therefore, 85 percent of the $700,000
allocated amount was expended.
Five major efforts were completed as identified by the International Hurricane
Research Center team for State Fiscal Year 2012. Please see Appendix B for FIU’s
Annual Report. The subject areas identified by FIU for SFY 2012 and a short summary
of conclusions are as follows:
1. Development of Hurricane Resilient Composite Structural Insulated Wall
Systems for Residential Buildings: The main purposes of a building envelope
are to protect occupants from ambient and extreme weather conditions, to regulate
environmental loads for building operation, and to provide safe, comfortable and
stable indoor conditions. The objective of this research was to evaluate the
performance of two different building envelope systems under simulated
environmental loads generated by the 12-fan Wall of Wind at Florida
International University (FIU). The experimental results indicate that the thermal
bridging effect can cause significant heat loss/gain through the building
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envelopes. In addition, thermal performance of building enclosure system in
different types of weather conditions (e.g., wind driven rain) could be different
and needs to be considered in the development of alternative building envelope
solutions. The data collected through this project will be useful in the
advancement of technological solutions of building envelope systems towards
achieving net-zero energy buildings.
2. Computational Evaluation of Wind Load on Residential Buildings with
Regular and Complex Roof Shapes: Wind induced loads are one of the most
critical design parameters for coastal construction, especially in Florida, where
buildings are subjected to the highest wind loads of the nation. The present study
attempts to evaluate wind loads on both common and complex roofs using a
numerical approach based on the technique of Computational Fluid Dynamics
(CFD) principles. The focus is on residential single family house roofs, which
incur the most damages during hurricane events. Both in-house computer
programs and commercial software were used in the study.
The models with complex roof shapes showed mixed pressure distribution on the
roof (positive and negative pressure) as opposed the regularly shaped models
where separation and reattachment location are clearly known. On both roof
models, high suction pressures were observed on areas close to the windward
edge and near the middle ridge. The highest magnitude roof suction pressures
were observed in the corner areas close to the edges for both roof types. On the
hip roof model, the highest suction pressure was observed when the wind came
from diagonal directions, while the highest suction pressures on the gable roof
model was observed when the wind comes perpendicular to the short dimension.
3. Estimation of Surface Roughness Using Airborne LiDAR Data: Surface
roughness is an important modeling parameter for determining impacts of
hurricane wind on buildings. The Florida Division of Emergency Management
(DEM) collected LiDAR data for coastal areas in Florida in 2007. IHRC
researchers developed methods (1) to extract terrains, buildings, trees from
LiDAR measurements, (2) to compute the surface roughness using extracted
terrains, buildings, and trees based on five surface roughness models, and (3)
compare the surface roughness values from LiDAR with those from the national
land cover datasets created based on Land sat imagery. The application of the
methods on two test sites in Miami shows that the algorithms classified terrain,
buildings, and trees successfully with minor errors. The comparison of LiDAR
derived roughness lengths with the NLCD based roughness length indicates that
two types of roughness values agree reasonably.
4. Reducing Losses From Extreme Hydro-meteorological Events: Insights
From a Survey of Florida Households: This research focused on risk
perceptions and mitigation behavior among a diverse sample of households from
across the State of Florida. Researchers investigated how households perceive the
annual threat of property damage from hurricanes, particularly from major
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hurricanes (Category 3, 4, and 5); projections of more destructive hurricanes due
to climate change; institutional accountability overall and institutional
effectiveness at disaster risk reduction. Researchers then explored households’
preferences for an array of mitigation measures and insurance reforms to enhance
coastal resilience, in addition to a households’ willingness to invest in a menu of
realistic, low and higher cost roofing and opening protection options. An analysis
of 1,710 survey responses from households across the State of Florida revealed
the following core findings:
a) A large majority of households (69%) support the creation of the proposed
‘Florida Pre-Disaster Mitigation Fund’, to sponsor an expansion of predisaster mitigation programs (with additional state funding above and
beyond federal dollars)
b) A large majority of households (76%) are highly supportive of increasing
setbacks along Florida’s shoreline to enhance coastal resilience.
c) A large majority of households (70%) are highly supportive of
strengthening Florida building codes to enhance coastal resilience.
d) A large majority of households (72%) are highly supportive of new
elevation standards for Florida’s roads and buildings to enhance coastal
resilience.
e) Only 42 % of households are highly supportive of continued State
surcharges and assessments to pay for hurricane losses after-the fact.
f) A large majority of households (73%) are highly supportive of a
comprehensive insurance program (combined flood and wind insurance
program).
FIU’s findings suggest avenues for potential risk reduction strategies that can be implemented by
federal, state, and local agencies, including county and municipal governments in vulnerable
coastal communities. Additional findings can be viewed in the full report.
5. Education and Outreach Programs to Convey the Benefits of Various
Hurricane Loss Mitigation Devices and Techniques:
This work promoted hurricane-loss mitigation and the objectives of the RCMP
and included the following:
a) Hurricane Mitigation & Hurricane Andrew 20th Anniversary Museum
Exhibition: The Miami Science Museum assisted International Hurricane
Research Center (IHRC) in developing and coordinating a new gallery of
hands-on, interactive exhibits and displays. The exhibits and displays
focus on the science and benefits of hurricane mitigation, preparedness,
hurricane forecasting and tracking and promoting a "culture of
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preparedness" for all natural hazards. This collaborative community
education outreach project partners the IHRC with the FDEM, MiamiDade County Emergency Management, the Miami Science Museum,
including collaboration with the National Hurricane Center, NOAA’s
Atlantic Oceanographic and Meteorological Laboratory (AOML) and the
Miami Office of the National Weather Service.
b) Hurricane Science, Mitigation & Preparedness Day (Feel the Force): The
IHRC and the Miami Science Museum partnered with Miami-Dade
County Emergency Management to develop, plan, coordinate and
facilitate Hurricane Science, Mitigation & Preparedness Day (Feel the
Force) at the Museum. Close to 2,000 people attended this public
education event that showcased hurricane science, mitigation,
preparedness and safety and IHRC Wall of Wind research and
demonstrations.
c) National Hurricane Survival Initiative: The IHRC collaborated with the
National Hurricane Survival Initiative (http://hurricanesafety.org/) and
their annual hurricane preparedness program, “Get Ready, America! The
National Hurricane Survival Test.” The 2012 version of the program
looked back on the devastation wrought by Hurricane Andrew in 1992, the
lessons learned since then, and what you need to know and do to stay safe
before, during and after hurricane season. This year’s broadcast
participation was the largest one ever, with 60 television network affiliate
stations from Texas to Maine.
d) Hurricane Andrew 20th Anniversary Event at the Miami Science
Museum: The Miami Science Museum assisted IHRC in planning this
special community event to commemorate the 20-year anniversary of
Hurricane Andrew’s landfall. Panel discussions occurred throughout the
day with high profile Andrew experts and the new Hurricane Andrew
exhibit was highlighted.
e) Hurricane Andrew 20th Anniversary Event and Grand Opening of the 12Fan Wall of Wind: IHRC partnered with Miami-Dade County Emergency
Management, the National Hurricane Center, NOAA’s Atlantic
Oceanographic and Meteorological Laboratory (AOML), the Miami
Office of the National Weather Service and the City of Homestead in
planning this official South Florida community event to commemorate the
20-year anniversary of Hurricane Andrew’s landfall.
f) Wall of Wind Neighborhood Open House: IHRC reached out to the local
FIU South Florida Community and invited local residents and families to
come and learn about the importance of hurricane mitigation and wind
engineering research through presentations, activities and tours and
demonstrations of the new 12-Fan Wall of Wind.
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D. Building Code Compliance and Mitigation - The purpose of this program is to provide
training and continuing education classes for Florida contractors. The original contract
for this work was entered into by the Florida Department of Community Affairs and
Building A Safer Florida, Inc. However, the contract has been amended and is now
between the Florida Department of Business and Professional Regulation and Building a
Safer Florida, Inc.
Section 553.841(3), F. S., requires all services and materials
Code Compliance and Mitigation Program be provided
corporation under contract with the Florida Department of
Regulation (DBPR). Therefore, DBPR with the assistance
managing the contract and processing of invoices.
under the Florida Building
by a private, non-profit
Business and Professional
of the Division has been
The total amount of funds expended for Building Code Compliance and Mitigation as of
June 30, 2012 was $826,843.15. Therefore, 89.39 percent of the allocated $925,000
amount was expended.
E. Residential Construction Mitigation Program (RCMP)
1. Wind Mitigation Retrofits - As stated, in the Division’s 2011 Annual Report, in
anticipation of the annual $10 million appropriation for State Fiscal year 2012 a
Notice of Funding Availability (NOFA) was issued in May 2011. The NOFA was
issued prior to the start of the State Fiscal Year 2012 in an effort to allow the full
twelve months for project implementation. A review panel appointed by the
Division selected appropriate eligible projects. The selection process was
competitive and projects were ranked based on published scoring criteria. A
Benefit-Cost analysis was performed on each retrofit project to assure that RCMP
funds were well-spent. Based on this evaluation process the Division contracted
with fourteen grant recipients to complete wind mitigation retrofits for homes
located across Florida.
Over the second half of State Fiscal Year 2012, the Division monitored the
progress of the individual wind mitigation retrofit projects and processed invoices
as submitted. Site visits were conducted and guidance was provided to the
recipients regarding contract compliance and specific wind mitigation
requirements. At the conclusion of the fiscal year, Division engineers inspected
each property to ensure that work was completed as required.
The fourteen grant recipients that were awarded wind mitigation retrofit contracts
for State Fiscal Year 2012 are shown below. The contract award amounts and
total expenditures as of June 30, 2012 are presented for each recipient. The total
contract award amount for wind mitigation retrofits was $1,672,200.41. At the
conclusion of State Fiscal Year 2012, a total of $1,432,503.79 was expended.
Therefore, eighty-six percent of the award was utilized for residential wind
mitigation retrofits projects.
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a) Retrofit Southwest Florida – This grant retrofitted nine homes within
Collier County.
Contract Award: $150,000
Total Expenditure: $150,000.00
b) City of Tamarac - This grant retrofitted five homes within Tamarac.
c) City of Delray Beach - This grant retrofitted eighteen homes within Palm
Beach County.
d) City of North Lauderdale – This grant retrofitted eleven single family
homes within the City of North Lauderdale.
e) Manatee County - This grant retrofitted thirteen single family homes
within Manatee County.
f) City of Jacksonville - This grant retrofitted six homes within Duval
County.
g) Homes for Independence - This grant retrofitted twenty-five single
family homes within Pinellas County.
h) Town of Century - This grant retrofitted ten single family homes within
Escambia County.
i) Hardee County - This grant retrofitted twelve single family homes within
Hardee County.
j) Archways, Inc. - This grant retrofitted four existing structures occupied
by clients possessing chronic, persistent, and severe mental illness within
Broward County.
Contract Award: $128,760.41
k) St. Lucie County - This grant retrofitted seven homes within St. Lucie
County.
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l) Rebuild NW Florida - This grant retrofitted fifty family homes within
Escambia & Santa Rosa Counties.
m) United Cerebral Palsy of NW Florida - This grant retrofitted five homes
within Escambia County.
n) ARC Gateway, Inc. - This grant retrofitted six existing structures
occupied by physically handicapped individuals within Escambia County.
The activity directly targets the needs of those persons will special needs
or perhaps limited resources such as the elderly, disabled or language
isolated individuals.
2. RCMP Outreach - The Division issued seven contracts with five separate
entities to conduct outreach programs to Florida homeowners and local
governments regarding wind mitigation across the state. These organizations
provided wind mitigation retrofit education to homeowners, local government
officials and staff through building code training, online residential retrofit
materials, and training of contractors for retrofit installation procedures. The total
contract award amount for outreach programs was $740,240.39. At the conclusion
of State Fiscal Year 2012, a total of $687,939.30 was expended. Therefore,
ninety-three percent of the award was utilized for public outreach programs.
The seven grants awarded outreach contracts for State Fiscal Year 2012 are
shown below. The contract award amounts and total expenditures as of June 30,
2012, are presented for each recipient.
a) City of North Lauderdale - produced a tri-fold brochure and held small
group workshops to educate residents about the need for residential wind
mitigation. The brochure included tips and resources that residents could
use to perform mitigation activities themselves or hire contractors to do
the work.
b) Florida State University - Outreach to local governments and
professionals in insurance, banking and real estate with tools for
promoting wind mitigation.
c) Florida Home Builders Association - Develop and conduct 18-20
workshops around the state for homeowners, demonstrating mitigation
techniques.
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d) Florida Home Builders Association - Develop and conduct seven (7) 7hour training events for construction industry around the state.
e) Florida’s Foundation - Develop and conduct twenty (20) workshops
around the state for homeowners, teaching wind mitigation techniques.
Total Expenditures: $76,112.00
f) Florida’s Foundation - Develop and conduct wind mitigation training at
five (5) events to reach local government staff and officials.
Total Expenditures: $51,764.00
g) Florida Association of Counties - Develop a course for local
governmental officials that incorporate all aspects of wind mitigation and
offer this five (5) times to local government across the state.
Specific Program Activities and Accounting - July 1, 2012 – December 31, 2012
A. Shelter Retrofits - According to Section 215.559 (2) (a) F. S., $3 million of the annual
$10 million appropriation for the Hurricane Loss Mitigation Program was directed to
retrofit existing public facilities to enable them to be used as public shelters. An annual
report of the state’s shelter retrofit program, entitled the Shelter Retrofit Report, is
prepared annually and separately submitted to the Governor and the Legislature pursuant
to section 252.385 F.S.
B. Tallahassee Community College - As required by Section 215.559 (1) (b) F. S.,
$2,800,000 was statutorily allocated to continue the Mobile Home Tie-Down Retrofit
Program, which is administered by the Tallahassee Community College (TCC). Based on
legislative direction the Division of Emergency Management provided this funding
directly to Tallahassee Community College.
C. Florida International University - As required by Chapter 215.559 (3), F. S., FIU is
allocated $700,000 from the annual appropriation to the Division for the Hurricane Loss
Mitigation Program.
Seven major efforts have been identified by the International Hurricane Research Center
team for RCMP FY 12-13 funding in the areas of structural mitigation analysis,
socioeconomic research and data dissemination to stakeholders and education/outreach:
Full-Scale Testing to Evaluate Code Prescriptive Design Wind Load and Attachment
Provisions for Hip, Ridge, and Eave Roof Tiles: Roof coverings are the most hurricane
vulnerable components of the building envelope system. Post storm reports indicated that
failure of tiled roofs during hurricane, in most cases, started either from hip, ridge or eave
tiles. This type of failure could be attributed to misrepresentation of design wind load for
those roof regions and/or inadequacy of code and standard provisions for hip, ridge and
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eave tiles attachments. The proposed project will investigate the causes of hip, ridge, and
eave tiles failure at high wind speed. The objectives of the project are:
1. Evaluate the wind induced load on hip, ridge, and eave tiles as prescribed in codes
for given building models;
2. Measure the uplift forces and pressures on those tiles using full-scale testing at the
newly built 12-fan Wall of Wind (WOW) facility at FIU;
3. Evaluation of the adequacy of code prescribed design wind load estimations for
hip, ridge, and eave tiles as they are located in flow separation and vortex regions;
4. Assessment of uplift resistance capacity of code prescriptive hip, ridge and eave
tiles attachment details under static load application;
5. High wind speed performance assessment of code prescriptive attachments for
hip, ridge and eave tiles under dynamic load induced by turbulent wind; and
6. Quarterly Reports and a Final Technical Report will be submitted covering in detail
the project results and findings.
Wind-induced Loads and Damage Mitigation Techniques on Florida Sunrooms The
proposed research project will examine the wind-induced loading on sunrooms attached to
low-rise buildings. Current wind standards and building codes of practice do not provide
sufficient guidelines on the design of sunrooms. A detailed parametric experimental study
will provide valuable information on the wind-induced load applied on sunrooms. The
acquired data will capture the effect of more complex wind flow on both surfaces of the
sunroom roof and will evaluate the net wind load. Most importantly, the findings will be
codified for future inclusion in wind standards and building codes of practice and will be
utilized to mitigate damage and develop retrofitting techniques for sunrooms. The following
deliverables have been established for this effort:
1. Detailed wind-induced pressure and force scheme on sunroom;
2. Design guidelines for future inclusion in State and National wind standards;
3. Evaluation of structural performance of current sunroom;
4. Damage mitigation and retrofitting techniques for sunroom; and
5. Quarterly Reports and a Final Technical Report will be submitted covering in detail
the project results and findings.
Wind Uplift of Roof Tiles, Shingles and Pavers - Roofing materials do not have a good record
of resilience against hurricane force winds. In hurricane winds materials such tiles, shingles, and
roof pavers or gravel ballast are frequently ripped off. The building itself then becomes
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vulnerable to additional damage through water infiltration and changes in internal pressure.
These weaknesses have caused considerable damage in the various tropical systems that have
affected the State of Florida. Building codes and standards specify roof wind pressures for a
few typical roof geometries but existing code provisions do not define well how to apply such
pressures roofing elements such as tiles, shingles, and pavers. The objective of this work would
be to develop guidance in code format for the design of roof shingles, tiles and pavers of
various sizes against hurricane wind forces. The results would also be applicable to the design of
green roofs where the earth and plant material are typically installed in pallets of various
dimensions. The benefit would be reduced damage to roofs, reduced losses due to the
consequent water infiltration and reduced damage to buildings in general through reduction of
the quantity of wind-borne debris. In the long term the knowledge developed would also assist
manufacturers and suppliers of roofing systems to make their products more resilient.
Deliverables for this effort include:
1. Report on literature review on uplift pressures on tiles, shingles and pavers;
2. Report on development and comparison of theoretical model with existing data on net
uplifts;
3. Report on Wall Of Wind test on pavers; and
4. Quarterly Reports and a Final Technical Report, including effectiveness of interlock
and strapping systems, preliminary guidelines for design against uplift.
Estimation of Surface Roughness Using Terrestrial LiDAR Measurements
The major damage from a hurricane to communities in the State of Florida is caused by high
wind and storm surges. Buildings, trees, and terrains, and the spatial arrangement of these
objects can greatly influence the turbulence structure of hurricane wind near the land surface.
High-resolution measurements of the terrain and surface roughness determined by the
arrangement of land surface objects are essential to understand and quantify the hurricane
damage to built structures and to create new hurricane resistant products. In order to accurately
estimate the surface roughness, large scale data on geometric shapes of buildings, trees, and
terrains are needed. We propose (1) to collect tree and building data at sample sites for various
types of land cover in South Florida using the terrestrial LiDAR system, and (2) to develop the
methods to extract buildings, trees, terrains from LiDAR measurements at these sites and to
estimate surface roughness parameters. Surface roughness models created from this research
will be used to determine hurricane wind interaction with various types of communities,
especially in urban settings. The deliverables of this project include:
1. Terrestrial LiDAR measurements for the selected areas in South Florida;
2. Methods to extract the terrain, buildings, and trees from terrestrial LiDAR
measurements;
3. Estimated surface roughness at the selected areas; and
Page 15
4. Quarterly reports and a final technical report will be submitted to cover the details
of the project.
Investigating Socio-economic Impacts of a Recent Hurricane and Understanding
Perceptions of Coastal Vulnerability
The most recent hurricane (Hurricane Sandy, 2012) devastated the Northeast region and still
many residents are suffering in the New York and New Jersey areas. Against this backdrop,
researchers propose to conduct a household survey to understand the socio-economic impacts of
Hurricane Sandy and resident’s preferences for hurricane risk mitigation. The survey data
collected through this project will allow us to estimate economic impacts of the recent hurricane.
Given that Florida has a significant number of populations living in coastal areas, the analysis
from this project will be very useful for planning purposes. For example, the analysis will be able
to provide insights how much total damages will be caused if a similar hurricane passes through
large urban areas in Florida (e.g. Miami). The social science research question addressed here is
novel, policy relevant and will provide useful inputs for planning purposes to the State of
Florida. The information will be useful to both academic community and decision-makers who
are working in addressing these challenges. Deliverables for this effort will include:
1. Summary of related research;
2. Details of survey design and the implementation of survey instrument;
3. Data analyses of survey responses; and
4. Results and findings of the survey and policy implications.
Education and Outreach Programs to Convey the Benefits of Various Hurricane Loss
Mitigation Devices and Techniques – For the 2012-13 performance period, the following
educational partnerships, community events, and outreach programs will be developed:
1. Wall of Wind Mitigation Challenge (WOW! Challenge): Teams comprised of
South Florida high school students will brainstorm innovative mitigation concepts
within a well-defined problem scope developed with IHRC academia. These
concepts will then be tested at the IHRC as part of a contest competition. The
student research will focus on developing hurricane mitigation techniques that will
lead to human safety, property loss reduction, insurance cost reduction, and a
"culture of preparedness" for natural disasters. This educational activity will also
increase student awareness in education and careers in area related to hurricane
mitigation. South Florida media and distinguished hurricane experts will be invited
to attend and participate in the competition, allowing for the opportunity for IHRC to
showcase its research projects and the Wall of Wind.
2. Museum Exhibit: A new interactive exhibit at the Miami Science Museum will
be developed and focused on hurricane science and mitigation. This new exhibit will
be an addition to the existing hurricane mitigation exhibition and provide an
Page 16
opportunity for the IHRC to promote its hurricane mitigation research to the
community. This collaborative community education outreach project will partner
the International Hurricane Research Center with the Miami Science Museum.
3. Hurricane Science, Mitigation & Preparedness Day (Feel the Force) - A
consultant will assist the IHRC in planning, coordinating and facilitating this public
education event at the Miami Science Museum. The event will showcase special
hands-on, interactive activities and demonstrations teaching hurricane science,
mitigation, preparedness and safety. This will include special learning activities for
parents and children, providing family fun throughout the day. South Florida media
and various distinguished hurricane experts will participate as guest speakers. This
collaborative community education outreach project will partner the International
Hurricane Research Center with the Florida Division of Emergency Management,
Miami-Dade County Emergency Management, the Miami Science Museum,
including collaboration with the National Hurricane Center and the Miami Office of
the National Weather Service.
4. National Hurricane Survival Initiative: A consultant will assist IHRC in
collaborating with the National Hurricane Survival Initiative and their annual
hurricane preparedness campaign for 2013.
5. Mitigation Event: This special event will help to focus policymakers and business
interests on the need for mitigation and promoting the benefits. The product of this
event should culminate in a three to five bullet point agenda to promote mitigation as
part of solving Florida’s insurance crisis and bring these ideas to the forefront of
the upcoming legislative session. This collaborative community education outreach
project will partner the International Hurricane Research Center with the Florida
Office of Consumer Advocate, and include collaboration with the hurricane
mitigation community.
D. Building Code Compliance and Mitigation - The State of Florida General
Appropriations Act for State Fiscal Year 2013 provided for a special allocation of $925,000 from
the Hurricane Loss Mitigation Program to fund the Building Code Compliance and Mitigation
program pursuant to Section 553.841, F. S. The purpose of this allocation is to provide training
and continuing education classes for Florida contractors.
E. Residential Construction Mitigation Program (RCMP) - The RCMP concentrates on
wind mitigation retrofits and outreach programs to Florida homeowners and local government
officials and its respective staff.
1. Wind Mitigation Retrofits - In anticipation of the annual appropriation for State
Fiscal year 2013, the Division issued a Request For Proposals (RFP) in May of 2012.
The RFP was issued prior to the start of the State Fiscal Year 2013 in an effort to allow
the full twelve months for project implementation. An evaluation committee was
appointed by the Division to evaluate submitted proposals solicited through the RFP
process as established by the State of Florida. Funds were awarded to local governments,
and non-profit organizations for wind mitigation retrofit projects. The total contract
Page 17
award amount for wind mitigation retrofit projects was $1,404,317.00. The ten grant
recipients that were awarded wind mitigation retrofit contracts for State Fiscal Year 2013
are as follows:
a) City of Cocoa Florida – Housing Rehabilitation and Mitigation
Improvement Project.
b) City of Melbourne – Melbourne Residential Retrofit Program 2012-2013.
c) Rebuild Northwest Florida – Residential Wind Retrofit Escambia and
Santa Rosa Counties.
d) Bay County – Salvation Army Rental Housing systemic Wind Retrofit.
Contract Award: $149.939.00
e) Town of Davie – Pre-Disaster Hardening/Mitigation Residential Program.
f) City of Tamarac – Residential Retrofit Program
g) Hardee County – Residential Wind Retrofit Project II
h) Community Action Program Committee – Ready, Set, Blow Residential
Retrofit.
i) St. Lucie County – St. Lucie County Housing Mitigation and
Preparedness Program
Contract Amount: $150,000.00
j) Town of Century – Century Residential Retrofit Program
2. Outreach Programs - RCMP’s outreach programs are designed for the Florida
homeowner and local government officials and their staff. The Division contracted with
four entities to update and conduct outreach programs to Florida homeowners and local
governments regarding wind mitigation retrofits across the state. These organizations
provided retrofit education to homeowners and local government officials and staff
through seminars, do it yourself presentations for retrofit installation procedures, building
code awareness, online residential retrofit materials, and training of local government
Page 18
officials and its staff regarding mitigation grant programs. The four
awarded contracts for State Fiscal Year 2013 are as follows:
entities are
a) Florida Home Builders – Residential Mitigation For Florida
Homeowners.
b) Florida’s Foundation – Make Mitigation Happen, Florida Homeowner
c) Florida PACE Funding Agency – Local Government Outreach
d) Florida Association of Counties – Wind Mitigation Training for County
Officials.
Contract Award: 108,000.00
OBSERVATIONS AND RECOMMENDATIONS
It is essential that the Division continue to work with Florida homeowners, local
governments, non-profit organizations and state agencies to reduce the risk of hurricane
losses. Research must continue to development stronger wind mitigation measures to
protect the citizens of Florida and its current and future housing stock. Therefore, the
following recommendations are made:
Observations and Recommendations
Observation - The Division, grant recipients, and contractors are continually under a
confined time constraint for awarding and expending the appropriated funds within one
fiscal year. Project solicitation, awarding, contracting, sub-contracting, actual mitigation
retrofits, and project closeout must be completed by the end of each fiscal year. This
condensed time frame does not allow the Division or its participants sufficient time to
take full advantage of the funding provided. The Division has moved the issuance of the
procurement documents into May of the preceding fiscal year appropriation and moved to
shorten its contract approval process. This approach has helped but more time is needed
to complete the cycle for wind mitigation retrofits and outreach programs.
Recommendation – Extend the funding and budget authority for the annual
appropriation for up to two years. This would allow the Division’s contracts to start upon
full execution and have a period of performance that would expire at the end of the
second fiscal year.
Page 19
Appendix A
2011-2012 ANNUAL REPORT TALLAHASSEE COMMUNITY COLLEGE
MOBILE HOME TIE DOWN PROGRAM
The program continues to grow as mobile home communities across Florida apply for the
program. Successful programs were completed in 13 mobile home communities across eight (8)
different Florida counties. Due to the economic constraints of the time, this was the first year the
program was responsible for removal and reinstallation of skirting on the individual homes. Over
one thousand seven hundred and seventy (1770) homes participated in the program to date.
Community nominations were again solicited throughout the previous fiscal years’ database and
from newly submitted nominations and letters of intent to be accepted into the program.
Tallahassee Community College and the Program Contractor, Windstorm Mitigation, Inc.
responded to over 500 parks and communities throughout the State. Due to the program
absorbing the cost of the skirting, Tallahassee Community College reviewed all past parks that
had been rejected to be sure the opportunity with the new guidelines was available to every
interested park. Tallahassee Community College contacted the Federation of Mobile
Homeowners (FMO) and Florida Manufactured Housing Association (FMHA) for any additional
parks or communities that may have an interest in the tie down program. Parks and communities
were contacted and a listing of over 2,000 parks and communities was updated for future use.
Each interested park was instructed to complete a Community Interest Verification Sheet
(attached). After phone contact with management and/or homeowners association
representatives, visual inspections via photos, websites and Google Earth these communities
were evaluated to determine the eligibility of a park for rating. The following criteria were used
when a park was chosen to be rated:
•
•
•
•
Interview with management and/or homeowners association representatives;
Soil compaction tests throughout the community;
Visual inspections of all homes within the community;
Intake training for the homeowners association representatives.
The following communities participated in the tie down program:
•
•
•
•
•
•
•
Tampa East, Hillsborough County, 21 homes
Tarpon Glen, Pinellas County, 82 homes;
Pinellas Cascades, Pinellas County, 161 homes;
Swiss Village, Polk County, 242 homes;
Whispering Pines, Highlands County, 45 homes;
Pinelake Village, Martin County, 194 homes;
Lake Highlander, Pinellas County, 176 homes;
Page 20
•
•
•
•
•
•
Lakeside Ranch, Polk County, 150 homes;
Golden Crest, Pinellas, 95 homes;
Spanish Lakes Golf Village, St. Lucie County, 266 homes;
Royal Oaks, Polk County, 47 homes;
Wayne Frier Mobile Home Park, Suwannee County, 103 homes; and
Camelot Lakes, Sarasota County, 188 homes.
For each park accepted into the program, homeowner’s meetings were offered. The program was
explained in detail and representative/s from Tallahassee Community College and/or Windstorm
Mitigation, Inc. remained until every question was answered. As always, an integral part of these
meetings were segments on resident safety and educating the homeowners to follow all
instructions from their local emergency management officials during weather related events.
Please refer any questions relating to this report or the Program in general to:
Amy Bajoczky
Tallahassee Community College
444 Appleyard Drive
Tallahassee, Florida 32304
850/201-8025
Page 21
TIE- DOWN PROGRAM
The State of Florida has allocated grant money through a contract with Tallahassee Community
College to implement the Manufactured Home Tie-Down Enhancement Program. This program
involves the enhancement of the tie-down and anchoring system of your home. The program
does not intend to bring existing mobile homes up to code but to make the home as wind
resistant as funding, physical characteristics and condition of premises permit. Please note:
Each home worked on may receive differing quantities of new equipment depending on size,
condition and physical characteristics.
This program is at NO COST to you. Skirting removal and re-installation on eligible homes is
now being offered with the Program. All fees paid to the contractor are paid directly by
Tallahassee Community College through a grant from the state. The additional tie-down and
anchoring systems could help minimize damage to the mobile home arising from windstorms,
tornadoes or hurricanes.
Eligibility to receive inspections and tie-down services is limited to the following:
1. A 60% mandatory participation rate is required and each manufactured home must be
located within the boundaries of the mobile home community.
2. The manufactured home must be built and installed prior to September 1999.
3. The minimum height requirement for the manufactured home shall be at least 15” inches
from the ground to the sidewall of the home.
4. The homeowner must properly complete and sign the AUTHORIZATION-TOPROCEED FORM.
5. Skirting removal and re-installation on eligible homes is included in the Program. All
skirting must be easily removable and able to be re-installed with minimal effort. Repair
or replacement of any skirting will be the sole responsibility of the homeowner. Please
note: Windstorm Mitigation Inc. reserves the right not to remove and re-install any
skirting which does not meet Program requirements. Tallahassee Community College and
the State of Florida are not responsible for the removal and re-installtion of the skirting.
6. The homeowner shall be responsible for the preparing and cleanup of the home site for
the enhancement project. Preparation may include removal and replacement of shrubbery,
furniture under carports and within screen rooms, etc.
7. To nominate your community for this program please send a letter of interest stating the
name, address of your community along with contact information to:
Tallahassee Community College
Attn: Amy Bajoczky
444 Appleyard Drive
Tallahassee, Florida 32304-2895
[email protected]
Page 22
2012-2013 Tie-Down Enhancement Program
Community Interest Verification
Date:
Community:
Contact:
Address:
County:
Telephone:
Email:
Please consider this a letter of interest for the Tie-Down Enhancement Program,
Here are the answers to the following questions.
1. Yes or No - Is your Community interested in qualifying for the Tie-Down Enhancement
Program? If yes, continue with questions.
2. Yes or No - If chosen to participate, will the owner(s) of the community allow the Program
access to the property?
3. Yes or No – Does your Community/HOA have the ability to organize and acquire the 60% of
eligible homes needed to participate in the Program?
4. How many homes are in your Community?
5. How many homes in the Community are vacant, up for sale or not occupied?
6. What year was your Community established?
7. What percentage of homes were installed before 1999?
%
8. What is the approximate height from the ground to the bottom of home (sidewall)?
Please measure.
9. What type of skirting is on the homes?
vinyl, stack, block. metal, slats, mortared, block, lattice, vinyl, siding to ground
10. Yes or No - Is the skirting easily removable?
Page 23
Appendix B
FIU
Annual Report
Page 24
A Resource for the State of Florida
HURRICANE LOSS REDUCTION
FOR HOUSING IN FLORIDA
FINAL REPORT
For the Period March 30, 2012 to July 31, 2012
A Research Project Funded by:
The State of Florida Division of Emergency Management
Through Contract #12RC-5S-11-23-22-369
Prepared by
The International Hurricane Research Center (IHRC)
Florida International University (FIU)
August 1, 2012
Final Report
Table of Contents
Executive Summary
Section 1
Development of Hurricane Resilient Composite Structural Insulated Wall
Systems for Residential Buildings (PI: Dr. Arindam Gan Chowdhury, Co-PI: Dr.
Nakin Suksawang)
Section 2
Computational Evaluation of Wind Load on Residential Roofs with Complex
Shape (PI: Dr. Girma Bitsuamlak, Co-PI: Dr. Amir Mirmiran)
Section 3
Investigating Household Perceptions of Coastal Vulnerability and Preferences for
Risk Mitigation (PI: Pallab Mozumder)
Section 4
Estimation of Surface Roughness Using Airborne LiDAR Data (PI: Keqi Zhang)
Section 5
Education and Outreach Programs to Convey the Benefits of Various Hurricane
Loss Mitigation Devises and Techniques (PI: Erik Salna)
Section 6
Section 1
2
Section 1
Executive Summary
Five major efforts were identified by the International Hurricane Research Center (IHRC) for the
Residential Construction Mitigation Program (RCMP) Fiscal Year 2011-2012 funding in the
areas of structural mitigation analysis, socioeconomic research, data dissemination to
stakeholders and education and outreach:
Development of Hurricane Resilient Composite Structural Insulated Wall Systems for
Residential Buildings (PI: Arindam Gan Chowdhury): The main purposes of a building
envelope are to protect occupants from ambient and extreme weather conditions, to regulate
environmental loads for building operation, and to provide safe, comfortable and stable indoor
conditions. Holistic experimental approaches for evaluating the performance of building
envelope systems continue to facilitate the process of choosing the best building envelope
material and construction method for a given application. The experimental approach presented
in this study focused on integrating a hygrothermal performance study (the performance under
heat, air, and moisture transfer) with the natural or environmental forcing functions (solar
radiation, wind, and wind-driven rain). The objective of this research was to evaluate the
performance of two different building envelope systems under simulated environmental loads
generated by the 12-fan Wall of Wind at Florida International University (FIU). The
experimental results indicate that the thermal bridging effect can cause significant heat loss/gain
through the building envelopes. In addition, thermal performance of building enclosure system in
different types of weather conditions (e.g., wind-driven rain) could be different and needs to be
considered in the development of alternative building envelope solutions. The data collected
through this project will be useful in the advancement of technological solutions of building
envelope systems towards achieving net-zero energy buildings.
Computational Evaluation of Wind Load on Residential Buildings with Regular and
Complex Roof Shapes (PI: Girma T. Bitsuamlak) - Wind induced loads are one of the most
critical design parameters for coastal construction, especially in Florida, where buildings are
subjected to the highest wind loads of the nation. ASCE 7 2005/2010 provides wind loads for the
design of Main Wind Force Resisting System (MWFRS), as well as Cladding and Components
of buildings. These provisions cover buildings with common shapes, such as buildings with Flat,
Gable, Hip, and Mono-slope roofs, under simple surrounding conditions. For complex roofs, and
surrounding conditions the code refers the practitioner to perform physical modeling in a
Boundary Layer Wind Tunnel (BLWT). Although these tests are viable for high-rise buildings
and other large complex projects, they may not be cost effective for residential houses. As a
result, there is a gap in the wind load information for the construction of low-rise buildings with
complex roofs.
Section 1
3
The present study attempts to evaluate wind loads on both common and complex roofs using a
numerical approach based on the technique of Computational Fluid Dynamics (CFD) principles.
The focus is on residential single family house roofs, which incur the most damages during
hurricane events. Both in-house computer programs and commercial software were used in the
study. The models with complex roof shapes showed mixed pressure distribution on the roof
(positive and negative pressure) as opposed the regularly shaped models where separation and
reattachment location are clearly known. On both roof models, high suction pressures were
observed on areas close to the windward edge and near the middle ridge. The highest magnitude
roof suction pressures were observed in the corner areas close to the edges for both roof types.
On the hip roof model, the highest suction pressure was observed when the wind came from
diagonal directions, while the highest suction pressures on the gable roof model was observed
when the wind comes perpendicular to the short dimension.
Estimation of Surface Roughness Using Airborne LiDAR Data (PI: Keqi Zhang) - Surface
roughness is an important modeling parameter for determining impacts of hurricane wind on
buildings. Remote sensing technology provides an effective way to estimate the surface
roughness in a large area. However, traditional optical remote sensing imagery does not provide
heights data of terrains, buildings, and trees required for the calculation of surface roughness.
Airborne LiDAR remote sensing overcome the disadvantage of the optical remote sensing by
providing direct measurements horizontal coordinates and vertical elevations of the objects on
the Earth surface. The Florida Division of Emergency Management (DEM) collected LiDAR
data for coastal areas in Florida in 2007. IHRC researchers developed methods (1) to extract
terrains, buildings, trees from LiDAR measurements, (2) to compute the surface roughness using
extracted terrains, buildings, and trees based on five surface roughness models, and (3) compare
the surface roughness values from LiDAR with those from the national land cover datasets
created based on Landsat imagery. The application of the methods on two test sites in Miami
shows that the algorithms classified terrain, buildings, and trees successfully with minor errors.
The comparison of LiDAR derived roughness lengths with the NLCD based roughness length
indicates that two types of roughness values agree reasonably.
Reducing Losses From Extreme Hydro-meteorological Events: Insights From a Survey of
Florida Households (PI: Pallab Mozumder) - With over $2.5 trillion of insured coastal
exposure, including over $1.25 trillion in residential exposure, situated in the heart of the
Atlantic Hurricane Basin, the State of Florida ranks as one of the most vulnerable places to
natural disaster losses in the world. This research focused on risk perceptions and mitigation
behavior among a diverse sample of households from across the State of Florida. Researchers
investigated how households perceive the annual threat of property damage from hurricanes,
particularly from major hurricanes (Category 3, 4, and 5); projections of more destructive
hurricanes due to climate change; institutional accountability overall and institutional
effectiveness at disaster risk reduction. Researchers then explored households’ preferences for
Section 1
4
an array of mitigation measures and insurance reforms to enhance coastal resilience, in addition
to a households’ willingness to invest in a menu of realistic, low and higher cost roofing and
opening protection options.
A database of contact information for over 400,000 households who had applied to the
My Safe Florida Home (MSFH) Program beginning in 2007 was acquired from the State of
Florida. A random sample of 40,000 households, whose email addresses were available, was
selected for the survey study. Analysis of 1,710 survey responses from households across the
State of Florida revealed the following core findings:

A large majority of households (69%) support the creation of the proposed ‘Florida Pre-Disaster
Mitigation Fund’, to sponsor an expansion of pre-disaster mitigation programs (with additional
state funding above and beyond federal dollars)

A large majority of households (76%) are highly supportive of increasing setbacks along
Florida’s shoreline to enhance coastal resilience.

A large majority of households (70%) are highly supportive of strengthening Florida building
codes to enhance coastal resilience.

A large majority of households (72%) are highly supportive of new elevation standards for
Florida’s roads and buildings to enhance coastal resilience.

Only 42 % of households are highly supportive of continued State surcharges and assessments to
pay for hurricane losses after-the fact.

A large majority of households (73%) are highly supportive of a comprehensive insurance
program (combined flood and wind insurance program).
Our findings suggest avenues for potential risk reduction strategies that can be implemented by
federal, state, and local agencies, including county and municipal governments in vulnerable
coastal communities. Additional findings can be viewed in the full report.
Mitigation Devises and Techniques (PI: Erik Salna) – IHRC staff developed and coordinated
educational partnerships, community events, and outreach programs. This work promoted
hurricane-loss mitigation and the objectives of the RCMP and included the following:
Hurricane Mitigation & Hurricane Andrew 20th Anniversary Museum Exhibition: The Miami
Science Museum assisted IHRC in developing and coordinating a new gallery of hands-on,
Section 1
5
interactive exhibits and displays. The exhibits and displays focus on the science and benefits of
hurricane mitigation, preparedness, hurricane forecasting and tracking and promoting a "culture
of preparedness" for all natural hazards. This collaborative community education outreach
project partners the IHRC with the Florida DEM, Miami-Dade County Emergency Management,
the Miami Science Museum, including collaboration with the National Hurricane Center,
NOAA’s Atlantic Oceanographic and Meteorological Laboratory (AOML) and the Miami Office
of the National Weather Service.
Hurricane Science, Mitigation & Preparedness Day (Feel the Force): The IHRC and the Miami
Science Museum partnered with Miami-Dade County Emergency Management to develop, plan,
coordinate and facilitate Hurricane Science, Mitigation & Preparedness Day (Feel the Force) at
the Museum. Close to 2,000 people attended this public education event that showcased
hurricane science, mitigation, preparedness and safety and IHRC Wall of Wind research and
demonstrations.
National Hurricane Survival Initiative: The IHRC collaborated with the National Hurricane
Survival Initiative (http://hurricanesafety.org/) and their annual hurricane preparedness program,
“Get Ready, America! The National Hurricane Survival Test.” The 2012 version of the program
looked back on the devastation wrought by Hurricane Andrew in 1992, the lessons learned since
then, and what you need to know and do to stay safe before, during and after hurricane season.
This year’s broadcast participation was the largest one ever, with 60 television network affiliate
stations from Texas to Maine.
Hurricane Andrew 20th Anniversary Event at the Miami Science Museum: The Miami Science
Museum assisted IHRC in planning this special community event to commemorate the 20-year
anniversary of Hurricane Andrew’s landfall. Panel discussions will occur throughout the day
with high profile Andrew experts and the new Hurricane Andrew exhibit will be highlighted.
Hurricane Andrew 20th Anniversary Event and Grand Opening of the 12-Fan Wall of Wind:
IHRC has partnered with Miami-Dade County Emergency Management, the National Hurricane
Center, NOAA’s Atlantic Oceanographic and Meteorological Laboratory (AOML), the Miami
Office of the National Weather Service and the City of Homestead in planning this official South
Florida community event to commemorate the 20-year anniversary of Hurricane Andrew’s
landfall.
Wall of Wind Neighborhood Open House: IHRC reached out to the local FIU South Florida
Community and invited local residents and families to come and learn about the importance of
hurricane mitigation and wind engineering research through presentations, activities and tours
and demonstrations of the new 12-Fan Wall of Wind.
Section 1
6
2012 IHRC Project Research Team
Principal Investigator
Principal Researchers:
Arindam Gan Chowdhury
Nakin Suksawang
Amir Mirmiran
Cheng-Xian (Charlie) Lin
Girma Bitsuamlak
Erik Salna
Pallab Mozumder
Keqi Zhang
FIU
FIU
FIU
FIU
FIU
FIU
FIU
FIU/IHRC
Wind Engineering
Civil Engineering
Civil Engineering
Civil Engineering
Wind Engineering
IHRC
Sociology
Research Associates and Assistants
Thomas Baheru
FIU
Ramtin Kargarmoakhar
FIU
Agerneh K. Dagnew
FIU
Edward Ledesma
FIU
Workamaw Warsido
FIU
Maryam Asghari Mooneghi FIU
FIU
Christian A. Vidal
Kristofer Shretha
FIU
Jie Huang
FIU
Yuepeng Li
FIU
Huiqing Lui
FIU
Evan Flugman
FIU
Civil Engineering
Civil Engineering
Civil Engineering
Civil Engineering
Civil Engineering
Civil Engineering
Civil Engineering
GeoScience
IHRC
IHRC
IHRC
Sociology
Administrative & Laboratory Staff
Walter Conklin
FIU
James Erwin
FIU
Roy Liu Marques
FIU
Donya Bernard
FIU
IHRC
IHRC
IHRC
IHRC
Section 1
7
FINAL REPORT
SECTION 2
Development of Hurricane Resilient Composite
Structural Insulated Wall Systems for Residential
Buildings
Prepared by
Dr. Arindam Gan Chowdhury and Dr. Nakin Suksawang
August 1, 2012
Development of Hurricane Resilient Composite Structural
Insulated Wall Systems for Residential Buildings
Prepared By:
Thomas Baheru (PhD Student)
Ramtin Kargarmoakhar (PhD Student)
Investigators:
PI: Arindam Gan Chowdhury, PhD, Assistant Professor
Co-PIs: Nakin Suksawang, PhD, Assistant Professor
Amir Mirmiran, PhD, P.E., Professor
Cheng-Xian (Charlie) Lin, PhD, Associate Professor
Research Scientists: James Erwin
Roy Liu Marques
Department of Civil and Environmental Engineering
Florida International University
In Partnership with
July 2012
Table of Contents
1. Introduction ..................................................................................................................... 1
2. Research Objectives ........................................................................................................ 5
3. Conduction ...................................................................................................................... 8
3.1. Introduction .............................................................................................................. 8
3.2. Test Setup .............................................................................................................. 11
3.3. Test Result and Discussion .................................................................................... 16
3.3.1. Infrared thermography .................................................................................... 16
3.3.2. Surface heat flux/ temperature measurements ................................................ 19
4. Convection .................................................................................................................... 31
4.1. Introduction ............................................................................................................ 31
4.2. Test Setup .............................................................................................................. 35
4.2.1. Instrumentation ............................................................................................... 38
4.2.2. Test protocol ................................................................................................... 40
4.3. Test Result and Discussion .................................................................................... 41
5. Conclusions ................................................................................................................... 55
6. Future Studies ............................................................................................................... 58
References
Executive Summary
Building envelope systems play a key role in the design phase of energy-efficient buildings. The
main purposes of a building envelope are to protect occupants from ambient and extreme weather
conditions, to regulate environmental loads for building operation, and to provide safe, comfortable and
stable indoor conditions. Thus, the design of building envelope systems involves many considerations.
The proper selection and design of a building envelope system should carry the notion of sustainability by
fulfilling the following four aspects: (1) resiliency: maintaining integrity in extreme events such as
hurricanes and recovering from these events with minimal effort, (2) durability: having an extended
service life and maintaining function with minimum need for maintenance and repair, (3) energy
efficiency: conserving non-renewable energy and profiting from renewable energy during the building
service life, and (4) cost effectiveness.
Holistic experimental approaches for evaluating the performance of building envelope systems
continue to facilitate the process of choosing the best building envelope material and construction method
for a given application. The experimental approach presented in this study focused on integrating a
hygrothermal performance study (the performance under heat, air, and moisture transfer) with the natural
or environmental forcing functions (solar radiation, wind, and wind-driven rain). The objective of this
research was to evaluate the performance of two different building envelope systems under simulated
environmental loads generated by the 12-fan Wall of Wind at Florida International University (FIU). The
experimental result indicates that the thermal bridging effect can cause significant heat loss/gain through
the building envelopes. In addition, thermal performance of building enclosure system in different types
of weather conditions (e.g., wind-driven rain) could be different and needs to be considered in the
development of alternative building envelope solutions. The data collected through this project will be
useful in the advancement of technological solutions of building envelope systems towards achieving netzero energy buildings.
Section 2
1
1. Introduction
Building envelope systems are designed to fulfill the requirements of protecting building
occupants from natural disasters and to satisfy daily comfort demands with minimal use of energy. These
two functional requirements involve the determination of building envelope performance under extreme
and normal weather conditions. While safety-enhancing advancements to building envelope systems have
been achieved to reduce the risks from recurring extreme weather conditions and/or natural disasters, such
as tropical storms and hurricanes, the technological advancement of building envelope systems to balance
the high energy demand under normal operating weather conditions has shown little development. In
recent decades, building energy demand has increased significantly, prompting the need for an alternative
building envelope system that can withstand hurricane wind loads and simultaneously provide economical
solutions to reduce daily energy consumption.
The energy efficiency of a building is highly dependent on the building’s envelope system. The building
envelope plays a major role in protecting occupants from ambient and extreme weather conditions,
regulating environmental loads for normal building operation, and providing a safe, comfortable and
stable indoor condition for occupants. Proper selection of building envelope systems for sustainability is
achieved by fulfilling the following four criteria: (1) resiliency: maintaining the integrity in extreme
events such as hurricanes and recovering from these events with minimal effort, (2) durability: having an
extended service life and maintaining functionality with minimal need for maintenance and repair, (3)
energy efficiency: conserving non-renewable energy and profiting from renewable energy during the
building’s service life, and (4) cost effectiveness. A well-chosen and well-designed building envelope
system is an effective means to reduce a building’s space heating/cooling loads, thereby improving
energy-efficiency.
The architectural and physical properties of a given building are the most important parameters that
influence energy consumption, and these include the building’s thermal mass, structural material and
shape. Building energy consumption is also greatly affected by local climate. The combined effect of
Section 2
2
these factors will impact the heat transfer and temperature distribution through building materials and
assemblies. Heat transfer and building temperature distribution are important for assessing energy use,
thermal movements, durability, and the potential for moisture problems. Three primary mechanisms of
heat transfer are:
i.
Conduction – the flow of heat through a material by direct molecular contact; contact occurs
within a material or through two materials in contact.
ii. Convection – the transfer of heat by the movement or flow of molecules (liquid or gas) with a
change in their heat content.
iii. Radiation – the transfer of heat by electromagnetic waves through a gas or vacuum; all objects
above absolute zero (zero degree Kelvin or Rankine) radiate heat energy. Building envelopes are
generally exposed to solar short wave radiation and sky long wave radiation; however, for the
building enclosure the net radiation heat transfer is of interest.
Building enclosures have all three types of heat transfer mechanisms interacting between the
exterior and interior environments. Conduction occurs in the solid section of the building envelop and is
mainly dependent on the material conductivity. Convection occurs between the surface of the enclosure
and environment, and it depends on the wind velocity, the wind direction with respect to the building, and
the building envelope’s roughness. Building surfaces also have a heat interaction with surrounding objects
through radiation. The surface color and temperature of the building surface, as well as surrounding
objects, are among the most important governing factors on the quantity of heat exchange.
To measure the conductive thermal performance and behavior of building and construction materials, Rvalue (thermal resistance) ratings are commonly used. This parameter shows the ratio of the temperature
difference across the surfaces of an object and the heat flux through it. The R-value is a measure of how
well an insulation product resists the flow of heat or cold. The R-value depends on the type of insulation,
including the material, thickness, and density. Higher R-values indicate greater insulating effectiveness of
Section 2
3
a given material. Thus, one way to improve the thermal performance of the building envelop is to choose
materials with high thermal resistances (i.e., a higher R-values), which would lower the heat conduction.
It is noted that when calculating the total R-value of a multilayered wall, all R-values of the individual
layers must be added.
Thermal properties of building enclosure materials have been studied by different researchers, and
different methods were proposed for finding thermal characteristics of various building materials
(Balocco et al. 2008; Cucumo et al. 2006; Evola et al. 2011). According to the literature, there is a
considerable discrepancy between the practical R-value of in situ building envelopes and the R-value that
is determined by laboratory measurement (Peng and Wu 2008). The thermal resistance of the wall
determined in the field might present a significant deviation from the resistance determined from the test
model. This deviation is due not only to the inevitable differences among supplies of the same material
made at different time periods, but also to the inevitable differences in the conditions of wall construction
(Laurenti et al. 2004). Due to current initiatives to improve and maximize energy efficiency, the need for
convergence between a building’s predicted and actual energy consumption has proven to be an essential
factor in the design and construction processes. Therefore, this deviation demonstrates the importance of
measuring a material’s thermal resistance in the real operating conditions, which, in most cases, results in
lower R-values than predicted in the laboratory.
It is well documented that ambient thermal losses from a building surface or a roof mounted solar
collector represent an important portion of the overall energy balance and depend heavily on the windinduced convection (Palyvos 2008). Convective heat transfer from the building enclosure surface is the
second mechanism of heat transfer. The convective heat transfer coefficient (CHTC) is the value used in
modeling the heat loss from the building enclosure surface to the ambient environment and is commonly
shown by h. According to the literature, improper use of CHTC can easily cause 20–40% errors in energy
demand calculations. However, the correct value for this parameter is not very well documented (Emmel
and Mendes 2005). Building envelope convection is influenced by a number of factors, such as wind
Section 2
4
velocity, wind direction, flow regime, building surface roughness, and temperature differences between
the external surface and ambient air. A better understanding of the convection on the external surface of a
building can provide a more accurate estimation of the heat load on the building. But the complex
relationship between building envelope convection and critical wind parameters makes it difficult to
accurately measure the convective heat transfer coefficients (CHTCs) on the external surface of any
building, and is subject to measurements under environmental conditions that cannot be controlled or
repeated.
The current study considers the effect of the first two heat transfer mechanisms on the thermal
performance of two different types of construction techniques:1) a traditional 2x4 stick frame building
with vinyl siding and 2) an innovative structural insulated panel (SIP) envelope. The heat transfer study
for these two building envelope systems was broken into two phases:
i. The first phase considered the conduction portion of the heat transfer and determined the thermal
resistance for the two building envelope systems. This section focused on experimental
measurements on building walls involving thermal bridges caused by structural components.
ii. The second phase sought to establish a comprehensive and quantitative prediction basis for the
convective heat transfer coefficient (CHTC) for building envelopes. A series of experiments were
performed simulating different environmental conditions. Multi-point measurements of surface
heat balance led to a distribution of the CHTC on an actual building envelope.
2. Research Objectives
The goal of this project was to investigate the thermal performance for two types of building
enclosure systems under simulated weather conditions. The research focused on evaluating the thermal
performance to minimize the cooling and heating load of residential buildings, which typically accounts
for 80% of the total home energy demand. The impact of the thermal bridging effect on the overall
thermal performance of the building enclosure was studied by comparing the thermal conductivity of the
Section 2
5
clear wall sections versus the sections with thermal bridging effects. Also the effect of wind direction,
velocity, and envelope materials on the (CHTC) distribution on the building enclosure was considered.
This knowledge will lay the foundation to mitigate adverse effects of thermal bridging and wind on the
thermal performance of envelope systems. The specific objectives of the project were:

Holistic testing of selected full-scale building envelope systems to evaluate impacts of thermal
bridging on the overall performance of the building enclosure. This specifically concerns the
quantitative evaluation of heat losses by thermal bridges.

Measuring the thermal resistance for two common types of buildings in-situ. Results of this study
were compared with laboratory test results to indicate the effects of testing conditions on the
thermal resistance.

Performing full-scale CHTC measurements on the walls and roof of two large-scale building
models, and comparing the results under different simulated environmental conditions. Simulated
environmental parameters included wind direction, wind speed, rain and sun.

Studying different types of building envelop materials to obtain the effects of the surface
roughness and color on the overall convection at the building surface. Also, roof results will be
compared with wall results to demonstrate the effects of orientation of building envelope subsystems (roof vs. walls) on the convection.

Evaluating the total effect due to all of the heat transfer mechanisms from the building envelope.
This will provide insight to the comparative effects of conduction, convection and radiation
mechanisms on the heat loss from the enclosure.
To achieve these goals, the scope of work for this project consisted of the following tasks:
i.
Construction of full-scale building models incorporated with selected building envelope systems.
Section 2
6
ii. Conducting performance-based testing of select full-scale building envelope systems under
ambient environmental conditions, and using the 12-fan Wall of Wind (WoW) facility to simulate
the external wind conditions.
iii. Using infrared (IR) photography to demonstrate the locations where thermal bridging occurs for
the different building envelope systems, and using finite element software for quantifying the
bridging effect numerically.
iv. Installing measurement equipment on the building surface to measure the heat transfer by
conduction and radiation from the building envelope.
v. Data analysis to determine the effects of ambient and environmental loads on building envelope
systems.
Section 2
7
3. Conduction
1.1. Introduction
The past few decades have seen tremendous improvements in the thermal efficiency of building
envelopes. The political risk of energy shortage in the 1970’s (Hirsta et al. 1982; Hirsta and O’Neal
1979), the strive for greater indoor comfort, the creation and implementation of measures to reduce
greenhouse gas emissions, and the rising cost of energy management in buildings, have all led to stricter
requirements and regulations for building performance. From both economic and environmental
conservation perspectives, it is beneficial to design buildings with high thermal insulation characteristics,
which will result in the long-term benefits of reduced cooling costs, and reduced environmental pollution.
Thus, an accurate understanding of a building envelope material’s thermal performance with respect to
environmental cooling and heating loads plays an important role in the design of energy efficient
buildings. In some conditions, the real R-value of building envelope components does not always agree
with the values advertised by manufacturers or those estimated during the design phase. Therefore, it is
important to measure and analyze the building envelope R-value in situ.
Two methodologies are commonly used for measuring the thermal resistance of the building materials
(Yesilata and Turgut 2007). One group of methods is known as steady-state measurement techniques,
which are based on establishing a temperature gradient over a known thickness of a sample to control the
heat flow from one side to the other side. The guarded hot-plate method, the heat-flow meter technique,
and the hot-box technique are the three main techniques used in steady-state measurements. In particular,
the heat-flow meter method is usually applied to test the insulation parameters of building envelopes in
situ because of its lightweight components and matching parts. Its main disadvantage is that the difference
between testing results and theoretical values is greater than that of the hotbox technique(Peng and Wu
2008).The other group of methods for thermal performance measurements is known as transient
(dynamic) measuring techniques, which were established during the last few decades for studying
materials with high thermal conductivities and for taking measurements at high temperatures. Contact
Section 2
8
techniques and optical techniques are the two main types of the transient methods. Optical transient
techniques are more expensive and more complicated in comparison with the contact techniques. For the
scope of this study, the heat flow meter technique was used for measuring the thermal resistance of the
two building enclosures. This method was advantageous because it had simple test set up requirements
and could be used for in situ measurement conditions.
As previously indicated, one of the simplest solutions for decreasing a building’s heat losses is to utilize
insulation materials during the construction of building envelopes. However, the thermal bridging effect
lowers the efficiency of thermal insulation installed in the building enclosure. A thermal bridge is defined
as a building element where a significant change in the thermal resistance occurs compared to that of the
envelope, due to the presence of materials with a higher thermal conductivity, as well as to the change in
the geometry of the fabric, as in the case of the junction between roofs, floors, ceilings and walls (EN ISO
10211-1 2007).As a result, a multi-dimensional heat flow is locally generated, which adds to the heat flow
normally transmitted through the envelope surfaces; this means that thermal bridges increase winter heat
losses and summer heat gains. Furthermore, the local reduction of the thermal resistance yields a decrease
in the temperature of the inner surface over the thermal bridge during the heating season, which might
cause condensation and mold growth; this would imply the deterioration of the building materials and a
reduction of the indoor air quality. This condition can also occur for the outer surface of the building in
the cooling season.
The problem of thermal bridges, appearing for example at the junction between two separately insulated
elements, or between a vertical and a horizontal element, is not always dealt with properly in the design
stage. Simplified calculation methods that neglect or reduce the impact of thermal bridges may lead to
significant deviation between predicted and actual thermal losses through the building’s envelope,
depending on the thermal insulation solution opted for. This leads to underestimated thermal losses during
the design process, during the insulation study, or in the various calculation methods, and, consequently,
to higher energy requirements in practice versus the design estimates. Moreover, in many countries,
Section 2
9
actual construction practices tend to only partially implement the insulation foreseen by regulations
because of construction difficulties, conflicting stability issues of the various building elements, the lack
of properly trained/qualified personnel and minimal or inefficient controls on behalf of the authorities
having jurisdiction.
Evola et al. (2011) identified two different types of thermal bridging effects: a linear thermal bridge and a
point thermal bridge. A linear thermal bridge occurs at the junction between two or more elements of the
building envelope. In this type, it is possible to identify an axis along which the orthogonal section of the
thermal bridge does not change. A point thermal bridge is located where the continuity of the insulation
material is locally interrupted at one point, such as three-dimensional corners.
Prior research has been carried out to determine the relative influence of thermal bridges in the overall
heat losses (Zalewski et al. 2010). A simple method is explained in ASHRAE2009 Fundamental
Handbook (ASHRAE 2009) for evaluation of thermal bridges at the interface between the wall sheathing
and the structural support system caused by the presence of a metal or wooden frame. This method is
based on integrating a linear thermal transmittance, and, in most cases, underestimates global heat losses
through the walls. Several researchers have tried to find more realistic thermal bridge estimates by using
two-dimensional numerical simulations, and they have compared their results to the values obtained from
simple estimation methods available in design codes (Deque et al. 2001; Dilmac et al. 2007; Larbi 2005).
Other researchers have analyzed the thermal bridging phenomena utilizing either three-dimensional
modeling or unsteady analyses (Hirsta et al. 1982; Kosny and Kossecka 2002; Mao and Johannesson
1997).
Thermal bridges occurring in the building envelope can be visualized through IR thermography, a
technology that has been increasingly implemented for building envelope studies over the past few years
(FivosSargentis et al. 2009; Fokaides and Kalogirou 2011;Zalewski et al. 2010). This technique is
advantageous for building envelope studies because it allows the visualization of heat losses: (1) in
Section 2
10
situ,(2) at a distance (without contact), (3) at the scale of the building, and (4) without intruding into the
building walls (non-destructive technique) (Zalewski et al. 2010).
Infrared thermography has many different applications in building enclosure performance analysis. IR
thermography can be used in building envelopes to detect heat losses, and to locate missing or damaged
thermal insulation in walls and roofs, thermal bridges, air leakage, and sources of moisture. IR
thermography can also be employed in building diagnostics for the determination of the thermo-physical
properties of building envelopes.Balaras and Argiriou (2002) suggested IR thermography in an exhaustive
review of thermal diagnostics for buildings, Wiggenhauser (2002) discussed the coupling of IR
thermography with moisture analysis in building materials, and Dufour et al. (2009)focused attention on
the use of IR thermography in the context of crack detection and their evolution in masonry walls.
Fokaides and Kalogirou (2011) used IR thermography to determine the overall heat transfer coefficient
(U-Value) for building envelopes.
1.2. Test Setup
The conduction portion of this project considered a study of the thermal resistance of the two
building envelope systems under investigation: 2x4 stick frame construction (Figure 1a) and SIP panels
(Figure 1b). The effect of thermal bridging on the overall performance of the two systems was evaluated.
Thermal performance data were obtained from in situ measurements performed on two large-scale,
single-story building models, each located on the College of Engineering and Computing Campus of
Florida International University (FIU) in Miami, FL, and exposed to ambient weather conditions for a
period of three days. Each building model measured 2.4 × 2.75 × 2.2 m (L × W × H; H refers to the
building eave height). Figure 2 illustrates the plan view of the two building models and the location of the
door and windows on each wall. Both building models were positioned such that the “front” wall—
defined for each building as the wall having a door—faced north, and the corresponding “side” wall faced
west. Figure 3 shows the typical section for the traditional wooden frame building, having plywood
sheathing, fiberglass insulation, and covered with gypsum board and vinyl siding as interior and exterior
Section 2
11
finishes, respectively. The SIP building was constructed from prefabricated cementitious SIP panels, a
in thick layer of polystyrene foam sandwiched between two fiber cement boards.
a.
b.
Figure 1: Building models: a) 2x4 stick-frame building; b) SIP building
.
Figure 2: Building plan dimensions (All dimensions are shown in inches)
Section 2
12
Figure 3: Stick-frame building envelope sections: Clear and thermal bridging sections
To generate a significant heat flux across the building envelopes, an air conditioning system was placed
inside the building models to create a considerable temperature difference between the interior and
exterior faces of the walls. The thermal bridges were identified by IR thermography on the outside surface
of the building models. For this study, a FLIR B400IR camera was used. The field of view of this camera
is 25° × 19°/1.31 (ft.) and the IR resolution is 320 ×240 pixels. The camera performed measurements in
the long-wavelength IR range of the electromagnetic spectrum (7.5–13 μm). In order to capture accurate
temperature from the thermogram, the emissivity of the surface was defined prior to using the IR camera.
The emissivity of the wall is defined as the ratio of emitted energy to the amount that would be radiated if
the wall were a perfect black body. Based on the emissivity table provided with the camera, the emissivity
for the different materials used in the construction of walls in this study was determined to be between
0.85 and 0.95. Thus, surface emissivity of the wall was adjusted to 0.9 for the experiments. Since the aim
of the thermography was only to capture the thermal bridging occurrences in the building envelope, no
measurement of surface emissivity was carried out. This assumption does not influence the accuracy of
the detection of the thermal bridging locations since there is no change of material on the surface. Also,
Section 2
13
according to Zalewski et al. (2010), the variations of surface emissivity induce negligible influence on the
surface temperature readings of the camera over the range 0.89–0.91.
The IR camera was located outside the building and was aligned along an axis perpendicular to the wall at
a distance of 6 ft and 10 ft for the side wall and front wall, respectively (Figure 4). A series of IR images
of the front and side walls of the two building models were acquired. All of the thermal images were
taken in the morning between 9:00 and 10:00 A.M. Since the conduction portion of this project was
studied in an unsheltered location, the building enclosures were directly exposed to the sunlight, which
could affect the accuracy of the temperature distribution. As a result, the pictures were taken on cloudy
days to minimize the effect of the direct solar irradiation.
Figure 4: Infrared camera setup
Omega HFS-4 heat flux sensors with embedded type K thermocouples were installed on the front and side
walls of each building model to capture the effect of different wall elements (e.g., doors and windows) on
the thermal behavior. The exterior sensor layouts for the two different walls are shown in Figure 5 for the
stick frame building and in Figure 6 for the SIP building. The interior heat flux sensors were installed on
the interior face of the wall at the same locations as the exterior sensors. The ambient temperature,
relative humidity and environmental wind speed and direction were recorded during the three day testing
period.
Section 2
14
Figure 5: Sensor layout for stick-frame building: a) Front wall; b) Side wall
Figure 6: Sensor layout for SIP building: a) Front wall; b) Side wall
It is noted that heat flux sensors provide only localized information and therefore cannot be used to assess
the overall heat flux over a surface that may lose energy non-uniformly. Thus, the sensor locations were
chosen to capture the thermal resistance of both the clear sections of the wall and the sections where
thermal bridging occurred. Building envelope thermal resistance was found through an experimental
calculation procedure based on the mean progressive method. According to the European standard EN
12494, this method is more precise when data is recorded for a period greater than 72 consecutive hours.
Section 2
15
1.3. Test Results and Discussion
1.3.1. Infrared thermography
Figures 7-11 show selected infrared thermograms taken during the testing period. Figure 7 shows
the front wall of the stick-frame building. The thermal leaks along the un-insulated building frame studs
are apparent. This photograph was taken from outside the building where the ambient temperature was
higher than the interior of the building. Significant heat loss through the vertical framework is apparent,
indicated by the vertical green lines. The window area has the lowest overall temperature on the wall
which means that the maximum heat flux is passing through this area. Also, it is evident that the wall
temperature increased with height. This phenomenon can be explained by the hot air stratifying and
moving upward, whereas colder air remained at the lower portion. It is also clear from the thermograms
that along the sections where two studs were placed together (e.g. framing along the window sides), the
thermal bridging effect is greater than sections where only one stud existed.
Figure 7: Thermal bridging in the front wall of the stick-frame building
Section 2
16
Figure 8: Thermal bridging in the side wall of the stick-frame building
The picture shown in Figure 8 illustrates the presence of thermal bridges in the side wall of the stickframe building. Vertical yellow lines along the window sides show the location where the thermal
bridging was happening. The temperature of these yellow lines was approximately 85°F; whereas the
surrounding wall had a temperature of 92°F. Thermal stratification is also apparent in the image, since the
wall temperatures increase with increasing height the wall. The most important observation is that there is
no exact symmetry about the middle axis. This asymmetry can be caused by the error in the construction
and it seems that the insulation is not properly installed in the left lower section of the window
(compression of the insulator or variable thickness of the insulation layer).
Figure 9: Thermal bridging in the front wall of the SIP building
Section 2
17
Figure 10: Thermal bridging in the side wall of the SIP building
Figures 9 and 10 show thermal images taken on the SIP building front and side walls, respectively. Both
of these figures show uniformity in the temperature distribution on the surface. Wall-to-wall connections,
wall-to-roof connections and the framing around the door and window are the only sections where
thermal bridging is apparent. Unlike the stick-frame building, no distortion is detected in the building
envelope. This can be explained by the fact that the enclosure is built with prefabricated panels, which
causes fewer errors during the construction process. Also, there is no structural framing member
connecting the two faces of the wall. This eliminates the thermal bridging for the SIP building that is
otherwise inherent to the stick-frame construction.
Figure 11: Thermal bridging inside the SIP building
Section 2
18
Figure 11 shows an inside portion of the SIP building. The ridge beam joining the two roof panels has the
highest temperature compared to the other sections inside the building – thus the ridge beam causes
thermal bridging and energy loss. Also, it is clear that the connections have higher temperatures in
comparison with the wall and roof panels. This can be explained both by the higher conductance of the
connections and by the air infiltration that occurs in these sections.
Based on the results presented in Figures 7- 11, IR thermography may clearly identify regions of thermal
bridging in the building envelope. Although IR thermography makes it possible to visualize problematic
regions, it does not allow for a quantitative evaluation of the impact of the thermal bridges on the total
performance (heat losses) of the wall, nor does it quantify energy consumption due to the thermal bridges.
In order to evaluate the thermal behavior of the two building envelope systems under investigation,
appropriate heat flow and temperature sensors were installed on the walls and connected to data
acquisition devices. These measurements are discussed in more detail in the subsequent section.
1.3.2. Surface heat flux/ temperature measurements
To obtain the coefficient of thermal resistance (R) for the building envelopes considered in this
study, the mean temperature difference was divided by the mean heat flux rate, where the average value
was taken over a period of 72 hrs (Haralambopoulos and Paparsenos 1998).The extended time span of the
tests mitigated the effect of the thermal mass on the final results, therefore, improving the outcome.
Although the tests for the two building systems were conducted separately, the external temperature was
comparable during each of the two experiments, rendering the results comparable as well. The following
equation was used to calculate the R-values at different sections:
∑
where
(
∑
, is heat flow is rate;
)
is interior surface temperature;
(eq. 1)
is exterior surface temperature;
enumerates the individual measurement interval, which was 1 min.
Section 2
19
The heat flux meters used were OmegaHFS-4 Thin-Film heat flux sensor with internal type K
thermocouple for temperature measurement. Sensors specifications are given in Table 1.
Table 1- Omega HFS-04 sensor specifications
Nominal Sensitivity (μV/Btu/ft2hr)
Max recordable heat flux (Btu/ ft2hr)
6.5
30,000
Response time (Sec)
0.60
Thermal capacitance (Btu per ft2°F)
0.02
Thermal Resistance (°F per Btu/ ft2hr)
0.01
Nominal Thickness (in)
0.007
Sensors were fixed to the wall surface with adhesive tape and connected to an OMB-DAQ Omega Data
acquisition system. As mentioned in Section 3.1, sensors were located in such a way that thermal
properties at both the clear section and the locations where thermal bridging existed.
Based on data collected from the front wall of the stick frame building, time histories of the temperature
differences between the two faces of wall, the heat flux through the wall, and the corresponding R-value
are given in Figures 12 – 14, respectively. Figure 12 shows that the maximum temperature difference
occurred at noon and the minimum temperature difference happened at midnight. The temperature
variations are due to the cyclic variations of the building environment itself. Figure 13a shows that heat
flux sensors on the clear section of the front wall measured unacceptable quantities. This may have
happened because of the error in connecting the wires to the data acquisition system. Figure 13b indicates
that the heat flux was minimum in the evening and maximum in the early morning. This can be explained
by the thermal mass effect. Figure 14 shows the thermal resistance value for the testing period of the
stick-frame front wall. From Figure 14b, it can be inferred that in areas where both horizontal and vertical
wood frame studs were present, the minimum R-value was achieved.
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20
a.
b.
Figure 12: Temperature difference for the sensors on the front wall of stick-frame building: a) Clear section; b)
Thermal bridging section
a.
b.
Figure 13: Heat flux through the front wall of stick-frame building: a) Clear section; b) Thermal bridging
section
Section 2
21
a.
b.
Figure 14: R-value for the front wall of stick-frame building: a) Clear section; b)Thermal bridging section
Figures 15 - 17 show respective plots of the temperature difference between the two faces of the wall, the
heat flux through the wall, and the corresponding R-value derived from the sensors located on the side
wall of the stick frame building. The temperature difference value on the side wall was higher than the
front wall because the side wall was a westward facing wall and received the sun radiation more severely
(Figures 12 versus Figure 15). Based on Figure 16, it was observed that the heat flux through the sensor
decreased as the height of the sensor on the wall decreased. This can be attributed to the stratification
phenomena causing the presence of warmer air with increasing heights. Furthermore, the larger section of
thermal bridging around sensors 26, and Figure 27, had a smaller quantity of heat flux measured because
of less concentration. Comparing Figures 14 and Figure 17, the R value for the thermal bridging section
was comparable, and, despite the larger value for the temperature difference, the overall R value was
about the same for the two walls.
Section 2
22
a.
b.
Figure 15: Temperature difference for the sensors on the side wall of stick-frame building: a) Clear section; b)
a.
b.
Figure 16: Heat flux through the side wall of stick-frame building: a) Clear section; b) Thermal bridging section
Section 2
23
a.
b.
Figure 17: R-value for the side wall of stick-frame building: a) Clear section; b)Thermal bridging section
Results for the stick frame building are summarized in Figure 18. Based on the overall results, the Rvalue for the clear section was larger than the thermal bridging sections’ R-values, as expected. The
variation between the R-values for the thermal bridging section on the front wall versus the thermal
bridging section on the side wall can be interpreted by the variation of the sensor locations (i.e. one sensor
was installed on a single stud while the other was installed over two studs). Also, different environmental
conditions on the two walls, including the exposure to the sun and wind, can cause some differences. The
constructional imperfections may be considered as another probable source, since there is not exact
uniformity in the distribution of the insulation material in traditional wood-framing construction
techniques.
Section 2
24
Figure 18: R-value for the stick-frame building
The temperature difference, the heat flux, and the R-value diagrams for the front wall of the SIP building
are presented in Figures 19, 20 and 21, respectively. Similar to the case of the stick frame building, the
maximum temperature difference for the thermal bridging section was lower than the clear section in the
SIP building; this was also confirmed by IR thermograms. Figure 20 shows that the heat flux through the
wall in the thermal bridging area was larger than that in the clear section. The variability between the Rvalues obtained for different points in the clear section is less for the SIP building when compared to the
stick-frame building, and this is due to the better uniformity of insulation in construction of the SIP
building (Figure 21).
a.
b.
Figure 19: Temperature difference for the sensors on the front wall of SIP building: a) Clear section; b)
Section 2
25
a.
b.
Figure 20: Heat flux through the front wall of the SIP building: a) Clear section; b) Thermal bridging section
a.
b.
Figure 21: R-value for the front wall of the SIP building: a) Clear section; b) Thermal bridging section
Section 2
26
The temperature difference, the heat flux, and the R-value diagrams for the side wall of the SIP building
are presented in Figures 22, Figure 23, and Figure 24, respectively. Figure 22 indicates that the
temperature difference between the faces of the enclosure was much higher for the clear section compared
to thermal bridging section (that is, for the same heat flux, a lower temperature difference means a lower
R value.). The thermal lag in the SIP building was smaller than the lag in the stick-frame building. This
may be explained by the fact that heavier materials were used in the construction of traditional stickframe buildings. Comparing Figures 21 and 24, it may be concluded that there was a considerable
difference between the R-value for the side wall versus the front wall of the SIP building. This was due
to the position of the sensors, which might not have completely captured the entire thermal bridging
effect. However, for the clear section, results were similar for the two buildings. A summary of the Rvalue for the SIP building is given in Figure 25.
a.
b.
Figure 22: Temperature difference for the sensors on the side wall of SIP building: a) Clear section; b) Thermal
bridging section
Section 2
27
a.
b.
Figure 23: Heat flux through the side wall of the SIP building: a) Clear section; b) Thermal bridging section
a.
b.
Figure 24: R-value for the side wall of the SIP building: a) Clear section; b) Thermal bridging section
Section 2
28
Since the size of the sensors was relatively small compared to the building elements, it was possible that
they would not capture the true behavior at the thermal bridging areas. Also, there was no certainty that a
unidirectional heat flow passed through the measurement zone. The experiment conducted in this study
demonstrated that the thermal heat losses caused by the thermal bridges could be evaluated. It also
provided complementary confirmation to the IR thermography. One of the shortcomings associated with
this experiment was that the measured heat fluxes were available only for limited locations and were
functions of the sensor’s dimensions. In order to evaluate how far these thermal bridges increased overall
heat losses from the wall, a larger number of flux meters would be required, covering a representative
area of the average characteristics of the wall.
Figure 25: R-value for the SIP building
Figure 26 shows the R-values measured for the window and the door on both buildings. The R-values
measured for the two buildings were similar in magnitude. However, there is a difference between the two
values for the windows, which can be related to the different positions of the sensors on the two
specimens. The R-values defined for the doors had less divergence compared to the windows. In the
market, heat loss of the window assembly is usually indicated in terms of the U-value, where the U-value
is defined as the reciprocal of the R-value. The lower the U-value, the greater a window’s resistance to
heat flow, and the better its insulating properties were. Windows were used in the two buildings with the
company U-factor equal to 1.05. Thus, the factory given R-value for the windows was equal to 0.95. In
other words, the measured in situ R-value for the window was four times smaller than what was tested in
the laboratory for the same material.
Section 2
29
a.
b.
Figure 26: R-value: a) Window; b) Door
According to the literature, there is a considerable discrepancy between the practical R-value of building
envelopes and the value that is declared by measurement in the laboratory (Peng and Wu 2008). This
deviation is due not only to the inevitable differences among supplies of the same material made at
different time periods, but also to the differences in the conditions of wall-building (Laurenti et al. 2004).
Section 2
30
4. Convection
4.1 Introduction
Convective heat transfer is a heat exchange mechanism between the building envelope system
and the outdoor air. It is a transfer of heat energy due to the movement of an air volume near a building
face. It can be caused by a temperature difference between the building surface and the outdoor air —
natural convection – or when the exchange is assisted by wind pressure – forced convection. Compared to
other mechanisms of heat transfer (conduction and radiation), convection heat transfer can, in some cases,
be accountable for higher percentages of the total heat transfer between a building’s outdoor and indoor
air conditions, especially in areas where there is frequent temperature fluctuation coupled with a windy
environment (Davies 2004). Convective heat transfer is affected by factors such as wind speed, surface
roughness, and building surface orientation with respect to wind direction.
Mathematically, convection heat flux is defined by Newton’s Law of Cooling, which is expressed in
Equation 2 as:
(
)
(eq. 2)
where qconv is the convective heat flux through a unit area of a given building envelope, h is the convective
heat transfer coefficient (CHTC), and Ts and T∞ are building surface temperature and air temperature
away from the building surface, respectively. Practically, it is difficult to measure the portion of the total
heat transfer caused by the convection process separately from the other heat transfer mechanisms. To
overcome this, the heat balance method is a common approach followed by scientists and researchers to
quantify the amount of convection heat loss or gain at the building surface. The heat balance equation is
written on the assumption of steady-state condition in which the rate of heat gain is equal to the rate of
heat loss. This requires the determination of heat gain through radiation such as short wave radiation from
the sun and long wave radiation from the surrounding environment. It is to be noted that a portion of the
incoming radiation heat from radiation will be lost through surface reflection and emission. Thus, it is the
net radiation that needs to be measured in the heat balance equation at the building surface.
Section 2
31
The net radiation heat combined with the convection heat gain or loss will induce directional conduction
heat transfer across the building envelope system. For a steady-state condition, the amount of conduction
heat transfer through the building enclosure depends on the material’s thermal resistance characteristics.
The heat balance on a typical building envelope surface shown in Figure 27 combines the heat gains and
losses due to net radiation, convection, and conduction processes. The heat balance equation is given as:
(eq. 3)
Figure 27: Heat balance at a building roof surface
The net radiation energy is calculated from the net short wave solar radiation gain (solar radiation minus
reflection) and net long wave radiation gain/loss from the surrounding environment (incoming long wave
Section 2
32
radiation minus long wave emission from the building surface as result of its temperature difference from
the surrounding air). It is expressed as:
(eq. 4)
Where qSR is net short wave radiation and qLin and qLout are heat fluxes from long wave radiation coming in
and being emitted from the building surface, respectively. The conduction heat flux at steady-state is a
function of the temperature gradient across the building envelope system, and is given in Equation 5 as:
(
)
(eq. 5)
where R is the thermal resistance characteristic of the building envelope system and Ts_out and Ts_in are
exterior and interior building surface temperature, respectively (see Section 3.3.2 for more details).
It is noted that the heat balance relationship given in Equation 3 assumed a dry building envelope surface,
and it would otherwise needed to include the amount of heat energy required in phase change processes,
such as shown in eq. 6 for evaporation of water. In reality, this could be the case if the building envelope
became wet after a very cold night or was drenched by wind-driven rain. The phase change process of
evaporation consumes a significant amount of the heat energy gained through radiation and convection
mechanisms as the specific heat capacity and latent heat of vaporization of water are relatively high
(4.186 KJ/Kg °K and 2,270 KJ/Kg, respectively).
(eq. 6)
The heat consumed in the evaporation process, qevap, can be estimated using the rate of water evaporation
calculations given in ASHRAE 2009 Fundamental Handbook.
̇
(eq. 7)
where hfg is heat of evaporation at a given temperature and
̇ is the rate of water evaporation, which is
given by
̇
Section 2
(
)
(eq. 8)
33
The terms ρw, ρ∞ are respectively defined as the vapor density at water surface and airstream, and they are
estimated based on the measured surface temperature using the natural vapor law. The term hM is defined
as the convective mass transfer coefficient, and its magnitude depends on Reynolds number, Sherwood
Number and diffusion coefficient of vapor at a given temperature. The building surface temperature can
be considered as the water surface temperature in calculating the vapor density at the water surface and
airstream.
In formulating the heat balance equation at different components of the building envelope (wall, roof,
etc.), the heat transfer due to the three mechanisms mentioned above should be considered based on the
prevailing conditions. For example, additional long wave radiation coming from the ground should be
considered in the case of heat balance measurements on the buildings’ walls and vertical surfaces.
Moreover, the conduction process at the roof component is usually coupled with convection due to the air
volume presence in the attic space, hence, called conduction-convection.
The effects of wind speed on the convective heat transfer at a building envelope surface have been studied
through field measurements (Hagishima and Tanimoto 2003; Liu and Harris 2007; Loveday and Taki
1996), wind tunnel experimentation, and computational fluid dynamics (CFD) modeling (Defraeye et al.
2010; Defraeye and Carmeliet 2010). Hagishima and Tanimoto (2003) developed a comprehensive and
qualitative relationship between wind velocity and CHTC over building surfaces using field
measurements. Outdoor surveys of wind speed and direction along with the thermal monitoring over the
surface of two 4-storey buildings were used to conduct the study. It showed that the CHTC has a strong
relation with wind speed when the temperature difference between building surface and the air exceeds
15°C (27°F), implying dominance of forced convection. (Hagishima and Tanimoto 2003) also found that
the distribution of CHTC over the building’s roof surface is highly correlated with the wind speed
distribution (dynamic pressure distribution) and turbulent energy near the building surface. Liu and Harris
(2007) conducted a similar study of wind-induced convective heat transfer on low-rise building surfaces
through field measurements. A test-panel mounted flush on one face of a square and single-story wooden
Section 2
34
building was used to create a controlled experimental setup of convective heat transfer. The test-panel,
which was built with an inside heat source to control and adjust the temperature difference between
outside air and the surface of the panel, was oriented to face the prevailing wind direction during the test.
Outside air temperature, surface temperature of the test panel, conductive heat flow input to the external
surface of the panel, incoming long wave radiation incident to the test panel, wind speed, and wind
direction data were collected to derive the relation of CHTC with wind speed for a given direction. The
study by Liu and Harris (2007) also found a series of CHTC and wind speed relationship for different
wind directions. The findings indicated that the values of CHTC for windward faces were higher than
those of the leeward wall faces(Liu and Harris 2007). Defraeye and Carmeliet (2010) presented a
methodology to assess the effect of wind on the convective heat transfer coefficient over a building
surface based on computational fluid dynamics (CFD) analysis. Though the temperature driven
convection and heat fluxes due to radiation are not considered, the results using the proposed
methodology were comparable with field tests. For example, the study of CHTC for different wind
direction with respect to a building model indicated that higher values of CHTC for building surfaces
facing direct wind(Defraeye et al. 2011; Defraeye and Carmeliet 2010).
4.2 Test Setup
Heat transfer measurements on large-scale building models were conducted at different wind
speeds and angles of attack to study the effects of wind and wind-driven rain on the convection heat
transfer process. The low speed wind flow field was simulated using the 12-fan WoW at FIU. The
simulation conducted in this study was targeted at a preselected eave height, with wind speeds of 15, 25,
35, and 45 mph to investigate the convective heat transfer under normal weather conditions. Detailed
measurements of free-stream wind characteristics (wind speeds and turbulence intensities) were
conducted using a vertical rake system developed specifically for wind speed measurement purposes. The
rake system consisted of pressure tubes mounted at various elevations in the wind field, and connected to
a 64-channel Scanivalve ZOC33 pressure scanner that records the dynamic pressure time history based on
Section 2
35
the static reference pressure. The dynamic pressure time histories were then converted to wind speeds and
presented in Figure 1. It is to be noted that the wind speed measurements were conducted at the center of
the test section and the values shown in Figure 1 are average of two measurements. The flow was fairly
uniform with turbulence intensities ranging between 3-10%. The corresponding average wind speeds
were 14.62, 24.50, 33.50, and 44.5 mph.
Figure 28: Wind profile at different wind speeds
Solar radiation was simulated with the wind to represent the exposure of buildings to sunny and windy
weather conditions. The solar radiation simulations were performed using three 107 mm spiral lamps
controlled by two XENON RC-847 units. The location and orientation of the solar lamp with respect to
the test building was determined based on the representation of typical solar radiation on a residential
building located in Miami, Florida during the month of September. This resulted in an azimuth angle of
40° from the building’s orientation for the 0° wind direction. The solar lamps were placed at a height of
15 ft above the ground, and radiated toward the center of the turntable to produce a typical afternoon
angel of incidence for solar radiation on the building model. The solar lamps were mounted on a framing
Section 2
36
system, and suspended using a forklift positioned outside the wind field. The forklift was able to relocate the solar lamps as the building model rotated on the turntable in order to simulate different wind
directions with respect to the test building on a sunny day.
Figure 29: Solar lamps location for 0° wind AOA
In addition to the wind/solar radiation simulation, wind-driven rain was simulated to study the convective
heat transfer coefficient over the building surface area in windy and rainy weather conditions. The winddriven rain was simulated using TEEJET full-cone spray nozzles arranged in a 3x4 grid pattern and
placed at the 12-fan WoW exit. A light rainy condition was used for the study, with an approximate rain
rate of 20 – 30mm/hr. Figure 30a and 30b show the simulation of solar radiation and wind-driven rain,
respectively.
a.
b.
Figure 30: Simulation of weather conditions: (a) Solar radiation; (b) Wind-driven rain simulation
Section 2
37
4.2.1 Instrumentation
The convection heat flux and convection heat transfer coefficients for different wind directions on
the roof and wall of the test buildings were determined separately using sensors to measure the heat fluxes
from conduction and radiation processes. Several parameters were monitored for 10 minutes during each
test to evaluate the convective heat flux: the short wave radiation from the solar lamps and reflection of
the building surface, the long wave radiation from the surrounding environment and emission from the
building surface, the conduction heat fluxes passing through the building envelope, the building surface
temperature, the indoor and outdoor temperatures, and the relative humidity. Two pairs of Kipp and
Zonen CPM3 pyranometers and CGR3 pyrgeometers were assembled back-to-back to capture the
radiation heat flux coming in and out the buildings surface (see Fig. 31). The pyranometer facing away
from the building surface measured the amount of incoming short wave radiation hitting the roof surface
while the one facing the building roof was used to collect the reflected short waves from the surface. It is
to be noted that the amount of reflection depends on the color and texture of the roof covering. Likewise,
the pyrgeometers were also placed back-to-back, one facing the building roof to measure the long wave
emission from the surface and the other facing outward to quantify the amount of long wave radiation
from the surrounding environment. Note that the intensity of long wave emissions from the roof surface is
a function of the roof surface temperature. The total long wave radiation from a single pyrgeometer
measurement at each sampling point was calculated as the pyrgeometer voltage output per heat flux
sensitivity plus long wave radiation depicted as housing temperature of the sensor itself (eq. 9). Note that
the second term in Equation 6 will cancel out in the net long wave radiation heat calculations as the
combined measurements from two pyrgeometers assembled back-to-back. An additional pyranometer and
pyrgeometer were required to determine the net radiation heat for a vertical wall of the building models.
The back-to-back pairs of pyranometers and pyrgeometers were placed horizontally on the ground to
measure the total radiation hitting the building wall surface. The additional sensors were placed facing the
wall to capture surface emission and reflection, respectively (Fig. 31).
Section 2
38
(eq. 9)
where Uemf is the voltage reading and S is the sensitivity of the sensor (µV/W/m2).
a.
b.
Figure 31: Net radiation measurements on stick-frame building: (a) Roof; (b) Wall
The conduction heat flux on the building surface was calculated as an average of the heat flux
measurements using Omega HFS-4 heat flux sensors installed on building surfaces. The roof of the
building models were instrumented with eight Omega HFS-4 heat flux sensor while six sensors were used
on the wall to measure the conductive heat flux.
Figure 32: Sensors layout on roof of the building for convective heat transfer tests
Section 2
39
All of the heat flux sensors had built-in Type K thermocouples which enabled them to detect the surface
temperature when connected to an Omega OMB-56 data acquisition system. Figure 32 shows the heat
flux and radiation sensors layout used during convection testing on the roof of the building. The indoor
air was conditioned during the test using a high capacity AC unit to recreate a typical building operational
temperature, to produce relative humidity conditions different from the outside air. Indoor and outdoor air
temperature and relative humidity parameters were monitored during each test using three ExTech SD800
CO2 Humidity/Temperature sensors placed inside the test building and in the laboratory, respectively.
These sensors have two units in which one contains the temperature and relative humidity sensors
covered with perforated plastic cover and the other unit holds the data logger system and connected to the
sensor unit with a data cable.
4.2.2 Test protocol
The convection test protocol was developed based on the different parameters involved in heat
transfer across a building envelope under normal operational weather conditions. Guided by the
objectives of the study, four target wind speeds (15, 25, 35, and 45 mph) were selected for testing at three
wind directions (0, 45, and 90 degrees) (see Fig. 29). Sunny weather was represented using solar lamps
that produce the full range of wavelengths when compared to the radiation spectrum of the sun. As the
testing represented a building model having a specific orientation and exposure to sun radiation, the solar
lamps were rotated with respect to the building to maintain a consistent exposure to the sun for the
various wind directions of interest. In contrast to the sunny weather, a light rainy weather condition was
also considered during the convective testing.
The tests were conducted in two categories based on the building envelope type: Stick-frame and SIP
building. The parametric study of wind speed, wind direction, rain rate, and positioning/application of
radiation from the solar lamps were applied in the same manner to both building models in order to
capture any effect from the type of materials used as enclosure systems for the two buildings. Table 2
summarizes the parameters considered in the testing, which resulted in total of 96 tests.
Section 2
40
Table 2: Summary of test parameters
Building Type
Wind Speed
Wind Angle of
Attack
Sensor
Location
Simulated Weather
Condition
2x4” Stick-frame
building
15
0
Roof
Sun & Wind
SIP building
25
45
Wall
Rain & Wind
35
90
45
4.3 Test Result and Discussion
Radiation and conduction heat fluxes through the surface of stick-frame and SIP buildings were
measured for 10 minutes with application of simulated solar radiation, wind, and rain. The measurements
were conducted on the roof and one of the side walls of the building models at different wind speeds and
three wind angles of attack. The convective heat gain or loss on the building surface was calculated
assuming the steady-state heat transfer condition in which the outside air and building surface temperature
remain constant during the test. This assumption was valid for most of the tests except when the test
involves application of wind-driven rain, the temperature at surface of the building exhibited large
variation due to temporary accumulation of rain water and removal and drying of the building surface by
the wind. Additional term of heat of water vaporization was considered in the heat balance equation in
tests where wind-driven rain is applied.
Parameters related to heat transfer through the building envelope, including short and long wave
radiations, conduction heat flux on the surface of the building, local air temperature, and relative humidity
were measured and analyzed to determine the wind induced forced convective heat gain or loss at
different wind speeds. In most of the tests, linear relationships with a positive slope between wind speed
and convective heat transfer coefficient (CHTC) were observed. These findings are consistent with
research performed by Hagishima and Tanimoto (2003); Liu and Harris (2007); Loveday and Taki
(1996). The positive slope indicates the increase of convective heat loss as the wind speed increases.
Section 2
41
However, a negative slope of convective heat loss was found when the building wall was in the leeward
direction with respect to the wind direction. Moreover, when the building surface was wetted due to winddriven rain, a highly negative value of CHTC with a negative slope (increasing with wind speed) was
observed. The significant dispersion of CHTC under a constant wind speed was observed as reported in
previous researches.
Figure 33 shows the conduction heat gain/loss at the roof surface of the stick-frame building as the wind
speed increased for a 0° wind of attack. The total duration of the test was 40 minutes, with the wind speed
increasing by 10 mph at 10 minute intervals, starting with a wind speed of 15 mph. The conduction heat
flux was stable over the roof surface area with average heat flux values of -3.54, 2.26, 0.11, and -5.68
W/m2 for 15, 25, 35, and 45 mph wind speeds respectively. The positive sign shows average heat loss
through conduction. The air and roof surface temperature were uniform throughout the test duration,
which validated the assumption of steady-state condition (see Fig. 34).
Figure 33: Conduction heat flux measurements on roof of 2x4 stick-frame building
Figure 34 shows that the roof surface temperature data from the thermocouples mounted on the roof
surface which were highly correlated with the air temperature measurements using SD800
CO2/Humidity/Temperature sensors. This indicates that the hot air with high relative humidity tends to
keep the building surface at a uniform temperature regardless of the increment in wind speed.
Section 2
42
Figure 34: Air and roof surface temperaturefor0°wind AOA
During the final 10 minutes of the test (when the wind speed was 45mph), the conduction heat fluxes
showed a relatively significant heat gain caused by the increase in local air temperature, which in turn
reduced the convective heat loss (See Fig. 35). This may be explained in terms of the heat balance
equation: as the surrounding air gets warmer, the convective air lost its cooling power over the building
surface and resulted in dispersed and lower value of CHTC (See Fig. 36).
Figure 35: Conduction, convection, and radiation heat fluxesfor0° wind AOA
The CHTC over the roof surface of the stick-frame building at 0° wind angle of attack is presented in
Figure 36. It has a mild positive slope, which indicates that the wind induced convection heat loss
Section 2
43
increased as the wind speed increased by only a small amount. A widely dispersed CHTC was observed
during the latter half of the test duration.
Figure 36: Convective heat transfer coefficient over the roof surface for 0° wind AOA
A different result was obtained for 45° wind AOA in which the convection process caused a significant
conduction heat loss at the beginning of the test duration. This represents the release of heat stored in the
building envelope material as a thermal mass. The temperature and relative humidity of the air increased
slightly during the test which caused an increase in the temperature of the surrounding environment and
thereby increasing the net long wave radiation heat gain at the building surface (see Fig 38). The net short
wave radiation heat gain was constant throughout the test duration. A mild increase in net radiation heat
gain was observed, which compensated the conduction heat loss with the aid of the forced convection
process. For the first thirty minutes of the test duration, the convection process caused continuously
decreasing conduction heat loss through circulation of the surrounding air and then became balanced with
the increasing net radiation heat gain at the building surface. A constant heat transfer through conduction,
convection, and radiation was observed in the last ten minutes of the test duration when the wind speed
was 45 mph. At this stage, the radiation heat gain was fully balanced by the convective heat loss with a
net zero heat transfer through conduction (See Fig. 40).
Section 2
44
Figure 41 presents the convective heat transfer coefficient (CHTC) during the test duration. Each 10
minutes testing duration is shown with the testing wind speed for the wind blowing in 45° with respect to
Figure 37: Conduction heat flux over roof surface for45° wind AOA
the building. The CHTC shows a negative slope, which indicates the wind induced convection heat
transfer decreases as the wind speed increases. This result was unexpected and could be related to high
amount of heat stored the building enclosure system before the testing starts. It is to be noted that this set
of test for which the wind was blowing in 45°, was conducted in the morning after the building interior air
had been conditioned with a lower temperature for the whole night before the testing date.
Section 2
Figure 38: Air and building surface temperature for45° wind AOA
45
Figure 39: Short and long wave radiation heat fluxes for 45° wind AOA
Figure 40: Conduction, convection, and radiation heat fluxes for 45° wind AOA
Figure 41: Convective heat transfer coefficient over the stick frame building roof surface for 45° wind AOA
Section 2
46
Figure 42 and Figure 43 show the time history of the three heat fluxes and the CHTC over the roof surface
of stick-frame building for 90° wind AOA, respectively. The wind induced convection process caused an
increasing heat loss for the first 20 minutes where the radiation was dominant and produced a net
conduction heat gain over the roof surface. The net long wave radiation heat gain decreased starting from
the second 10 minutes of the test duration due to a minor reduction in the local air temperature. Constant
values of radiation, convection, and conduction heat fluxes were observed for the remaining half time of
the test duration in which the net radiation heat gain was cancelled out with the high wind speed induced
forced convection heat loss over the roof surface. These two half time durations of heat transfer processes
Figure 42: Conduction, convection, and radiation heat fluxes for 45° wind AOA
Figure 43: Convective heat transfer coefficient over the SIP building roof surface for 45° wind AOA
Section 2
47
exhibited different distribution of CHTC with time and as the wind speed increases. In the first half time,
the convective process caused a lower amount of heat loss at an increasing rate. This process continued
until the conduction heat gain over the building roof surface reduced to a much lower value of heat flux.
In the last half time of the test duration, the CHTC showed high correlation with time and wind speed,
having a positive slope indicated the increase in convection heat loss as the wind speed increased. It is to
be noted that the variations of CHTCs at a specific wind speed were significant even for the last 20
minutes (8 to 15 and 12 to 18 for 35 and 45 mph, respectively).
Similar tests are conducted for the SIP building with the application of simulated solar radiation and
wind. The analyses results indicated the same pattern with a little higher value of CHTC for SIP building
for different wind angles of attack. A relative higher value of conduction heat losses were observed
during the SIP building tests, which show the difference in the thermal performances of the two building
envelope materials. In both types of buildings, the wind induced convection heat transfer tends to slow
down the conductive heat gain/loss and ultimately balances the radiation heat gain. Figure 44 and Figure
45 present the three types of heat fluxes and the convective heat transfer coefficient (CHTC) over the roof
surface of the SIP building, respectively.
Figure 44: Conduction, convection, and radiation heat fluxes over the roof surface of SIP building for 45° wind AOA
Section 2
48
Figure 45: Convective heat transfer coefficient over the roof surface of SIP building for 45° wind AOA
CHTCs with negative slope and lower values were observed for SIP building when it is subjected to wind
at 90° AOA (See Fig. 46). The negative slope indicates that the forced convection process has an effect of
minimizing the conduction heat loss at the roof surface. This effect is due to the wake formation when the
building is exposed to wind parallel to the ridge. The wake volume holds the hot and moist air (air with
high relative humidity percentage) close to the roof surface of building creating conduction heat gain.
This process can be coupled with a secondary temperature driven convection process due to the
temperature difference between the windy air at the top of the wake volume and the roof surface.
Figure 46: Convective heat transfer coefficient over the roof surface of SIP building for 90° wind AOA
Section 2
49
The convective heat transfer over the surface of the two building models was studied under the
application of simulated wind-driven rain condition. The buildings were subjected to a constant rain rate
for the first one minute while the wind speed was kept constant for the whole 10 minutes test duration.
The aim was to study the effect of short duration rain events on the three mechanisms of heat transfer and
the response of the two types of building materials in such weather conditions. As mentioned earlier,
additional term of heat of water vaporization is considered in the heat balance equation considering the
significant amount of heat that was being consumed for evaporation of rain water from the building
surface. The test results indicated that the large amount of heat required for the evaporation of rain water
was supplied to the system through convection of warm ambient air. The presence of rain water over the
building roof surface area also affected the conduction heat gain/loss at the roof surface of the buildings.
Conduction heat loss was observed during the application of wind-driven rain and then followed by
significant heat gain in the drying process during the remaining 9 minutes of the test duration.
The pyranometers measured about zero radiation heat per unit area of the roof surface due to the absence
of short wave solar radiation. The long wave radiation emitted from the roof surface was much lower
when the building was exposed to wind-driven rain as the rain water kept the roof surface temperature to
be less warm. Although the presence of rain lowers the temperature of the surrounding environment and
the long wave radiation heat gain, the net long wave radiation heat gain over the building surface was
much higher as compared to its value in simulation of sunny weather conditions. This might not be true in
actual rain events where the temperature of the surrounding environment also lowers down at the same
scale with the building surface area. However, since the percentage of net radiation heat is much lower
than the convection and heat of evaporation, the effect of relatively higher temperature of the surrounding
environment and magnitude of net long wave radiation is very minimal.
Section 2
50
Figure 47: Net short and long wave radiation heat fluxes for wind-driven rain test at 0° wind AOA
Figure 48: Conduction, convection, radiation, and evaporation heat over the roof surface for wind-driven rain
test at 0° wind AOA
Figure 49: Convective heat transfer coefficient over the roof surface for wind-driven rain test at 0° wind AOA
Section 2
51
The test results also indicated that the supply of convective heat to the system and its consumption in the
evaporation process increased with time and wind speed which is also exhibited in large slope value of
the linear relationship of CHTC with wind speed (See Fig 49). Though high variations of CHTCs at a
specific wind speed were observed during the tests, the correlations with time were much better when the
building was exposed to wind-driven rain.
Figure 50: Conduction heat gain on roof surface of stick-frame versus SIP building during wind-driven rain test
at 45° wind AOA
Figure 50 shows the conduction heat gain at surface of the two types of building envelope systems (Stickframe versus SIPs) after the application of wind-driven rain for one minute duration. The difference in
conduction heat gain/loss shown in Figure 50 is related to the thermal resistance and heat absorption
capacity of the two building envelope materials.
The wind induced forced convection heat gain/loss tests were also conducted on the building wall system
with the instrumentation setup shown in Figure 31. The test results indicated that the wall surface of the
stick-frame building gained significant amount of heat through conduction comparing to the roofing
system with the same level of exposure to wind speed and simulated solar radiation. It is to be noted that
the temperature differences between the air and the building surface were similar for both wall and roof
tests. In contrary to the roof tests, the conduction heat gain at wall surface of the SIP building was found
Section 2
52
to be less that the stick-frame building. This suggested that the roofing system of the stick-frame building
has better heat resistance and thermal absorption capacity. The net radiation heat gain on the surface of
the wall was found to be about the same magnitude as for the roof surface of the building. The side wall
of the building used for this study was unfortunately exposed to a significant wake volume containing
relatively warmer air for the three wind angles of attack. This is believed to be the reason for the
conduction heat gain during the tests. The CHTCs over the wall surface of the building showed a linearly
decreasing relationship with time and wind speed for the same reason as the roof when the building is
exposed to wind from 90° AOA. The wake volume caused high accumulation of warm air in the vicinity
of the wall. The reduction in CHTCs and conduction heat gain on the wall surface was caused by the
change in the wake volume as the wind increased and as the wind direction changes from 0 to 90 degrees
(see Fig. 53).As mentioned earlier, there is a high probability that this phenomenon could be coupled with
secondary natural convection process driven by temperature difference between building surface and the
boundary air surface forming the wake region. It is also noted that the CHTCs on the wall surface of stickframe building were higher than the SIP building for similar exposure of simulated weather conditions.
Figure 51 and Figure 52 show the heat fluxes through the three heat transfer mechanisms and CHTC on
the wall surface of the stick-frame building for 0° wind angle of attack.
Figure 51: Conduction, convection, and radiation heat fluxes over the wall surface of stick-frame building for 0° wind
Section 2
53
Figure 52: Convective heat transfer coefficient over the wall surface of stick-frame building at 0° wind AOA
Similar convection test results as the roof tests were found for the wall surface of the buildings under
simulated wind-driven rain condition. The conduction heat gain over the wall surfaces of the two building
envelope systems (2x4” stick-frame and SIP) were found to be about the same magnitude (the SIP
envelope system showed slightly higher amount of conduction heat fluxes in some instants of the test
duration). Note that the vertical side walls of the buildings (sometimes facing leeward) may not get the
same amount of rain as windward building surfaces. Figure 54 presents the comparisons of conduction
heat transfer through the walls of the two building envelope systems after wind driven rain exposure.
Figure 53: Comparison of CHTC at the wall surface of the stick-frame building for different wind AOA
Section 2
54
Figure 54: Conduction heat gain on wall surface of stick-frame versus SIP building during wind-driven rain
test at 45° wind AOA
5. Conclusions
Based on the discussions mentioned above, the following conclusions can be made:
The relative importance of thermal bridges increases in the energy balance of recent highly
insulated buildings. It requires a very detailed attention with the components of the envelope and their
implementation. Moreover, to ensure an extended life cycle for the buildings, it is vital to reduce these
singularities as much as possible in order to avoid any problem of internal condensation which would be
detrimental to the framework and to the performances of insulating materials. This need requires the
synchronous employment of two approaches, the in-situ spot measurements and the infrared
thermography analysis of the building envelope. Thermal bridging can be visualized precisely using Infra
–Red camera. It is clear from the thermograms that wood studs create thermal bridging in the stick-frame
building. However, for the SIP building no thermal bridging occurs within the wall panels and it only
happens at the wall to wall and roof to wall connections. IR thermography answers only part of the
problem by converting surface variations of temperature into nuances of color on a thermogram.
Furthermore, with no quantitative evaluation of heat fluxes, the thermogram can only be used with care
because many parameters may disturb readings (variations of surfaces emissivity, room temperature,
relative moisture, parasitic infrared radiations, solar radiations, etc.).
Section 2
55
The mean progressive method used here just requires testing the heat-flow rate on the inside
surface of building envelopes and both the inside and outside surface temperatures of building
construction. The magnitudes of the calculated R-values obtained in this study are not consistent with the
values published from laboratory testing. The in situ results presented in this report are smaller than the
values given for the controlled environmental condition in the laboratory. In fact, one of main test errors
of in-situ measurement of the thermal resistance of building construction occurs from heat-flow meters,
including their measurement accuracy. The measured heat flux values are influenced by the placing of the
sensor. It would therefore be desirable to know the exact placing of the heat flux sensor to judge whether
they effectively correspond to the mean heat flux passing through the wall. In practice, heat-flow meters
need to be plastered on the surface or embedded in specimens. In this case, since the heat condition on the
surface is altered, it will lead to the change of temperature fields in the specimen and around the heat-flow
meter. All of these will result in the difference between the test results and the real cases. Also, the Rvalue measurement with the use of a heat-flux meter is strongly affected by environmental conditions, in
particular by the temperature difference between inside and outside. The smaller the temperature
difference the less precise the measurement.
Apart from the errors in the heat flux meters readings, the main reasons for these differences
might be that the test buildings might not be dry enough or they might be affected by thermal bridges,
such as beams and pillars, or the un-controlled climate under field conditions. Certainly other factors,
such as errors in the thermocouples, heat-flow meters, and in the temperature and heat flux data loggers
may also contribute to these differences. The test wall studied contains a window, which constitutes a
geometric and thermal singularity. It is advisable to conduct heat flux measurements on walls without
singularities or in areas far off from them. A further cause of possible measured heat flux variability is
that of any indoor air stratification that could increase the heat flux from bottom to top (in the winter)
owing to an increase in air temperature.
Section 2
56
In addition to the general outcomes of this study that were presented earlier, following detailed
information can be concluded furthermore. Stick-frame building results suggest that the heat losses in
front of the wood frame are approximately 1.5 times the clear section. For the SIP building, the effect of
thermal bridging is more severe and the heat loss through the edges is twice as for the clear panel section.
However, smaller area is affected by the thermal bridging phenomena in the SIP building compared to the
stick-frame building. Also, because of the fact that SIP panels are prefabricated and were produced more
precisely, more uniform thermal behavior is observed. Comparing the clear section with the thermal
bridging section for the two buildings, average temperature difference is larger for the clear section; while
heat flux is completely opposite and is larger for the thermal bridging area.
Full-scale measurements of wind induced convective heat transfer were conducted on two types
of building envelope systems. The test data analysis indicated that the convective heat transfer coefficient
over the building surface varies with wind speed, wind direction, surface conditions (including overnight
condensation and exposure to wind-driven rain), and orientation of the building surface with respect to the
wind direction (windward versus leeward). The building envelope thermal resistance and heat absorption
capacity also affect the CHTC at a given wind speed.

The convective heat transfer coefficient (CHTC) increased with wind speed in most of tests.
However, the variation of CHTC at a specific wind speed was significantly high.

The wind induced convective heat loss/gain is higher for windward building surfaces as compared
to the leeward faces.

Conduction heat gain at the building surface increased when the building models were exposed to
wind-driven rain. The convection process delivers significant amount of heat for evaporation of rain
water from the building surface. The prefabricated SIP building showed higher amount of surface
conduction heat gain compared to the 2x4” stick frame building after wind-driven rain exposure.
Section 2
57
6. Future Studies
This research includes information about two heat transfer mechanisms from the building
enclosure. Following items can be investigated in future studies to improve the results and/or to increase
the knowledge about the topics that are not covered here:
The following points can be considered in future study of related subject:

Heat flux sensors are one of the main sources of error in defining the thermal resistance of the
building envelope. Therefore, introducing a new method which measures the thermal resistance of
the enclosure based on the temperature only will enhance the outcome. One of the different
alternatives possible is to assume that the thermal flow is constant through the whole wall
thickness. By this assumption, the heat flux through the surface air film attached to the wall is equal
to the flux inside the wall. By measuring the air temperature adjacent to the building surface and
surface temperature of the wall, thermal resistance of the wall can be computed from:
where
exterior surface temperature,
temperature close to the surface,
= interior surface temperature,
= wall thermal resistance,
= interior air
= interior surface
air film thermal resistance.

Considering the effect of the thermal capacity of the wall material on the thermal behavior of the
enclosure. Also, defining the quantitative value of the thermal capacity of the two systems and
comparing them with each other.

Finite number of sensors can be installed on the walls to capture the thermal properties of the
system. Using a numerical modeling of the system will make it possible to study the behavior of the
wall as a continuous system. Accuracy of the modeling can be validated by comparing the results of
Section 2
58
the current study with the outcome of the modeling. Finite element modeling of the system will also
be useful in case of improving the design parameters. Different design alternatives can be studied
more easily using computer modeling.

Convective heat transfer coefficient as a function of the location on the building envelope.
Distribution of the convective heat transfer coefficient on the building envelope can be determined
by measuring the heat balance components at more locations. These points should be representative
of the effective wind load distribution on the surface.

In this report, only three wind angles of attack were considered for the tests. Other angles of attack
can also be studied to increase the knowledge on the effect of this parameter.

Two environmental conditions were simulated in the experiments including: sunny and rainy
weather conditions. The experiments can be further expanded to consider a situation where the
building is exposed to sun right after the rain.
Section 2
59
References
1. EN ISO 10211-1. (2007). "Thermal Bridges in Building Construction - Heat Flows and Surface
Temperatures Detailed Calculations."
2. ASHRAE. (2009). "Fundamental Handbook." American Society of Heating, Refrigeration and AirConditioning Engineers, ASHRAE.
3. Balaras, C. A., and Argiriou, A. A. (2002). "Infrared thermography for building diagnostics." Energy
and Buildings, 34, 171-183.
4. Balocco, C., Grazzini, G., and Cavalera, A. (2008). "Transient analysis of an external building
cladding." Energy and Buildings, 40, 1273–1277.
5. Cucumo, M., Rosa, A. D., Ferraro, V., Kaliakatsos, D., and Marinelli, V. (2006). "A method for the
experimental evaluation in situ of the wall conductance." Energy and Buildings, 38, 238-244.
6. Davies, M. G. (2004). "Building Heat Transfer." John Wiley & Sons Ltd., First ed., Chichester, UK.
7. Defraeye, T., Blocken, B., and Carmeliet, J. (2010). "CFD analysis of convective heat transfer at the
surfaces of a cube immersed in a turbulent boundary layer." International Journal of Heat and
Mass Transfer, 53, 297-308.
8. Defraeye, T., Blocken, B., and Carmeliet, J. (2011). "Convective heat transfer coefficients for exterior
building surfaces: Existing correlations and CFD modeling." Energy Conversion and
Management, 52.
9.
Defraeye, T., and Carmeliet, J. (2010). "A methodology to assess the influence of local wind
conditions and building orientation on the convective heat transfer at building surfaces."
Environmental Modelling & Software, 25, 1813-1824.
10. Deque, F., Ollivier, F., and Roux, J. (2001). "Effect of 2D modeling of thermal bridges on the energy
performance of buildings: numerical application on the Matisse apartment." Energy and
Buildings, 33, 583-587.
11. Dilmac, S., Guner, A., Senkal, F., and Kartal, S. (2007). "Simple method for calculation of heat loss
through floor/beam-wall intersections according to ISO 9164." Energy Conversion and
Management, 48, 826–835.
12. Dufour, M. B., Derome, D., and Zmeureanu, R. (2009). "Analysis of thermograms for the estimation
of dimensions of cracks in building envelope." Infrared Physics & Technology, 52, 70-78.
13. Emmel, M. G., and Mendes, N. (2005). "Analysis of wind-driven flow and external convective heat
transfer coefficients for the bestest model building." Ninth International IBPSA Conference,
Montréal, Canada.
14. Evola, G., Margani, G., and Marletta, L. (2011). "Energy and cost evaluation of thermal bridge
correction in Mediterranean climate." Energy and Buildings, 43, 2385–2393.
15. FivosSargentis, G., Chatzimpiros, A., and Symeonidis, N. (2009). "Determination Method of thermal
conductivity of building parts in situ through IR Imaging by minimizing the influence of
environmental parameters." Proceedings of the 11 th International Conference on Environmental
Science and Technology, Crete, Greece.
16.
Fokaides, P. A., and Kalogirou, S. A. (2011). "Application of infrared thermography for the
determination of the overall heat transfer coefficient (U-Value) in building envelopes." applied
Energy, 88, 4358–4365.
Section 2
60
17. Hagishima, A., and Tanimoto, J. (2003). "Field measurements for estimating the convective heat
transfer coefficient at building surfaces." Building and Environment, 38, 873-881.
18. Haralambopoulos, D. A., and Paparsenos, G. F. (1998). "Assessing the thermal insulation of old
buildings - The need for in situ spot measurements of thermal resistance and planar infrared
thermography." Energy Conversion and Management, 39(1/2), 65-79.
19. Hirsta, E., Barnes, R. W., Cohn, S. M., Corum, K. R., and Greene, D. (1982). "Effects of improved
energy efficiency on US energy use: 1973–1980." Energy and Buildings, 7(11), 897-907.
20. Hirsta, E., and O’Neal, D. (1979). "Contributions of improved technologies to reduced residential
energy growth." Energy and Buildings, 2(3), 217–224.
21. Kosny, J., and Kossecka, E. (2002). "Multi-dimensional heat transfer through complex building
envelope assemblies in hourly energy simulation programs." Energy and Buildings, 34, 445-454.
22. Larbi, A. B. (2005). "Statistical modelling of heat transfer for thermal bridges of buildings." Energy
and Buildings, 37, 945-951.
23. Laurenti, L., Marcotullio, F., and Monte, F. d. (2004). "Determination of the thermal resistance of
walls through a dynamic analysis of in-situ data." International Journal of Thermal Sciences 43,
297-306.
24. Liu, Y., and Harris, D. J. (2007). "Full-scale measurements of convective coefficient on external
surface of a low-rise building in sheltered conditions." Building and Environment, 42, 2718-2736.
25. Loveday, D. L., and Taki, A. H. (1996). "Convective heat transfer coefficients at a plane surface on a
full-scale building facade." INT. J. Heat Mass Transfer, 39(8), 1729-1742.
26.
Mao, G., and Johannesson, G. (1997). "Dynamic calculation of thermal bridges." Energy and
Buildings, 26, 233-240.
27. Palyvos, J. A. (2008). "A survey of wind convection coefficient correlations for building envelope
energy systems’ modeling." Applied Thermal Engineering, 28, 801-808.
28. Peng, C., and Wu, Z. (2008). "In situ measuring and evaluating the thermal resistance of building
construction." Energy and Buildings, 40, 2076-2082.
29.
Wiggenhauser, H. (2002). "Active IR-applications in civil engineering." Infrared Physics &
Technology, 43, 233-238.
30. Yesilata, B., and Turgut, P. (2007). "A simple dynamic measurement technique for comparing
thermal insulation performances of anisotropic building materials." Energy and Buildings, 39,
1027-1034.
31. Zalewski, L., Lassue, S., Rousse, D., and Boukhalfa, K. (2010). "Experimental and numerical
characterization of thermal bridges in prefabricated building walls." Energy Conversion and
Management, 51, 2869-2877.
Section 2
61
FINAL REPORT
SECTION 6
Computational Evaluation of Wind Load on
Residential Roofs with Complex Shape
Prepared by
Girma T. Bitsuamlak, Amir Mirmiran, Agerneh K. Dagnew, Edward Ledesma,
Workamaw Warsido, Maryam Asghari Mooneghi, and Christian A. Vidal
August 1, 2012
Executive summary
Wind induced loads are one of the most critical design parameters for coastal construction, especially
in Florida, where buildings are subjected to the highest wind loads of the nation. ASCE 7 2005/2010 provides wind loads for the design of Main Wind Force Resisting System (MWFRS), as well as Cladding
and Components of buildings. These provisions cover buildings with common shapes, such as buildings
with Flat, Gable, Hip, and Mono-slope roofs, under simple surrounding conditions. For complex roofs,
and surrounding conditions the code refers the practitioner to perform physical modeling in a Boundary
Layer Wind Tunnel (BLWT). Although these tests are viable for high-rise buildings and other large complex projects, they may not be cost effective for residential houses. As a result, there is a gap in the wind
load information for the construction of low-rise buildings with complex roofs. The present study attempts to evaluate wind loads on both common and complex roofs using a numerical approach based on
the technique of Computational Fluid Dynamics (CFD) principles. The focus is on residential single family house roofs, which incur the most damages during hurricane events. Both in-house computer programs
and commercial software were used in the study. To win the confidence of practicing engineers, all CFDbased wind load evaluations on roofs were validated by comparison with similar cases obtained from reliable and industry-wide accepted BLWT experimental data collected during the DEM 2010-11 projects
data from NSF Grant CMMI-0928563 project. The present comparisons are limited to the numerical
evaluation of mean pressure coefficient values.
KEYWORDS
CFD, LES, BLWT, Inflow turbulence, Low-rise buildings, Wind pressure, Complex roof shapes, Surrounding condition, Sub-urban.
Section 3
2
Table of Contents
Executive summary................................................................................................................... 2
1
Introduction ...................................................................................................................... 5
1.1
2
Commonly used residential roofs in the state of Florida .......................................... 6
Methodologies for wind load evaluation of low-rise buildings ....................................... 8
2.1
General provision of codes and standards ................................................................ 8
2.1.1
ACSE7-05 provisions ........................................................................................ 8
2.1.2
Highlights of the ASCE 7-10 wind provision ................................................... 9
2.2
2.2.1
Full-scale measurements.................................................................................... 9
2.2.2
Wind Tunnel .................................................................................................... 10
2.3
3
4
Physical modeling for wind load evaluation ............................................................. 9
CFD based computational approach ....................................................................... 11
Wind tunnel experiment for validation of CFD simulations .......................................... 12
3.1
Gable and Hip roof buildings .................................................................................. 12
3.2
Low-rise buildings with complex roof shapes ........................................................ 15
Outline of the CFD modeling for wind load evaluation................................................. 19
4.1
CAD preparation of the model buildings ................................................................ 20
4.2
The LES model ....................................................................................................... 22
4.3
Computational domain and boundary conditions ................................................... 22
4.4
Inflow turbulence for LES simulation .................................................................... 26
Section 3
3
Computational grid, spatial, and temporal discretization schemes ......................... 27
4.5
5
Results and discussions .................................................................................................. 29
5.1
Wind-induced responses of regularly shape low-rise residential buildings............ 30
5.1.1
Gable roof building .......................................................................................... 30
5.1.2
Hip roof............................................................................................................ 33
5.2
Wind-induced responses low-rise houses with complex roof shapes ..................... 37
6
Conclusions .................................................................................................................... 42
7
Acknowledgements ........................................................................................................ 44
8
References ...................................................................................................................... 44
Appendix A: FL27 House Model Dimensions ....................................................................... 46
Appendix B: FL30 House Model Dimensions........................................................................ 49
Appendix C: FL27 Neighboring Houses ............................................................................... 53
Appendix D: FL30 Neighboring Houses ................................................................................ 61
Section 3
4
1 Introduction
Hurricanes are among the most costly natural hazards to impact residential construction in
the coastal regions of the United States. During the 20th Century, Florida was second to Texas in
the number of direct hurricane hits; 57 total categorized storms with 24 of those being major
storms (Categories 3, 4, and 5 with winds 110 mph and higher). That means homeowners somewhere in Florida were hit by a hurricane every other year. During 2004-05, Florida was hit by
eight hurricanes, resulting in 2.9 million claims and $31.3 billion in insured losses (Blessing et
al., 2009). Roof systems are exposed to higher loading than any other building element and are
subjected to wind forces from many directions (Smith et. al, 1991). Suction pressures on the surface of the roof and roof corner vortices can lift both roof cladding and sheathing, and cause further structural damage. The wind flow patterns over a roof are complicated because of the various possible shapes of a roof and its sensitivity to the wind characteristics. While gable roofs
comprise the majority of architectural form on engineered low-rise buildings, which are generally subject to deemed-to-comply provisions of building codes and standard, the overall housing
stock of Florida exhibit a myriad of roof shapes. Post disaster studies have revealed that similar
standards of residential low-rise construction of different geometric forms have suffered a disparity in wind-induced damage (Meecham, 1992). In the case of wind loads on complex-shaped
roofs of residential buildings, few researches have been done so far. Building codes and standards refer to physical model testing for wind load evaluation of buildings with complex configurations such as typical residential construction with complex roof shapes and architectural features. Although these tests are viable for high-rise buildings and other large complex projects,
they may not be cost effective for residential houses. Nowadays, commendable efforts are being
done to determine wind effects on buildings and structures computationally. The evolution of
Section 3
5
computational wind engineering (CWE) based on the techniques of CFD is making numerical
evaluation of wind loads an attractive proposition for the design community. This is particularly
true in light of the positive trends in hardware and software technology development. The main
purpose of this research project is to evaluate wind-induced loads on low-rise residential buildings with complex roof shapes computationally, and validate results using experimentally obtained data. The outcome of the investigations will contribute to these computational efforts being made in order to support the case where building codes and standards in the future may begin
to also consider CWE as one of the commonplace tools for wind load evaluation, especially for
the design of low-rise residential houses.
1.1 Commonly used residential roofs in the state of Florida
There are many roof shapes in residential construction with gable, hip, and flat roofs being
the most common. Dutch hip, gambrel, mono slope, and many shape combinations are also possible for these structures. In residential buildings, gable roofs prevail, especially for older homes
in part because they are easier and cheaper to build. The Residential Construction and Mitigation
Program data in Florida is representative of a sample of 1103 homes of which 56% had gable
roofs, 29% had hip roofs, 10% had a combination of both and 5% were of other types. 85% of
the buildings were single story homes. Although gable and hip roofs are shown to be the dominant shape in low-rise residential roof designs (Hamid, 2005), many of these roofs exhibit some
complexities due to non-symmetrical floor plans or additions made to the original home. Table 1
shows the results of a survey done for three counties in Florida. The prevailing roof type for the
surveyed counties is gables with 70%, while hips account for around 23%.
Section 3
6
Table 1: Low-rise building roof types proportions (Pita et al., 2009)
County
Gable
Hip
Volusia
73%
21%
Marion
69%
26%
St. Lucie
67%
21%
Flat
6%
1%
-
A typical gable roof building has vertical side walls that extend all the way to the top tip
of an upside-down V. A gable roof generally consists of common trusses of triangular form,
yielding a constant cross section throughout the building length (Figure 1(a)). A hip roof has
sloping ends and sloping sides down to the roof eave line (Figure 1 (b)). The central part has the
same cross section as the gable roof, but the ends of the hip roof are framed differently. To
achieve the hip ends, the structural framing usually consists of diagonal hip trusses spanning
from the building roof corners to a girder truss located at the hip/ridge intersection. Hip roofs are
believed to be less prone to damage than gable roofs for several reasons: they slope in four directions; the sloping faces enhance the performance of the roofing material; they generate less uplift
and are structurally better braced; they laterally brace the primary roof trusses, or rafters, and
support the top of the end walls of the home against lateral wind forces; and they eliminate the
hinge formed between a gable end and a gable-end wall. Several post disaster investigations on
wind-induced damage to building roofs reveal that hip roofs have performed better than the gable
roofs during severe cyclones (FEMA, 1992) and (Sparks et al., 1985).
(a)
(b)
Figure 1: Typical (a) gable and (b) Hip roof buildings
Section 3
7
2 Methodologies for wind load evaluation of low-rise buildings
2.1 General provision of codes and standards
2.1.1 ACSE7-05 provisions
The design wind pressures on buildings in the United States are determined using ASCE
7-10 provisions. ASCE 7-10 provides wind loads for the design of Main Wind Force Resisting
System (MWFRS) as well as Cladding and Components of buildings using one of the three following methods: (a) Method 1-Simplified Procedure (b) Method 2-Analytical Procedure and (c)
Method 3-Wind Tunnel Procedure. The limitations for using each of these methods have been
provided in the code. These provisions cover buildings with common shapes, such as buildings
with Flat, Gable, Hip, and Mono-slope roofs, under simple surrounding conditions (Figure 2).
While the existing wind load design guides do provide wind pressure coefficients for regular
shape building, for complex situations one should refer to a physical model testing in a boundary
layer wind tunnel (BLWT). The determination of these wind loads on a building is directly dependent on experimentally determined pressure coefficients from previous wind tunnel tests. If
these pressure coefficients for a particular building shape do not exist, engineers must perform
wind tunnel tests to estimate the design wind loads.
Figure 2: Typical types of roofs addressed in wind codes and standards.
Section 3
8
2.1.2 Highlights of the ASCE 7-10 wind provision
The new ASCE 7 2010 issue for Minimum Design Loads for Buildings and other structures
comes with changes in the wind design, especially in the design wind of speed in hurricane prone
regions and wind debris. The design wind speed is specified as the ultimate speed directly, with
return period 700 years (or 1700 years for important buildings). It is more consistent with the
approach used in earthquake design and will tend to focus attention more directly on the behavior of the structure in ultimate failure mode (Irwin 2009).
2.2 Physical modeling for wind load evaluation
2.2.1 Full-scale measurements
The ever rising economic losses and human casualties associated with natural disasters
caused by frequent occurrence of severe wind storms is shaping the wind engineering research in
dramatic ways. To deal with this novel, large-scale and full-scale research facilities are being
built around the world. The Wall of Wind (WoW) facility at Florida International University is
one of the first major initiatives to operate a full-scale facility dedicated to hurricane damage
mitigation (Leatherman et al., 2007). The insights from WoW full-scale experiment results are
very significant for the wind engineering community in terms of understanding the performance
of buildings under hurricane winds and devising mitigation plans to increase the resilience of
low-rise buildings under such loads. One noticeable advantage of this type of facility is that its
ability for testing a full-sized residential building, single story dwelling. Residential construction
is typically built using prescriptive building codes, and is characterized by the use of materials
with large variability and structures with significant static indeterminacy making load paths and
overall performance difficult to ascertain. These new facilities are able to deal with these issues
Section 3
9
by bringing realistic wind loads to full-scale structures, enabling the development of improved
building code requirements, product safety standards and loss models. In addition it also eliminates some of the issues associated with model scale testing such as boundary layer wind tunnel.
2.2.2 Wind Tunnel
Boundary layer wind tunnel testing is the most commonly used industry wide accepted wind
engineering tool for evaluating of wind loads on structures ranging from low-rise to high-rise
buildings with complex configurations, and from bridge aerodynamics to topographic study. For
large structures, the generic load provisions derived from analytical methods in building codes
and standards are often insufficiently precise for an optimal design. Though most often defined
as minimum design loads, the loads derived from code analytical methods represent an upper
bound covering the majority of cases for standard building shapes. Wind tunnel tests can predict
wind-induced effects on structures, addressing some of the difficulty encountered by codes, by
accounting for project-specific factors such as the aerodynamic effect of the actual shape of the
structure, the influence of adjacent buildings and topography, detailed wind directionality effects,
and aero-elastic interaction between the structural motion and airflow. Testing in the wind tunnels involve the modeling of the structure, representing all its geometrical characteristics to a
properly chosen scale (keeping in view blockage effects), the prescription of the incoming wind
flow properties that properly characterize the flow statistics of the study site, choice of resolution
for wind pressure measurement on the surface of the target model building which should take
cognizance of the importance of edge/corner pressures against the overall average, and the
choice of the reference pressure.
Section 3
10
2.3 CFD based computational approach
The evolution of computational wind engineering (CWE) is making numerical evaluation
of wind loads an attractive proposition. This is particularly true in light of the positive trends in
hardware and software technology development. Practical applications of CFD are widespread
in areas such as pedestrian level wind evaluation, where mean wind velocities are required for
evaluating comfort issues (Stathopoulos and Wu, 2004; Hanjalić and Kenjereš, 2008) and for
building ventilation design applications (Jiru and Bitsuamlak, 2010). Some of the works in CFD
applications for wind load evaluation includes non-linear Reynolds-averaged Navier-Stokes
(RANS) modeling for full-scale low-rise buildings such as the Silsoe Cube (Wright and Easom,
2003), the computational prediction of flow-induced pressure fluctuations on the Texas Tech
University (TTU) test building (Senthooran et al., 2004) and the computation of pressure on TTU
(Selvam, 1996). Also, there has been CFD research on tall buildings such as the Aerodynamics
of Commonwealth Advisory Aeronautical Research Council (CAARC) model building – a
benchmark tall building used to calibrate wind tunnels around the world -- (Dagnew et al., 2009,
2010; Braun and Awruch, 2009; and Huang et al., 2007) ; Large Eddy Simulation (LES) of flow
and building wall pressure in the center of Tokyo (Nozu et al,. 2008); LES of wind effect on a
full-scale supper-tall building (Huang et al., 2010); flow around high-rise buildings using various
turbulence models by Tominaga et al (2008a); topographic studies over complex terrains (Tamura et al 2007, Stathopoulos, 1999, 2002; Ishihara et al., 1999, Bitsuamlak et al., 2004, 2005b, and
2007). More recently, exponential growth in computing technologies have helped analyze 3D
complex wind flow fields using LES and Direct Numerical Simulation (DNS) with reasonable
computational cost and enabled wind load estimation with high accuracy (Tamura et al 2008).
Section 3
11
Some countries have already established working groups to investigate the practical applicability of CWE and develop recommendations and guidelines for efficient implementation and use
for wind resistant design of actual buildings and for assessing pedestrian level winds, within the
framework of the Architectural Institute of Japan (AIJ) (Tamura et al. 2008, (Tominaga et al.,
2008) and the European cooperation in the field of scientific and technical research (COST,
2007; Franke, 2006). AIJ provides methods for predicting wind loading on buildings by RANS
and LES. While COST Action 732 (COST732, 2007) outline a best practice guideline for successful CFD simulation of wind flows in the urban environment using steady RANS equations.
Wind loads for residential buildings are affected in a complex way by many factors, such as
incoming wind characteristics (wind speed, turbulence intensity, integral length scales, etc.), topography and surface roughness, immediate surroundings, building/roof shape and orientation.
Hence, before getting to the wind load evaluation phase any CWE simulation should put an effort to incorporate all of these factors in a manner that is as realistic as possible in order to produce a usable outcome. Hence an attempt will be first to produce global wind loads (i.e. Wind
load for MWFRS design) followed by local wind loads (i.e. wind load for cladding and components design). Once validated in comparison with wind tunnel data, computational models/methods will be valuable and cost-effective wind engineering tools for practitioners.
3 Wind tunnel data for validation of CFD simulations
3.1 Gable and Hip roof buildings
Wind tunnel tests were conducted on small scale models of one-story single-family residential buildings to study the distribution of roof pressure. For the current investigation, two different roof geometries were fabricated, a 3:12 slope gable roof model (Figure 3(a)), and a 3:12
Section 3
12
slope hip roof model (Figure 3 (b)). Photographs of the gable and hip roof buildings models in
the wind tunnel are also shown in Figs. 3 and 4 respectively.
(b)
(a)
Figure 3: Wind tunnel testing of Gable and Hip roof buildings.
The scale model of the house was constructed from Plexiglass at a length scale of 1:15 and
has dimensions of L=1.2m(4ft), W=0.6 m(2ft) and H=0.3m(1.1ft) where L and W denote the
longer and shorter widths respectively and H denotes the roof ridge height. At this size, the maximum building model blockage ratio in the wind tunnel was approximately 9% (only marginally
higher than the maximum blockage of 8% given in ASCE-7 (2010). The wind tunnel tests were
carried out at RWDI’s boundary layer wind tunnel facility in Florida USA. The wind tunnel has
a cross-section of 2.13m x 2.44m (7ft x 8ft) and the test model was placed on a turntable located
13.3m (43.5ft) downstream of the tunnel entrance. An attempt was made to generate only the
lower part of the atmospheric boundary layer at a relatively large scale. The test was conducted
in an open terrain exposure for which the mean wind speed (prescribed by power law) and turbulence intensity profiles as well as the longitudinal turbulence spectrum of the simulated open exposure are shown in Figure 4(a) and (b), respectively. The simulation of mean wind speed and
turbulence intensity profiles approaching the modeled area of the BLWT were accomplished
Section 3
13
through a combination of turbulence-generating spires installed at the upwind end of the tunnel
and a long working section with floor roughness elements. The target open terrain profile was
generated by a configuration of 2-dimensional trapezoidal spires (19 inches wide at the floor, 15
inches wide at the ceiling) and 1.5 inch tall triangular floor roughness elements. The Reynolds
numbers in the present study was calculated to be 7.84E+05. The mean wind speed profile fits
well with a target profile obtained with a power law exponent of 0.15(~1/6.5). Moreover, the
turbulence intensity profile also fits well to a target profile of 1/ln(z/zo) recommended based on
the large-scale depression system measurements Holmes (2007). A full-scale value of 0.02m was
taken for the roughness coefficient zo as per the recommendation of ASCE7 (2010) for exposure
category C. The normalized longitudinal turbulence spectrum also shows a good match with von
Karman spectrum.
35
0
1
TI(%)
10 20 30 40 50 60 70 80 90 100
V (measured)
30
0.1
TI (measured)
f*SU(f)/σ2
25
0.01
Z(m)
20
15
Measured
von Karman
0.001
10
5
0
a) 0.0
0.2
0.4
(a)
0.6
V/V30m
0.8
1.0
1.2
0.0001
0.001
0.01
0.1
(b)
1
10
f*Lv/U
Figure 4: (a) mean wind speed and turbulence intensity profile and (b) longitudinal turbulence
spectrum at 5.64m (building height) of the simulated open exposure.
Section 3
14
Pressure taps were installed on the gable and hip roof models following the tap layouts
shown in Figure 5(a) and (b), respectively. Pressure readings on the building envelope were taken by connecting the pressure taps to Scanivalve pressure scanners with 0.053in (1.34mm) diameter PVC tubes. For each wind direction, the data was collected for a 90 s duration at a sampling
frequency of 512Hz. Each test was conducted for wind directions ranging from 0o to 90o at 10o
intervals including the diagonal (45o) direction. The data collected was low-pass filtered with a
transfer function to account for signal distortion effects resulting from vibration of the tubes.
(a)
(b)
Figure 5: (a) Gable and (b) hip building model roof, soffit, and wall pressure tap layouts and
wind angle of attack (AoA).
3.2 Low-rise buildings with complex roof shapes
Field measurments were conducted on two house models with complex roof shapes. The
code names for these study buildings are called FL27 and FL30, respectively. Both FL27 and
FL30 are two of the 42 homes in the Florida Costal Monitoring Program (FCMP). These buildings were instrumented during the landfall of hurricane Ivan and pressure time-history data were
recorded on the actual houses. FL-27 is a one-story single-family residence located in Gulf
Breeze, Florida (Figure 6). The gable roof consists of multiple levels, with the main ridge at 6m
Section 3
15
elevation above grade. Twenty-four absolute pressure transducers were mounted at the corner
and edge locations on the roof to measure external dynamic pressures. An additional absolute
pressure sensor is connected to an RM Young pressure port to minimize dynamic wind pressure.
Figure 6: Photographs of the actual FL-27 house showing anemometer location and pressure sensor (after Liu et al., 2009)
Wind tunnel tests as part of NSF Grant CMMI-0928563 were also conducted on the two
house models with complex roof shapes. The wind tunnel tests were conducted on the two houses to provide comparison data of the field measurements. The BLWT conducted at the UWO
assumed a suburban exposure, where according to ASCE7-10, the exposure B (suburban exposure) is defined as areas where the ground surface roughness includes urban and suburban areas,
Section 3
16
wooded areas, or other terrain with numerous closely spaced obstructions having the size of single-family dwellings or larger, and where it prevails in the upwind direction for a distance greater than 1500 ft (460 m). The mean velocity and turbulence intensity profile measured at the
UWO wind tunnel for the complex were used for the CFD simulation (Figure 7). From the
Google images provided in Figure 8, both FL27 and FL30 are surrounded with 1 or 2 story single-family dwellings on one side and wooded area on the other side, within a radius of 0.5 miles
(800 m). The wind tunnel accounted the surrounding terrain within 1 mile radius of the target
building (Figure 9). The radius of the surrounding assumed for the BLWT study took into consideration of the model building size. And the resulting stretch length was assumed to be sufficient enough to develop the inner boundary layer. Since suburban terrains for low-rise buildings
(i.e., at large scales) are challenging to model in boundary layer wind tunnels, as there are limited
variations one can achieve. Thus, the changes in roughness with the 0.5 mile (0.8 km) radius
were not modeled in detail.
14
Experiment (UWO)
12
U (m/s)
Iu (%)
Elvation (m)
10
CFD
U (m/s)
Iu (%)
8
6
4
2
0
0.0
0.2
0.4
Turbulence intensity
Section 3
0.6
0.8
1.0
1.2
1.4
1.6
Normalized Wind Speed
17
Figure 7: Mean wind speed referenced at mean roof height, h, and turbulence intensity profile in
the suburban exposure (zo = 0.23 m) in full-scale dimensions (NSF Grant CMMI-0928563).
Figure 8: Google image of surrounding exposures of study buildings FL27 (top) and FL30 (bottom) (after Kopp and Gavanski -- part of NSF Grant CMMI-0928563-- 2010; Liu et al., 2009)
The building models were instrumented with pressure taps. Figure 9 (a) shows the 1: 50 scale
model of LF27 which contains 496 taps on its roof. Figure 9 (b) shows the Fl30 model building
with 474 taps systematically distributed on the roof. The boundary layer simulation of the model
buildings with neighboring houses were done by placing the test house model at the center of the
turn table (Figure 10). The surrounding houses located within a radius of 250 ft (full-scale) were
built and placed on the turntable.
Section 3
18
(a)
(b)
Figure 9: Wind tunnel models of houses with complex roof shapes: (a) house model FL27 and
(b) house model FL30 (after Kopp and Gavanski, 2010 -- part of NSF Grant CMMI-0928563--).
Figure 10: Wind tunnel setup of study houses with neighboring buildings: FL27 with neighboring house (left) and FL30 with neighboring houses (right) (after Kopp and Gavanski, 2010 -- part
of NSF Grant CMMI-0928563--)
4 Outline of the CFD modeling for wind load evaluation
The CFD evaluation of wind effects on buildings involves various modeling steps. The preprocessing/simulation steps start from the conceptual modeling to the CAD preparation and to
generation of high quality computational meshes. Due to the complexity of the wind/structure
interaction, care should be taken during the model preparation phase. Those follow the selection
of appropriate turbulence modeling, such as RANS, LES, and hybrid RANS/LES models, which
Section 3
19
can realistically capture the important structures of the wind flow. To ensure the best use of the
turbulence models in getting the accurate numerical prediction of wind-induced effects, the sizing of the computational domain and prescription of the boundary conditions should also be carefully defined. Parallel simulations were carried out using commercially available software known
as Ansys Fluent14 (Ansys Inc. 2012). All simulation were carried in the Multidisciplinary Analysis Inverse Design Robust Optimization and Control Laboratory (MAIDROC) lab, which has
272 processor parallel computing nodes, using 28 CPUs. The following sections describe in detail the procedures and the modeling principles used in the present research project.
4.1 CAD preparation of the model buildings
The geometrical modeling for the CFD simulation mimics the wind tunnel setup building
models. For the regular shape models, gable and hip roof buildings, an isolated building case
have been investigated using 1:15 scale. Figure 11 show the 3D perspective view of the model
buildings considered for the preliminary numerical simulations. For buildings with complex roof
shapes the dimensions and the surrounding context for the LES simulation were determined from
a combination of the wind tunnel model photos and information provided by the University of
Florida and UWO research group (Figure 12) (part of NSF Grant CMMI-0928563). The scale
adopted for the LES simulation is the same as the scale of the BLWT testing, i.e. 1:50. Figure 13
shows the topology of FL27 and FL30 with the surrounding buildings inside the idealized turntable of the computational domain. The full-scale dimensions of the isolated building with complex roof shapes are shown in the Appendices A and B. The complete geometrical information
such as model dimensions, wind direction, name of the neighboring buildings are in the Appendices C and D.
Section 3
20
(a)
(b)
Figure 11: Three-dimensional perspective drawings of residential buildings: (a) Gable and (b)
Hip
(a) FL27
(b) FL30
Figure 12: CAD models of single house models with complex roof shapes
(a) FL27
(b) FL30
Figure 13: Geometrical models of the FCMP residential houses with neighboring buildings
Section 3
21
4.2 The LES model
LES is a multi-scale computational modeling approach that offers a more comprehensive
way of capturing unsteady flows. The use of LES as a wind load evaluation tool has been significantly improved in recent years through the following numerical techniques (a) numerical generation of transient inflow turbulence (Kraichan, 1970; Lund et al., 1998; Nozawa et al., 2002,
2005; Smirnov et al., 2001; Batten et al., 2004 ), (b) development of efficient sub-grid scale turbulence modeling techniques suitable for unsteady three-dimensional boundary separated flows,
and (c) numerical discretization with conservation of physical quantities for modeling complicated geometry (Tamura et al. 2008). Because of these advancements, LES holds promise to become the future computational wind engineering (CWE) modeling for which turbulent flow is of
pivotal importance (Tamura, 2008; Tucker and Lardeau, 2009; Sagaut and Deck, 2009). In the
present study, the Dynamic Smagornisky-Lilly subgrid-scale (SGS) model based on Germano et
al. (1996) and Lily (1992) have been employed. In this method the Smagornisky constant, C s , is
computed dynamically according to the resolved scales of motion inside the domain.
4.3 Computational domain and boundary conditions
The computational domain (CD) defines the region where the flow field is computed. The size
of the CD should be large enough to accommodate all relevant flow features that will have potential effects on altering the characteristics of the flow field within the region of interest
(Franke, 2006, COST 2007, AIJ 2008). In addition, the sizing also should take into account the
computational overhead that will be incurred by using an excessively large domain. For the present study multiple steady state preliminary simulations were conducted to size the computational domains and the combination of sizes which resulted in a blockage ratio of less than 5% were
used for the main simulations. Where the blockage ratio is defined as the ratio of the projected
area of the surfaces of the model buildings in the flow direction to the area of the inlet boundary.
Table 2 summarizes the dimensions of the models, the computational domain, and the resulted blockage ratio of the cases considered in the present study. For FL27 and FL 30 with neigh-
Section 3
22
boring houses the CD were sized using the maximum building height within the vicinity of the
target model and the resulting blockage ratio were 7 and 6% respectively.
Boundary conditions (BC) represent the effect of the surroundings that have been cut off by
the CD and idealize the influence of the actual flow environment under consideration. BCs could
dictate the solution inside the CD and have significant effects on the accuracy of the solution. At
the inlet boundary, the mean wind velocity profile can be prescribed using either the power law
or log-law profile. For the regular shape models the mean wind speed with power law (   0.15 )
and turbulent intensity defined for an open terrain (Figure 4). The wind speed and turbulent intensity profiles of the complex roof shape buildings (FL27 and FL30) measured at the UWO
wind tunnel were applied for the CFD simulation. The mean velocity profile was prescribed by
the power law (with exponent   0.1658 ), and the turbulence intensity profile was found by
curve fitting (Figure 7). For velocities, no-slip boundary is commonly used at solid walls
(COST, 2007). A symmetry boundary condition was employed at the top and lateral surfaces of
the CD. Since details of the flow variables were not known prior to the simulation, an outflow
boundary was applied at the outlet plane. Figure 14 shows a typical CD and boundary conditions
modeling for the benchmark simulations.
Section 3
23
Figure 14: Computational domain and boundary conditions: Gable roof model
The computational domain and boundary conditions were setup for buildings with complex
roof shape after careful CAD modeling and topology cleanup, as shown in Figure 15. For the
case with the neighboring buildings the CD size were increased to accommodate the surrounding
buildings (Figure 16).
Section 3
24
Figure 15: Computational domain and boundary conditions for FL27 model building.
Figure 16: Computational domain and boundary conditions of FL27 and Fl30 with neighboring
houses.
Section 3
25
Table 2: Dimension of the mode buildings and blockage ratio of the computational domains
Case
Building dimensions
Wind direction
Blockage ratio
(L x W x H) (in)
(degree)
(%)
3
0
4
Gable
48 x 24 x 12.875
45
Hip
48 x 24 x 12.875
Fl27
16.48 x 15.30 x 4.48
FL30
13.2 x 11.52 x 4.08
90
0
45
90
120
1.7
4.3
4
1.7
4
120
4.4
4.4 Inflow turbulence for LES simulation
For the present study, in addition to the mean velocity profile, transient velocity fluctuations
were superimposed at the inlet boundary of the LES simulations. A method called the discretizing and synthesizing random flow generation (DSRFG) for the transient inflow turbulence,
which has the flexibility to prescribing any arbitrary 3D spectrum for the amplitude of the fluctuation such as the von Karman spectral (Huang et al., 2010) were used. The synthesized velocity
field is presented below for discussion purposes and the detailed formulation and derivation can
be found in the original paper
u ( x ,t ) 
K ma
N
 [ p
m  k 0 n 1
m ,n
~ ~
~ ~
cos( k m ,n x  m ,n t ) q m ,n sin( k m ,n x  m ,n t )]
(1)
Where p m ,n and q m ,n are the vector form of the fluctuation amplitude. For inhomogeneous and
anisotropic turbulence the distribution of k m ,n is done by remapping the surface of the sphere
after the components of P m ,n and q m ,n are aligned with the energy spectrum. In addition to the
flexibility of prescribing any arbitrary 3D spectrum, the DSRFG method uses the length scale (
Section 3
26
L s  L2u  L2v  L2w ) as a scaling factor and this resulted in the generation of spatially correlated
flow fields with the relevant length scales.
For velocities, no-slip boundary was used at solid walls of the model buildings. For the
near-wall regions high resolution meshes were applied for all the LES simulations cases. Symmetry boundary condition was employed at the top and lateral surfaces of the CD. Since details
of the flow variables are not known prior to the simulation, outflow boundaries were applied at
the outlet plane.
4.5 Computational grid, spatial, and temporal discretization schemes
The computational grids were generated using Ansys Meshing CutCell Cartesian meshing
algorithm, which is a very powerful mesh-generating tool. This mesh tool has a unique ability to
generate a large fraction of hexahedral cells in complex configurations. The mesh operation involves a two-stage inflation process to resolve the inner boundary layer and generate sufficient
quality for convergence. For all the cases considered in the present study the cell’s skew ranges
between 0 and 0.55, and the orthogonal quality lies between 0.5 and 1 and the computational
cells’ sizes were fixed to produce an aspect ratio of less than 3. Successive adaptations have been
done to refine the cells’ sizes and resolve the near-wall region of the model buildings. In the inner sub-layer region, the boundary layer meshes were inflated from the ground surface and the
first cells were placed at a distance y p  0.0005 m with a stretching ratio of 1.05. This ensured
y  to be less than 5 units. In addition, the computational domain was subdivided into multi-body
parts to have better control and distribution of the computational grid points around the model
building and wall boundary. Figure 17 show the step-by-step computational grid generation of
gable and hip roof models. For complex roof shapes, the area within the vicinity of the sharp
Section 3
27
edges were treated by clustering very fine grid cells around, to help negotiate the change of the
topology (Figure 18).
Figure 17: Computational mesh for gable and hip roof models
Figure 18: Computational mesh for Fl27 and FL30 model buildings
For discretization of convection terms central-differencing based schemes give the least
numerical diffusion and the best accuracy compared to the upwind schemes, as demonstrated by
Marinuzzi and Tropea (1993). However, for high Re flows in the wake region, such as the present cases, this scheme can become unstable, giving unphysical oscillations (wiggles). The
bounded central differencing (BCD) scheme, essentially based on the normalized variable diaSection 3
28
gram (NVD) approach (Leonard 1991) together with a convection ‘boundedness’ criterion can
detect and remove these wiggles in the wake region. Because of this the BCD scheme has been
used for all the simulations of the present study. For temporal discretization, second-order
schemes are advised for most computational wind engineering applications and have been used
in the present study. A second-order scheme for pressure discretization has been applied. For
pressure-velocity coupling, the Pressure Implicit with Splitting of Operators (PISO) algorithm
with skewness and neighboring correction is recommended for the transient simulation and has
been used in all LES simulation. PISO is based on the higher degree of the approximate relation
between the corrections for pressure and velocity (Ansys Inc. 2011). The simulations have been
carried out at the supercomputer center at Florida International University. The parallel computations have been carried out using 28 CPUs. A computational time step of 0.001s with 5 subiterations, per time step, was used in all the simulations. First the simulation run for enough flow
time and once the solution reached a stable condition, the fluctuating pressure data were recorded
for 2s flow-time. Also, for the residuals a strict convergence criterion of 10 5 has been applied
to ensure full convergence of the simulations.
5 Results and discussions
Aerodynamic forces develop over the body surface of structures immersed in a turbulent
flow field. These forces are usually obtained by integration of pressures and shear stresses developed on the fluid-structure interaction owing to the flow action. These forces can be calculated
from the time-history of the pressure data. In the present work, pressure coefficients obtained
from the CFD simulations and the wind tunnel tests were converted into non-dimensional mean
Section 3
29
pressure coefficients ( C p ), normalized with the dynamic pressure head, defined using the following expression:
CP 
P  Po
1
ρaVH2
2
2
where C p is the non-dimensional pressure coefficient, P is the pressure measured on the building
roof surface, Po is the reference pressure,  is the density of air, and V H is the reference wind
speed at mean roof height of the building.
5.1 Wind-induced responses of regularly shape low-rise residential roofs
5.1.1 Gable roof building
For the numerical investigation of wind-induced effects on the low-rise buildings two cases
for three wind directions were considered (Table 3).
Table 3 Cases considered for LES and BLWT studies: Gable roof
Case
Roof type
Case 1
Case 2
Case 3
Gable
Gable
Gable
Terrain exposure
Open
Open
Open
Azimuth (degree)
00
450
900
The time average of the pressure time-history data of the LES simulations were calculated
and compared with the BLWT. Figure 19 shows the mean pressure coefficients measured at the
pressure taps located at centerline of the roofs, as shown in Figure 5(a) and (b). For the straight
wind (00 AoA) the LES predicted well except at the taps near the edge of the roof where flow
separates. For the oblique wind directions (case 1 and 2) the LES resulted in a good prediction
comparable to the BLWT result. Figure 20 shows the comparisons of the mean pressure coefficients contour plot for the BLWT and LES. From the illustration it can be seen that for Case 1
Section 3
30
the oncoming flow separated at the leading edge of the roof and remain separated to the lee-ward
region of the roof (Figure 20 (a) and (b)). This introduced very high suction pressure at the
windward edge of the roof and the suction intensified at the ridge edge. For the oblique wind
AoA (Case 2) the distribution of the pressure contours show the formation of corner vortices,
responsible for uplift wind forces (Figure 20 (c) and (d)). Large eddies on the longer side of the
roof and on the shorter side of the roof small structure with high fluctuation were formed. This
type of fluctuating pressure could initiate the failure of roof coverings. For the 900 wind direction (Case 3) the oncoming flow reattached back to the roof at lee-ward and as a result part of the
roof in the reattachment region experienced a low positive pressure (Figure 20 (d) and (e)).
Overall there is a good agreement between the LES simulations of the gable roof models with
and the experiments.
0
Cp (mean)
-0.5
-1
Exp.
LES (CFD)
-1.5
-2
-2.5
0
0.2
0.4 x/L 0.6
0.8
1
(a) Case 1
Section 3
31
Cp (mean)
0
-0.5
-1
Exp.
LES (CFD)
-1.5
-2
0
0.2
0.4 x/L 0.6
0.8
1
(b) Case 2
0.5
Cp (mean)
0.25
0
Exp.
-0.25
LES (CFD)
-0.5
0
0.2
0.4 x/L 0.6
0.8
1
(c) Case 3
Figure0 19: Comparison of mean pressure coefficient of LES and BLWT data: (a) 00, (b) 450, and
(c) 90 wind AoA.
Section 3
32
(a) BLWT
(b) CFD-LES
(c) BLWT
(d) CFD-LES
(f) CFD-LES
(d) BLWT
Figure 20: Wind
tunnel
and
CFD
contour
map
of
mean
pressure
coefficients
on the gable roof
building: (a) 00, (b) 450, and (c) 900 wind angle of attack.
5.1.2 Hip roof
LES was used to assess the performance of hip roof building in comparison with the gable
roof and its response for wind directionality effects three wind directions have been considered
for the numerical and wind tunnel simulations. Table 4 summarizes the cases studied for the hip
roof building.
Table 4: Cases considered for LES and BLWT studies: Gable and hip roof buildings
Case
Roof type
Case 1
Hip
Section 3
Terrain exposure
Open
Azimuth (degree)
00
33
Case 2
Case 3
Hip
Hip
450
900
Open
Open
Figure 21 shows the comparison of the mean pressure coefficients of the hip roof. The mean
Cp is computed from the time-history of pressure recorded during the LES simulation and the
BLWT testing. The LES simulations predicted the mean C p
very well, especially for the 00
wind AoA. There is slight over-prediction of the time-averaged C p for the oblique wind. For
Case 3 (900) the Cp measured at the centerline of the hip roof showed very small pressure coefficients ( C p  0 ). This is in line with the response of the gable roof building for the same wind
direction (Figure 19(c)).
0
Cp (mean)
-0.5
-1
Exp.
-1.5
LES (CFD)
-2
-2.5
0
0.2
0.4
x/L
0.6
0.8
1
(a)
Section 3
34
0
Cp (mean)
-0.5
-1
Exp.
LES (CFD)
-1.5
-2
0
0.2
0.4
x/L
0.6
0.8
1
0.4
x/L
0.6
0.8
1
(b)
0.5
Cp (mean)
0.25
0
Exp.
-0.25
LES (CFD)
-0.5
0
0.2
(c)
Figure0 21 Comparison of mean pressure coefficient of LES and BLWT data: (a) 00, (b) 450, and
(c) 90 wind AoA on the roof of a Hip roof building.
Figure 22 shows the contour map of a typical gable roof under turbulent wind field. The LES
reproduced most of the important flow features such as separation, re-attachment and corner vortices on the surface of the building. Quantitatively, there is a good agreement between the LES
and the BLWT results.
Section 3
35
(a) BLWT
(b) CFD
(c) BLWT
(d) CFD
(e) BLWT
(f) CFD
Figure 22:0 Wind tunnel
and CFD contour map of mean pressure coefficients for hip roof building: (a) 0 , (b) 450, and (c) 900 wind angle of attack.
Section 3
36
The use of a time-history approach and transient inflow turbulence contributed to the improved prediction of wind loads for oblique wind direction, which usually for such type of wind
the numerical simulations fail to accurately estimate the wind effects. The assumption of constant integral length and turbulent intensities in the lateral and vertical directions, due to the size
of the mode scale it was not possible to measure these properties in the lower part of the study
buildings during the wind tunnel testing, attributed to the slight discrepancies of the LES results.
5.2 Wind-induced responses low-rise houses with complex roof shapes
As stated in the introduction section, the design wind load evaluation of building with complex roof shapes are not covered in the codes such as ASCE7. As part of this research project
two buildings with a typical complex roof shapes that are common in the State of Florida have
been investigated for wind pressure loads, numerically. The CFD results were compared with
BLWT data generously provided by the UF/UWO research group. Table 5 shows the cases considered, the terrain exposure and the wind directions. Time-history of pressure data were recorded at the pressure taps systematically distributed on the critical section of the roofs (Figure 23).
Table 5 Cases considered for LES and BLWT studies: Complex roof shap buildings
Case
FL27
FL27 with neighboring
FL30
FL30 with neighboring
Section 3
Roof type
Complex
Complex
Complex
Complex
Terrain exposure
Exposure B
Exposure B
Exposure B
Exposure B
Azimuth (degree)
1200
1200
1200
1200
37
(b)
(a)
Figure 23: distribution of pressure taps for LES simulation: (a) FL27 and (b) FL30
The mean pressure coefficients of the LES simulations and the BLWT data for complex roof
FL27 were plotted along the pressure tap lines and compared, as shown in Figure 24. The prediction of the time-averaged Cp by the LES follows the same pattern as the experimental data.
Figure 25 shows the mean pressure coefficient profiles for the FL30 measure at the highlighted
taps. Overall the mean pressure coefficient profile of the LES simulations near the ridge showed
some discrepancies from the experiment. The pressure load distribution on the roofs these models displayed a variation that is completely different from the one by the regular shape models.
However, considering the sharp edge of the building and assumption of used in translating the
wind tunnel data to the CFD modeling (such as the assumption of constant integral length in the
lateral and span, turbulent intensity), the results are very encouraging.
Section 3
38
LES (No neighboring houses)
BLWT (No neighboring houses)
LES (With neighboring houses)
BLWT (With neighboring houses)
0.5
0.3
Cp mean
0.0
-0.3
-0.5
-0.8
-1.0
15
20
25
30
35
40
45
y coordinate (ft)
(a)
BLWT(No neighboring houses)
LES (with neighboring houses)
BLWT(with neighboring houses)
0.2
0.0
Cp mean
-0.2
-0.4
-0.6
-0.8
-1.0
0
5
10
15
20
25
30
35
40
y coordinate (ft)
(b)
Figure 24: Mean pressure coefficient of FL27 from CFD and BLWT: (a) plot along the east of
the roof and (b) plot along west side of the roof.
Section 3
39
BLWT (no neighboring houses)
BLWT (with neighboring houses)
0.2
0.0
Cp mean
-0.2
-0.4
-0.6
-0.8
-1.0
10
15
20
25
30
35
40
45
y coordinate (ft)
(a)
LES (no neighboring houses)
BLWT (no neighboring houses)
BLWT (with neighboring houses)
0.2
0.0
Cp mean
-0.2
-0.4
-0.6
-0.8
-1.0
35
40
45
50
55
60
x coordinate (ft)
(b)
Figure 25: Mean pressure coefficients of FL30 from CFD and BLWT: (a) plot along the east of
the roof and (b) plot along west side of the roof.
Section 3
40
The contour map of the mean pressure coefficients shown in Figure 26 illustrates how the
neighboring buildings could affect the wind load distribution of the target or study building. The
interference and sheltering effects resulting from the surrounding houses modified the contour
map. In some case it increased in the pressure loads in another instance it increases the suction
pressure load. These highlights the importance of consider these effects when evaluating the design wind loads for roofs of irregular shapes buildings.
(a) FL 27: isolated house
(b) FL27: With neighboring houses
(c) FL30: isolated houses
(d) FL30: With neighboring houses
Figure 26: CFD counter maps of mean pressure coefficients
To illustrate the importance of studying wind directionality, velocity streamlines of two wind
direction were studied. Figure 27 and Figure 28 show the surface velocity streamlines of FL27
Section 3
41
and FL30 with neighboring buildings, respectively. The neighboring structures clearly changed
the flow dynamics of at the incidence plane of the study buildings. Because of these, channeling,
sheltering, and wake effects were observed.
(a)
(b)
Figure 27: Surface velocity streamlines of FL27 with neighboring houses: (a) 00 and (b) 1200
(a)
(b)
Figure 28: Surface velocity streamlines of FL30 with neighboring houses: (a) 00 and (b) 1200
6 Conclusions
Numerical investigation of wind-induce pressure loads on building with regular and complex
roof shapes were carried out using the technique of LES. The study of the roof pressure distribuSection 3
42
tion revealed that the mean pressure coefficient predicted by the LES simulation is in a good
agreement with the wind tunnel data. The study also showed that oblique angle wind could introduce uplift pressure loads. The models with complex roof shapes showed mixed pressure distribution on the roof (positive and negative pressure) as opposed the regularly shaped models
where separation and reattachment location are clearly known. In the futures, other wind load
statics such as fluctuating (rms), peak minimum, and peak maximum pressure coefficients could
be extracted to reveal the readiness of numerical approach.
LES captured the common fact that mean pressures on the gable roof are generally higher
than those on the hip roof which has been confirmed by a number of similar previous studies as
well. On both roof models, high suction pressures were observed on areas close to the windward
edge and near the middle ridge. This makes sense physically since these are the areas where flow
separation is expected to occur. The highest magnitude roof suction pressures were observed in
the corner areas close to the edges for both roof types. On the hip roof model, the highest suction
pressure was observed when the wind comes from diagonal directions, while the highest suction
pressures on the gable roof model was observed when the wind comes perpendicular to the short
dimension.
LES was found very useful for complex roof cases, where building standards and codes do
not provide design wind loads. The mean pressure coefficients between LES and wind tunnel
data revealed there is a general agreement between the two. The flow visualization from LES
could be useful to rationally encourage design of low rise building for wind performance. It is
fair to conclude that CFD simulation such as LES can be used as an alternative tool for wind
pressure load evaluation of low-rise building at least for preliminary design.
Section 3
43
7 Acknowledgements
The financial support from Florida Department of Emergency Management (FDEM) is gratefully acknowledged. We are grateful to Drs Gurley, Prevat, Masters and Kopp for providing us
the wind tunnel data used for the complex roof comparison (NSF Grant CMMI-0928563).
8 References
ASCE-7 2010. ASCE/SEI 7-10 Minimum design loads for buildings and other structures. American Society of Civil Engineers, Reston, VA.
ASCE (2010), Minimum Design Loads for Buildings and Other Structures, Reston, VA, American Society of Civil Engineers.
Ansys Inc. (2012), “Ansys Fluent 14 User's Guide”, Ansys Inc., Lebanon. 13.0.
Batten, P., Goldberg, U., Chakravarthy, S. (2004), “Interfacing statistical turbulence closures
with large-eddy simulation”, AIAA Journal 42(3), 485–492.
Blessing, C., Chowdhury, A. G., Lin, J., Huang, P. (2009), “Full-scale validation of vortex suppression techniques for mitigation of roof uplift”, Engineering Structures, 31(12), pp. 2936–
2946.
Bitsuamlak, G.T., Stathopoulos, T., Bédard, C., (2007)..“Modeling the Effect of Topography on
Wind Flow Using a Combined Numerical–Neural Network Approach”, Journal of Computing
in Civil Engineering, ASCE, Vol. 21, No. 6, pp 384-392
Bitsuamlak, G.T., Stathopoulos, T., Bédard, C., (2005) “Effect of upstream hills on design wind
load: a computational approach”, Wind and Structures. Vol. 9, No. 1, pp. 37-58.
Bitsuamlak, G.T., Stathopoulos, T., Bédard, C., (2004) “Numerical evaluation of turbulent flows
over complex terrains: A review”, Journal of Aerospace Engineering, Vol. 17, No 4, pp. 135145.
Braun, A.L., Awruch, A.M. (2009). Aerodynamic and aeroelastic analyses on the CAARC
standard tall building model using numerical simulation. Computers and Structures 87, 567581.
COST (2007) “Best practice guideline for the CFD simulation of flows in the urban environment”, COST Action 732.
Dagnew, A.K., Bitsuamalk,
G.T., Ryan, M. (2009) “ Computational evaluation of wind pressures
on tall buildings”, 11th American conference on Wind Engineering. San Juan, Puerto Rico.
Federal Emergency Management Agency (1992). “ Building performance in hurricane Andrew
in Florida observations”, Recommendations and Technical Guidance", Federal Insurance
Administration, USA.
Franke. J. 2006. Recommendations of the COST action C14 on the use of CFD in predicting pedestrian wind environment.
Germano, M. U. Piomelli, P. Moin, and W. H. Cabot. 1996. Dynamic Subgrid-Scale Eddy Viscosity Model". In Summer Workshop. Center for Turbulence Research, Stanford, CA.
Hamid, S., (2005), “Exposure and vulnerability components of the Florida Florida public hurricane loss projection model” , Engineering team Final Report, Laboratory for insurance, finance, and economic research, International Hurricane Research Center, Florida International
University, 1.
Hanjalic´,K., Kenjeres,S., 2008. Some developments in turbulence modeling for wind and environmental engineering. J.Wind Eng. and Ind.Aerodyn.96,1537–1570.
Holmes, J., 2007. Wing loading of structures, 2nd ed. Taylor & Francis.
Huang and Q.S. Li. 2010. Large eddy simulation of wind effects on a super-tall building. Wind
and Structures, Volume 13, Number 6, November 2010.
Section 3
44
Huang, S., Li, Q.S., Xu, S. 2007. Numerical evaluation of wind effects on a tall steel building by
CFD. Journal of Constructional Steel Research 63, 612-627.
Irwin, P. A. (2008), "Bluff body aerodynamics in wind engineering”, Journal of Wind Engineering and Industrial Aerodynamics, 96, pp. 701-712.
Ishihara, T., Hibi, K., Oikawa, S. 1999. A wind tunnel study of turbulent flow over a threedimensional steep hill. Journal of Wind Engineering and Industrial Aerodynamics 83, 95-107.
Jiru, T.E., Bitsuamlak, G.T., Computational fluid dynamics applications in predicting natural
ventilation performance of buildings: A review, International Journal of Natural Ventilation,
Vol. 9, No. 2, 131-147, (2010).
Leatherman, S.P., Gan Chowdhury, A., Robertson, C. J. (2007), "Wall of Wind Full-Scale, Destructive Testing of Houses and Hurricane Damage Mitigation," Journal of Coastal Research.
Lilly, D.K., 1992. A Proposed Modification of the Germano Subgrid-Scale Closure Model. Physics of Fluids. 4. 633–635.
Lund, T.S., Wu, X., Squires, K.D., 1998. Generation of turbulent inflow data for spatiallydeveloping boundary layer simulations. J. of Computational Physics140, 233-258.
Martinuzzi, R., Tropea, C., 1993. The flow around surface-mounted, prismatic obstacles placed
in a fully developed channel flow. J. Fluids Eng 115, 85–92.
Meecham, D., (1992), “The improved performance of hip roofs in extreme winds - A case
study”, Journal of Wind Engineering and Industrial Aerodynamics, 43(1–3), pp. 1717–1726.
Nozawa, K., Tamura, T. (2002), “Large eddy simulation of the flow around a low-rise building
immersed in a rough-wall turbulent boundary layer”, J. Wind Eng. And Ind. Aerodyn. 90,
1151–1162.
Nozu.T., Tamura.T, Okuda.Y, Sanada.S. 2008. LES of the flow and building wall pressures in
the center of Tokyo. J. Wind Eng. Ind. Aerodyn. 96, 1762–1773.
Kraichnan R.H. (1970), “Diffusion by a random velocity field”, Physics of Fluids, 13(1), pp. 22–
31.
Sagaut, P., Deck, S. & Terracol, M. (2006), “Multiscale and multiresolution approaches in turbulence”, London, UK: Imperial College Press.
Senthooran,S., Lee,D.D., Parameswaran,S., 2004. A computational model to calculate
the
flow-induced pressure fluctuations on buildings. J.Wind Eng. and Ind.Aerodyn. 92, 1131–
1145
Selvam, R.P., 1997. Computation of pressures on Texas Tech University building using large
eddy simulation. J. Wind Eng. Ind. Aerodyn. 67–68, 647–657.
Smirnov, R., Shi, S., Celik, I., (2001), “Random flow generation technique for large eddy simulations and particle-dynamics modeling”, J. of F. Eng. 123, 359–371.
Smith, Lee, T., and McDonald, J. R., (1991), "Roof Wind Damage Mitigation: Lessons from
Hugo" In Hurricane Hugo One Year Later, Benjamin A. Sill and Peter R. Sparks, Editors.
New York: American Society of Civil Engineers
Sparks, P.R., Baker, J., Belville, J., Perry, D.C., (1985), “Hurricane Elena, gulf coast, August
29–September 2”, committee on natural disasters, Commission on Engineering and Technical
Systems, Natural Research Council, USA.
Stathopoulos. T., Wu, H. 2004. Computational Fluid Dynamics (CFD) for pedestrian winds. Proceedings of the 2004 Structures Congress, Nashville, TN.
Tamura, T., 2008.Towards practical use of LES in wind engineering. J. Wind Eng. And Ind.
Aerodyn. 96, 1451–1471.
Tamura, T., Nozawa, K., Kondo, K. 2008. AIJ guide for numerical prediction of wind loads on
buildings. J.Wind Eng. and Ind.Aerodyn 96, 1974-1984.
Tamura, T, Okuno.A, Sugio.Y, 2007. LES analysis of turbulent boundary layer over 3D steep
hill. J. Wind Eng. Ind. Aerodyn.95, 1463-1475.
Tominaga, Y., Mochida, A., Murakami, S., Sawaki, S. 2008a. Comparison of various revised k–
ε models and LES applied to flow around a high-rise building model with 1:1:2 shape placed
within the surface boundary layer. Journal of Wind Engineering and Industrial Aerodynamics
96, 389-411.
Section 3
45
Tucker, P. G, & Lardeau, S. (2009), “Applied large eddy simulation”, Phil. Trans. R. Soc. A July
28, 367 (1899) 2809-2818.
Wright, N.G., Easom, G.J. 2003. Non-linear k–e turbulence model results for flow over a building at full-scale 27(12), 1013-1033.
Appendix A: FL27 House Model Dimensions
Section 3
46
Figure A. 1: Roof Layout view
Figure A. 2: North Wall Elevation
Section 3
47
Figure A. 3: South Wall Elevation
Figure A. 4: West Wall Elevation
Figure A. 5: West Wall Elevation
Section 3
48
Figure A. 6: Wall Perimeter Plan
Appendix B: FL30 House Model Dimensions
Section 3
49
Figure B. 1: Roof Layout view
Figure B. 2: North Wall Elevation
Section 3
50
Figure B. 3: South Wall Elevation
Figure B. 4: East Wall Elevation
Figure B. 5: West Wall Elevation
Section 3
51
Figure B. 6: Wall Perimeter Plan
Section 3
52
Appendix C: FL27 Neighboring Houses
Figure C. 1: Proximity Model Layout
Section 3
53
Figure C. 2: E1
Figure C. 3: E2
Section 3
54
Figure C. 4: NE1
Figure C. 5: NE2
Section 3
55
Figure C. 6: NW1
Figure C. 7: NW2
Section 3
56
Figure C. 8: SE1
Figure C. 9: SE
Section 3
57
Figure C. 10: SW1
Figure C. 11: SW2
Section 3
58
Figure C. 12: W1
Figure C. 13: W2
Section 3
59
Figure C. 14: W3
Section 3
60
Appendix D: FL30 Neighboring Houses
Figure D. 1: Proximity Model Layout
Section 3
61
Figure D. 2: 4012
Figure D. 3: 4014
Section 3
62
Figure D. 4: 4015
Figure D. 5: 4017
Section 3
63
Figure D. 6: 4019
Figure D. 7: 4021
Section 3
64
Figure D. 8: 4013, 4029
Figure D. 9: 4025
Section 3
65
Figure D. 10: 4026
Figure D. 11: 4027
Section 3
66
Figure D. 12: 4030
Section 3
67
FINAL REPORT
SECTION 4
Investigating Household Perceptions of Coastal
Vulnerability and Preferences for Risk
Mitigation
Prepared by
Pallab Mozumder, PhD, Evan Flugman
August 1, 2012
Table of Contents
Executive Summary
6
1.0 Introduction
12
2.0 Background
15
3.0 Methodology
26
4.0 Results
30
5.0 Discussion
74
6.0 Conclusions
79
References
82
Appendices
94
Section 4
2
List of Figures
Figure
Page
Figure 1. Endemic Vulnerability Diagram
16
Figure 2. The 2004 and 2005 Hurricane Seasons
17
Figure 3. The 2004 Hurricane Season: Overlapping Damage Swaths
18
Figure 4. Survey Respondents by Florida County
28
Figure 5a. Concern a Major Hurricane Will Damage Home This Year
32
Figure 5b. Concern a Major Hurricane Will Damage Home Within Ten Years
32
Figure 6a. Concern a Major Hurricane Will Cause Significant Damage to Home This Year
33
Figure 6b. Concern a Major Hurricane Will Cause Significant Damage to Home Within Ten Years
33
Figure 7. Models Foresee More Category 4 and 5 Hurricanes in a Warmed Climate
34
Figure 8. The Economic Threat of More Destructive Hurricanes Due to Climate Change
35
Figure 9. Institutional Accountability Overall
36
Figure 10. Institutional Effectiveness: Disaster Risk Reduction
37
Figure 11. Enhancing Resilience with Land-Use and Building Codes
38
Figure 12. The My Safe Florida Home Program
39
Figure 13. A Publically Accessible Mitigation Database
41
Figure 14. A ‘Florida Pre-Disaster Mitigation Fund’
42
Figure 15. Willingness-to-Pay to Support a ‘Florida Pre-Disaster Mitigation Fund’
43
Figure 16. Hurricane Surcharges and Assessments
44
Figure 17. Multi-Year and Long-Term Flood Insurance Contracts
46
Figure 18. A Comprehensive Insurance Program
47
Figure 19. Tax-Free Catastrophe Savings Accounts
48
Figure 20. A Mitigation Choice Experiment: Roof System
52
Figure 21. A Mitigation Choice Experiment: Opening Protection
53
Figure 22. Certainty Level of Mitigation Choice Preferences
54
Section 4
3
List of Figures (continued)
Figure
Page
Figure 23. Gender of Respondents
56
Figure 24. Age of Respondents
56
Figure 25. Educational Background of Respondents
57
Figure 26. Ethnicity of Respondents
58
Figure 27. Marital Status of Respondents
59
Figure 28. Household Size of Respondents
59
Figure 29. Number of Children in Respondents’ Household
60
Figure 30. Household Income of Respondents
61
Figure 31. Political Affiliation of Respondents
63
Figure 32. Years Respondents Living in Florida
63
Figure 33. Mortgage Holders Among Respondents
64
Figure 34. Home Values of Respondents
64
Figure 35. Year Respondents’ Homes Built
65
Figure 36. Building Code Level of Respondents’ Roofing Systems
66
Figure 37. Building Code Level of Respondents’ Opening Protection
67
Figure 38. Square Footage of Respondents’ Homes
68
Figure 39. Roof Covering on Respondents’ Homes
69
Figure 40. Roof Shape of Respondent’s Homes
69
Figure 41. Percentage of Respondents with Wind Insurance
70
Figure 42. Percentage of Respondents with Flood Insurance
71
Figure 43. Percentage of Respondents with Mandatory Flood Insurance
71
Figure 44. Number of Damaging Hurricane Hits of Respondents in 2004 and 2005
72
Figure 45. Strategic Resilience Diagram
78
Section 4
4
Table
List of Tables
Page
Table 1. Residential Storm Surge Exposure Estimates for 10 U.S. Cities
20
Table 2. Survey Respondents by Florida County
29
Table 3. Mitigation Choice Experiment: Roof System
94
Table 4. Mitigation Choice Experiment: Opening Protection
94
Table 5. Concern, Accountability, and Effectiveness Variables
95
Table 6. Mitigation and Insurance Variables
96
Table 7. Mitigation Choice Experiment Variables
97
Table 8. Socio-demographic Variables
98
Table 9. Respondents’ Home Characteristics and Insurance Variables
99
Table 10. Hurricane Experience and Structural Damage History Variables
100
Table 11. Interior Damage and Water Intrusion Variables
101
Section 4
5
Executive Summary
1.0 Introduction and Background
Vulnerability to natural hazards is exacerbated by a complex array of inter-related chronic and
emergent challenges, which undermine the effective design and implementation of disaster risk
reduction policies and social-ecological resilience. As unsustainable development (underinvestment in risk reduction measures and environmental degradation) continues virtually unabated
in historically hazard-prone and increasingly strained mega-urban landscapes, catastrophic losses
from extreme hydro-meteorological events are fueling an untenable rise in annual government
response and recovery costs and long-term risks for consumers and taxpayers.
The near and longer-term impacts of climate change will amplify vulnerability, particularly in
the world’s low-lying coastal zones and among the poorest and most vulnerable members of
society. Moreover, substantial barriers impede the pursuit of status-quo altering pathways of
systemic resilience; namely, insufficient resources and the high cost of mitigation measures,
limited direction and leadership, and lack of institutional frameworks to initiate and sustain a
holistic, social-ecological response.
A transformational, all-hazards, disaster risk reduction initiative is required to mitigate the
rising tide of catastrophes, effectively insure against future natural hazards, and adapt to climate
change. Now is the time to invest in the sustainability of our coastal communities, the benefits
will reverberate far beyond the shore.
1.1 Florida: A State of Risk
With over $2.5 trillion of insured coastal exposure, including over $1.25 trillion in residential
exposure, situated in the heart of the Atlantic Hurricane Basin, the State of Florida ranks as one of
the most vulnerable places to natural disaster losses on the planet.
Communities across the State of Florida were devastated by the 2004 and 2005 Hurricane
Seasons. In a 44-day period in 2004, Floridians endured four powerful hurricanes collectively
exceeding the losses from Hurricane Andrew (1992). Roughly 20 % of Florida’s residential
building stock was damaged or destroyed (over one million homes). As a result, The National
Flood Insurance Program (NFIP) was saddled with $20 billion in debt, Florida’s quasi-public
insurance system went into a state of upheaval, one that it is still far from recovering from, and
private insures fled the state.
Section 4
6
Although Floridians have been sparred over the past few years, future hurricanes are
inevitable. Hurricanes cannot be prevented, but their effects can be mitigated. Most of the
damage from wind/wind-borne debris and wind-driven rainwater intrusion could have been
significantly reduced. For instance, homes constructed to the most-up-to date building codes
suffered appreciably fewer losses in 2004 and 2005 than homes constructed to older standards,
including structural damage, as well as costly interior damage from wind-driven rainwater
intrusion.
While the U.S. has dramatically reduced the loss of life from natural hazards, economic losses
are skyrocketing. The ability of the U.S. to mitigate and insure against natural hazards and adapt
to climate change can be greatly improved.
1.2 Research Objectives
This research focused on risk perceptions and mitigation behavior among a diverse sample of
households from across the State of Florida. We investigated how households perceive the
annual threat of property damage from hurricanes, particularly from major hurricanes (Category
3, 4, and 5); projections of more destructive hurricanes due to climate change; institutional
accountability overall and institutional effectiveness at disaster risk reduction.
We then explored households’ preferences for an array of mitigation measures and insurance
reforms to enhance coastal resilience, including willingness to support stricter building codes and
zoning laws, multi-year flood insurance contracts, a comprehensive insurance program, and,
among other things, a ‘Florida Pre-Disaster Mitigation Fund’, to sponsor an expansion of predisaster mitigation programs (with additional state funding above and beyond federal dollars)
focused on strengthening the built environment before hurricanes strike.
We also examined households’ willingness to invest in a menu of realistic, low and higher cost
roofing and opening protection options, in a Mitigation Choice Experiment, when presented with
a package of salient vulnerability information, including a video presentation of the costly losses
associated with wind-driven rainwater intrusion and the cost-effectiveness of response options,
as well as, a meaningful incentive.
We also collected households’ socio-demographic information and housing characteristics, as
well as an overview of recent hurricane experiences and damages.
We discuss our findings in the context of enhancing coastal social-ecological resilience, by
developing innovative mitigation technologies, products, policies, and public engagement
strategies, to minimize the loss of life and property from all-hazards, in Florida and beyond.
Section 4
7
3.0 Methodology
3.1 The Survey Instrument
An in-depth online survey was developed for households, including questions on some of the
major challenges facing Florida. The survey consisted of five sections including 40 attitudinal
and behavioral questions (yes/no, multiple-choice, rating scales, check all, and referendum style
questions).
The survey made use of the latest scientific data, models, and risk assessments available. Before
implementation, the survey was also extensively tested by a diverse sample of government
experts and decision-makers and academicians.
3.2 Sample Selection
A database of contact information for over 400,000 households who had applied to the
My Safe Florida Home (MSFH) Program beginning in 2007 was acquired from the State of
Florida. A random sample of 40,000 households, whose email addresses were available, was
selected for the survey study.
3.3 Data Collection
Households were contacted by email beginning on Wednesday, May 2, 2012, and invited
through a brief letter including a link to the survey website to complete an online questionnaire
on or before June 1, 2012.
The invitation specified that the survey was voluntary, but that their participation was very
important. Households were asked to please fill out the questionnaire to the best of their ability.
They were informed that responses were strictly confidential, and no individual respondents
could be identified. Two email reminders were sent during the third and fifth Wednesdays of
May 2012.
Of the 40,000 email invitations, 38 requests were returned with bad addresses. Adjusted for
undeliverable emails, the response rate was 4.3 %, with 1,710 usable responses received over 31
days, including households from 46 of 67 Florida counties.
4.0 Results
Analysis of 1,710 survey responses from households across the State of Florida revealed the
following core findings:
‘Florida Pre-Disaster Mitigation Fund’
A large majority of households (69%) support the creation of the proposed ‘Florida Pre-Disaster
state funding above and beyond federal dollars)
Land-Use Planning
A large majority of households (76%) are highly supportive of increasing setbacks along
Florida’s shoreline to enhance coastal resilience.
A large majority of households (74%) are highly supportive of stricter density restrictions in
Florida’s low-lying areas to enhance coastal resilience.
Florida Building Codes
A large majority of households (70%) are highly supportive of strengthening Florida building
codes to enhance coastal resilience.
A large majority of households (72%) are highly supportive of new elevation standards for
Florida’s roads and buildings to enhance coastal resilience.
Institutional Accountability
The federal government received an overall accountability score of 5.3 out of 10; the State of
Florida received a 5.5; Counties received a 5.3; Municipalities received a 5.2; Insurance
companies received a 6.0.
Disaster Risk Reduction Effectiveness
The federal government received a disaster risk reduction effectiveness score of 4.8 of 10; the State
of Florida received a 5.2; Counties received a 5.2; Municipalities received a 4.9; Insurance
companies received a 4.5.
Section 4
9
Florida’s Property Insurance System: Post-Loss Financing
Only 42 % of households are highly supportive of continued State surcharges and assessments to pay
for hurricane losses after-the fact.
Multi-Year Flood Insurance Contracts
Only 45 % of households are highly supportive of 5-year flood insurance contracts.
A Comprehensive Insurance Program
A large majority of households (73%) are highly supportive of a comprehensive insurance
program (combined flood and wind insurance program).
Mitigation Choice Experiment
We examined households’ willingness to invest in a menu of realistic, low and higher cost
roofing and opening protection options, in a Mitigation Choice Experiment, when presented with
a package of salient vulnerability information, including a video presentation of the costly losses
associated with wind-driven rainwater intrusion and the cost-effectiveness of response options,
as well as, a meaningful incentive.
The experiment proceeded as follows: the Information Treatment group received the salient
vulnerability information package ahead of the Mitigation Choice Experiment; the Incentive
Treatment group received an incentive in the form of a matching grant of up to $10,000, but not
the vulnerability information; the Combined Treatment group received both the vulnerability
information and the $10,000 matching grant.
Households’ roofing preferences are presented in Figure 4. Option A ($13,000) included a new
hurricane-rated roof covering, secondary water barrier, strengthening of the roof deck, reinforced
roof-to-wall connections, bracing gable-ends and reinforcing soffits. Option B ($7000) included
an interior hurricane spray-foam adhesive, reinforced roof-to-wall connections, bracing gableends and reinforcing soffits.
Section 4
10
5.0 Discussion and Conclusions
Survey findings can provide systematic information on multiple scales. Our findings suggest
avenues for potential risk reduction strategies that can be implemented by federal, state, and local
agencies, including county and municipal governments in vulnerable coastal communities.
The design and implementation of robust mitigation policies can help break the cycle of disaster:
reducing the loss of life, property damage, local economic disruption, the cost and time of response
and recovery, the risk of repeated disaster, as well as the financial burden on state and local
communities and the nation as a whole.
Not all natural hazards events have to become widespread natural disasters. From South
Florida to Jacksonville, Fort Myers to Tampa Bay, Orlando to the Panhandle, and beyond,
coastal vulnerability is amplified by unsustainable development (underinvestment in disaster risk
reduction and environmental degradation). The main drivers include: hallow state and local
growth management plans and imprudent federal floodplain management, overcome by
development pressures; inconsistent state and local policies to adopt and enforce stricter building
codes; weak federal, state and local mandates and distorted incentives to avoid or reduce risk,
including over-reliance on billions of dollars in annual federal disaster relief and recovery
spending; unsound federal and state insurance subsidies promoting systemic risk; trillions of
dollars in deteriorating infrastructure; as well as fragmented programs to protect or restore
natural capital.
The 2004 and 2005 hurricane seasons were a wake-up call, not an aberration – a lesson in
resilience thinking. Today, in most coastal areas across the U.S., exposure to natural hazards is
growing and vulnerability is increasing, resilience is increasing in certain aspects, but decreasing in
others. In short, the ability of Florida, and the U.S. as a whole, to mitigate and insure against
natural hazards and adapt to climate change can be greatly improved. This involves setting new
priorities, including the strategic investment of new dollars, knowing that every $1 spent on
mitigation saves society an average of $4.
Transitioning our institutions and coastal communities to address these dynamic challenges
will require a combination of novel decision-making criteria, public-private partnerships,
regulatory and market-based mechanisms, and significant investments in physical and social
infrastructure. Expanded investment in meteorology and climatology, as well as risk reducing
measures, is essential to marshal resources and lay the foundation for institution building and
strategic pre-disaster risk reduction initiatives.
Section 4
11
1.0 Introduction
Vulnerability to natural hazards is exacerbated by a complex array of interrelated chronic
and emergent challenges, which undermine the effective design and implementation of disaster
risk reduction policies and social-ecological resilience (Allenby and Fink, 2005; Walker et al., 2009;
Alesch et al., 2011; Leonard, 2012). As unsustainable development (i.e., underinvestment in risk
reduction measures and environmental degradation) continues virtually unabated in historically
hazard-prone and increasingly strained mega-urban landscapes, catastrophic losses from extreme
hydro-meteorological events are fueling an untenable rise in annual government response and
recovery costs and long-term risks for consumers and taxpayers (ASCE, 2009; Cummins et al.,
2010; Barnosky et al., 2012; Kunruether et al., 2012).
The near and longer-term impacts of climate change will amplify vulnerability to natural
hazards, particularly in the world’s low-lying coastal zones and among the poorest and most
vulnerable members of society (Milly et al., 2008; IPCC, 2012; Martinich et al., 2012). Moreover,
substantial barriers impede the pursuit of status-quo altering pathways of systemic resilience;
namely, insufficient resources and the high cost of mitigation measures, limited direction and
leadership, and lack of institutional frameworks to initiate and sustain a holistic response
(Adger and Barnett 2009; Hallegatte, 2009; Moser and Elkstrom, 2010; Kates et al., 2012).
A transformational, all-hazards, disaster risk reduction initiative is required to mitigate the rising
tide of catastrophes, effectively insure against future natural hazards, and adapt to climate change
(Heinz et al., 2009; Folke et al., 2010; Mutter, 2010; Llyods, 2011; Smith et al., 2011; Park et al., 2012).
Now is the time to invest in the sustainability of our coastal communities, the benefits will
reverberate far beyond the shore.
1.1 Florida: A State of Risk
With over $2.5 trillion of insured coastal exposure, including $1.25 trillion in insured
residential exposure, situated in the heart of the Atlantic Hurricane Basin, the State of Florida
Section 4
12
ranks as one of the most vulnerable places to natural disaster losses on the planet (Hartiwg, 2010;
Florida Tax Watch, 2011). The urban, coastal population and economic centers of South Florida,
Tampa Bay, and Jacksonville are at high-risk of potentially catastrophic, Katrina-scale, losses from
major hurricanes (Category 3, 4, and 5), today and increasingly so, for the foreseeable future
(FASS, 2010; Zhang, 2010; Bjarnadottir et al., 2011a; CoreLogic 2011; Genovese et al., 2011).
Unfortunately, we do not appear to be heeding the warnings with respect to reducing population
growth and development in high-risk coastal areas (Emmanuel et al., 2006; Chapin et al., 2007;
Sun, 2011). By 2030, Florida’s population is projected to increase by roughly 10 million people,
from 19 million to 29 million people (U.S. Census Bureau, 2005; U.S. Census Bureau, 2011).
1.2 Strategic Resilience in A New Era of Risk
While the U.S. has dramatically reduced the loss of life from natural hazards, economic
losses are skyrocketing (U.S. GAO 2007a; Hartiwg, 2012; NOAA, 2012). Since 1980, combined
decadal losses from natural disasters have increased seven-fold (NRC, 2011). Cummins et al.
(2010) project federal disaster assistance could total $7 trillion over the next seventy-five years.
Today, in most coastal areas throughout the U.S., exposure to natural hazards is growing and
vulnerability is increasing, resilience is increasing in certain aspects, but decreasing in others.
In short, the ability of Florida, and the U.S. as a whole, to mitigate and insure against natural
hazards and adapt to climate change can be greatly improved (Godschalk, 2003; Leatherman and
White, 2005; NRC, 2006; U.S. GAO, 2007b; NRC, 2009; FEMA, 2011; Michel-Kerjan and
Kunreuther, 2011; U.S. GAO 2011a; U.S. GAO 2011b). There is however, opportunity in this
crisis, and there is evidence to back it up – every dollar spent on mitigation saves society about
four dollars in recovery costs, helps stabilize the property insurance market and reduce premiums
(MMC, 2005; Heinz et al., 2009; Llyods, 2011).
Section 4
13
1.3 Research Objectives
This research focused on the risk perceptions and mitigation decision-making among a
diverse sample of households from across the State of Florida. More specifically, we investigated
how households perceive the annual threat of property damage from hurricanes, particularly the
threat of major hurricanes (Category 3, 4, and 5), the risk of more destructive hurricanes due to
climate change, institutional accountability overall and the effectiveness of institutions at
managing coastal vulnerability. Exploring households’ preferences for an array of mitigation
measures and insurance reforms to enhance coastal resilience, we investigated their willingness
to support stricter building codes and zoning laws, a comprehensive insurance program, multiyear flood insurance contracts, and among other things, a proposed ‘Florida Pre-Disaster
state funding above and beyond federal dollars) focused on strengthening the built environment
before hurricanes strike. We also examined households’ willingness to invest in a menu of
realistic, low and higher cost roofing and opening protection options in a Mitigation Choice
Experiment, when presented with a package of salient vulnerability information, including a
video presentation of the costly losses associated with wind-driven rainwater intrusion and the
cost-effectiveness of response options, as well as, meaningful incentives.
We then collected households’ socio-demographic information and housing
characteristics, including a profile of households’ insurance coverage. Lastly, we gathered
information regarding households’ recent hurricane experiences and damage histories, including
interior damages from wind-driven rainwater intrusion. We discuss our findings in the context of
enhancing coastal social-ecological resilience, by developing innovative mitigation technologies,
Section 4
14
products, policies, and public engagement strategies, to minimize the loss of life and property
from all-hazards, in Florida and beyond.
2.0 Background
Eight of the ten costliest hurricanes in U.S. history occurred within the last decade,
seven of ten impacted the State of Florida, incurring vast losses to life and property, with national
repercussions (Blake et al., 2007; NOAA, 2012). “The result of an accelerating spiral of risk-taking…
the increasing mismatch between assets-at-risk and investments in risk reducing measures”
(Kunreuther et al., 2012, p.2).
From South Florida to Jacksonville, Fort Myers to Tampa Bay, Orlando to the Panhandle
and beyond, coastal vulnerability is amplified by unsustainable development (underinvestment in
disaster risk reduction measures and environmental degradation) (see Figure 1). The main drivers
of unsustainable development include: hallow state and local growth management plans and
imprudent federal floodplain management, overwhelmed by development pressures; inconsistent
state and local policies to adopt and enforce stricter building codes; weak federal, state, and local
mandates and distorted incentives to avoid or reduce risk, including over-reliance on annual federal
disaster relief and recovery spending; unsound federal and state insurance subsidies promoting
systemic risk; trillions of dollars in deteriorating infrastructure; as well as fragmented programs to
protect or restore natural capital, including natural storm buffers (Platt, 1999; Miletti and Gailus,
2005; Salvesen, 2005; Burby, 2006; Comfort, 2006; Bagstad at al., 2007; Gaddis et al., 2007;
Deyle et al., 2007; Brody et al., 2010; Holladay and Schwartz, 2010; Berke et al., 2012).1
1
Critical infrastructure, “the lifelines of our coastal cities – are already facing pressures they
were not designed to withstand” (Heinz, 2009). Of particular concern: water management
Section 4
15
Figure 1. Endemic Vulnerability Diagram
Unsustainable
Development
Natural Hazards
Under Investment in
Mitigation &
Environmental
Degradation
Near & Long Term
Impacts
Present & Future
Frequency & Intensity
Subjective
Risk Perception
& Decision Bias
Climate Change
Endemic
Vulnerability
Public & Private
Financial Risks
Uninsured &
Underinsured
2.1 ‘What’s Past is Prologue’ in Hurricane Alley
Coastal communities across the State of Florida and the Gulf of Mexico were
overwhelmed by the 2004 and 2005 hurricane seasons; inland counties (where building codes
remain less stringent) were hard hit as well (see Figure 2) (FEMA, 2005a; NRC, 2006).2 The
systems (surface and ground-water) responsible for water supply, treatment, and flood control
(Milly et al., 2008; ASCE, 2009; Tryhorn, 2010; Rosenzweig et al., 2011; AWWA, 2012). The
U.S. Environmental Protection Agency (EPA) recently filed a complaint against Miami-Dade
County to upgrade its 7,500 mile wastewater collection treatment system and plants, said to be in
violation of the Clean Water Act, the estimated cost is $1 billion (Rabin and Morgan, 2012)
2
Four hurricanes battered Florida in 2004, including three major hurricanes with sustained winds
of at least 178 km/hr (Category 3, 4, and 5), a first in recorded history. Florida was hit by
Section 4
16
“physical and human devastation [unequivocally demonstrated] the consequences both to
individuals and to society of not being prepared…despite ample warnings” (Leatherman and
Williams, 2008, p.7). The devastation of 2004 and 2005 was a wake-up call, not an aberration –
a lesson in resilience thinking (FEMA, 2005b; NSB, 2007; Liu, 2011).
Figure 2. The 2004 and 2005 Hurricane Seasons
Source: AIR Worldwide
In a 44-day period in August and September of 2004, Floridians endured four powerful
hurricanes (three major hurricanes), collectively exceeding the losses from Hurricane Andrew
(1992). Many communities experienced direct hits from two to three hurricanes following
Hurricanes Charley (4), Frances (2), Ivan (3), and Jeanne (3) (Franklin et al., 2006). The 2005
season was the most active on record, five hurricanes made landfall in the U.S., including four
major hurricanes. Florida was hit by Dennis (3), Katrina (1), and Wilma (3) (Beven et al. 2008).
Section 4
17
similar paths, intensifying their impacts (see Figure 3). Along the Northwest corner of the
Florida Panhandle (counties exempt from the minimum building code requirements), were
flattened by Hurricane Ivan (2004) and again by Hurricane Dennis (2005) (U.S. GAO, 2007a).
In the wake of the 2004 hurricane season, roughly 20 % of Florida’s residential building stock
was damaged or destroyed (one million single-family homes) (FEMA, 2005b; FDFS, 2010).3
Figure 3. The 2004 Hurricane Season: Overlapping Damage Swaths
Source: The Palm Beach Post
At a cost of over $220 billion (insured and uninsured losses), the eight landfalling
hurricanes of 2004 and 2005 resulted in extensive loss of life, displaced families, millions of
severely damaged or destroyed homes, commercial and industrial buildings and critical
3
As of 2006, the Florida panhandle was no longer exempt from meeting the requirements of the
Florida Building Code (U.S.GAO, 2007a).
Section 4
18
infrastructure facilities and economic disruption (NOAA, 2012). The devastation showed that the
U.S. “remains notably vulnerable to catastrophic damage and loss of life from natural hazards”
(NSB, 2007, p. 8-9). The National Flood Insurance Program (NFIP) was saddled with $20 billion
in debt, the quasi-public Florida Insurance System went into a state of upheaval, on par if not
worse than in the aftermath of Hurricane Andrew and one that it is still far from recovering from,
and private insures fled the state. In the aftermath of the 2004 and 2005 hurricane seasons, Florida
and Gulf Coast residents are struggling regarding the availability and affordability of property
insurance and there are serious risks for state and federal taxpayers (Grace and Klein, 2009;
Hartwig, 2010; Macdonald et al., 2010; Michel-Kerjan, 2010; Hartwig and Wilkinson, 2012;
Lehrer and Lehmann, 2012; Michel-Kerjan et al., 2012).
2.2 ‘Clear and Present Danger’
Since 1970 Florida’s population has nearly tripled, growing from 6.8 million residents to
more than 19 million residents in 2012, and coastal development in areas most at risk from
hurricanes and flooding boomed, in clear conflict with long-term mitigation goals, policies,
growth management plans, etc. (U.S. Census Bureau, 2011) (see Table 1).4 This extraordinary
transformation has dramatically impacted Florida’s coastal environment, as well as the state’s
ability to limit local coastal development inside and direct populations away from coastal high
hazard areas (CHHAs) and to maintain or reduce evacuation times within the larger hurricane
4
Now the nation’s forth most populous state, Florida is on pace to become the nation’s third
largest state (surpassing New York). Florida’s population was only 2.7 million in 1950, and less
than 0.5 million in 1900 (U.S. Census Bureau, 2011).
Section 4
19
vulnerability zones (HVZs) as mandated under the 1985 Growth Management Act (Burby, 2005;
U.S. GAO 2007a; Deyle et al., 2008; Ntelekos et al., 2009; Brody et al., 2010; Sun, 2011).5
The Miami Metropolitan Area (fifth largest urban area and third largest skyline in the U.S.) ranks
number one in the world in for cities with assets exposed to coastal flooding (Hanson et al., 2011).6
Loss estimates if the Great Miami Hurricane of 1926 struck today range from $140-$225 billion
(U.S. GAO, 2007a; Pielke et al., 2008).
Table 1. Residential Storm-Surge Exposure Estimates for 10 U.S. Cities
Source: CoreLogic
5
The CHHA is defined as the evacuation zone for a Category 1 hurricane; the HVZ is defined as
the area that would be evacuated for a Category 3 hurricane (Deyle et al., 2008).
6
From 1990 to 2010, Miami-Dade County’s population grew by 29 %, Broward grew by 39 %,
and Palm Beach County grew by 53 % (U.S. Census Bureau, 1990; U.S. Census Bureau, 2012).
Section 4
20
Measured by area, more than 80 % of low-lying land in Florida (below one-meter) is highly
developed or intermediate, compared with only 45 % of coastal land from Georgia to Delaware
(Titus et al., 2009). With over 75 % of its population living along the coast, Florida accounts for
30 % of all insured property along the Atlantic and Gulf of Mexico coasts, including 40 % of the
subsidized National Flood Insurance Program (NFIP) portfolio (Zaharan et al., 2009; Hartwig,
2010; Michel-Kerjan and Kousky, 2010).7 Meanwhile, the government-run Florida Citizens
Property Insurance Corporation, intended to be the insurer of last resort for property owners who
were legitimately unable to find coverage in the private market, is now the largest insurance
provider in the state and the third largest underwriter of property insurance in the country. With
1.4 million heavily subsidized policies in force, a total exposure of $500 billion, and $3 billion in
total premiums with surcharge, Florida Citizens has more than 23 % of the state’s personal and
commercial residential policies in force (2.5 times the second largest provider (Cole et al., 2011;
Florida TaxWatch, 2011; Florida Citizens, 2012; Lehrer and Lehmann, 2012).8
7
By law 25 % of flood insurance policies, mostly older ‘grandfathered’ pre-FIRM properties in
Special Flood Hazard Areas (SFHAs), receive discounted premium rates that reflect only 3540 % of actual flood risk. These subsidized properties experience as much as five times more
flood damage than properties with ‘full rates’. Criticism has also been raised that ‘full-rate’
properties don’t accurately reflect flood risk either (Michel-Kerjan, 2010; U.S. GAO, 2011a).
8
“Subsidies effectively shift the cost of assessments from the plan’s policyholders to policyholders
and taxpayers …dilute the message of risk that actuarially sound premiums send to coastal dwellers.
The effect is to encourage and enable even more vulnerable coastal development, further increasing
residual market exposure and increasing the burden on taxpayers” (Hartwig and Wilkinson, 2012, p.25).
Section 4
21
At over 2,170 kilometers (1,350 linear miles), Florida has the longest coastline in the
contiguous U.S., including over 1,327 kilometers (825 miles) of sandy beaches, half of which are
experiencing critical erosion (FDEP, 2011).9 From 1790 to 1990, Florida’s wetlands declined by
nearly 50 %, from 19.575 million acres to 10.275 million acres (FDEP, 2011). A 0.6 meter rise in
sea-level could transform over 50 % of the Everglades freshwater marsh into a saltwater system
and contaminate the unconfined, surficial Biscayne Aquifer (the sole source of drinking water for
over 6 million South Floridians) (Kimball, 2008; FDEP, 2011).10
The near and longer-term impacts of climate change, as well as fragmented programs to
protect and restore Florida’s natural capital, including natural storm buffers, amplify vulnerability,
particularly for the poorest and most vulnerable members of society (Costanza, et al., 2008;
Anthoff et al., 2011; Mozumder et al., 2011; Hallegatte, 2012; Jevrejeva et al., 2012; Martinich
et al., 2012; Schaeffer et al., 2012). “That vulnerability, combined with its highly concentrated
coastal population, means that Florida will be a case study for other states and the world –
either in how to prepare for rising sea levels or in what happens if you don’t” (Schrope, 2010, p.36).
To avoid the unmanageable and manage the unavoidable, Florida’s institutions and communities
will need to adapt. “A refined ability to identify where the most challenging barriers might lie
affords the opportunity to better allocate resources and strategically design processes to
overcome them…at all levels of decision-making” (Moser and Ekstrom, 2010, p.6). In this
9
Critical erosion refers to shoreline where public or private development, recreational interests,
wildlife habitat, or cultural resources are threatened, in need of protection, or lost (FDEP, 2011).
10
The Kissimmee River - Lake Okeechobee - Everglades watershed (KOE) and Floridan aquifer
system supports a $100 billion dollar agricultural sector and provides drinking water for
Florida’s over 19 million residents (FOCC, 2008; FOCC, 2010).
Section 4
22
context, expanded investment in meteorology and climatology, as well as adaptation, is essential
to marshal resources and lay the foundation for institution building and strategic pre-disaster risk
reduction initiatives (Higgins, 2008; FOCC, 2010; Kunreuther et al., 2011; U.S. GAO, 2011b;
Yohe et al., 2011; Flugman et al., 2012; USGCRP, 2012).11
2.3 Offsetting Risk With Resilience
Although Floridians have been sparred over the past few years, future landfalling
hurricanes are inevitable. “Our nation would be wise to reduce the risk to this investment and
population” (Chowdhury et al., 2009, p.1). Hurricanes cannot be prevented, but their physical
and socioeconomic impacts can be substantially reduced with increased community resiliency
and built environment sustainability (MMC, 2005; Bitsuamlak et al., 2009; Godschalk et al.,
2009; Canbek et al., 2011; Canino et al., 2011; Chowdhury et al., 2011; Simiu et al., 2011).
Most of the structural damage from wind/wind-borne debris and the interior damage from
wind-driven rainwater intrusion suffered during the 2004 and 2005 hurricane seasons could have
been significantly reduced (FEMA, 2005a; FEMA, 2005b). Homes constructed to the most-up-to
date Florida building codes suffered appreciably fewer losses in 2004 and 2005 (up to 60% fewer
losses) than homes constructed to older standards, including structural damages and costly interior
damages from wind-driven rainwater intrusion - mold growth and damage to interior contents/
furnishings, finishes (ceilings, walls, flooring), and utilities (electrical, mechanical, plumbing) and
11
“Given the prospects for a 4◦C world [average global temperature increase of 4◦C or more],
adaptation needs to be reconceptualized away from the incremental handling of residual risk to
preparing for continuous (and potentially transformational) adaptation” (Smith et al., 2012, p.212).
Section 4
23
subsequent additional living expenses (IBHS, 2007; RMS, 2009).12 An array of low and higher
cost mitigation measures involving improved construction methods and retrofitting techniques
exist for opening protection (e.g., installing impact-resistance windows and skylights, entry and
sliding doors and garage doors or approved shutter or panels, and by securing and protecting vents)
Similarly improved construction methods and retrofitting techniques for roof systems (e.g.,
installing new hurricane-rated roof coverings, exterior strengthening of the roof deck, exterior
secondary water barriers, reinforcing roof-to-wall connections, bracing gable ends, reinforcing
soffits, and applying interior hurricane spray foam adhesive for interior secondary water barrier
and rood deck strengthening) is proven to substantially reduce hurricane-related damages
(FDFS, 2009; Barbeau et al., 2010; Lopez et al., 2010; Salzano et al., 2010; Gurley and Masters
2011; IBHS, 2011; Pinelli et al., 2011).
According to IBHS (2009), roof cover damage continues to be the largest, most frequent
source of non-surge failures and losses related to hurricanes. Much is known about how to
effectively retrofit roofs, and it should be the first place owners and builders address in order to
reduce future losses. Analysis by RMS (2009) concluded that roof upgrades could reduce 100year storm losses by up to 55 % (20% more than by improving opening protection alone). Nearly
all of the more than 400,000 homes inspected under the My Safe Florida Home (MSFH) Program
(98%) were said to be in need of new roofs (FDFS, 2009). However, confronting the significant
upfront costs of mitigation remains a major obstacle to homeowners’ widespread adoption of
12
For example, full-scale testing by Bitsuamlak et al. (2009) found that heavier weight asphalt
secondary water barriers reduced wind-driven rainwater intrusion into the roof deck by as much
as 75% than lighter weight versions and as much as 90% less for heavier weight synthetic
secondary water barriers than lighter weight versions.
Section 4
24
resilience-enhancing measures, particularly for lower-income households (U.S. GAO, 2007a;
NRC, 2011). RMS (2009) estimates that the benefit of each $1 spent under the MSFH Program
could be increased to $2.75 with the inclusion of roof mitigation matching grants and expansion to
other locations in the state.
Of the roughly five million single-family homes in Florida, over 85 % were built prior to
the implementation of a stronger statewide building code in 2002 (FDSF, 2009). “Risk is offset
by resilience…this resilience is a direct function of both capacity and economic prosperity
(O’Brien et al., 2006, p.72-73). Households (and often contractors) face difficult choices and
often hear conflicting information about risks and response options (Peacock, 2003; Sadowski et
al., 2008; Bubeck et al., 2010; Lindell and Perry, 2011).13 The delivery of salient vulnerability
information coupled with practical mitigation options and incentives, to homeowners living in
hurricane prone communities in Florida and beyond, is a formidable, yet essential component of
disaster risk reduction (NRC, 2006; U.S. GAO, 2007a).
Coastal communities on low-lying barrier islands, coastal floodplains, and in deltaic
regions are disproportionately vulnerable to hydro-meteorological events and environmental
degradation. The 2004 and 2005 hurricane seasons were catastrophic evidence of the need for
reform of mitigation policies in coastal areas. The physical and socioeconomic destruction,
disproportionately affecting the poor and most vulnerable members of our society, revealed
failures at all level of government – “a host of policies, plans, and practices gone badly awry”
13
For example, in 2010 revisions to the Florida Building Code (FBC) significantly extended the
High Velocity Hurricane Zone (HVHZ) building code requirements further inland in the south
central portion of Florida, but removed large stretches of coast along the Panhandle and in the
northeast corner of the state around the highly populous Jacksonville area (FBC, 2010).
Section 4
25
(Comfort, 2006, p. 501). Kunreuther refers to this investment-mitigation gap, “the combination of
underinvestment in protection coupled with the general taxpayer financing losses after-the-fact”,
as the natural disaster syndrome (Kunreuther et al., 2012, p.3). With billions of dollars spent on
rescue and recovery after hurricanes strike, $9 trillion of insured coastal property along the
Atlantic and Gulf Coasts, and trillions more in development projected over the next several
decades, it is clear that we can and must be doing more to enhance resilience before future
hurricanes strike (NSB, 2007; Heinz et al., 2009; FEMA, 2011; NRC, 2011).
3.0 Methodology
This research focused on the risk perceptions and mitigation behavior among a diverse
sample of households from across the State of Florida. More specifically, we investigated how
households perceive the annual threat of property damage from hurricanes, particularly the threat
posed by major hurricanes (Category 3, 4, and 5), the risk of more destructive hurricanes due to
climate change, institutional accountability overall and institutional effectiveness at disaster risk
reduction. Exploring households’ preferences for an array of mitigation measures and insurance
reforms to enhance coastal resilience, we investigated their willingness to support stricter building
codes and zoning laws, a comprehensive insurance program, multi-year flood insurance contracts,
and among other things, a proposed ‘Florida Pre-Disaster Mitigation Fund’, to sponsor an
expansion of pre-disaster mitigation programs (with additional state funding above and beyond
federal dollars) focused on strengthening the built environment before hurricanes strike. We also
examined households’ willingness to invest in a menu of realistic, low and high cost roofing and
opening protection options in a Mitigation Choice Experiment, when presented with a package of
salient vulnerability information, including a brief video presentation of the costly losses associated
with wind-driven rainwater intrusion and the cost-effectiveness of response options, as well as,
Section 4
26
meaningful incentives. We then collected households’ socio-demographic information and housing
characteristics, including a profile of households’ insurance coverage. Lastly, we gathered
information regarding households’ recent hurricane experiences and damage histories, including
structural damage and interior damage from wind-driven rainwater intrusion.
3.1 The Survey Instrument
An in-depth online survey was developed for households, including questions on some of
the major challenges facing Florida. The survey consisted of five sections including 40 attitudinal
and behavioral questions (yes/no, multiple-choice, rating scales, check all, and referendum style
questions). The survey made use of the latest scientific data, models, and relevant assessments
available. Before implementation, the survey was also extensively tested by a diverse sample of
government experts and decision makers and academicians.
3.2 Sample Selection
A database of contact information for over 400,000 households who applied to the My
Safe Florida Home (MSFH) Program beginning in 2007 was acquired from the State of Florida.
A random sample of 40,000 households, whose email addresses were available, was selected for
the purposes of our survey study.
3.3 Data Collection
Households were contacted by email beginning on Wednesday, May 2, 2012, and invited
through a brief letter including a link to the survey website to complete an online questionnaire
on or before Friday, June 1, 2012. The invitation specified that the survey was voluntary, but that
Section 4
27
their participation was very important. Households were asked to please fill out the questionnaire
to the best of their ability. They were informed that their responses were strictly confidential, and
that no individual respondents could be identified. They were also informed that responses would
be used for statistical analysis, and would only be released in summary format. Two email
reminders were sent during the third and fifth Wednesdays of May 2012. Of the 40,000 email
invitations, 38 requests were returned with bad addresses. Adjusted for undeliverable emails, the
overall response rate was 4.3 %, with 1,710 usable responses received over 31 days, including
households from 46 of 67 Florida counties where respondents’ zip codes were reported (See
Figure 4). Findings indicating respondents’ substantial variations in risk perceptions, mitigation
preferences, and backgrounds are presented in the next section.
Section 4
28
Figure 4. Survey Respondents by Florida County
Section 4
29
Table 2. Survey Respondents by Florida County
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
22.
23.
24.
25.
26.
27.
28.
29.
30.
31.
32.
33.
34.
35.
36.
37.
38.
County
Alachua
Baker
Bay
Bradford
Brevard
Broward
Calhoun
Charlotte
Citrus
Clay
Collier
Columbia
Desoto
Dixie
Duval
Escambia
Flagler
Franklin
Gadsden
Gilchrist
Glades
Gulf
Hamilton
Hardee
Hendry
Hernando
Highlands
Hillsborough
Holmes
Indian River
Jackson
Jefferson
Lafayette
Lake
Lee
Leon
Levy
Liberty
N
3
0
11
0
86
169
0
25
12
5
25
0
1
0
14
68
9
2
0
0
0
0
0
0
1
13
1
65
1
22
0
0
0
15
54
14
0
0
39.
40.
41.
42.
43.
44.
45.
46.
47.
48.
49.
50.
51.
52.
53.
54.
55.
56.
57.
58.
59.
60.
61.
62.
63.
64.
65.
66.
67.
County
Madison
Manatee
Marion
Martin
Miami-Dade
Monroe
Nassau
Okaloosa
Okeechobee
Orange
Osceola
Palm Beach
Pasco
Pinellas
Polk
Putnam
Santa Rosa
Sarasota
Seminole
St. Johns
St. Lucie
Sumter
Suwannee
Taylor
Union
Volusia
Wakulla
Walton
Washington
N
Total
0
30
6
25
132
9
4
47
0
42
7
112
25
101
14
0
36
32
22
20
26
13
0
1
0
32
7
5
1
1,365
Notes: We received zip code locations for 1365 of 1710 responses; 46 of 67 Florida counties.
Section 4
30
4.0 Results
We present detailed findings of key survey results below. Survey findings are presented
in five phases. Part One of the results section covers respondents’ risk perceptions, assessments
of institutional accountability overall and institutional effectiveness at disaster risk reduction;
Part Two focuses on respondents’ preferences for an array of mitigation measures and insurance
reforms; Part Three presents the results of the Mitigation Choice Experiment; Part Four provides
a detailed description of respondents’ socio-demographic backgrounds and home characteristics,
including a profile of respondents’ insurance coverage; and Part Five explores respondents’
hurricane experiences and damage histories, including respondents’ structural damages and
interior damages from hurricane wind-driven rainwater intrusion. The definitions and descriptive
statistics of all variables and survey responses are also presented in tabular form in Appendix B.
Section 4
31
4.1 Risk Perception, Institutional Accountability and Effectiveness
Survey findings begin with Florida households’ concern about the annual risk of
damaging hurricane landfalls, particularly the threat of major hurricanes (Category 3, 4, and 5).
Respondents’ concern regarding a major hurricane damaging their home this hurricane season
(2012) and concern regarding a major hurricane damaging their home within the next ten years
are presented in Figures 5a and 5b respectively. With 1,689 reporting, 37.95 % of respondents
expressed being highly concerned about a major hurricane damaging their home this hurricane
season, 33.99 % were moderately concerned, and 28.06 % were minimally concerned. Regarding
a major hurricane damaging their home within the next ten years, over half of respondents (51.89%)
expressed being highly concerned, 27.45 % were moderately concerned, and 20.66 % were
minimally concerned (N=1694).14
Households were then presented with a more specific, significant hurricane loss event, a
major hurricane causing more damage to their home than 10 % of its assessed value, this
hurricane season and within the next ten years, and asked to rate their level of concern.
Regarding a major hurricane causing more damage to their home than 10 % of its assessed value
this hurricane season, slightly more than one-third of respondents (36.00%) expressed being
highly concerned, 30.25 % were moderately concerned, and 33.75 % were minimally concerned
(N=1689). Regarding a major hurricane causing more damage to their home than 10 % of its
assessed value sometime within the next ten years, approximately half of respondents (49.82 %)
expressed being highly concerned, 25.12 % were moderately concerned, and 25.00 % were
minimally concerned (N=1692) (see Figures 6a and 6b).
14
Responses for concern were collapsed from a 0 to 10 scale into three categories (0-3 minimally
concerned, 4-6 moderately concerned, and 7-10 highly concerned).
Section 4
32
Figure 5a. Concern a Major Hurricane Will Damage Home This Year
Highly
Concerned
38%
Moderately
Concerned
34%
Minimally
Concerned
28%
N=1689
Figure 5b. Concern a Major Hurricane Will Damage Home Within Ten Years
Highly
Concerned
52%
Moderarely
Concerned
27%
Minimally
Concered
21%
N=1694
Section 4
33
Figure 6a. Concern a Major Hurricane Will Cause Significant Damage to Home This Year
Moderately
Concerned
30%
Highly
Concerned
36%
Minimally
Concerned
34%
N=1689
Figure 6b. Concern a Major Hurricane Will Cause Significant Damage to Home
Within Ten Years
Highly
Concerned
50%
Moderately
Concerned
25%
Minimally
Concerned
25%
N=1692
Section 4
34
Scientists in both meteorology and climatology recently concluded that global warming
will cause hurricanes to shift toward stronger storms with intensity increases of roughly 2-11 %
by 2100 (See tropical cyclone models in Figure 7). They calculate approximately double the
number of Category 4 and 5 hurricanes by 2100, leading to an increase in damages of roughly 30 %
(Bender et al., 2010; Knutson et al., 2010).15
Figure 7. Models Foresee More Category 4 and 5 Hurricanes in a Warmed Climate
Source: Bender et al., 2010
Households were presented with this information and asked if they considered the
projections of more Category 4 and 5 hurricanes due to climate change poses a credible economic
threat to Florida. With 1,481 reporting, less than half of respondents (43.75%) considered the
economic threat of more destructive hurricanes linked to climate change highly credible, 40.85 %
considered it moderately credible, 15.40 % minimally credible (see Figure 8).16 In comparison, a
majority of Florida experts and decision makers surveyed in 2010 (61%) regarded more
destructive hurricanes linked to climate change as a highly credible economic threat to Florida
(21% moderately credible, 18% minimally credible) (Mozumder and Flugman, 2010).
15
More recent research warns of the amplification of storm surge flooding due to sea-level rise
(Hoffman et al., 2010; Emmanuel, 2011; Lin et al., 2012; Strauss et al., 2012; Tebaldi et al., 2012).
16
Responses for credibility were collapsed from a 0 to 10 scale into three categories (0-3
minimally credible, 4-6 moderately credible and 7-10 highly credible).
Section 4
35
Figure 8. The Economic Threat of More Destructive Hurricanes Due to Climate Change
Moderately
Credible
41%
Highly Credible
44%
Minimally
Credible
15%
N=1481
Institutional Accountability Overall
Overall accountability ratings for federal, state, county, and municipal governments, as
well as, insurance companies are presented in Figure 9. With 1,406 reporting, respondents gave the
federal government an overall accountability score of 5.32 on a 10-point scale, where 0 is ‘not
accountable at all’ and 10 is ‘highly accountable’. The State of Florida received an overall
accountability score of 5.49 (N=1406); county governments received an overall accountability
score of 5.34 (N=1406); municipalities received an overall accountability score of 5.20 (N=1406);
and insurance companies received an overall accountability score of 6.05 (N=1406). The average
overall accountability score was 5.48 out of 10.
Section 4
36
Figure 9. Institutional Accountability Overall
N=1406
Federal Government
5.32
State Government
5.49
County Government
5.34
Municipal Government
5.20
Private Insurance Companies
6.05
0
1
2
3
4
5
6
7
8
9
10
Institutional Effectiveness: Disaster Risk Reduction (DRR)
Not all natural hazards events (earthquakes, floods, hurricanes, tornadoes, wildfires) have
to be widespread natural disasters. Respondents where asked to rate how effective the federal,
state, county, and municipal governments and insurance companies are at managing our
vulnerability to natural hazards and disaster risk reduction, on a scale from 0 to 10, where 0 is ‘not
effective at all’ and 10 is highly effective’, the results are presented in Figure 10. With 1,400
reporting, respondents rated the federal government’s effectiveness at disaster risk reduction a
4.84 score out of 10; the State of Florida received a DRR effectiveness score of 5.17 (N=1394);
county governments received a DRR effectiveness score of 5.21 (N=1396); municipalities
received a DRR effectiveness score of 4.9 (N=1391); and insurance companies received a DRR
effectiveness score of 4.51 (N=1392). The average DRR effectiveness score was 4.93.
Section 4
37
Figure 10. Institutional Effectiveness: Disaster Risk Reduction
Federal Government Effectiveness
N=1395
4.84
State Government Effectiveness
5.17
County Government Effectiveness
5.21
Municipal Government Effectiveness
4.90
Private Insurance Company Effectiveness
4.51
0
1
2
3
4
5
6
7
8
9
10
4.2 Mitigation Measures and Insurance Reforms
Households were asked to rate their support for an array of measures related to building
codes and land use to enhance Florida’s coastal resilience, results are presented in Figure 11.
With 1,459 reporting, a large majority of respondents (76.29%) were highly supportive of
increasing Florida’s setbacks along shorelines to enhance coastal resilience (16% were
moderately supportive, 8% were minimally supportive).17 Similarly, a large majority of
respondents (74.11%) were highly supportive of creating stricter density restrictions in Florida’s
low-lying areas (N=1456). With 1,461 reporting, 69.54 % of respondents were highly supportive
of strengthening Florida building codes, and 72.15 % were highly supportive of establishing new
elevation standards for roads and buildings in Florida to enhance coastal resilience (N=1458).
17
Responses for support were collapsed from a 0 to 10 scale into three categories (0-3 minimally
supportive, 4-6 moderately supportive and 7-10 highly supportive).
Section 4
38
Figure 11. Enhancing Resilience With Land-Use and Building Codes
100%
N=1459
90%
80%
76%
70%
74%
72%
70%
60%
50%
40%
30%
20%
0%
17%
16%
10%
8%
Increasing
Shoreline Setbacks
20%
19%
9%
Stricter Density
Restrictions
10%
9%
Updating Elevation
Standards
Strengthening
Building Codes
Highly Supportive
Moderately Supportive
Minimally Supportive
Similarly, a large majority of Florida experts and decision makers surveyed in 2010
(80.6%) were highly supportive of increasing setbacks along shorelines and creating stricter
density restrictions in low-lying coastal areas (79.1%) to enhance coastal resilience. Like Florida
households’, a large majority of Florida experts and decision makers (72.5 %) were also highly
supportive of establishing new elevations standards and strengthening building codes (66.9%) to
enhance coastal resilience (Mozumder and Flugman 2010).
Section 4
39
In 2006, the Florida State Legislature appropriated $250 million to create the My Safe
Florida Home (MSFH) Program. Over a 3-year period, the MSFH Program provided over
400,000 free home inspections, Home Structure Ratings, recommended mitigation
improvements, and information on insurance discounts. In addition, 35,000 homeowners
received matching grants of up to $5,000 to strengthen their homes. Approximately $93 million
were allocated in matching grants under the MSFH Program, which expired on June 30, 2009
(FDFS, 2009). Households were asked if they supported continuing the MSFH Program (see
Figure 12). A large majority of respondents (74.02%) supported continuing the MSFH Program,
compared with 25.98 % who did not (N=1682).18
Figure 12. The My Safe Florida Home Program
Yes
74%
No
26%
N=1682
18
In 2010, a large majority of Florida experts and decision makers (77%) supported continuing
(refunding) the MSFH Program (23% were opposed). Most (86%) supported continuing the
MSFH Program cosponsored (program costs shared) by FEMA (14% were opposed).
Section 4
40
Under the My Safe Florida Home (MSFH) Program, every homeowner received a
‘hurricane resistance rating’ (0-100 scale) (Home Structure Ratings) following an inspection of
their home’s building code status and mitigation features (FDFS, 2009). In Florida, this
information is currently collected on a voluntary basis to reflect mitigation in insurance pricing
(RMS, 2009). Often individuals looking to purchase a new home, especially first-time buyers,
are often unaware of the benefits and costs of mitigation features (U.S. GAO, 2007a). In both
cases, up-to-date comprehensive inspections/inspection reports are highly beneficial to the
consumer. This could be accomplished in a variety of ways, in our survey we proposed the
creation of a publically accessible database of residential hurricane resistance ratings (statewide),
including records of mitigation improvements, similar to other public information currently
available in the real estate market (e.g., year built, property taxes, sales history, etc.).
Households were asked to rate how supportive they were of a publicly accessible database
of Home Structure Ratings, including records of mitigation improvements similar to other public
information currently available in the real estate market (e.g., year built, taxes, sales history).
With 1,647 reporting, approximately half of respondents (49.73%) were highly supportive of a
publicly accessible database of Home Structure Ratings, 29.5 % were moderately supportive, and
20.77 % were minimally supportive (see Figure 13).19
19
Nearly two-thirds of Florida experts and decision makers surveyed in 2010 (63.0%) were
highly supportive of a publicly accessible database of Home Structure Ratings (19.0%
moderately supportive, 18.0% minimally supportive) (Mozumder and Flugman, 2010).
Section 4
41
Figure 13. A Publically Accessible Mitigation Database
Highly
Supportive
50%
Moderately
Supportive
29%
Minimally
Supportive
21%
N=1647
‘A Florida Pre-Disaster Mitigation Fund'
Pre-disaster mitigation refers to measures outside the context of response and recovery
(actions taken before disaster strikes). Effective pre-disaster mitigation strategies can break the
cycle of disaster: loss of life, property damage, local economic disruption, costly response and
recovery, and repeated disaster (FEMA, 2011). In this context, households were asked if they
would support a proposed ‘Florida Pre-Disaster Mitigation Fund’, to sponsor an expansion of predisaster mitigation programs (with additional state funding above and beyond federal dollars)
focused on strengthening the built environment before hurricanes strike, programs such as the My
Safe Florida Home (MSFH) Program, the Infrastructure Protection Program, etc., the results are
Section 4
42
presented in Figure 14. With 1,687 reporting, a large majority of respondents (68.79%) supported
the creation of the proposed ‘Florida Pre-Disaster Mitigation Fund’ (31.21 % were against).20
Figure 14. A ‘Florida Pre-Disaster Mitigation Fund
Support
69%
Against
31%
N=1687
Willingness-to-Pay to Support a ‘Florida Pre-Disaster Mitigation Fund’
Households were then asked to consider how much their household was willing to pay each
year to contribute to the proposed ‘Florida Pre-Disaster Mitigation Fund’. Households were asked
to please consider their household budget when answering the question as if they were in an actual
payment situation. They were also asked to note that state programs compete for funding, and that
20
In 2010, a majority of Florida experts and decision makers surveyed (60.5%) supported the
creation of a proposed ‘Florida Adaptation Fund’ to mobilize resources and support proactive
measures focused on increasing coastal resilience and minimizing the adverse impacts of climate
change (39.5% were against) (Mozumder and Flugman, 2010).
Section 4
43
allocating funds for this program could potentially constrain financing of other projects. However,
if households contributed to the proposed ‘Florida Pre-Disaster Mitigation Fund’, programs such as
the MSFH Program could be continued. Households were given a random amount from $5-$500
and asked if they were willing to pay that much each year to contribute to the proposed ‘Florida
Pre-Disaster Mitigation Fund’ The average amount households were asked to contribute to the
proposed ‘Florida Pre-Disaster Mitigation Fund’ was $248.61, the results are presented in Figure
15. With 1,400 reporting, 25.86 % of respondents were willing-to-pay to contribute to the proposed
‘Florida Pre-Disaster Mitigation Fund’, (74.14% were unwilling-to-pay). Households were then
asked how certain they were of the answer they just gave about their willingness to pay each year
to contribute to the proposed ‘Florida Pre-Disaster Mitigation Fund’. Respondents rated their
certainty level at 8.24 out of 10 (N=1396).
Figure 15. Willingness-to-Pay to Support a ‘Florida Pre-Disaster Mitigation Fund’
Willing-to-Pay
26%
Not
Willing-to-Pay
74%
N=1400
Section 4
44
Florida’s Property Insurance System: Post-Loss Financing
The State of Florida funds the insurance system to pay for hurricane damage after-the-fact
with surcharges, regular assessments, and emergency assessments (hurricanes taxes) on nearly
every insurance line in the state (auto, casualty and property insurance) (Cole et al., 2011; Florida
Tax-Watch, 2011; Hartwig and Wilkinson, 2012; Lehrer and Lehmann, 2012). The quasi-public
entities Florida Citizens Property Insurance Corporation (CPIC), Florida Hurricane Catastrophe
Fund (FHCF), and the Florida Insurance Guarantee Association (FIGA) have the power to issue
surcharges and assessments to cover hurricane losses after-the-fact. Households were asked how
supportive they were theses surcharges and assessments, noting that elimination of these fees
would likely cause currently subsidized insurance premiums to rise, the results are presented in
Figure 16. With 1,437 reporting, 41.89 % of respondents were highly supportive of continuing
surcharges and assessments, 31.46 % moderately supportive, 26.65 % minimally supportive.
Figure 16. Hurricane Surcharges and Assessments
Highly
Supportive
42%
Moderately
Supportive
31%
Minimally
Supportive
27%
N=1437
Section 4
45
A statewide survey of 805 Floridians by the American Consumer Institute (ACI) found
that 70 % were concerned about being assessed hundreds or thousands of dollars in hurricane
taxes, because the state’s Hurricane Catastrophe Fund may run out of money to pay insurance
claims. Respondents were almost split when it came to be willing to pay a little more in property
insurance to avoid insolvencies and taxes, but support increased to 55 % when the amount was
specified at less than $15 a month (ACI, 2011).
The National Flood Insurance Program: Low Retention Rates
Since 1980, the National Flood Insurance Program’s (NFIP) exposure quadrupled, to
over 5.6 million policies and more than $1.2 trillion. Analysis by Michel-Kerjan et al. (2012)
revealed that only 36 % of new policies issued by the NFIP between 2001 and 2009 were still in
place five years after they were purchased. Insurance penetration in flood prone areas is only
about 50%. The inability of the NFIP to enroll and retain homeowners has serious implications
for post-flood federal disaster assistance (U.S. GAO, 2011a; Jaffee et al. 2011; Michel-Kerjan
and Kunreuther 2011; Michel-Kerjan et al. 2012).
Households were asked to rate their support for multi-year (5-year) flood insurance
contracts as well as long-term (30-year) flood insurance contracts 5-year flood insurance
contracts, the results are presented in Figure 17. With 1,394 reporting, 44.69 % of respondents
were highly supportive of multi-year (5-year) flood insurance contracts, 30.27 % were
moderately supportive, and 25.04% were minimally supportive. In contrast, only 30.23 % of
respondents were highly supportive of long-term (30-year) flood insurance contracts, 27.84 %
were moderately supportive, 41.93 % were minimally supportive (N=1376).
Section 4
46
Figure 17. Multi-Year and Long-Term Flood Insurance Contracts
N=1394
100%
N=1376
90%
80%
70%
45%
30%
60%
50%
40%
30%
30%
28%
20%
10%
25%
0%
5-Year Flood Insurance
Contracts
42%
30-Year Flood Insurance
Contracts
Highly Supportive
Moderately Supportive
Minimally Supportive
Section 4
47
Comprehensive Insurance
In the aftermath of several recent hurricanes much debate was focused on whether
damages were caused by flooding and should be covered under flood insurance policies or
damages were caused by wind and should be covered by wind insurance policies (MacDonald et
al., 2010). A comprehensive all-hazards insurance program (fire, flood, hurricane, earthquake,
tornado, etc.), which could pool policy holders with diversified risks may create a more
stabilized insurance mechanism and reduce uncertainty about the availability and affordability of
coverage (Kunreuther, 2006; U.S. GAO 2008). Households were asked to rate how supportive
they were of the creation of a comprehensive insurance program that combines flood and wind
coverage, the results are presented in Figure 18. With 1,426 reporting, a large majority of
respondents (73.49%) were highly supportive of a comprehensive insurance program, 18.05 %
were moderately supportive, and 8.06 % were minimally supportive.
Figure 18. A Comprehensive Insurance Program
Highly
Supportive
74%
Moderately
Supportive
18%
Minimally
Supportive
8%
N=1426
Section 4
48
Tax-Free Catastrophe Savings Accounts
Tax-free household catastrophe savings accounts could permit households to establish
reserves for future losses not covered by wind or flood insurance policies (U.S. GAO, 200X).
Households were asked how much they supported the creation of tax-free catastrophe savings
accounts, the results are presented in Figure 19. With 1,428 reporting, 62.32 % of respondents
were highly supportive of tax-free catastrophe savings accounts, 22.76 % were moderately
supportive, and 14.92 % were minimally supportive.
Figure 19. Tax-Free Catastrophe Savings Accounts
Highly
Supportive
62%
Moderately
Supportive
23%
Minimally
Supportive
15%
N=1428
Section 4
49
4.3 A Mitigation Choice Experiment
We examined homeowners’ willingness to invest in a menu of realistic, low and higher
cost roofing and opening protection options, when presented with a package of salient vulnerability
information, including a full-scale video presentation by the Insurance Institute for Business and
Home Safety (IBHS) on the costly interior losses associated with wind-driven rainwater intrusion
and the cost-effectiveness of response options, as well as, a meaningful incentive, in the form of
a matching grant of up to $10,000, to reduce the upfront costs of mitigation (IBHS, 2011).21
The experiment proceeded as follows: the Information Treatment group, 25% of the
sample of households, received the salient vulnerability information package ahead of the
Mitigation Choice Experiment; the Incentive Treatment group, 25% of the sample of households,
received an incentive in the form of a matching grant of up to $10,000 within the Mitigation
Choice Experiment, but did not receive the salient vulnerability information package; the
Combined Treatment group, 25% of the sample of households, received both the salient
vulnerability information package ahead of the Mitigation Choice Experiment and the $10,000
matching grant. The Baseline group did not receive either the salient vulnerability information
package or the $10,000 matching grant.
The Incentive Treatment version of the Mitigation Choice Experiment appears below,
showing the details of Options A and B associated with the roofing and opening protection options:
21
The full-scale demonstration conducted by IBHS illustrates how wind-driven rainwater can
penetrate a home’s roof system, and how applying a relatively simple and inexpensive secondary
water barrier, such as sealing the roof deck ($500 for a 2000 square foot home), can substantially
reduce rainwater intrusion, mold, and the associated damage to interior contents/furnishings,
finishes (ceilings, walls, flooring), and utilities (electrical, mechanical, plumbing) (IBHS, 2011).
Section 4
50
Analysis of Florida claims data from the 2004 and 2005 hurricane seasons revealed
that homes constructed with the most up-to-date building codes suffered as much as
60 % FEWER LOSSES than homes constructed to older building codes.
Please review the Roofing and Opening Protection Options below, including:
(1) Estimated cost, (2) Home Structure Rating, and (3) Estimated insurance
premium savings for each option.
In answering the questions below please consider your household budget as if you
are making actual home improvement decisions in a real payment situation.
Please also note that there are potential alternatives to the mitigation options shown here.
Now suppose that you are given a matching grant up to $10,000 (you receive $1 for
mitigation for every $1 you spend on mitigation), to reduce your mitigation costs.
Section 4
51
Which Roofing and Opening Protection Options, if you had to choose, do you most prefer?
Roofing
■ Option A
■ Option B
■ My roof was installed after August 1994 in Miami-Dade and Broward Counties
(meets 1994 South Florida Building Code)
■ My roof was installed after March 2002 in all other Florida Counties
(meets 2001 Florida Building Code)
■ No improvement at this time
Opening Protection
■ Option A
■ Option B
■ My opening protection was installed after August 1994 in Miami-Dade and Broward Counties
(meets 1994 South Florida Building Code)
■ My opening protection was installed after March 2002 in all other Florida Counties
(meets 2001 Florida Building Code)
■ No improvement at this time
The results of the Mitigation Choice Experiment are presented in Figures 20 and 21
(detailed tables also appear in Appendix A). In regards to both roof systems and opening
protection, the package of salient vulnerability information in the Information Treatment was not
shown to have a positive impact on households’ mitigation decision making when compared
with the Baseline sample. Households’ were not more likely to pursue mitigation Options A or B
over the no improvements at this time option, nor were they likely to ‘shift up’ from the lower
cost Option B to the higher cost Option A for either roofing or opening protection. In fact,
households in the Information Treatment were less likely to invest in mitigation. In contrast, the
$10,000 matching grant incentive significantly increased households’ willingness to invest in
mitigation and to ‘shift up’ to the higher cost Option A in regards to both roofing and opening
protection options in the Incentive and Combined Treatment groups. Implications of these
findings appear in the Discussion Section.
Section 4
52
Figure 20. Mitigation Choice Preferences: Roof System
30%
Option A
Baseline
N=899
Option B 17%
Option A or B
47%
None
53%
26%
Option A
Information Treatment
Option B
20%
Option A or B
46%
None
54%
43%
Option A
Incentive Treatment
Option B 18%
Option A or B
61%
None
39%
35%
Option A
Combined Treatment
(Information
and Incentive)
Option B
23%
Option A or B
None
41%
Option A
Overall
Option B
58%
34%
20%
Option A or B
None
54%
46%
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Section 4
53
Figure 21. Mitigation Choice Preferences: Opening Protection
Baseline
N=820
26%
Option A
28%
Option B
54%
Option A or B
46%
None
24%
Option A
Information Treatment
Option B
21%
Option A or B
45%
54%
None
Option A
Incentive Treatment
Option B
33%
30%
63%
Option A or B
37%
None
Option A
Combined Treatment
(Information
and Incentive)
Option B
29%
Option A or B
62%
None
Overall
0%
38%
Option A
29%
Option B
28%
57%
Option A or B
None
Section 4
33%
43%
10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
54
In a follow-up to the Mitigation Choice Experiment, households were asked to express how
certain they were of the answers regarding their mitigation preferences on a scale form 0 to 10,
where 0 is ‘not certain at all’ and 10 is ‘highly certain’ (See Figure 22). In the Baseline group,
respondents rated their certainty level at 7.93 out of 10 (N=223); respondents in the Information
Treatment group rated their certainty level at 7.98 out of 10 (N=251); respondents in the
Incentive Treatment group rated their certainty level at 7.65 out of 10 (N=284); respondents in
the Combined Treatment group rated their certainty level at 7.87 out of 10 (N=265). The overall
certainty level of respondents regarding their mitigation preferences was 7.85 out of 10 (N= 1023).
Figure 22. Certainty Level of Mitigation Choice Preferences
N=1023
Baseline Certainty
7.93
Information Treatment Certainty
7.98
Incentive Treatment Certainty
7.65
Combined Treatment Certainty
7.87
Overall Certainty
7.85
0
Section 4
1
2
3
4
5
6
7
8
9
55
10
4.4 Socio-Demographic Information and Housing Characteristics
Definitions and descriptive statistics of socio-demographic information collected from
households are presented below (gender, age, ethnicity, educational background, marital status,
household size, number of children, income, political affiliation, and years living in Florida).
In addition, information collected regarding housing characteristics is also reported (home value,
mortgage status, year built, square footage, roof shape, roof covering, level of opening protection
and roofing, and insurance coverage).
This data will be incorporated with survey responses regarding mitigation and insurance
preferences in future analysis using applied multivariate regression analysis and simultaneous
(multiple regression) equation models to explore the underlying drivers of mitigation behavior
among Florida households particularly with regards to their willingness to support and contribute
to the proposed ‘Florida Pre-Disaster Mitigation Fund’ and with regards to their decision making
in the Mitigation Choice Experiment.
The gender and age distributions of respondents are presented in Figures 23 and 24
respectively. With 1,417 reporting, 42.91 % of respondents identified themselves as female,
57.09 % as male. Regarding age, respondents reported having an average age of 58.76 years,
ranging from 30 to 91 years of age. Nearly 80 % of respondents (78.30%) reported being over 50
years of age. Approximately 5 % of respondents (4.96%) reported being 30-39 years of age,
16.74 % reported being 40-49 years of age, 28.30 % reported being 50-59 years of age, 31.11 %
reported being 60-69 years of age, 15.87 % reported being 70-79 years of age, and 3.02 % reported
being 80 years of age and above (N=1392).
Section 4
56
Figure 23. Gender of Respondents
Female
43%
Male
57%
N=1417
Figure 24. Age of Respondents
80+
3%
30-39
5%
40-49
17%
70-79
16%
50-59
28%
60-69
31%
N=1392
Section 4
57
The educational level and ethnicity of respondents are presented in Figures 25 and 26.
Regarding educational background, 10.55 % of respondents reported obtaining a doctorate or
professional degree, 23.19 % a masters degree, 31.07 % a bachelors degree, 9.23 % an associates
degree, 19 % some college, no degree, and 6.94 % a high school diploma or equivalent (N=1397).
With 1,390 reporting, 82.30 % of respondents identified themselves as Caucasian, 7.41 % as
Hispanic/Latino, 4.82 % as African-American, 0.86 % as Asian, and 4.6 % as Other ethnicity.
Figure 25. Educational Background of Respondents
N=1397
Doctorate/Professional Degree
11%
Masters Degree
23%
Bachelors Degree
Associates Degree
31%
9%
Some College, No Degree
High School Graduate or Equivalent
0%
Section 4
19%
7%
10%
20%
30%
58
Figure 26. Ethnicity of Respondents
100%
N=1390
90%
82%
80%
70%
60%
50%
40%
30%
20%
10%
0%
Caucasian
7%
5%
Hispanic/Latino
AfricanAmerican
5%
1%
Asian
Other
Respondents’ marital status, household size, and number of children (under 18 years of age)
are presented in Figures 27, 28, and 29 respectively. With 1,412 reporting, 7.93 % of respondents
identified themselves as single, 3.82 % as living together, 70.54 % as married, 10.13 % as
divorced, 6.02 % as widowed, and 1.56 % as other. Regarding household size, 15.68 % of
respondents reported living alone, 50.39 % of respondents a two member household, 16.54 % of
respondents a household of three, 11.14 % of respondents a household of four, and 6.25 % of
respondents a household of five or more (N=1409). With 1,402 reporting, 77.32 % of
respondents indicated that they do not have any children, 11.63 % of respondents have one child,
8.49 % of respondents have two children, and 2.56 % of respondents have three or more
children. More specifically, 5.49 % of respondents indicated that they have one or more children
less than five years of age in their household, 94.51 % of respondents have none (N=1403).
Section 4
59
Figure 27. Marital Status of Respondents
N=1412
80%
70%
70%
60%
50%
40%
30%
20%
10%
8%
10%
0%
6%
4%
Single
Living
Together
Married
Divorced
2%
Widowed
Other
Figure 28. Household Size of Respondents
One
N=1409
16%
Two
50%
Three
17%
Four
Five or More
0%
Section 4
11%
6%
10%
20%
30%
40%
50%
60%
60
Figure 29. Number of Children in Respondents’ Household
One Child
12%
No Children
77%
Two Children
8%
Three or More
Children
3%
N=1402
The household income of respondents is presented in Figure 30. With 1,310 reporting,
23.74 % of respondents indicated earning less than $50,000 in household income before taxes for
the previous year (3.36% earning less than $20,000, 3.59% earning $20,000-$29,999, 8.17%
earning $30,000-$39,999, and 8.63% earning $40,000-$49,999); 43.44 % indicated earning
between $50,000 and $100,000 (10.08% earning $50,000-$59,999, 9.01% earning $60,000$69,999, 10.23% earning $70,000-$79,999, 7.18% earning $80,000-$89,999, and earning 6.95%
$90,000-$99,999); 20.30 % of respondents indicated earning between $100,000 and $150,000
(6.79% earning $100,000-$109,999, 3.74% earning $110,000-$119,999, 4.35% earning
$120,000-$129,999, 2.37% earning $130,000-$139,999, and 3.05% earning $140,000-$149,999);
7.02 % indicated earning between $150,000 and $200,000 (2.52% earning $150,000-$159,999,
1.53% earning $160,000-$169,999, 1.22% earning $170,000-$179,000, 1.30% earning $180,000$189,999, 0.46% earning $190,000-$199,999); 3.21 % indicated earning between $200,000 and
$250,000 (1.07% earning $200,000-$209,999, 0.84% earning $210,000-$219,999, 0.31% earning
$220,000-$229,999, 0.53% earning $230,000-$239,999, and 0.46% earning $240,000-$249,999);
and 2.29 % indicated earning $250,000 and above.
Section 4
61
Figure 30. Household Income of Respondents
$250,000 and above
2%
$240,000-$249,999
1%
$230,000-$239,999
1%
$220,000-$229,999
1%
$210,000-$219,999
1%
$200,000-$209,999
1%
$190,000-$199,999
1%
$180,000-$189,999
1%
$170,000-$179,000
1%
$160,000-$169,999
1%
$150,000-$159,999
2%
$140,000-$149,999
$130,000-$139,999
N=1310
3%
2%
$120,000-$129,999
4%
$110,000-$119,999
4%
$100,000-$109,999
7%
$90,000-$99,999
7%
$80,000-$89,999
7%
$70,000-$79,999
10%
$60,000-$69,999
9%
$50,000-$59,999
10%
$40,000-$49,999
9%
$30,000-$39,999
8%
$20,000-$29,999
Less than $20,000
0%
Section 4
4%
3%
2%
4%
6%
8%
10%
12%
62
The political affiliation of respondents is presented in Figure 31. With 1,317 reporting,
36.52 % of respondents identified themselves as republicans, 28.32 % of respondents identified
themselves as democrats, 15.49 % of respondents identified themselves as independents, 15.49 %
of respondents identified themselves as having no affiliation, 2.89 % of respondents identified
themselves as other, and 1.29 % of respondents identified themselves as libertarians.
Figure 31. Political Affiliation of Respondents
50%
N=1317
45%
40%
35%
37%
30%
28%
25%
20%
15%
16%
10%
15%
5%
0%
Republican
Democrat
Independent No Affiliation
3%
1%
Other
Libertarian
Respondents’ years living in Florida are presented in Figure 32. With 1,415 reporting,
respondents’ indicated an average of 29.65 years living in Florida, ranging from 1 to 79 years.
Nearly all respondents (97.81%) reported living in Florida for more than five years, 90.32 % of
respondents reported living in Florida for more than ten years, 75 % of respondents (74.84%) for
more than fifteen years, and nearly half of respondents (47.77%) for more than 25 years.
Section 4
63
Figure 32. Years of Respondents Living in Florida
Over 65 Years
2%
61-65 Years
2%
56-60 Years
N=1415
4%
50-55 Years
6%
46-50 Years
5%
41-45 Years
7%
36-40 Years
8%
31-35 Years
10%
26-30 Years
10%
21-25 Years
10%
16-20 Years
11%
11-15 Years
13%
6-10 Years
5 Years or Less
0%
10%
2%
2%
4%
6%
8%
10%
12%
14%
The percentage of respondents with mortgages and respondents’ home values are
presented in Figures 33 and 34. With 1,405 reporting, approximately two-thirds of respondents
(66.62%) indicated that they currently have a mortgage, 33.38 % do not. Regarding home values,
nearly three-quarters of respondents (73.84%) indicated owning homes worth less than $300,000
(20.89% worth less than $150,000, 24.71% worth $150,000-$200,000, 16.93% worth $200,000$250,000, and 11.31% worth $250,000-$300,000). Approximately one-quarter of respondents
(26.15%) indicated owning homes worth $300,000 or more (2.95% worth $300,000-$350,000,
2.81% worth $350,000-$400,000, 6.63% worth $400,000-$450,000, 8.00% worth $450,000$500,000, and 5.76% worth more than $500,000) (N=1388).
Section 4
64
Figure 33. Mortgage Holders Among Respondents
No
33%
Yes
67%
N=1405
Figure 34. Home Values of Respondents
Less than $150,000
$150,000-$200,000
25%
$200,000-$250,000
17%
$250,000-$300,000
11%
$300,000-$350,000
3%
$350,000-$400,000
3%
$400,000-$450,000
7%
$450,000-$500,000
Greater than $500,000
0%
Section 4
N=1388
21%
8%
6%
5%
10%
15%
20%
25%
30%
65
The year respondents’ homes were built is presented in Figure 35. With 1,588 reporting,
the average year in which homes were built was 1981.48, with a range of 1900 to 2006. Most of
the respondents’ homes (96.54%) were built after 1950. Approximately 20 % of the respondents’
homes were built between 1950 and 1970 (9.07% were built from 1950-1959 and 10.83% were
built from 1960-1969). Over three-quarters of the respondents’ homes (76.64%) were built after
1970 (17.32% were built from 1970-1979, 20.72% were built from 1980-1989, 25.25% were
built from 1990-1999, and 13.35% were built from 2000-2006). However, most of the
respondents’ homes (91.69%) were constructed before implementation of the more stringent
2001 Florida Building Code (and before the 1994 South Florida Building Code).
Figure 35. Year Respondents’ Homes Built
N=1588
30%
25%
25%
21%
20%
17%
15%
13%
11%
9%
10%
4%
5%
0%
Before
1950
Section 4
1950-1959 1960-1969 1970-1979 1980-1989 1990-1999 2000-2006
66
Respondents’ level of opening protection (windows, skylights, entry and sliding glass
doors) and roofing systems are presented in Figures 36 and 37 respectively. With 1,391
reporting, only one-third of respondents (35.37%) indicated that their roof system was installed
after August 1994 in Miami-Dade and Broward Counties (meets 1994 South Florida Building
Code) or after March 2002 in all other Florida Counties (meets 2001 Florida Building Code)
(N=1391). Similarly, with regards to opening protection, only one-third of respondents (33.87%)
indicated that their opening protection was installed after August 1994 in Miami-Dade and
Broward Counties (meets 1994 South Florida Building Code) or after March 2002 in all other
Florida Counties (meets 2001 Florida Building Code) (N=1240).
Figure 36. Level of Respondents’ Roofing Systems
35%
Roofing System
Meets 2001 Florida
Building Code
65%
Roofing System
DOES NOT
Meet 2001 Florida
Building Code
N=1391
Section 4
67
Figure 37. Level of Respondents’ Opening Protection
34%
Opening Protection
Meets 2001 Florida
Building Code
66%
Opening Protection
DOES NOT
Meet 2001 Florida
Building Code
N=1240
The square footage of respondents’ homes is reported in Figure 38. With 1,595 reporting,
respondents indicted owning homes with an average of 2104.63 square feet (ranging from 800 to
6,500 square feet). Over half of respondents (54.24%) reported owning homes with less than
2,000 square feet (17.24% of respondents reported owing homes of less than 1,500 square feet
and 37.00% of respondents reported owing homes of 1,501-2,000 square feet). Approximately
one-quarter of respondents (24.01%) reported owning homes of 2,001-2,500 square feet,
13.11 % of respondents reported owning homes of 2,501-3,000 square feet, and 8.65 % of
respondents reported owing homes of greater than 3,000 square feet.
Section 4
68
Figure 38. Square Footage of Respondents’ Homes
N=1595
Greater than 3,000
9%
2,501-3,000
13%
2,001-2,500
24%
1,501-2,000
37%
Less than 1,500
17%
0%
5%
10%
15%
20%
25%
30%
35%
40%
The roof covering type and roof shape of respondents’ homes are presented in Figures 38
and 39 respectively. With 1,564 reporting, a large majority of respondents (70.08%) indicated
having shingle roof coverings on their homes, compared with 20.52 % of respondents with tile
roof coverings, 6.52 % of respondents with metal roof coverings, and 2.88 % of respondents with
concrete roof coverings. Regarding roof shape, approximately half of respondents (49.43%)
reported having hip shaped roofs on their homes, 46.82 % of respondents reported having gable
roofs, and 3.75 % of respondents reported having flat roofs (N=1493).
Section 4
69
Figure 38. Roof Covering on Respondent’s Homes
Tile
20%
Shingle
70%
Metal
7%
Concrete
3%
N=1564
Figure 39. Roof Shape of Respondent’s Homes
Gable
47%
Hip
49%
N=1493
Section 4
Flat
4%
70
Insurance Profile of Respondents
With 1,427 reporting, all respondents indicated that they have property insurance, and a
large majority of respondents (69.03%) indicated that they have wind coverage (See Figure 40).
Regarding flood insurance, a large majority of respondents (61.05%) indicated that have flood
insurance, and approximately half of respondents (49.12%) indicated that they had a mandatory
flood insurance purchasing requirement (respondents with a mortgage who live in the flood zone)
(See Figures 41 and 42).
Figure 40. Percentage of Respondents with Wind Insurance
Have Wind
Insurance
69%
Don't Have
Wind Insurance
31%
N=1427
Section 4
71
Figure 41. Percentage of Respondents with Flood Insurance
Have Flood
Insurance
61%
No Flood
Insurance
39%
N=1427
Figure 42. Percentage of Respondents with Mandatory Flood Insurance
Mandatory
Flood Insurance
13%
Not Mandatory
87%
N=1427
Section 4
72
4.5 Hurricane History of Respondents
Lastly, we present respondents’ recent hurricane experiences and an overview of
damages (number of damaging hurricanes, exterior damages including interior damages from
water intrusion/penetration and loss totals). The distribution of respondents who experienced
damage to their property from hurricanes in 2004 and 2005 is presented in Figure 43. With 1,710
reporting, approximately half of respondents (49.00%) indicated experiencing property damage
from one or more hurricanes in 2004 and 2005. Approximately one-third of respondents
(33.74%) experienced property damage from one hurricane, 10.23 % experienced property
damage from two hurricanes, 4.21 % experienced property damage from three hurricanes, and
0.82 % experienced property damage from four hurricanes. Hurricane Wilma damaged the most
respondents’ homes (N=375), followed by Hurricanes Frances (194), Charley (180), Ivan (152),
Jeanne (151), Katrina (99) and Dennis (38).
Figure 43. Respondents Damages From 2004 and 2005 Hurricanes
60%
51%
N=1710
49%
50%
40%
34%
30%
20%
10%
10%
4%
1%
0%
Section 4
73
Exterior/Structural Damage
Respondents indicated 903 instances of exterior/structural damage from hurricane winds,
wind-borne debris and wind-driven rainwater; in particular, damage from downed trees and tree
limbs, as well as, damage from broken and dislodged exterior equipment (rooftop mounted or
ground level), and damage from accessory structure failure (screen enclosure, porch, carport).
More specifically, respondents indicated 856 reports of exterior/structural damage ranging from
failure of roof-coverings, roof decks, roof-to-wall connections, wall coverings/cladding, soffits,
vents (gable-end, ridge, roof, soffit, turbine), gutters and downspouts, and shutters, as well as,
broken windows, skylights, entry and sliding glass doors, and garage doors.
Interior Damage From Wind-Driven Rainwater Intrusion
With 773 reporting, respondents indicated numerous entry points of damaging winddriven rainwater intrusion (roof, walls, windows, entry doors, soffits, and vents).
672 reports of interior damage including damage to interior contents/furnishings, interior finishes
damage (ceilings, walls, flooring), ceiling collapse, utilities damage (electrical, mechanical,
plumbing), and mold growth.
Respondents’ loss histories will be further analyzed and incorporated with survey
responses regarding mitigation and insurance preferences in future analysis using applied
multivariate regression analysis and simultaneous (multiple regression) equation models to
explore the underlying drivers of mitigation behavior among Florida households particularly
with regards to their willingness to support and contribute to the proposed ‘Florida Pre-Disaster
Mitigation Fund’ and with regards to their decision making in the Mitigation Choice Experiment.
Section 4
74
5.0 Discussion
Survey findings can provide systematic information on multiple scales. Our findings
suggest avenues for potential risk reduction strategies that can be implemented by federal, state,
and local agencies, including county and municipal governments in vulnerable coastal communities.
While the U.S. has dramatically reduced the loss of life from natural hazards, economic
losses are skyrocketing (U.S. GAO 2007a; Hartiwg, 2012; NOAA, 2012). “There are reasonable
steps to counter those threats, and we as a nation are not yet taking them” (Heinz et al., 2009, p.3).
Today, in most coastal areas throughout the U.S., exposure to natural hazards is growing and
vulnerability is increasing, resilience is increasing in certain aspects, but decreasing in others.
In short, the ability of Florida, and the U.S. as a whole, to mitigate and insure against natural
hazards and adapt to climate change can be greatly improved, this involves setting new priorities
(Godschalk, 2003; Leatherman and White, 2005; NRC, 2006; U.S. GAO, 2007b; NRC, 2009;
FEMA, 2011; Michel-Kerjan and Kunreuther, 2011; U.S. GAO 2011a; U.S. GAO 2011b).
The National Flood Insurance Program operates a voluntary program called the
Community Rating System (CRS) the purpose of which is to encourage communities to improve
flood resiliency by providing incentives to implement prevention and awareness measures in the
form of premium discounts. The CRS Program works on a 1-10 point system where a score of 1
requires the most points and communities with this score receive the highest discounts and a
score of 10 where communities receive the least points and do not receive a discount). As of
2010, the average score among participating Florida communities was 7.18, and no communities
had a rating better than 5. Since 2005, less than half of the 179 Florida communities in the CRS
Program improved their score (47%) (Ntelekos et al., 2009; FDEP, 2010).
Section 4
75
From South Florida to Jacksonville, Fort Myers to Tampa Bay, Orlando to the Panhandle
and beyond, coastal vulnerability is amplified by unsustainable development (underinvestment in
disaster risk reduction measures and environmental degradation). The main drivers of
unsustainable development include: hallow state and local growth management plans and
imprudent federal floodplain management, overcome by development pressures; inconsistent state
and local policies to adopt and enforce stricter building codes; weak federal, state and local
mandates and distorted incentives to avoid or reduce risk, including over-reliance on billions of
dollars in annual federal disaster relief and recovery spending; absurd federal and state insurance
subsidies promoting systemic risk; trillions of dollars in deteriorating infrastructure; as well as
fragmented programs to protect or restore natural capital, including natural storm buffers (Platt,
1999; Miletti and Gailus, 2005; Salvesen, 2005; Burby, 2006; Comfort, 2006; Bagstad at al., 2007;
Gaddis et al., 2007; Deyle et al., 2007; Brody et al., 2010; Holladay and Schwartz, 2010; Berke
et al., 2012). The near and longer-term impacts of climate change, will amplify vulnerability,
particularly in the world’s low-lying coastal zones and among the poorest and most vulnerable
members of society (Milly et al., 2008; IPCC, 2012; Martinich et al., 2012).22
Without institutional frameworks and clear rules of engagement, adaptive capacity is low,
and adaptation is highly limited and fragmented (Vogel et al. 2007; U.S. GAO 2011b). “Capacitybuilding comes down to resources and commitments to ensure that these resources are
effectively utilized. This is a formidable challenge” (O’Brien et al., 2006, p.74). Substantial
22
“Climate change will test the ability of governments to lead, as never before. Trade-offs will
be necessary in the choices policymakers must make - between the urgency of today’s problems
and the need to prepare for future risks, the world in which our children and grandchildren live
and thrive” (WRI, 2011, p.13)
Section 4
76
barriers exist that impede the pursuit of status-quo altering pathways of systemic resilience;
namely, insufficient resources and the high cost of mitigation measures, limited direction and
leadership, and lack of institutional frameworks to initiate and sustain a holistic response
(Adger and Barnett 2009; Hallegatte, 2009; Moser and Elkstrom, 2010; Kates et al., 2012).
5.1 A Wake-Up Call, Not An Aberration
Not all natural hazards events (earthquakes, floods, hurricanes, tornadoes, wildfires) have
to become widespread natural disasters. According to the National Science Board Task Force on
Hurricane Science and Engineering, “the imperative to act has never been clearer, nor have the
science, technology, and intellectual capacity needed to address the challenge been more capable
of rising to the occasion…to translate new knowledge into operational practice in ways that yield
tangible benefits to society” (NSB, 2007, p.25-26).
Disaster risk reduction involves both structural (hard) and nonstructural (soft), pre- and
post-disaster measures undertaken to make citizens, communities, states, and the nation more
resilient to natural hazards, reduce losses, save lives, stabilize insurance markets and reduce
premiums (Godschalk, 2003; Heinz et al., 2009; Comfort et al., 2011; Llyods, 2011). “Given
projections related to climate change, combined with demographic and economic trends that
suggest population growth in higher risk coastal areas, the nation could face a future of more
disasters, resulting in greater loss of life, greater economic impacts, and greater social disruption…
communities prepared for the most common disruptions are those most likely to adapt in the face
of more severe or unexpected threats” (NRC, 2011, p.3). The design and implementation of robust
pre-disaster mitigation policies can break the cycle of disaster: reducing the loss of life, property
damage, local economic disruption, the cost and time of response and recovery, the risk of repeated
Section 4
77
disaster, and the financial burden on local communities and the nation as a whole (McDaniels et
al., 2008; Heinz, et al., 2009; FEMA, 2011). In short, “the economic viability of the state of Florida
and many other Atlantic and Gulf Coast states depends upon hurricane mitigation [it] “is a
necessary condition for available and affordable insurance, which is paramount to sustain the
economy of coastal states” (Chowdhury et al., 2009, p. 9).
Structural disaster risk reduction measures (‘hardening measures’) are generally
engineering and technology-based solutions such as the construction of new flood control
systems, restoration of wetlands and other natural infrastructure, relocations/land exchanges,
retrofitting existing buildings and systems (e.g., residential, commercial, industrial, and public
buildings including critical infrastructure), as well as closing pervasive exemptions and
loopholes.23 Nonstructural disaster risk reduction measures include policy-based measures, such
as enacting stricter risk-based land use policies (e.g., adopting zoning ordinances that steer
development away from areas subject to flooding, storm surge or coastal erosion, including nobuild and no-rebuild zones, setbacks and rolling easements), implementing more stringent
building codes for new construction, improving professional training and community education/
outreach programs, providing specialized interest free or long-term mitigation loans, initiating
23
For example, most homes are built to or slightly above the 100-year base flood elevation,
where there is a 1 % chance of being equaled or exceeded in a single year. Homes built to this
level have a 26 % chance of being flooded or demolished over the life of a 30-year mortgage and
a 40 % chance over a 50-year period. IBHS (2009) found that building homes to or slightly
above the 500-year base flood elevation reduces the chance of flooding to 10 % over a 50-year
period. As such, IBHS strongly recommends that the NFIP raise the minimum base flood
elevation requirements to reduce future property losses in all exposed areas.
Section 4
78
and enforcing mandatory insurance purchase requirements, offering multi-year and long-term
insurance contracts, setting risk-based (actuarially sound) premiums, offering comprehensive
insurance, raising participation and retention rates and reducing repetitive losses, and providing
financial assistance including matching grants and insurance discounts to facilitate mitigation
(see Figure 45) (Kunreuther, 2006; Higgins, 2008; Nichols and Bruch, 2008; Ruppert, 2008;
Jaffe et al., 2010; Michael-Kejan, 2010; Schrope, 2010; Cheong, 2011).24
Figure 45. Strategic Resilience Diagram
Systematic
Risk Assessment
& Evidence-Based
Decision Making
Strategic
Resilience
All-Hazards
Mitigation
Structural &
Nonstructural
Measures
Training &
Education/Outreach
Innovative
Technologies/Products
& Public Engagement
Strategies
24
Insurance Reform
Public & Private
Climate Change
Adaptation
Structural &
Nonstructural
Measures
Approximately 70,000 properties, less than 1 % of the NFIP portfolio, account for over 16 %
of claims payments since 1978 (Michel-Kerjan, 2010; U.S. GAO, 2011a).
Section 4
79
The 2004 and 2005 hurricane seasons were a wake-up call, not an aberration – a lesson in
resilience thinking (FEMA, 2005b; NSB, 2007; Liu, 2011). The goods news is that with “critical
need [comes] immediate opportunity…reducing the physical and economic risks associated with
coastal hazards is not only critical, but is also often cost-effective. We must now give high
priority to implementing adaptation strategies to protect the natural and built environments on
which society depends ” (Heinz et al., 2009, p.5). This requires the strategic investment of new
dollars, knowing that every $1 spent on mitigation saves society an average of $4. Investment is
essential to marshal resources and lay the foundation for bold pre-disaster mitigation initiatives
(Ganderton 2005; MMC 2005; Rose et al. 2007; U.S. GAO, 2007a; Godschalk et al., 2009).
6.0 Conclusions
Vulnerability to natural hazards is exacerbated by a complex array of interrelated chronic
and emergent challenges, which undermine the effective design and implementation of disaster
risk reduction policies and social-ecological resilience (Allenby and Fink, 2005; Walker et al., 2009;
Alesch et al., 2011; Leonard, 2012). “Transformational [physical, socioeconomic, and institutional]
adaptations will be required…given local vulnerabilities and in the face of such possible driving
forces as relatively severe climate change and other stresses. If serious disruptions are to be avoided,
vulnerable parties should consider anticipatory transformations” (Kates et al., 2012, p.7160).
As unsustainable development (i.e., underinvestment in risk reduction measures and
environmental degradation) continues virtually unabated in historically hazard-prone and
increasingly strained mega-urban landscapes, catastrophic losses from extreme hydrometeorological events are fueling an untenable rise in annual government response and recovery
costs and long-term financial risks for consumers and taxpayers (ASCE, 2009; Kunruether et al.,
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80
2009; Cummins et al., 2010; Barnosky et al., 2012). According to the GAO “a comprehensive
strategic framework would help define common national goals, establish joint strategies,
leverage resources, and assign responsibilities among stakeholders. No state in the country is
immune to the risk from a natural hazard, be it floods, hurricanes, earthquakes, tornadoes, or
wildland fires and large percentages of the U.S. population live in areas susceptible to more than
one of these hazards. In particular, the coastal areas of the country…Florida and California, the
need for a comprehensive strategic framework for natural hazard mitigation takes on new
significance because these areas are subject to multiple hazards” (U.S. GAO, 2007a, p.54).
Given these complex challenges, federal, state, and local decision makers have a
responsibility to act boldly. A national imperative exists to confront these challenges –
challenges that won’t solve themselves, only grow more expensive to solve over time (Holling, 1973;
Pielke, 2007; Wood, 2009a). Unfortunately, there is “a complete absence of decision-making
among leaders in the government” today, a pervasive, institutional paralysis. “Someone needs to
meld these ideas into a vision of how to move forward, sculpt them into policies that can make a
difference in peoples’ lives and then build a majority to deliver on them. Those are called leaders.
…And, today, across the globe and across all political systems, leaders are in dangerously
short supply” (Friedman, 2011). Moving beyond conventional decision-making strategies and
antiquated management regimes will require political, social, and economic capital. Decisionmakers and society as a whole must recognize the need for transformative action in response to
changing conditions on the ground (and with respect to the climate), and prioritize investment
accordingly.25
25
“Capacity to adapt includes not only the preconditions necessary to enable adaptation, including
social and physical elements, but also the ability to mobilize them…These may, in part, be facilitated
by enhancing the interaction between science, policy and practice” (Park et al., 2012, p.151)
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81
6.1 Pre-Disaster Mitigation: A Paradigm Shift
Transitioning our institutions and coastal communities to address these dynamic
challenges will require a combination of novel decision-making criteria, public-private
partnerships, regulatory and market-based mechanisms, and significant investments in physical
and social infrastructure. Expanded investment in meteorology and climatology, as well as risk
reducing measures, is essential to marshal resources and lay the foundation for institution building
and strategic risk reduction initiatives (Vogel et al., 2007; Agrawala and Fankhauser 2008;
McDaniels et al., 2008; Willliamson et al., 2010; Grossmann and Morgan, 2011; Aerts and
Botzen, 2011; Interagency Climate Change Adaptation Task Force 2011; USGCRP, 2012).
Absent the development and implementation of a holistic and well-coordinated
national strategic resilience initiative, setting priorities for state and local governments, including:
(1) a comprehensive all-hazards mitigation program; (2) public and private insurance reform; and
(3) a robust climate change adaptation action-plan – extreme hydro-meteorological events and
other natural hazards will continue to burden Floridians and beyond with billions of dollars in
avoidable losses and hardship (Walker et al., 2004; Kettl, 2006; Wood, 2009b; Pelling, 2011;
Solecki et al., 2011). “If this resilient cities initiative sounds too ambitious, think back to what is
at stake” (Godschalk 2003, p.141-142). Historic losses, contemporary vulnerability, and future
risk projections, dictate that our ability to design and implement effective risk reduction policies is
more important than ever. A transformational, all-hazards, disaster risk reduction initiative is
required to mitigate the rising tide of catastrophes, effectively insure against natural hazards, and
adapt to climate change – a paradigm shift in national, state, and local risk reduction policy
(Heinz et al., 2009; Folke et al., 2010; Mutter, 2010; Llyods, 2011; Smith et al., 2011; Park et al.,
2012). Now is the time to invest in the sustainability of our coastal communities, the benefits will
reverberate far beyond the shore.
Section 4
82
References
American Consumer Institute (ACI) (2011) ACI Surveys Finds Floridians Support Fixing the
State’s Insurance CAT Fund. The American Consumer Institute: Center for Citizen Research.
Adger, W.N., Barnett, J. (2009) Reasons for concern about adaptation to climate change.
Environment and Planning A 41 (12), 2800–2805.
Aerts, C.J.H. and Botzen, W.C.W. (2011) Flood-resilient waterfront development in New York
City: Bridging flood insurance, building codes, and flood zoning. Annals NY Academy Sciences
1227, 1-82.
Agrawala, S., Fankhauser, S. (2008) Economic aspects of adaptation to climate change: Costs,
benefits and policy instruments. OECD, Paris.
Allenby, B., Fink, J. (2005) Toward inherently secure and resilient societies. Science 309 (10),
1034–1036.
Alesch, D.J., Arendt, L.A., Petak, V.J. (2011) Natural Hazard Mitigation Policy:
Implementation, Organizational Choice, and Contextual Dynamics. Springer, New York.
American Society of Civil Engineers (ASCE) (2009) Report Card for America’s Infrastructure.
American Water Works Association (AWWA) (2012) Buried No Longer: Confronting
America’s Water Infrastructure Challenge.
Anthoff, D., Nicholls, R.J., Tol, R.S.J. (2010) The economic impact of substantial sea-level rise.
Mitig Adapt Strateg Glob Change 15 (4), 321–335.
Bagstad, K.J., Stapleton, K., D'Agostino, J.R. (2007) Taxes, subsidies and insurance as drivers of
U.S. coastal development. Ecological Economics 63 (2-3), 285–298.
Barbeau, D.N., Grimsley, L.F., White, L.E., El-Dahr, J.M., Lichtveld, M. (2010) Mold exposure
and health effects following Hurricanes Katrina and Rita. Annu Rev Public Health 31, 165–78
Barnosky, A.D., Hadly, E.A., Bascompte J., Berlow, E.L., Brown, J.H., Fortelius, M., Getz,
W.M., Harte, J., Hastings, A., Marquet, P.A., Martinez, N.D., Mooers, A., Roopnarine, P.,
Vermeij, G., Williams, J.W., Gillespie, R., Kites, J., Marshall, C., Matzke, N., Mindell, D.P.,
Revilla, E., Smith, A.B. (2012) Approaching a state shift in Earth’s biosphere. Nature 486
(7401), 52–58.
Bender MA, Knutson TR, Tuleya RE, Sirutis JJ, et al 2010. Modeled Impact of Anthropogenic
Warming on the Frequency of Intense Atlantic Hurricanes. Science 327, 5964, 454–458.
Berke, P., Smith, G., Lyles, W. (2012) Planning for Resiliency: Evaluation of State Hazard
Mitigation Plans under the Disaster Mitigation Act. Natural Hazards Review 13 (2), 139–149.
Section 4
83
Beven, J.L., Avila, L.A., Blake, E.S., Brown, D.P., Franklin, J.L., et al. (2008) Annual summary:
Atlantic hurricane season of 2005. Mon Wea Rev 136, 1109–1173.
Bitsuamlak, G.T., Chowdhury, A.G., Sambare, D. (2009) Application of a full-scale testing
facility for assessing wind-driven-rain intrusion. Building and Environment 44, 2430–2441.
Bjarnadottir, S., Li, Y., Stewart, M.G. (2011a) Social vulnerability index for coastal communities
at risk to hurricane hazard and a changing climate. Natural Hazards 59, 1055–1075.
Bjarnadottir, S., Li, Y., Stewart, M.G. (2011b) A probabilistic-based framework for impact and
adaptation assessment of climate change on hurricane damage risks and costs. Structural Safety
33 (3), 173–185.
Blake, E.S., Rappaport, E.N., Landsea, C.W. (2007) The deadliest, costliest, and most intense
U.S. tropical cyclones from 1851 to 2006. NOAA NWS TPC-5.
Brody, S.D., Kang, J.E., Bernhardt, S. (2010) Identifying factors influencing flood mitigation at the
local level in Texas and Florida: the role of organizational capacity. Natural Hazards 52 (1), 167–
184.
Bubeck, P., Botzen, W.J.W., Aerts, J. C. J. H. (2012) A Review of Risk Perceptions and Other
Factors that Influence Flood Mitigation Behavior. Risk Analysis in press.
Burby, R.J. (2005) Have state comprehensive planning mandates reduced insured losses from
natural disasters? Natural Hazards Review 6 (2), 67–81.
Burby, R.J. (2006) Hurricane Katrina and the paradoxes of government disaster policy: bringing
about wise governmental decisions for hazardous areas. Annals Amer Acad Pol Soc
Sci 604 (1), 171–191.
Canbek, C., Mirmiran, A., Chowdhury, A.G., Suksawang, N. (2011) Development of fiberreinforced polymer roof-to-wall connection. J Composites For Construction 15 (4), 644–652.
Canino, I., Chowdhury, A.G., Mirmiran, A. Suksawang, N. (2011) Triaxial load testing of metal
and FRP roof-to-wall connectors. J Architectural Engineering 17 (3), 112–120.
Carmin, J., Nadkarni, N., Rhie, C. (2012) Progress and Challenges in Urban Climate Adaptation
Planning: Results of a Global Survey. Cambridge, MA: MIT.
Chapin, T.S., Connerly, C.E., Higgins, H.T. (eds.), (2007). Growth Management in Florida:
Planning For Paradise. Ashgate Publishing Limited, England.
Cheong S.M. (2011) Policy solutions in the U.S. Climatic Change 106 (1), 57–70.
Chowdhury, A.G., Simiu, E., Leatherman, S.P. (2009) Destructive Testing under Simulated
Hurricane Effects to Promote Hazard Mitigation. Natural Hazards Review 10 (1), 1-10.
Section 4
84
Chowdhury, A.G., Bitsuamlak, G.T., Fu, T.C., Kawade, P. (2011) Study on roof vents subjected
to simulated hurricane effects. Natural Hazards Review 12 (4), 158–165.
Cole, C.R., Macpherson, D.A., Maroney, P.F., McCullough, K.A., Newman, Jr, J.W., Nyce, C.
(2011) The use of postloss financing of catastrophic risk. Risk Mgmt Insur Rev 14 (2), 265–298.
Comfort, L.K. (2006) Cities at risk: Hurricane Katrina and the drowning of New Orleans.
Urban Affairs Rev 41 (4), 501–516.
Comfort, L.K., Boin, A., Demchak, C.C., (2010). Designing Resilience: Preparing for Extreme
Events. Univ. Pittsburg Press, Pittsburg, PA.
CoreLogic (2011) 2011 CoreLogic Storm Surge Report: Residential Storm-Surge Exposure
Estimates for 10 U.S. Cities.
Costanza, R., Perez-Maqueo, O., Martinez, M., Sutton, P., Anderson S., Mulder, K. (2008) The
value of coastal wetlands for hurricane protection. Ambio 37(4), 241-8.
Cummins, J.D., Suher, M., Zanjani, G. (2010) Federal financial exposure to natural catastrophe risk.
In Measuring and managing federal financial risk (ed.) D. Lucas, National Bureau of Economic
Research (NBER), Univ Chicago Press, Chicago, 61–96.
Deyle, R.E., Chapin, T.S., Baker, E.J. (2007) Are we any safer? An evaluation of Florida’s
hurricane hazard mitigation planning mandate. Ch. 11 in Growth management in Florida:
planning for paradise. Chapin, T.S., Connerly, C.E., Higgins, H.T. (eds.), (2007). Ashgate
Publishing Limited, England.
Deyle, R.E., Chapin, T.S., Baker, E.J. (2008) The proof of the planning is in the platting: an
evaluation of Florida's hurricane exposure mitigation planning mandate. JAPA 74 (3), 349–370.
Emanuel, K., Anthes, R., Curry, J., Elsner, J., Holland, G., Klotzbach, P., Knutson, T., Landsea,
C., Mayfield, M., Webster, P. (2006) Statement on the U.S. hurricane problem. July 25, 2006.
Emmanuel, K. (2011) Global warming effects on U.S. hurricane damage. Weather Climate
Society 3, 261–268.
Federal Emergency Management Agency (FEMA) (2005a) Summary report on building
performance: 2004 hurricane season. U.S. Department of Homeland Security.
Federal Emergency Management Agency (FEMA) (2005b) Hurricanes’ Impact on Florida’s
Building Codes and Standards: We Have Been Given a Wake Up Call. FEMA, Mitigation
Section, Technical Services Branch DR-1539/1545/1551/1565 – Orlando – LTRO.
Federal Emergency Management Agency (FEMA) (2011) The FEMA Mitigation and Insurance
Strategic Plan for 2012-2014. U.S. Department of Homeland Security.
Section 4
85
Federal Emergency Management Agency (FEMA) (2012) National Preparedness Report.
U.S. Department of Homeland Security.
First American Spatial Solutions (FASS) (2010) First American Storm Surge Report: Residential
storm surge exposure estimates for 13 U.S. cities. First American Corp.
Florida Building Code (FBC) (2010) Chapter 44, High Velocity Hurricane Zones. Florida
Building Commission.
Folke, C., Carpenter, S.R., Walker, B., Scheffer, M., Chapin, T., Rockström, J. (2010) Resilience
thinking: integrating resilience, adaptability and transformability. Ecology and Society 15 (4), 20.
Florida Citizens Property Insurance Corporation (Citizens) (2012) Monthly Policy Counts:
Current Month Reports. Policies in Force Snapshot as of May 31, 2012.
Florida Department of Environmental Protection (FDEP) (2010) FACT 2010: Florida
Assessment of Coastal Trends. Florida Coastal Management Program.
Florida Department of Environmental Protection (FDEP) (2011) Critically eroded beaches in
Florida. Bureau of Beaches and Coastal Systems, Division of Water Resource Management.
Florida Department of Financial Services (FDFS) (2009) My Safe Florida Home Annual Report.
Florida Oceans and Coastal Council (FOCC) (2008) Phase II Florida’s Ocean and Coastal
Economy. Prepared by the National Ocean Economics Program (NOEP).
Florida Oceans and Coastal Council (FOCC) (2010) Climate Change and Sea-Level Rise in
Florida: An Update of “The Effects of Climate Change on Florida’s Ocean and Coastal
Resources.” [2009 Report] Tallahassee, FL.
Florida State Board of Administration (FSBA) (2012) Florida Hurricane Catastrophe Fund:
Fiscal Year 2010-2011 Annual Report.
Florida TaxWatch (2011) Risk & Reform: A Florida Taxwatch Anlaysis Of Florida’s Property
Insurance System. Florida TaxWatch Research Institute, Tallahassee, FL.
Flugman, E., Mozumder, P., Randhir, T. (2012) Facilitating adaptation to global climate change:
Perspectives from experts and decision makers serving the Florida Keys. Climatic Change 112
(3-4), 1015–1035.
Franklin, J.L., Pasch, R.J., Avila, L.A., Beven, J.L., Lawrence, M.B., et al. (2006) Annual
summary: Atlantic hurricane season of 2004. Mon Wea Rev 134, 981–1025.
Friedman, T.L. (2011). Who’s the Decider? The New York Times Opinion Pages, November 15,
2011.
Section 4
86
Gaddis, E.B., Miles, B., Morse, S., Lewis, D. (2007) Full-cost accounting of coastal disasters in
the U.S. Implications for planning and preparedness. Ecological Economics 63 (2-3), 307–318.
Ganderton, P. (2005) Benefit-cost analysis of hazard mitigation: the current state of what counts
and how to count it. Mitig Adapt Strateg Glob Change 10, 445-465.
Genovese, E., Hallegatte, S., Dumas, S. (2011) Damage Assessment from Storm Surge to Coastal
Cities: Lessons from the Miami Area. Lect Notes Geoinformation Cartography 1(1), 21-43.
Godschalk, D.R. (2003) Urban hazard mitigation: creating resilient cities. Natural Hazards
Review 4 (3), 136–143.
Godschalk, D.R., Rose, A., Mittler, E., Porter, K., West, C.T. (2009) Estimating the Value of
Foresight: Aggregate Analysis of Natural Hazard Mitigation Benefits and Costs. J Environ
Planning Manage 52 (6), 739-756.
Grace MF, Klein RW 2009. The Perfect Storm: Hurricanes, Insurance, and Regulation. Risk
Management and Insurance Review 12, 1, 81-124.
Grossmann, I. Morgan, M.G. (2011) Tropical cyclones, climate change, and scientific uncertainty:
what do we know, what does it mean, and what should be done? Climatic Change 108, 543–579.
Gurley, K. R., Masters, F. J. (2011) Post-2004 Hurricane Field Survey of Residential Building
Performance. Natural Hazards Review 12 (4), 177-183.
Hallegatte, S. (2009) Strategies to adapt to an uncertain climate change. Global Environmental
Change 19, 240–247.
Hallegatte, S. (2012) A framework to investigate the economic growth impact of sea level rise.
Environmental Research Letters 7, 015604.
Hanson, S., Nicholls, R., Hallegatte, R.S., Corfee-Morlot, J., Herweijer, C., Chateau, J. (2011)
A global ranking of port cities with high exposure to climate extremes. Climatic Change 104 (1),
89–111.
Hartwig, R.P. (2010) Elephants in the Room: Big Problems in Florida’s Insurance Markets that
Nobody Wants to Discuss. Insurance Information Institute (III).
Hartwig, R.P. (2012) The Legacy of Hurricane Andrew: What Has Been Learned Over the Past
20 Years? Insurance Information Institute (III).
Hartwig, R.P., Wilkinson, C. (2012) Residual Market Property Plans: From Markets Of Last
Resort To Markets Of First Choice. Insurance Information Institute (III).
Higgins, M. (2008) Legal and policy impacts of sea level rise to beaches and coastal property.
Sea Grant Law Policy J 1 (1), 43–64.
Section 4
87
H. John Heinz Center for Science, Economics and the Environment (Heinz), Ceres, et.al. (2009)
Resilient Coasts: A Blueprint For Action.
Hoffman, R.N., Dailey, P., Hopsch, S., Ponte, R.M., Quinn, K., Hill, E.M., Zachary, B. (2010)
An estimate of increases in storm surge risk to property from sea level rise in the first half of the
twenty-first century. Weather Climate Society 2 (4), 271–293
Holladay, J.S., Schwartz, J.A., (2010) Flooding the Market: The Distributional Consequences of
the NFIP. Policy Brief No. 7. Institute for Policy Integrity, New York University School of Law.
Holling, C. S. (1973) Resilience and stability of ecological systems. Annual Rev Ecology
Systematics 4, 1–23.
Insurance Institute for Business & Home Safety (IBHS) (2004) Hurricane Charley: the benefits
of modern wind resistant building codes on hurricane claim frequency and severity.
Insurance Institute for Business & Home Safety (IBHS) (2009) Hurricane Ike: nature’s force vs.
structural strength.
Interagency Climate Change Adaptation Task Force (2011) Federal Actions for a Climate
Resilient Nation. White House Council on Environmental Quality (CEQ), Office of Science and
Technology Policy (OSTP), National Oceanic and Atmospheric Administration (NOAA).
IPCC (2012) Managing the Risks of Extreme Events and Disasters to Advance Climate Change
Adaptation. A Special Report of Working Groups I and II of the Intergovernmental Panel on
Climate Change [Field, C. B., Barros, V., Stocker, T.F., Qin, D., Dokken, D., et al. (eds.)].
Cambridge University Press, Cambridge, UK and New York, NY, USA, 582 pp.
Jaffee, D., Kunreuther, H., Michel-Kerjan, E. (2010) Long-term property insurance. J Insur Reg
29 (7), 167–187.
Jevrejeva, S., Moore, J.C., Grinsted, A. (2012) Sea level projections to AD2500 with a new
generation of climate change scenarios. Global and Planetary Change 80-81, 14–20.
Kates, R.W., Travis, W.R., Wilbanks, T.J. (2012) Transformational adaptation when incremental
adaptations to climate change are insufficient. Proc Natl Acad Sci USA 109 (19), 7156–7161.
Kettl, D.F., (2006) Is the worst yet to come? ANNALS American Academy Political Social
Science 604 (1), 273-287.
Kimball D 2008. As quoted in The Preservation Predicament. C. Dean, The New York Times
Tuesday, January 29, 2008.
Knutson TR, McBride JL, Chan J, Emanuel K, Holland G, Landsea C., Held, I., Kossin, J.P.,
Srivastava, A.K., Sugi, M. (2010) Tropical cyclones and climate change. Nature Geoscience 3,
157-163.
Section 4
88
Kunreuther, H. (2006) Has the time come for comprehensive natural disaster insurance? In On
Risk and Disaster: Lessons Fom Hurricane Katrina (eds.) R.J. Daniels, D.F. Kettl, H.
Kunreuther. UPENN Press, Philadelphia PA.
Kunreuther, H., Michel-Kerjan, E. Ranger, N. (2011) Insuring climate catastrophes in Florida: an
analysis of insurance pricing and capacity under various scenarios of climate change and
adaptation measures. Working Paper # 2011-07.
Kunreuther, H., Meyer, R., Michel-Kerjan, E. (2012) Overcoming Decision Biases to Reduce
Losses from Natural Catastrophes. In Behavioral Foundations of Policy, E. Shafir (ed.),
Princeton Univ. Press.
Leatherman, S.P., White, G.F. (2005) Living on the edge: the coastal collision course. Natural
Hazards Observer 30 (2), 5–6.
Lehrer, E., Lehmann, R.J. (2012) Workable Solutions for Florida’s Challenging Insurance
Problems. The James Madison Institute Backgrounder 70, 1-28.
Leonard, A. (2012) Indices for resilience and global viability. J. Organizational Transformation
and Social Change 9 (1), 75–80.
Lin, N., Emanuel, K., Oppenheimer, M., Vanmarcke, E. (2012) Physically based assessment of
hurricane surge threat under climate change. Nature Climate Change 2, 462–467.
Lindell, M.K., Perry, R.W. (2011) The protective action decision model: theoretical
modifications and additional evidence. Risk Analysis 32 (40), 613-632.
Liu, A. (2011) Resilience and Opportunity: Lessons from the U.S. Gulf Coast after Katrina and
Rita. The Brookings Institution.
Llyod’s (2011) Managing the escalating risks of natural catastrophes in the United States.
Emerging Risk Reports.
Lopez, C., Masters, F.J., Bolton, S. (2010) Water penetration resistance of residential window
and wall systems subjected to steady and unsteady wind loading. Building and Environment 45,
1373–1388.
Macdonald, J.W., Dixon, L., Zakaras, L. (2010) Residential insurance on the U.S. Gulf Coast in
the aftermath of Hurricane Katrina: a framework for evaluating potential reforms. RAND
Institute for Civil Justice and Institute for Gulf States Policy.
Martinich, J., Neumann, J., Ludwig, L., Jantarasami, L. (2012) Risks of sea level rise to
disadvantaged communities in the United States. Mitig Adapt Strateg Glob Change in press.
Section 4
89
McDaniels, T., Changb, S., Colec, D., Mikawozc, J., Longstaff, H. (2008) Fostering resilience to
extreme events within infrastructure systems: Characterizing decision contexts for mitigation and
adaptation. Global Environmental Change 18 (2), 310–318.
Michel-Kerjan, E.O. (2010) Catastrophe economics: the National Flood Insurance Program.
J Economic Perspectives 24 (4), 165–186.
Michel-Kerjan, E.O., Kousky, C. (2010) Come rain or shine: evidence on flood insurance
purchases in Florida. J Risk Insur 77 (2), 369–397.
Michel-Kerjan, E.O., Kunreuther, H. (2011) Disaster management: redesigning flood insurance.
Science 333 (6041), 408–409.
Michel-Kerjan, E.O., Lemoyne de Forges, S., Kunreuther, H. (2012) Policy tenure under the U.S.
National Flood Insurance Program (NFIP). Risk Analysis 32 (4), 644–658.
Mileti, D.S., Gailus, J.L. (2005) Sustainable development and hazards mitigation in the United
States: disasters by design revisited. Mitig Adapt Strat Glob Change 10 (3), 491–504.
Milly, P.C.D., Betancourt, J., Falkenmark, M., Hirsch, R.M., Kundzewicz, K.W., Lettenmaier,
D.P., Stouffer, R.J., (2008) Stationarity is dead: whither water management? Science 319 (5863),
573–574.
Moser, S.C., Ekstrom, J.A. (2010) A framework to diagnose barriers to climate change
adaptation. Proc Natl Acad Sci USA 107 (51), 22026–22031.
Mozumder, P., Flugman, E., (2010) Facilitating Hurricane Mitigation And Enhancing
Coastal Socioeconomic Resilience. Prepared for the Division of Emergency Management,
Florida Department of Community Affairs.
Mozumder, P. Flugman, E., Randhir, T. (2011) Adaptation behavior in the face of global climate
change: survey responses from experts and decision makers serving the Florida Keys. Ocean &
Coastal Management 54, 37–44.
Multihazard Mitigation Council (MMC) (2005) Natural hazard mitigation saves: An independent
study to assess the future savings from mitigation activities. National Institute of Building Sciences.
Mutter, J. (2010) Disasters widen the rich–poor gap. Nature 466 (7310), 1042.
National Oceanic and Atmospheric Administration (NOAA) (2012). Billion dollar U.S.
weather/climate disasters. National Climatic Data Center (NCDC).
National Research Council (NRC) (2006) Facing Hazards and Disasters: Understanding Human
Dimensions. Committee on Disaster Research in the Social Sciences: Future Challenges and
Opportunities. National Academies Press, Washington DC.
Section 4
90
National Research Council (NRC) (2009) Informing Decisions in a Changing Climate. Panel on
Strategies and Methods for Climate-Related Decision Support. National Academies Press,
Washington DC.
National Research Council (NRC) (2011) Building Community Disaster Resilience Through
Private-Public Collaboration. Committee on Private-Public Sector Collaboration to Enhance
Community Disaster Resilience, Geographical Science Committee. National Academies Press,
Washington DC.
National Science Board (NSB) (2007) Hurricane warning: The critical need for a national
hurricane research initiative. Task Force on Hurricane Science and Engineering.
Nichols, S.S., Bruch, C. (2008) New frameworks for managing dynamic coasts: legal and policy
tools for adapting U.S. coastal zone management to climate change. Sea Grant Law Policy J 1
(1), 19–42.
Ntelekos, A.A., Oppenheimer, M., Smith, J.A., Miller, A.J. (2009) Urbanization, climate change
and flood policy in the United States. Climatic Change 103 (3-4), 597−616.
O’Brien, G., O’Keefe, P., Rose, J., Wisner, B. (2006) Climate change and disaster management.
Disasters 30 (1), 64−80.
Park S.E., Marshall, N.A., Jakku, E., Dowd, A.M., Howden, S.M., Mendham, E., Fleming, A.
(2012) Informing adaptation responses to climate change through theories of transformation.
Global Environmental Change 22 (1), 115–126.
Peacock, W.G. (2003) Hurricane Mitigation Status and Factors Influencing Mitigation Status
among Florida’s Single-Family Homeowners. Natural Hazards Review 4(3), 149−158.
Pelling, M. (2011) Adaptation to Climate Change: From Resilience to Transformation.
Routledge New York, NY.
Pielke, Jr., R.A. (2007) The Case For A Sustainable Climate Policy: Why Costs And Benefits
Must Be Temporally Balanced. Univ. Pennsylvania Law Review 155, 1843−1857.
Pielke, Jr., R., Gratz, J., Landsea, C., Collins, D., Saunders, M., Musulin, R. (2008) Normalized
Hurricane Damage in the United States: 1900–2005. Natural Hazards Review 9 (1), 29–42.
Pinelli, J.P., Pita, G., Gurley, K., Torkian, B., Hamid, S. Subramanian, C. (2011) Damage
characterization: application to Florida public hurricane loss model. Natural Hazards Review 12
(4), 90-95.
Platt, R.H. (1999) Disasters and Democracy: The Politics of Extreme Natural Events. Island
Press Washington DC.
Rabin, C., Morgan, C. (2012) Feds file complaint, demand Miami-Dade County fix faulty sewer
Section 4
91
lines. Miami Herald Wednesday, May 5, 2012.
Risk Management Solutions (RMS) (2009) Analyzing the effects of the My Safe Florida Home
Program on Florida insurance risk.
Rosenzweig, C., Solecki, W.D., Blake, R., Bowman, M., Faris, C., Gornitz, V., Horton, R.,
Jacob, K., LeBlanc, A., Leichenko, R., Linkin, M., Major, D.C., O’Grady, M., Patrick, L.,
Sussman, E., Yohe, G., Zimmerman, R. (2011) Developing coastal adaptation to climate change
in the New York City infrastructure-shed: Process, approach, tools, and strategies. Climatic
Change 106, 93–127.
Ruppert, T.K. (2008) Eroding long-term prospects for Florida’s beaches: Florida’s coastal
construction control line program. Sea Grant Law Policy J 1(1), 65–98.
Sadowski, N.C., Sutter, D. (2008) Mitigation motivated by past experience: prior hurricanes and
damages. Ocean & Coastal Management 51, 303–313.
Salvesen, D. (2005) The Coastal Barrier Resources Act: Has It Discouraged Coastal
Development? Coastal Management 33 (2), 181-195.
Salzano, C.T., Masters, F.J., Katsaros, J.D. (2010) Water penetration resistance of residential
window installation options for hurricane-prone areas. Building and Environment 45, 1373–1388.
Schaeffer, M., Hare, W., Rahmstorf, S., Vermeer, M., (2012) Long-term sea-level rise implied
by 1.5◦C and 2◦C warming levels. Nature Climate Change
Schrope, M. (2010) Unarrested development: despite the threat of rising sea levels, the drive to
develop Florida’s coastline continues. Nature Reports Climate Change 4, 36–38.
Simiu, E., Bitsuamlak, G., Chowdhury, A., Li, R., Tecle, A., Yeo, D. (2011) Testing of
residential homes under wind loads. Natural Hazards Review 12 (4), 166–170.
Smith, M.S., Horrocks, L., Harvey, A., Hamilton, C. (2011) Rethinking adaptation for a 4oC world.
Phil. Trans. Royal Society A 369, 196–216.
Solecki, W., Leichenko, R., O’Brien, K. (2011) Climate change adaptation strategies and disaster
risk reduction in cities: connections, contentions, and synergies. Current Opinion Environmental
Sustainability 3, 135–141.
Strauss, B.H., Ziemlinski, R., Weiss, J.L., Overpeck, J.T. (2012) Tidally adjusted estimates of
topographic vulnerability to sea level rise and flooding for the contiguous United States.
Environmental Research Letters 7 (1), 1–12.
Sun, L.G. (2011) Smart growth in dumb places: sustainability, disaster, and the future of the
American city. Brigham Young Univ. Law Review 2157–2202.
Section 4
92
Tebaldi, C., Strauss, B.H., Zervas, C.E. (2012) Modelling sea level rise impacts on storm surges
along US coasts. Environmental Research Letters 7(1), 1–11.
Titus, J.G., Hudgens, D.E., Trescott, D.L., Craghan, M., Nuckols, W.H., Hershner, C.H.,
Kassakian, J.M., Linn, C.J., Merritt, P.G., McCue, T.M., O’Connell, J.F., Tanski, J., Wang, J.
(2009) State and local governments plan for development of most land vulnerable to rising sea
level along the U.S. Atlantic coast. Environmental Research Letters 4, (4) 1–7.
Tryhorn, L. (2010) Improving policy for stormwater management: implications for climate
change adaptation. Weather Climate Society 2 (2), 113–126.
United Nations International Strategy for Disaster Reduction (UNISDR) (2011) Global
Assessment Report on Disaster Risk Reduction (GAR): Revealing Risk, Redefining
Development.
U.S. Census Bureau (2005) State Interim Population Projections by Age and Sex: 2004–2030.
U.S. Census Bureau, Population Division.
U.S. Census Bureau (2011). Florida People QuickFacts. State & County QuickFacts.
U.S. Global Change Research Program (USGCRP) (2012) National Global Change Research
Plan 2012-2021: A Strategic Plan for the U.S. Global Change Research Program.
U.S. Government Accountability Office (U.S. GAO) (2007a) Natural Hazard Mitigation: Various
Mitigation Efforts Exist, But Federal Efforts Do Not Provide a Comprehensive Strategic Framework.
GAO-07-403.
U.S. Government Accountability Office (U.S. GAO) (2007b) Climate Change: Financial Risks
To Federal and Private Insurers in Coming Decades Are Potentially Significant. GAO-07-285.
U.S. Government Accountability Office (U.S. GAO) (2008) Natural Catastrophe Insurance:
Analysis of a Proposed Combined Federal Flood and Wind Insurance Program. GAO-08-504
U.S. Government Accountability Office (U.S. GAO) (2011a) Action Needed to Improve
Administration of the National Flood Insurance Program. GAO-11-297.
U.S. Government Accountability Office (U.S. GAO) (2011b) Climate Change Adaptation:
Aligning funding With Strategic Priorities. GAO-11-876T.
Verchick, R.R.M. (2010) Facing Catastrophe: Environmental Action for a Post-Katrina World.
Harvard Univ Press, Cambridge.
Vogel, C., Moser, S.C., Kasperson, R.E., Dabelko, G.D. (2007) Linking vulnerability,
adaptation, and resilience science to practice. Global Environmental Change 17 (3-4), 349–364.
Walker, B., Holling, C.S., Carpenter, S.R., Kinzig, A. (2004) Resilience, adaptability and
Section 4
93
transformability in social–ecological systems. Ecology and Society 9 (2), 5.
Walker, B., Barrett, S., Polasky, S., Galaz, V., Folke, C., Engström, G., Ackerman, F., Arrow, K.,
Carpenter, S., Chopra, K., Daily, G., Ehrlich, P., Hughes, T., Kautsky, N., Levin, S., Mäler, K.G.,
Shogren, J., Vincent, J., Xepapadeas, T., de Zeeuw A. (2009) Looming global-scale failures and
missing institutions. Science 325, 1345–1346.
Wilbanks, T.J., Kates, R.W., (2010) Beyond adapting to climate change: embedding adaptation in
responses to multiple threats and stresses. Annals Assoc American Geographers 100 (4), 719–728.
Wood, M.C. (2009a) Advancing the sovereign trust of government to safeguard the environment
for present and future generations (Part I): ecological realism and the need for a paradigm shift.
Environmental Law 39 (1), 43-89.
Wood, M.C. (2009b) Advancing the sovereign trust of government to safeguard the environment
for present and future generations (Part II): instilling a fiduciary obligation in governance.
Environmental Law 39 (1), 91-139.
World Resources Institute (WRI), United Nations Development Programme, United Nations
Environment Programme, World Bank (2011) Decision Making in a Changing Climate:
Adaptation Challenges and Choices. World Resources Report 2010-211.
Yohe, G., Knee, K., Kirshen, K. (2011) On the economics of coastal adaptation solutions in an
uncertain world. Climatic Change 106 (1), 71–92.
Zahran, S., Weiler, S., Brody, S.D., Lindell, M.K., Highfield, W.E. (2009) Modeling national food
insurance policy holding at the county scale in Florida, 1999–2005. Ecological Economics 68 (10),
2627–2636.
Zhang, K. (2010) Analysis of non-linear inundation from sea-level rise using LIDAR data: a case
study for South Florida. Climatic Change 106 (4), 537−365.
Section 4
94
Appendix A. Mitigation Choice Experiment
Table 3. Mitigation Choice Experiment: Roof System
Baseline
Information
Treatment
Incentive
Treatment
Combined
Treatment
(Information and
Incentive)
Overall
Option A
(high cost)
Option B
(low cost)
None
Total
39
Option A
or
Option B
109
70
121
230
30.43%
55
16.96%
44
47.39%
99
52.61%
115
100.00%
214
25.70%
101
20.56%
42
46.26%
143
53.74%
90
100.00%
233
43.35%
78
18.03%
52
61.38%
130
38.63%
92
100.00%
222
35.14%
23.42%
58.56%
41.44%
100.00%
304
177
481
418
899
33.81%
19.69%
53.50%
46.50%
100.00%
Note: An additional 99 respondents in Baseline reported Roofing at 2001 Florida Building Code (or South Florida
Building Code), 139 in Information Treatment, 121 in Incentive, 105 in Combined Treatment, 464 in total.
Table 4. Mitigation Choice Experiment: Opening Protection
Baseline
Information
Treatment
Incentive
Treatment
Combined
Treatment
(Information and
Incentive)
Overall
Option A
(high cost)
Option B
(low cost)
None
Total
58
Option A
or
Option B
113
55
95
208
26.44%
43
27.89%
38
54.33%
81
45.67%
96
100.00%
177
24.29%
75
21.47%
70
44.76%
145
54.24%
85
100.00%
230
32.61%
67
30.43%
60
63.04%
127
36.96%
78
100.00%
205
32.68%
29.27%
61.95%
38.05%
100.00%
240
226
466
354
820
29.27%
27.56%
56.83%
43.17%
100.00%
Note: An additional 86 respondents in Baseline reported Opening Protection at 2001 Florida Building Code (or 1994
South Florida Building Code), 129 in Information Treatment, 93 in Incentive Treatment, and 82 in Combined
Treatment, 390 total.
Section 4
95
Appendix B: Definitions and Descriptive Statistics of Key Variables
Table 5. Concern, Accountability, and Effectiveness Variables: Definitions and Descriptive Statistics
Variable
Concern
Season
+10% Concern
Season
Concern
Ten-years
+10% Concern
Ten-Years
Federal Govt.
Accountability
State Govt.
Accountability
County Govt.
Accountability
City Govt.
Accountability
Insurance Comp.
Accountability
Federal Govt.
Effectiveness
State Govt.
Effectiveness
County Govt.
Effectiveness
City Govt.
Effectiveness
Insurance Comp.
Effectiveness
Section 4
Definition
Concern a major hurricane (Category 3, 4, and 5) will damage home this
hurricane season (0-10; 0. is not concerned at all, 10. is highly concerned).
Concern a major hurricane (Category 3, 4, and 5) will cause more damage to
home than 10% of its assessed value this hurricane season (0-10; 0. is not
concerned at all, 10. is highly concerned).
Concern a major hurricane (Category 3, 4, and 5) will damage home within
the next ten years (0-10; 0. is not concerned at all, 10. is highly concerned).
Concern a major hurricane (Category 3, 4, and 5) will cause more damage to
home than 10% of its assessed value within the next ten years (0-10; 0. is not
concerned at all, 10. is highly concerned).
How accountable overall is the federal government (0-10; 0. is not
accountable at all, 10. is highly accountable).
How accountable overall is the State of Florida government (0-10; 0. is not
How accountable overall is your county government (0-10; 0. is not
How accountable overall is your city government (0-10; 0. is not accountable
at all, 10. is highly accountable).
How accountable overall are private insurance companies (0-10; 0. is not
How effective is the federal government at managing vulnerability to natural
hazards (0-10; 0. is not effective at all, 10. is highly effective).
How effective is the State of Florida government at managing vulnerability to
natural hazards (0-10; 0. is not effective at all, 10. is highly effective).
How effective is your county government at managing vulnerability to
natural hazards (0-10; 0. is not effective at all, 10. is highly effective).
How effective is your city government at managing vulnerability to natural
How effective are insurance companies at managing vulnerability to natural
96
N
1689
Mean
5.37
SD
2.83
Min
0
Max
10
1689
5.10
3.00
0
10
1694
6.17
2.96
0
10
1692
5.94
3.08
0
10
1404
5.32
3.14
0
10
1406
5.49
2.30
0
10
1406
5.34
2.89
0
10
1406
5.20
2.96
0
10
1406
6.05
3.36
0
10
1400
4.84
2.74
0
10
1394
5.17
2.60
0
10
1396
5.21
2.61
0
10
1391
4.90
2.69
0
10
1392
4.51
2.90
0
10
Table 6. Mitigation and Insurance Variables: Definitions and Descriptive Statistics
Variable
Pre-Disaster
Mitigation
Fund
WTP Mitigation
Fund
Definition
Support creation of ‘Florida Pre-Disaster Mitigation Fund’ to finance programs
focused on strengthening built environment before hurricanes strike
(0-1; 0. if no, 1. if yes).
Household’s willingness-to-pay each year to contribute to the proposed
‘Florida Pre-Disaster Mitigation Fund’ given random number generator
amount between $5-$500 (0-1; 0. if no, 1. if yes).
Random WTP
Random number generator for willingness-to-pay each year to contribute to
Amount
the proposed ‘Florida Pre-Disaster Mitigation Fund’, between $5-$500.
Certainty WTP Certain household is willingness-to-pay each year (0-10; 0. is not certain at
all, 10. is highly certain).
MSFH Program Support continuation of the My Safe Florida Home (MSFH) Program in a
proposed referendum (0-1; 0. vote no (against), 1. vote yes (in support).
Mitigation
Support creation of publically accessible database of ‘Home Structure Ratings’,
Database
including records of mitigation improvements (0-10; 0. is not supportive at all,
10. is highly supportive).
Hurricane
Assess economic threat of more Category 4 and 5 hurricanes in Florida due
Threat
to climate change (0-10; 0. not credible at all and 10. highly credible).
Building
Support strengthening Florida building codes to enhance coastal resilience
Codes
(0-10; 0. not supportive at all and 10. highly supportive).
Shoreline
Support increasing Florida shoreline setbacks to increase coastal resilience
Setbacks
(0-10; 0. not supportive at all and 10. highly supportive).
Elevation
Support establishing new elevation standards for roads/buildings in Florida to
Standards
enhance coastal resilience (0-10; 0. not supportive at all and 10. highly supportive).
Density
Support creating stricter density restrictions in low-lying areas to enhance
Restrictions
coastal resistance (0-10; 0. not supportive at all and 10. highly supportive).
Surcharges/
Florida should continue to finance the insurance system to pay for hurricane
Assessments
damage after-the-fact with surcharges, regular assessments, and emergency
assessments (0-10; 0. not supportive at all and 10. highly supportive).
5-Year Flood
Support creation of multi-year (5-year) flood insurance contracts (0-10; 0. not
Contracts
supportive at all and 10. highly supportive).
30-Year Flood
Support creation of long-term (30-year) flood insurance contracts (0-10;
Contracts
0. not supportive at all and 10. highly supportive).
Comprehensive Combine wind and flood insurance into a comprehensive program for hurricane
Insurance
prone coastal areas (0-10; 0. not supportive at all and 10. highly supportive).
Catastrophe
Support creation of tax-free catastrophe savings accounts (0-10; 0. not
Savings Accounts supportive at all and 10. highly supportive).
Section 4
97
N
Mean
SD
Min
Max
1687
0.69
0.46
0
1
1400
0.26
0.44
0
1
1400
248.61
141.34
5
500
1396
8.24
2.28
0
10
1682
0.74
0.44
0
1
1647
6.06
3.28
0
10
1481
5.81
2.57
0
10
1461
7.36
2.72
0
10
1459
7.84
2.69
0
10
1458
7.54
2.70
0
10
1456
7.73
2.78
0
10
1437
5.49
3.28
0
10
1394
5.67
3.37
0
10
1376
4.40
3.52
0
10
1426
7.64
2.64
0
10
1428
6.87
3.08
0
10
Table 7. Mitigation Choice Experiment Variables: Definitions and Descriptive Statistics
Variable
Vulnerability
Information
Package
Roofing
Overall
Openings
Overall
Roofing
Baseline
Openings
Baseline
Roofing
Information
Openings
Information
Roofing
Incentive
Openings
Incentive
Roofing
Combined
Openings
Combined
Certainty
Overall
Certainty
Baseline
Certainty
Information
Certainty
Incentive
Certainty
Combined
Section 4
Definition
Received salient vulnerability information package treatment, Insurance Institute
for Business and Home Safety (IBHS) video demonstration of secondary
water barrier performance against rainwater intrusion (0-1; 0. if no, 1. if yes).
Roofing system selected in Mitigation Choice Experiment (0-2; 0. if none at
this time or at code, 1. if lower cost Option B, 2. if higher cost Option A).
Opening protection selected in Mitigation Choice Experiment (0-2; 0. if none
at this time or at code, 1. if lower cost Option B, 2. if higher cost Option A).
How certain household is of protection options selected in the Mitigation
Choice Experiment (0-10; 0. is not certain at all, 10. is highly certain).
Choice Experiment (0-10; 0. is not certain at all, 10. is highly certain.
How certain household is of protection options selected in the ‘Mitigation
98
N
1647
Mean
0.49
SD
0.50
Min
0
Max
1
899
0.73
0.77
0
2
820
0.84
0.83
0
2
230
0.64
0.76
0
2
208
0.82
0.84
0
2
214
0.69
0.80
0
2
177
0.67
0.80
0
2
233
0.79
0.73
0
2
230
0.93
0.82
0
2
222
0.82
0.79
0
2
205
0.91
0.82
0
2
1023
7.85
2.11
0
10
223
7.93
2.01
0
10
251
7.98
2.01
0
10
284
7.65
2.20
0
10
265
7.87
2.18
0
10
Table 8. Socio-demographic Variables: Definitions and Descriptive Statistics
Variable
Gender
Definition
Gender (0-1; 0. male, 1. female).
N
1417
Mean
0.43
SD
0.50
Min
0
Max
1
Age
Age.
1392
58.83
11.56
30
91
Education
1397
3.76
1.44
1
6
1390
3.06
0.68
1
5
1412
2.40
1.07
1
6
Household Size
Highest level of education completed (1-6; 1. if high school graduate or
equivalent, 2. if some college, no degree, 3. if associates degree, 4. if
bachelors degree, 5. if masters, 6. doctorate or professional degree).
Ethnicity (1-5; 1. if african-american, 2. if asian, 3. if caucasian, 4. if
hispanic/latino, 5. other).
Current marital status (1-5; 1. if single, 2. if married; 3. if living together; 4.
if divorced, 5. if widowed, 6. if other).
Household size.
1409
2.42
1.07
1
5
Children
Number of children (under 18) in household.
1402
0.37
0.78
0
5
Children
Under 5
Household
Income
Number of children under 5 in household.
1403
0.07
0.29
0
2
Total household income, before taxes, for the past year (2011) (1-19, 1. if
less than $20,000, 2 if $20,000-$29,999, 3 if $30,000-$39,999, 4. if $40,000$49,999, 5. if $50,000-$59,999, 6. if $60,000-$69,999, 7. if $70,000$79,999, 8. if $80,000-$89,999, 9. if $90,000-$99,999, 10. if $100,000$109,999, 11. if $110,000-$119,999, 12. if $120,000-$129,999, 13. if
$130,000-$139,999, 14. if $140,000-$149,999, 15. if $150,000-$159,999,
16. if $160,000-$169,999, 17. if $170,000-$179,000, 18. if $180,000$189,999, 19. if $190,000-$199,999, 20. $200,000-$209,999, 21. $210,000$219,999, 22. $220,000-$229,999, 23. $230,000-$239,999, 24. $240,000$249,999, and 25. $250,000 and above).
Political affiliation (1-7; 1. if democrat, 2. if independent, 3. if libertarian, 4.
if republican, 5. if no affiliation, 6. if other).
Number of years living in Florida.
1310
8.42
5.31
1
25
1317
3.04
1.59
1
6
1415
29.65
16.49
1
79
Ethnicity
Marital Status
Political
Affiliation
Years Florida
Section 4
99
Table 9. Respondents’ Home Characteristics and Insurance Variables: Definitions and Descriptive Statistics
Variable
Home Value
N
1388
Mean
3.60
SD
2.52
Min
1
Max
9
Mortgage
Definition
Home’s approximate value (1-9; 1. if less than $150,000, 2. if $150,000$200,000; 3. if $200,000-$250,000, 4. if $250,000-$300,000, 5. if $300,000$350,000, 6. if $350,00-$400,000, 7. if $400,000-$450,000, 8 .if $450,000$500,000, 9. if greater than $500,000).
Home mortgage (0-1; 0. if no, 1. if yes).
1405
0.67
0.47
0
1
Year Built
Year home built.
1588
1981.48
17.01
1900
2006
At Code
Openings
Opening protection was installed after August 1994 in Miami-Dade and
Broward Counties and meets 1994 South Florida Building Code or after March
2002 in all other Florida Counties and meets 2001 Florida Building Code.
Respondents roof system was installed after August 1994 in Miami-Dade and
Broward Counties and meets 1994 South Florida Building Code or after March
2002 in all other Florida Counties and meets 2001 Florida Building Code.
Home’s number of levels (stories) (1-3; 1. if one level, 2. if two levels; 3. if
three levels).
Home’s square footage.
1172
0.34
0.47
0
1
1391
0.35
0.48
0
1
1615
1.17
0.45
1
3
1595
2104.63
728.86
800
6500
Home’s roof cover type (1-4; 1. if tile, 2. if shingle, 3. if metal, 4. if
concrete).
Home’s roof shape (1-3; 1. if hip, 2. if gable; 3. if flat).
1564
1.92
0.62
1
4
1493
1.54
0.57
1
3
Household has property insurance (0-1; 0. if no, 1. if yes).
1424
1
0
0
1
Household has wind insurance (0-1; 0. if no, 1. if yes).
985
1
0
0
1
Household has flood insurance (0-1; 0. if no, 1. if yes).
1435
0.61
0.49
0
1
Household is required to purchase flood insurance (lives in flood zone and
has a mortgage) (0-1; 0. if no, 1. if yes).
865
0.29
0.45
0
1
At Code
Roofing
Levels/Stories
Square Footage
Roof Covering
Roof Shape
Property
Insurance
Wind
Insurance
Flood
Insurance
Mandatory
Flood
Section 4
100
Table 10. Hurricane Experience and Structural Damage History Variables: Definitions and Descriptive Statistics
Variable
Hurricane Hits
Definition
Total hurricane hits experienced in 2004 and 2005.
N
1710
Mean
0.70
SD
0.88
Min
0
Max
4
Wind/Bourne
Debris
Wind-Driven
Rainwater
Downed Trees
Home was damaged by wind/wind-borne debris from hurricane/tropical storm
(0-1; 0. not damaged, 1. damaged).
Home was damaged by wind-driven rainwater from hurricane/tropical storm
Home was damaged by downed trees/tree limbs from hurricane/tropical storm
Home was damaged by broken/dislodged exterior equipment (rooftop mounted
or ground level) from hurricane/tropical storm (0-1; 0. not damaged, 1. damaged).
Home was damaged by accessory structure failure (screen enclosure, porch,
carport) from hurricane/tropical storm (0-1; 0. not damaged, 1. damaged).
Home suffered roof cover failure from hurricane/tropical storm (0-1; 0. not
damaged, 1. damaged).
Home suffered roof deck failure from hurricane/tropical storm (0-1; 0. not
Home suffered roof-to-wall connection failure from hurricane/tropical storm
Home suffered wall covering/cladding failure from hurricane/tropical storm
Home suffered shutter failure from hurricane/tropical storm (0-1; 0. not
Home suffered broken windows, skylights, entry doors, sliding glass doors
from hurricane/tropical storm (0-1; 0. not damaged, 1. damaged).
Home suffered garage door failure from hurricane/tropical storm (0-1; 0. not
Home suffered soffit/vent failure (gable-end, ridge, roof, soffit, turbine vents)
from hurricane/tropical storm (0-1; 0. not damaged, 1. damaged).
Home suffered gutter/downspout failure from hurricane/tropical storm (0-1;
0. not damaged, 1. damaged).
281
1
0
0
1
205
1
0
0
1
318
1
0
0
1
77
1
0
0
1
22
1
0
0
1
124
1
0
0
1
38
1
0
0
1
111
1
0
0
1
92
1
0
0
1
126
1
0
0
1
124
1
0
0
1
38
1
0
0
1
111
1
0
0
1
92
1
0
0
1
Dislodged
Exterior Equip.
Accessory
Struct. Failure
Roof Cover
Failure
Roof Deck
Failure
Roof-to-Wall
Failure
Wall Covering
Failure
Shutter
Failure
Window/Door
Failure
Garage Door
Failure
Soffit/Vent
Failure
Gutter
Failure
Section 4
101
Table 11. Interior Damage and Water Intrusion Variables: Definitions and Descriptive Statistics
Variable
Contents/
Furnishings
Interior
Finishes
Ceiling
Collapse
Utilities
Damage
Mold
Growth
Water Roof
Water Walls
Water Windows
Water Doors
Water Soffits/
Vents
Water Damage
Covered
Section 4
Definition
Home suffered interior contents/furnishings damage from hurricane/tropical
storm (0-1; 0. not damaged, 1. damaged).
Home suffered interior finishes damage (ceilings, walls, flooring) from
hurricanes/tropical storm (0-1; 0. not damaged, 1. damaged).
Home suffered ceiling collapse from hurricane/tropical storm (0-1; 0. not
Home suffered utilities damage (electrical, mechanical, plumbing) from
hurricane/tropical storm (0-1; 0. not damaged, 1. damaged).
Home suffered mold growth from hurricane/tropical storm (0-1; 0. not
Damage to interior contents, finishes (ceilings, walls, flooring), and utilities,
caused directly by wind-driven rainwater intrusion through the roof (0-1; 0.
not damaged, 1. damaged).
caused directly by wind-driven rainwater intrusion through the walls (0-1;
caused directly by wind-driven rainwater intrusion through the windows (0-1;
caused directly by wind-driven rainwater intrusion through the doors (0-1; 0.
not damaged, 1. damaged).
caused directly by wind-driven rainwater intrusion through soffits/vents (0-1;
All interior damage covered by your insurance (0-1; 0. not all interior damage
covered, 1. all interior damage covered.
102
N
126
Mean
1
SD
0
Min
0
Max
1
210
1
0
0
1
73
1
0
0
1
99
1
0
0
1
91
1
0
0
1
344
1
0
0
1
148
1
0
0
1
134
1
0
0
1
80
1
0
0
1
67
1
0
0
1
442
0.62
0.48
0
1
Section 4
103
FINAL REPORT
SECTION 5
Estimation of Surface Roughness Using Airborne
LiDAR Data
Prepared by
Keqi Zhang, Kristofer Shretha, Jie Huang, Yuepeng Li, and Huiqing Lui
August 1, 2012
Table of Contents
Executive Summary
2
1 Introduction
3
2 Classification of Terrain, Vegetation, and Building LiDAR Points
4
2.1 Derivation of digital terrain model
5
2.2 Creation of Ground and non-ground masks
6
2.3 Building point segmentation
7
2.4 Tree points segmentation
10
3 Calculation of Roughness Length
11
4 Application of the Algorithms to Test Datasets
15
4.1 Single house residential area
16
4.2 Apartment residential area
21
5 Discussion and Summary
Section 5
25
1
Executive Summary
Surface roughness is an important parameter for determining impacts of hurricane wind on
buildings. The remote sensing technology provides an effective way to estimate the surface
roughness in a large area. However, traditional optical remote sensing imagery does not provide
heights data of terrains, buildings, and trees required for the calculation of surface roughness.
Airborne LiDAR remote sensing overcome the disadvantage of the optical remote sensing by
providing direct measurements horizontal coordinates and vertical elevations of the objects on
the Earth surface. The Florida Division of Emergency Management collected LiDAR data for
the coastal area in Florida in 2007. We have developed methods (1) to extract terrains,
buildings, trees from LiDAR measurements, (2) to compute the surface roughness using
extracted terrains, buildings, and trees based on five surface roughness models, and (3) compare
the surface roughness values from LiDAR with those from the national land cover datasets
created based on Landsat imagery. The application of the methods to two test sites in Miami
shows that the algorithms classified terrain, buildings, and trees successfully with minor errors.
The comparison of LiDAR derived roughness lengths with the NLCD based roughness length
indicates that two types of roughness values agree reasonably.
Section 5
2
1 Introduction
One of the major damages caused by hurricanes in the State of Florida is wind impacts on
buildings. The impacts of hurricane wind on buildings are not only determined by the magnitude
and duration of high wind, but also by the interaction between the features including terrain,
vegetation, and buildings above the Earth surface and the wind within the boundary layer.
Assessment of surface roughness is thus a critical component of modeling wind effects on
buildings. The surface roughness is characterized by the roughness length, denoted z0, which is a
function of the height and spacing of buildings, trees, and other obstructions. Methods for
calculating z0 can be generalized into two categories: those based on field wind observations and
those based on the morphology and spatial arrangement of surface elements. The calculation of
the surface roughness based on field wind measurements is accurate, but is limited by the
availability of wind observation stations. Often, the sparse wind observation stations cannot
capture the characteristics of the interaction between the wind and roughness elements within the
boundary layer during a hurricane. The method based on spatial arrangement of the elements on
the Earth surface is more practical for estimating surface roughness for a large area.
Remote sensing imagery provides an effective way to estimate the surface roughness for a large
area. For example, the national land cover dataset (NLCD) from Landsat images with a 30 m
spatial resolution [1] is commonly used to estimate z0. Roughness values are associated with
land cover classes based on the study of micrometeorological experiments measuring wind flows
over various types of surfaces. FEMA's HAZUS model (www.fema.gov/hazus/) uses the NLCD
to calculate the surface roughness for estimating hurricane wind impacts. The advantage of the
Section 5
3
NLCD is that it provides national coverage, the disadvantage is that the NLCD lacks important
information such as heights of trees and buildings for surface roughness calculation.
The light detection and ranging (LiDAR) technology overcome the disadvantage of the optical
remote sensing technology used by Landsat by providing direct measurements horizontal
coordinates and vertical elevations of the objects on the Earth surface. The Florida Division of
Emergency Management collected airborne LiDAR data for the Florida's coastal areas
vulnerable to hurricane surge inundation in 2007. A LiDAR dataset includes measurements for
all earth surface features scanned by a laser sensor. In order to compute z0, several feature
classes must be extracted from the LiDAR data—specifically, terrain, buildings and vegetation.
The objective of this research is to develop methods to extract surface elements such as buildings
and trees from LiDAR measurements and to estimate the surface roughness based on extracted
surface elements.
2 Classification of Terrain, Vegetation, and Building LiDAR Points
Many approaches have been developed to extract features such as buildings from a LiDAR
dataset. These methods include roof topology vertex minimization in a rectilinear approximation
of building outlines [2]; parametric roof composition based on roof-topology graph searching
[3]; region-growing based on plane-fitting [4]; rectilinear probabilistic representation of free,
occupied, and hidden space via occupancy grids [5]; and eigen analysis of Voronoi
neighborhoods with fuzzy k-means clustering [6]. In this study, we have improved the feature
extraction method developed by Zhang et al. [4, 7] for the usage in mixed urban environments
with vegetation cover.
Section 5
4
The process for classification of irregularly-spaced raw data points {x, y, z} into ground,
building, and tree / vegetation points is outlined in Fig. 1. A square mesh {xi , yi } is fitted to the
data extents with horizontal spacing slightly smaller than the expected average posting of
LIDAR data; we will subsequently refer to the indices of this grid as (m, n) for image processing
purposes. A value z i is created for each grid cell location in {xi , yi } , or each image index
(m, n) , by fitting Delaunay triangles to {x, y, z} and employing linear interpolation. The
resulting set of points {xi , yi , zi } (or pixels) is used to create a digital earth model, building
surfaces mask, and tree / vegetation mask.
Fig. 1. Algorithm flowchart for automated classification of LiDAR data.
2.1 Derivation of digital terrain model
The rationale for feature extraction is that topographic changes occur in large scales and are
usually gradual with a large degree of spatial autocorrelation in a neighborhood, while the
Section 5
5
elevation change between buildings or trees and the ground is drastic. Therefore, the difference
in elevation change in a neighborhood (window) can be used to separate ground and non-ground
measurements in a LiDAR dataset [8]. Morphological filters can derive ground points by
removing points for buildings and trees from a set of LiDAR measurements using the operations
based on the elevation surface from LiDAR measurements [9]. Ground points are selected from
the LiDAR points {xi , yi , zi } based on combinations of morphological opening and closing
operations. The opening operation is achieved by performing an erosion of the dataset followed
by dilation, and the closing operation is accomplished by carrying out dilation first and then
erosion. It is often difficult to detect all non-ground objects of various sizes using a fixed
window size of dilation and erosion operations. Zhang et al. [10] developed a progressive
morphological filter to separate ground and nonground LiDAR points using varying window
sizes. By gradually increasing the filtering window and using elevation difference thresholds,
the measurements of vehicles, vegetation, and buildings are removed while the ground data is
preserved. After the filtering of the LiDAR points, a digital terrain model (DTM) {xg , y g , z g } is
generated by interpolating ground points. A problem with this procedure is the removal of lowelevation objects less than the window size, but the terrain reduction is expected to be minor with
a small window. An accuracy analysis shows 3% errors committed by the terrain filter in a
random sample of 648 measurements [9].
2.2 Creation of ground and non-ground masks
The next step is to produce a mask for the set of points {xi , yi , zi } for separating ground and nonground points. To do this we employ the simplest form of image segmentation: thresholding.
The LiDAR elevations z i and z g are scaled to use as intensities in the creation of two grayscale
Section 5
6
images f1 (m, n) and f 2 (m, n) with common indices. A composite image f 3 (m, n) is created
based on the difference in heights z  zi  z g , or the intensity difference between f1 and f 2 .
The composite image is then segmented into “object” C1  and “background” C2  pixels by
utilizing a threshold value zT ; a constant selected based on the expected minimum building
height:
C1  f 3 (m, n) | f 3 (m, n)  zT
C2  f 3 (m, n) | f 3 (m, n)  zT
This helps to reduce the contribution of reflecting objects such as trimmed bushes [4],
automobiles, and humans. The ground mask is created by labeling object pixels in C1  1 and
background pixels in C2  0 . The non-ground mask is the inverse of this operation. Binary
images are created by coloring each pixel white or black depending on the labels.
2.3 Building point segmentation
The set of points {xi , yi , zi } can be expressed as the combination of three rectangular component
arrays (each of size m  n ). The kernel 1 0  1 is used to propagate a one-dimensional
digital filter in both directions across each component array. The individual components of the
normal vector at each vertex {xi , yi , zi } are generated by taking the cross-product of the filter
outputs. An image related to the normal of the fitted surface can be generated by using the XYZ
components as RGB channels; an example is shown in Fig. 2. Computing correlation between
surface normal vectors in local neighborhoods gives an estimate of co-planarity. A twodimensional digital filter with kernel
Section 5
7
1 1 1
1 0 1


1 1 1
is used to compute the local correlation in each component array. Employing this particular type
of 2-D kernel is often referred to as operating with “8-connectivity.” The dot product between
the filter output and each vertex normal (with component magnitudes inversely scaled by the
sum of the digital coefficients) is computed to yield the angles between vectors, resulting in the
set g1  {xi , yi ,  i } . Co-planar points should coincide within a certain angular tolerance related
to the height accuracy of the LIDAR system. For example, if it is expected that the maximum
vertical error of observations is ±10 cm and points are separated by 1 m in the horizontal, the
angle between co-planar points should be no more than 5.71°. The thresholding of the array by
T  5.71 yields co-planar points D1 and non-co-planar points D2 :
D1  g1 (m, n) | g1 (m, n)  T
D2  g1 (m, n) | g1 (m, n)  T
.
We can again create a mask by labeling D1  1 / D2  0 , and generate the requisite binary image.
Fig. 2. Left: Top view of a three-dimensional scene with elevations colored by height. Right: Example of
surface norm image when XYZ components are set to RGB channels. Note that the norms for the pixels
of flat roof facets have the same values (blue color).
Section 5
8
The employment of an angular tolerance inside / outside the expected vertical resolution of the
system in thresholding operations has advantages and disadvantages. A smaller or “stricter”
tolerance will classify only strongly co-planar points. A larger or “looser” tolerance will also
classify weakly co-planar points. The latter can potentially yield more true positives, but at the
expense of increased false positives. Thick tree canopies / high vegetation (no ground
penetration by the laser beam) that vary slowly in height may be falsely classified if the angular
tolerance value is set too large. However, the characteristic roughness of a typical building roof
necessitates a looser tolerance than the cutoff for tree / vegetation discrimination; setting the
tolerance to this stricter value often results in partial footprint reconstruction. We propose a
method of combining information derived from using a lower and upper tolerance. Two binary
images are thus generated related to thresholded points of g1  {xi , yi ,  i } : a strict mask and a
loose mask.
In order to remove contributions from planar ground surfaces, we simply evaluate the union of
the aforementioned non-ground mask and strict / loose masks. Further morphological operations
are performed on both resulting images. All 8-connected components that have fewer than n p
grid cells are eliminated. n p is set to a value corresponding to the 20% of the expected
minimum building size which varies based on the grid spacing of the dataset. This eliminates
undesirable data related to small isolated objects such as fences, light poles, and power lines.
The procedure may also eliminate useful data, however—data related to air conditioning units,
pipes, overhangs, etc. A flood-fill operation is thus performed for 4-connected background
neighbors; this fills in holes. A hole is a set of background cells that cannot be reached by filling
in the background from the edge of the image. Shapes in the image are then thickened by adding
Section 5
9
cells to object outlines, and unconnected cells with two-neighbor connectivity are bridged. All
8-connected components that have fewer than nb grid cells are eliminated. nb is set to a value
corresponding to the expected minimum building size.
The centroid of each isolated object in the strict mask is calculated by evaluating the center of
mass in the object region. For complex roof geometry or roofs with significant non-linear
elements (air conditioning units, industrial piping, ventilation structures), we expect to only
partially classify the overall building footprint using the strict angular tolerance. For each strict
object centroid, the closest loose object centroid is located. Information from the resulting object
in the loose mask is used to refine partial building footprints. The selective merging of loose
mask information into the strict classifier mask creates a final binary image, or building mask.
We use this image to segment building points from the LIDAR dataset {xi , yi , zi } .
2.4 Tree points segmentation
Leaf-on tree points are now identified via the union between non-ground and inverse building
images. We will call this the non-building mask. Morphological operations can help to isolate
small, sparsely populated, or low density objects. All 8-connected components that have fewer
than nt grid cells are eliminated. nt is set to a value corresponding to the expected minimum
tree size in leaf-on conditions that varies based on the grid spacing of the dataset. This
eliminates responses from tall grasses, small shrubs, and tree skeletons (i.e., full leaf-off
conditions in deciduous forest). Similar to the building classifier, the process may introduce
underestimation bias into eventual effective roughness calculations. The benefits related to more
Section 5
10
accurate estimation of individual tree centers in the next step outweighs the inclusion of objects
with small plan area, however. Objects whose total area is a small fraction of their overall
bounding box are removed from the mask; this helps to eliminate contributions from non-linear
building edges. If one side of an object’s bounding box is much larger than the other, the object
is also eliminated from the mask—this helps with tall shaped hedges that may have escaped
earlier thresholding.
Finally, individual tree centers are determined based on local maxima crown analysis. A dilation
operation is applied to non-building / non-ground points to indicate possible tree centers. The
type and size of the structuring element object can be specified to maximize performance; for
example, a disk shaped object with a radius of 10-15 grid cells is normally used.
3 Calculation of Roughness Length
The morphometric methods for computing surface roughness was first proposed by Kutzbach
[11] and Counihan [12]; the former based on natural wind flow over uniform arrangements of
bushel baskets, and the latter on wind tunnel simulations over regular arrays of cubic elements.
The results of these studies were simplified equations for calculating z 0 based on average
geometric characteristics:
z0,Kutzbach  P
1.13
zH ,
and z0,Counihan  1.08P  0.08zH
Section 5
11
The plan area aspect ratio ( P ) is calculated by dividing the lot area of obstacles ( AP ) by the
total lot area ( AT ). The average obstacle height is z H . Each of these early models was intended
to be used for certain values of P ; ≤ 0.29 and 0.1 - 0.25, respectively.
Lettau [13] proposed a formula based on Kutzbach’s observations of wind-profile modifications
over Lake Mendota:
2
z0,Lettau  0.5 z H Ly
n
.
AT
This equation includes the width of the average cross-sectional object surface intercepted by
wind flow ( L y ) and number of objects ( n ). The formula is valid for P up to 0.3 [14]. Lettau’s
relationship for surface roughness estimation had been used for almost three decades by
meteorologists and wind-tunnel engineers.
Rapauch [15] used dimensional analysis together with physical hypotheses about the scales
controlling roughness element wakes and their interaction to predict a bulk drag coefficient
 u* 2 
 2  of a rough surface at height z H . The roughness length is calculated by:
U 
 h 



z 
U
z0, Rapauch  zH 1  d  exp    h  h  ,
u*
zH 





 exp  2cd 1F 0.5  1 

with zd  z H 1 
0.5



2
c

d1 F


and

u  
u*
 min (cs  cr F ) 0.5 ,  *   .
Uh

 U h  max 
Section 5
12
This includes the drag coefficient of the substrate surface ( c s ) at height z H in the absence of
roughness elements, the drag coefficient of an isolated element mounted on the surface ( c r ), the
von Karman constant (  ), the roughness-sublayer influence function ( h ), the frontal area

n 
 , and a free parameter ( cd 1 ). Rapauch suggested to use the
aspect ratio   F  L y z H
AT 

u 
following values: cs  0.003 , cr  0.3 ,  *   0.3 ,   0.4 , h  0.193 , and cd 1 =7.5 to
 U h  max
calculate roughness length. Note that Rapauch’s equations have no plan area restrictions.
Macdonald et al. [16] provided an improvement on the Lettau’s formula so that it would not be
limited to low roughness element densities. They proposed an improved model that included an
explicit description of the object drag coefficient and modifications related to the peak in the z 0
vs. area density curve:
 

z 
C 
z  
z0,MacDonald  z H 1  d  exp  0.5 D2 1  d F 
  zH  
 zH 
 

0.5

,


with zd  z H 1  AP P  1 .
The drag coefficient here is C D , A controls curve convexity, and  is an extra multiplicative
factor to incorporate drag correction factors. Suggested values are CD  1.2 , A  4.43 , and
  1.
Section 5
13
Bottema [17] proposed an analytical model specifically intended for a regularly-spaced urban
environment based on in-plane sheltering displacement height:


k
,
z0,Bottema  zH  zdp1 exp  
0.5 



0
.
5
C
F dh




with zdp1  z H
z dp1
Lx  0.33Lea  Lbo 
for low densities Wx  Lea  Lbo ,
Dx



Wx 
Wx
Lx  0.33 2 

L

L
ea
bo 

 zH
for high densities Wx  Lea  Lbo ,
Dx

Lea  Lbo  4
Ly Z H
0.5Ly  Z H

,


Ly


L
and Cdh  1.2 max 1  0.15 x ,0.82  min  0.65  0.06
,1.00  .


ZH
ZH




The values Dx and D y refer to the characteristic mean dimensions of an urban array, with
Wx  Dx  Lx and Wy  Dy  Ly . Bottema [17] also suggested a second set of equations for a
“staggered” urban orientation where the wind flow is oblique to the roughness elements and the
 Wy

 1 :
along-track obstacle spacing is high 
L

 y

z dp1  z H


Lx  0.33( Lea  L bo )
for low densities Wx  Dx  Lea  Lbo ,
2 Dx

W  Dx 
 Wx  Dx
Lx  0.33 2  x
Lea  Lbo 

and zdp1  z H
for high densities Wx  Dx  Lea  Lbo .
2 Dx

Section 5



14

 Wy
When the along-track obstacle spacing is low 
 1 :
L

 y




Wx  Dx 
 Wx  Dx
 Lx  0.33 2 

 Wy  
Lea  Lbo 



zdp1 
zH  +
 L 

2
D
x
 y 








Wx 
Wx 
 Lx  0.33 2 
 Wy  
Lea  Lbo 

1 

zH  .




Ly 
Dx





We used identified building and vegetation objects from LiDAR datasets to calculate the surface
roughness using the five models including z0, Le (Lettau), z 0, Ra (Rapauch), z 0,Ma (Macdonald),
z0,Bo , N (Bottema-normal), and z0,Bo ,S (Bottema-staggered). In the next section, we provide
roughness estimates over the full range of wind directions for various categories of land cover,
and compare the surface roughness values based on LiDAR measurements with those based on
the NLCD.
4. Application of the Algorithms to Test Datasets
In July through September 2007, airborne LIDAR data was collected in the areas vulnerable to
storm surge flooding in Florida for the Division of Emergency Management. Flights were
conducted using a Leica ALS50 system, and parameters were set to provide a point density
sufficient to support a maximum final post spacing of 4 ft (1.24 m) for unobscured areas. The
average horizontal spacing for this data is 0.81 ft (26 cm). A vertical accuracy assessment was
Section 5
15
performed using a standard method to compute the root mean square error (RMSE) based on a
comparison of ground control points and filtered LIDAR points. Resulting analysis indicates
that we can expect 0.35 ft (11 cm) of RMSE error.
4.1 Single house residential area
A single house residential area in the northeast Miami with a number of buildings and directly
adjacent trees was selected for testing the algorithms. Satellite imagery (November 2007) of this
region is shown in Fig. 3. Fig. 4 shows a top view of gridded LiDAR data with features are
colored by the automatic classifier. Building points are represented by a blue mask, non-building
points by a green mask, and tree centers by red stars. A minor classification error occurs at (X,
Y) = (924500, 539550), where a tall shaped hedge is merged into a building outline. This was an
expected outcome, as the planar classification algorithm may struggle to differentiate highly
dense, slowly varying canopies from building surfaces. Three-dimensional shaded surfaces
created from gridded LIDAR data are shown in Fig. 5. Fig. 6 shows results of the planar
classifier plotted over the DTM. Fig. 7 shows results of the tree / vegetation classifier over the
DTM.
Section 5
16
Fig. 3. Satellite imagery (November 2007) of residential Miami area, courtesy of Google Earth. The
analysis region is indicated by a red box.
Fig. 4. Gridded single house residential LiDAR data with elevations colored by height (grayscale). Top
view of image with specific features colored by automatic classifier: building points (blue mask), nonbuilding points (green mask), and tree centers (red stars). Units are feet, State Plane, Florida East zone.
Section 5
17
Fig. 5. Three-dimensional shaded surfaces created by gridding all LiDAR points for a single house
residential area. Units are feet, State Plane, Florida East zone.
Fig. 6. Three-dimensional shaded surfaces created from results of planar classifier plotted over DTM in a
single house residential area. Units are feet, State Plane, Florida East zone.
Section 5
18
Fig. 7. Three-dimensional shaded surfaces created from results of tree/vegetation classifier over DTM in a
single house residential area. Units are feet, State Plane, Florida East zone.
To provide a comparison of our LiDAR-driven results to 2006 NLCD based values, effective
surface roughness was also calculated by averaging 30x30 m NLCD grid cells in the indicated
region. NLCD has four types of developed classifications (Open, Low, Medium, and High) in
this test dataset. We chose values for NLCD classes (Table 1) based on the descriptions of
homogenous surface types from Wieringa [18], and specific descriptions of urban surface form
Grimmond [14].
Table 1. NLCD classes and associated z 0 ranges in a residential area.
NLCD class
NLCD key #
Open water
Developed, open space
Developed, low intensity
Developed, medium intensity
Developed, high intensity
11
21
22
23
24
Section 5
Associated description
(Wieringa / Grimmond)
Sea
Concrete-short grass
Low height and density
Medium height and density
Tall and high density
z 0 range
0.0002
0.0002-0.02
0.3-0.8
0.7-1.5
0.8-1.5
19
An image of the roughness for the residential Miami area created from the 2006 NLCD is shown
in Fig. 8. Three land cover classes are present in this residential Miami image: Open Water (11,
blue); Developed, Low Intensity (22, light red); and Developed, Medium Intensity (23, dark red).
It is noteworthy that two isolated Medium Intensity pixels in the NLCD dataset were generated
by the coincidence of the locations of pixels and buildings. Taking the average over 40 cells in
Fig. 8, z 0 for the area based on NLCD LULC classes is 0.47 m. A plot of z 0 values estimated
using the automated LIDAR classifier is shown in Fig. 9, with the NLCD estimation represented
by a straight line. The roughness as a function of wind direction is shown for five models—
Lettau, Rapauch, MacDonald, and Bottema (normal and staggered).
Fig. 8. National Land Cover Database (2006) 30 meter resolution image of residential Miami area,
courtesy of Multi-Resolution Land Cover. Three classes are shown: open water (11, blue); developed,
low intensity (22, light red); and developed, medium intensity (23, medium red). The analysis region is
indicated by a black box.
Section 5
20
Fig. 9. z 0 calculations in residential Miami area using averaged NLCD and models by Lettau (Le),
Rapauch (Ra), Macdonald (Ma), and Bottema (Bo N: normal; Bo S: staggered) for changing wind
directions.
4.2 Apartment residential area
Satellite imagery (November 2007) of an urban area in west Miami, Florida is shown in Fig. 10.
Fig. 11 shows a top view of gridded LIDAR data with features colored by automatic classifier.
Returns from parked cars and several small trees do not meet the minimum area requirements for
classifiers. Three-dimensional shaded surfaces created from the gridded data, planar classifier,
and tree / vegetation classifier are shown in Figs. 12, 13, and 14. An NLCD image of the area
(Fig. 15) was used to calculate a z 0 value of 0.87 m. A plot of z 0 values estimated using the
automated LIDAR classifier is shown in Fig. 16.
Section 5
21
Fig. 10. Satellite imagery (November 2007) of urban Miami area, courtesy of Google Earth. The analysis
region is indicated by a red box.
Fig. 11. Gridded urban Miami LIDAR data with elevations colored by height (grayscale). Top view of
image with specific features colored by automatic classifier: building points (blue mask), non-building
points (green mask), and tree centers (red stars). Units are feet, State Plane, Florida East zone.
Section 5
22
Fig. 12. Three-dimensional shaded surfaces created from gridded urban Miami LIDAR data. Units are
feet, State Plane, Florida East zone.
Fig. 13. Three-dimensional shaded surfaces created from results of planar classifier plotted over DEM in
urban Miami. Units are feet, State Plane, Florida East zone.
Section 5
23
Fig. 14. Three-dimensional shaded surfaces created from results of tree / vegetation classifier over DEM
in urban Miami. Units are feet, State Plane, Florida East zone.
Fig. 15. National Land Cover Database (2006) 30 meter resolution image of urban Miami area, courtesy
of the Multi-Resolution Land Characteristics Consortium. Two classes are shown: developed, low
intensity (22, light red) and developed, medium intensity (23, medium red). The analysis region is
indicated by a black box.
Section 5
24
Fig. 16. z 0 calculations in urban Miami area using averaged NLCD and models by Lettau, Rapauch,
Macdonald, and Bottema for changing wind directions.
5 Discussion and Summary
We have shown that LiDAR data can be used as an independent, comprehensive source for
effective surface roughness calculation. A methodology for automatic classification of ground,
building, and tree / vegetation surfaces was described, and classifier performance related to
known features within Miami, Florida datasets was evaluated. Average obstacle characteristics
were calculated based on detected feature geometries. Derived parameters in two study sites are
given in Table 2. Between the five chosen methods of z 0 estimation (Lettau / Rapauch /
Macdonald / Bottema-normal / Bottema-staggered), there are fairly clear trends. Lettau’s basic
equation provided the largest values. Since the plan area (  P ) was larger than 0.3 in each of the
studies, which is beyond the effective range of the Lattau’s formula, it follows that these results
may be an overestimation. Of the other four equations, Rapauch’s method consistently
Section 5
25
produced the largest values, with Macdonald, Bottema-staggered, and Bottema-normal generally
following in that order.
Table 2. Obstacle parameters derived for a single house (MiaSin) and apartment (MiaApart) residential area in

Miami: number of obstacles ( n ), average height ( z H ), plan surface area ratio  P 





surface area ratio  F 
Ly Z H 
 , and average aspect surface area ratio
Dx D y 
AP
AT

 , average frontal


z 
 S  H  . Minimum, mean, and

Wx 

maximum surface roughness values are given for the following models: Lettau ( z 0, Le ), Macdonald ( z 0,Ma ),
Rapauch ( z 0, Ra ), Bottema-normal ( z0, Bo , N ), Bottema-staggered ( z 0, Bo ,S ), and NLCD ( z0, NLCD ).
zH
P
F
S
204
6.81
0.45
0.38
1.48
181
6.65
0.33
0.31
0.92
Study
Site
n
MiaSin
MiaApart
(m)
z0,Le
z0,Ma
z0,Ra
z0,Bo , N
z 0, Bo ,S
z0, NLCD
(m)
1.00
1.29
1.43
0.76
1.02
1.14
(m)
0.32
0.39
0.43
0.52
0.65
0.70
(m)
0.80
0.83
0.91
0.82
0.86
0.89
(m)
0.02
0.10
0.27
0.19
0.25
0.39
(m)
0.03
0.19
0.51
0.37
0.51
0.79
(m)
0.47
0.87
In comparing single house parameters to apartment parameters, it may seem counterintuitive to
note that effective roughness decreased as values of  P and  F increased. However, upon
z
consultation of curves for normalized length  0
 zH

 as a function of  F [14], it is clear that the

Rapauch, Macdonald, and Bottema models display downward trends for  F  0.3 .
In comparison of the NLCD based z 0 value with its LiDAR counterparts, it can be concluded
that there is reasonable overall agreement (Figs. 9 and 16). The z 0 value estimated by the
NLCD in each case is bounded by plotted curves for Rapauch and Macdonald. The Lettau
model generated z 0 value above the NLCD z 0 due to the straightforward linear dependence in
the formula. Bottema’s equations produce fairly significant underestimation in both single
Section 5
26
house and apartment areas; results plotted using the staggered version of the equation begin to
move in between the envelope bounded by Rapauch / Macdonald curves. For future work, a
larger number of studies should be conducted to evaluate whether these trends persist over a
wide range of residential / city area. The current classification algorithm may need to be
improved for use in heavily industrialized cities; roofs with significant non-linear structures near
building edges (industrial piping and air conditioning units) still tend to cause issues.
Section 5
27
References
[1]
C. Homer, C. Huang, L. Yang, B. Wylle and M. Coan, "Development of a 2001 national
land-cover database for the United States," Photogrammetric Engineering and Remote
Sensing, vol. 70, pp. 829-840, 2004.
[2]
B. C. Matei, H. S. Sawhney, S. Samarasekera, J. Kim and R. Kumar, "Building
segmentation for densely built urban regions using aerial LIDAR data," in IEEE
Conference on Computer Vision and Pattern Recognition, Anchorage, AK 2008, p. 8.
[3]
V. Verma, R. Kumar and S. Hsu, "3D building detection and modeling from aerial
LIDAR data," in IEEE Computer Society Conference on Computer Vision and Pattern
Recognition, 2006, pp. 2213 - 2220.
[4]
K. Zhang, J. Yan and S. C. Chen, "Automatic construction of building footprints from
airborne LIDAR data," IEEE Transactions on Geoscience and Remote Sensing, vol. 44,
pp. 2523-2533, 2006.
[5]
T. C. Yapo, C. V. Stewart and R. J. Radke, "A probabilistic representation of LiDAR
range data for efficient 3D object detection," in IEEE Computer Society Conference,
Computer Vision and Pattern Recognition Workshops, Anchorage, AK, 2008, p. 8.
[6]
A. Sampath and J. Shan, "Segmentation and reconstruction of polyhedral building roofs
from aerial lidar point clouds," IEEE Transactions on Geoscience and Remote Sensing,
vol. 48, 2010.
[7]
K. Zhang, J. Yan and S. C. Chen, "A Framework for automated construction of building
models from airborne LIDAR measurements," in Topographic Laser Ranging and
Scanning: Principles and Processing, J. Shan and C. K. Toth, Eds., ed: CRC Press, 2008,
pp. 511-534.
[8]
K. Zhang and D. Whitman, "Comparison of three algorithms for filtering airborne
LIDAR data," Photogrammetric Engineering and Remote Sensing vol. 71, pp. 313-324,
2005.
[9]
K. Zhang, S. C. Chen, D. Whitman, M. L. Shyu, J. Yan and C. Zhang, "A progressive
morphological filter for removing non-ground measurements from airborne LIDAR
data," IEEE Transactions on Geoscience and Remote Sensing, vol. 41, pp. 872-882,
2003.
Section 5
28
[10]
K. Zhang, S. C. Chen, D. Whitman, M. L. Shyu, J. Yan and C. Zhang, "A progressive
morphological filter for removing nonground measurements from airborne LIDAR data,"
Geoscience and Remote Sensing, IEEE Transactions on, vol. 41, pp. 872-882, 2003.
[11]
K. Kutzbach, "Investigations of the modifications of wind profiles by artificially
controlled surface roughness," Department of Meteorology, University of Wisconsin,
1961.
[12]
J. Counihan, "Wind tunnel determination of the roughness length as a function of fetch
and density of three-dimensional roughness elements," Atmospheric Environment, vol. 5,
pp. 637-647, 1971.
[13]
H. Lettau, "Note on aerodynamic roughness-parameter estimation on the basis of
roughness-element description," Journal of Applied Meteorology, vol. 8, pp. 828-832,
1969.
[14]
C. Grimmond, "Aerodynamic roughness of urban areas derived from wind observations,"
Boundary-Layer Meteorology, vol. 89, pp. 1-24, 1998.
[15]
M. R. Raupach, "Simplified expressions for vegetation roughness length and zero-plane
displacement as functions of canopy height and aera index," Boundary-Layer
Meteorology, vol. 71, pp. 211-216, 1994.
[16]
R. W. MacDonald, R. F. Griffiths and D. J. Hall, "An improved method for the
estimation of surface roughness of obstacle arrays: A comparative study of the land use
and built form of 110 schemes," Atmospheric Environment, vol. 32, pp. 1857-1864, 1998.
[17]
M. Bottema, "Urban roughness modeling in relation to pollutant dispersion,"
Atmospheric Environment, vol. 31, pp. 3059-3075, 1997.
[18]
J. Wieringa, "Roughness‐dependent geographical interpolation of surface wind speed
averages," Quarterly Journal of the Royal Meteorological Society, vol. 112, pp. 867-889,
1986.
Section 5
29
FINAL REPORT
SECTION 6
Education and Outreach Programs to Convey the
Benefits of Various Hurricane Loss Mitigation Devises
and Techniques
Prepared by
Erik Salna
August 1, 2012
Mitigation Devices and Techniques
Executive Summary:
Erik Salna, IHRC Associate Director, developed and coordinated educational partnerships, community events,
and outreach programs. This work promoted hurricane-loss mitigation and the objectives of the RCMP.
Educational Outreach:
Hurricane Mitigation & Hurricane Andrew 20th Anniversary Museum Exhibition:
The Miami Science Museum assisted IHRC in developing and coordinating a new gallery of hands-on,
interactive exhibits and displays. This new and unique exhibition at the Museum provides an opportunity for
the IHRC to showcase to the community its hurricane mitigation research including the new 12-Fan Wall of
Wind. The exhibits and displays focus on the science and benefits of hurricane mitigation, preparedness,
hurricane forecasting and tracking and promoting a "culture of preparedness" for all natural hazards. In
addition, special attention is given to the 20th Anniversary of Hurricane Andrew, recounting the tremendous
impact it had on the community and the resultant changes in hurricane mitigation and preparedness programs.
This collaborative community education outreach project partners the International Hurricane Research Center
with the Florida Division of Emergency Management, Miami-Dade County Emergency Management, the
Miami Science Museum, including collaboration with the National Hurricane Center, NOAA’s Atlantic
Oceanographic and Meteorological Laboratory (AOML) and the Miami Office of the National Weather Service.
Community Partnerships:
Hurricane Science, Mitigation & Preparedness Day (Feel the Force):
Erik Salna, IHRC Associate Director, and the Miami Science Museum partnered with Miami-Dade County
Emergency Management to develop, plan, coordinate and facilitate Hurricane Science, Mitigation &
Preparedness Day (Feel the Force) at the Museum. Close to 2,000 people attended this public education event
that showcased hurricane science, mitigation, preparedness and safety and IHRC Wall of Wind research and
demonstrations. Families were educated and entertained throughout the day with tremendous support from
IHRC partners and South Florida agencies. This event received great attendance and coverage by the South
Florida media. In fact, the media coverage resulted in a Total Publicity Value amounting to $11,898.82. This
resulted in great positive visibility in the community for IHRC, FIU and FLDEM’s message of mitigation.
Targeted Outreach Events:
National Hurricane Survival Initiative:
Erik Salna, IHRC Associate Director, collaborated with the National Hurricane Survival Initiative
(http://hurricanesafety.org/) and their annual hurricane preparedness program, “Get Ready, America! The
National Hurricane Survival Test.” The 2012 version of the program looked back on the devastation wrought
by Hurricane Andrew in 1992, the lessons learned since then, and what you need to know and do to stay safe
before, during and after hurricane season. This year’s broadcast participation was the largest one ever, with 60
television network affiliate stations from Texas to Maine. The National Hurricane Survival Initiative poll is one
of the leading elements in a public education campaign, with partnerships with the National Emergency
Management Association, The Salvation Army, International Hurricane Research Center, National Hurricane
Center and corporate partners Plylox™ and Nestlé Waters North America.
Hurricane Andrew 20th Anniversary Event at the Miami Science Museum: The Miami Science Museum
assisted IHRC in planning this special community event to commemorate the 20-year anniversary of Hurricane
Andrew’s landfall. It is an opportunity to remember those who lost their lives, look back at the devastating
Section 6
2
effects it had on South Florida and look ahead to continued efforts and progress in hurricane mitigation,
preparedness, response and recovery. Panel discussions will occur throughout the day with high profile Andrew
experts and the new Hurricane Andrew exhibit will be highlighted.
Hurricane Andrew 20th Anniversary Event and Grand Opening of the 12-Fan Wall of Wind: IHRC has
partnered with Miami-Dade County Emergency Management, the National Hurricane Center, NOAA’s Atlantic
Oceanographic and Meteorological Laboratory (AOML), the Miami Office of the National Weather Service and
the City of Homestead in planning this official South Florida community event to commemorate the 20-year
anniversary of Hurricane Andrew’s landfall. After remembering the past, the event will look ahead with the
Grand Opening of the new 12-Fan Wall of Wind. Many high profile VIPs will participate and attend.
Wall of Wind Neighborhood Open House:
IHRC reached out to the local FIU South Florida Community and invited local residents and families to come
and learn about the importance of hurricane mitigation and wind engineering research through presentations,
activities and tours and demonstrations of the new 12-Fan Wall of Wind.
Wall of Wind Media Day:
IHRC is planning a special media event for local, state and national media to come and learn about the
importance of hurricane mitigation and wind engineering research through presentations, tours and
Educational Outreach:
Hurricane Mitigation & Hurricane Andrew 20th Anniversary Museum Exhibition:
The Miami Science Museum assisted IHRC in developing and coordinating a new gallery of hands-on,
interactive exhibits and displays at the Miami Science Museum. This new and unique exhibition provides an
opportunity for the IHRC to showcase to the community its hurricane mitigation research, including the new
12-Fan Wall of Wind. The exhibits and displays focus on the science and benefits of hurricane mitigation,
preparedness, hurricane forecasting and tracking and promoting a "culture of preparedness" for all natural
hazards. In addition, special attention is given to the 20th Anniversary of Hurricane Andrew, recounting the
tremendous impact it had on the community and the resultant changes in hurricane mitigation and preparedness
programs. South Florida media, public and private school children, hurricane experts, various agencies,
community groups and organizations and the general public will visit and participate in the exhibition
throughout the hurricane season. This collaborative community education outreach project partners the
International Hurricane Research Center with the Florida Division of Emergency Management, Miami-Dade
County Emergency Management, the Miami Science Museum, including collaboration with the National
Hurricane Center, NOAA’s Atlantic Oceanographic and Meteorological Laboratory (AOML) and the Miami
Office of the National Weather Service.
Section 6
3
Exhibits Include:
12-Fan Wall of Wind
The 12-Fan Wall of Wind Exhibit provides an interactive, hands-on experience and looks very similar to the
real IHRC research facility at Florida International University. Museum visitors will assemble model home
structures accompanying the exhibit and then test them against wind produced by the Wall of Wind. In
addition, a video screen will show examples of wind testing by the real Wall of Wind. Display panels will tell
the IHRC story and focus on the hurricane mitigation research being done by the four IHRC labs.
P-3 Cockpit
The P-3 Cockpit Exhibit also provides an interactive, hands-on experience and tells the story of NOAA’s
Hurricane Hunters. Museum visitors will learn about hurricane reconnaissance missions and how data is
gathered while flying into the eye of the storm.
Section 6
4
Hurricane Andrew 20th Anniversary
The Hurricane Andrew Exhibit will coincide with the 20th Anniversary of its South Florida landfall. Museum
visitors will see a collection of real historical artifacts related to Andrew, including physical items, pictures and
survivor stories. In addition, a video screen will show archived TV media coverage before, during, and after the
storm.
Community Partnerships:
Hurricane Science, Mitigation & Preparedness Day (Feel the Force) – June 11th, 2011
Erik Salna, IHRC Associate Director, and the Miami Science Museum partnered with Miami-Dade County
Emergency Management to develop, plan, coordinate and facilitate Hurricane Science, Mitigation &
Preparedness Day (Feel the Force) at the Museum. Close to 2,000 people attended this public education event
that showcased hurricane science, mitigation, preparedness and safety and IHRC Wall of Wind research and
demonstrations. Families were educated and entertained throughout the day with tremendous support from
IHRC partners and South Florida agencies.
IHRC Wall of Wind researchers provided demonstrations with the Small Scale 6-Fan Wall of Wind. As a
special mitigation learning activity, parents and children constructed small model homes throughout the day and
actually had them tested by the Small Scale 6-Fan Wall of Wind.
Special interactive exhibits and demonstrations included:
 Small Scale 6-Fan Wall of Wind
 Hurricane Simulator
 TV Hurricane Broadcast Center
Various distinguished hurricane experts participated as guest speakers:
 Jack Beven, Senior Hurricane Specialist, National Hurricane Center
 Robert Molleda, Warning Coordination Meteorologist, National Weather Service-Miami
 Dr. Robert Rogers, Meteorologist, NOAA/AOML Hurricane Research Division
 Curt Sommerhof, Director, Miami-Dade Emergency Management
Section 6
5
Special guests and presentations:
 Tsunami Tim
 Roary The Panther, Florida International University Mascot
This event received great attendance and coverage by the South Florida media. In fact, the media coverage
resulted in a Total Publicity Value amounting to $11,898.82. This resulted in great positive visibility in the
community for IHRC, FIU and FLDEM’s message of mitigation. The following media representatives
participated:
 Bonnie Schneider, CNN News
 Max Mayfield, WPLG Ch. 10 ABC-TV
 Trent Aric, WPLG Ch. 10 ABC-TV
 Luis Carrera, Telemundo 51, WSCV-TV
Miami-Dade County EM at Museum.
Section 6
NHC & NWS-Miami at Museum.
6
Hurricane Simulator at Museum.
Interactive TV weather studio at Museum.
Small Scale 6-Fan Wall of Wind (WOW) testing area.
Section 6
Learning about dropsondes & the Hurricane Hunters.
Presentation by former NHC Director, Max Mayfield.
Future hurricane wind engineer building model home.
7
Parents & children building model homes for WOW.
FIU’s Roary the Panther & Wall of Wind.
Creating lightning effects with a plasma ball.
Section 6
Parents & children building model homes for WOW.
Another future hurricane wind engineer.
Electrifying experience learning about lightning.
8
Targeted Outreach Events:
National Hurricane Survival Initiative:
Erik Salna, IHRC Associate Director, collaborated with the National Hurricane Survival Initiative
(http://hurricanesafety.org/) and their annual hurricane preparedness program. The 2012 version of, “Get
Ready, America! The National Hurricane Survival Test”, looked back on the devastation wrought by
Hurricane Andrew in 1992, the lessons learned since then, and what you need to know and do to stay safe
before, during and after hurricane season.
The program offers a retrospective of record-breaking storm seasons; takes the viewer inside some of the most
exciting hurricane research currently underway, and offers reality-based advice concerning insurance and how
best to handle a claim when necessary. “Get Ready, America!”, produced by Ron Sachs Communications,
emphasizes the critical need for personal responsibility before, during and after each storm season. IHRC
contributed information regarding hurricane mitigation for protecting your home and business and the changes
in the twenty years since Andrew.
This year’s broadcast participation was the largest one ever, with 60 television network affiliate stations from
Texas to Maine. Plus, there were numerous secondary broadcasts on government cable channels and school
systems throughout Florida and two other states!
The National Hurricane Survival Initiative poll is one of the leading elements in a public education campaign,
with partnerships with the National Emergency Management Association, The Salvation Army, International
Hurricane Research Center, National Hurricane Center and corporate partners Plylox™ and Nestlé Waters
North America.
Hurricane Andrew 20th Anniversary Event at the Miami Science Museum:
The Miami Science Museum assisted IHRC in planning this special community event to commemorate the 20year anniversary of Hurricane Andrew’s landfall. It is an opportunity to remember those who lost their lives,
look back at the devastating effects it had on South Florida and look ahead to continued efforts and progress in
hurricane mitigation, preparedness, response and recovery. Panel discussions will occur throughout the day
with high profile Andrew experts and the new Hurricane Andrew exhibit will be highlighted.
Section 6
9
Hurricane Andrew 20th Anniversary Event and Grand Opening of the 12-Fan Wall of Wind:
IHRC has partnered with Miami-Dade County Emergency Management, the National Hurricane Center,
NOAA’s Atlantic Oceanographic and Meteorological Laboratory (AOML), the Miami Office of the National
Weather Service and the City of Homestead in planning this official South Florida community event to
commemorate the 20-year anniversary of Hurricane Andrew’s landfall. After remembering the past, the event
will look ahead with the Grand Opening of the new 12-Fan Wall of Wind. Many high profile VIPs will
participate and attend.
Wall of Wind Neighborhood Open House:
IHRC reached out to the local FIU South Florida Community and invited local residents and families to come
and learn about the importance of hurricane mitigation and wind engineering research through presentations,
activities and tours and demonstrations of the new 12-Fan Wall of Wind.
Wall of Wind Media Day:
IHRC is planning a special media event for local, state and national media to come and learn about the
importance of hurricane mitigation and wind engineering research through presentations, tours and
Section 6
10

2012 annual report - FloridaDisaster.org

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