HAZUS-MH Hurricane Wind 
Model Validation Study  
Florida 

Hurricanes Charley and Ivan 

HAZUS-MH MR-2 

April 2007 



ABBREVIATIONS 

ARC  American Red Cross 
CECBL  Concrete, Engineered Commercial Building, Low-Rise 
CECBM  Concrete, Engineered Commercial Building, Mid-Rise 
FDEM  Florida Division of Emergency Management 
FDOIR  Florida Department of Insurance Regulation 
FDOT  Florida Department of Transportation 
FEMA  Federal Emergency Management Agency 
FHA  Florida Hospital Association 
GIS  Geographic Information System 
HAZUS-MH  Hazards United States-Multi-Hazard 
HMTAP  Hazard Mitigation Technical Assistance Program 
JFO  Joint Field Office 
MECBL  Masonry, Engineered Commercial Building, Low-Rise 
MECBM  Masonry, Engineered Commercial Building, Mid-Rise 
MLR1  Masonry, Low-Rise, Industrial/Warehouse/Factory Buildings 
NCDC  National Climatic Data Center 
NEMIS  National Emergency Management Information System 
NOAA  National Oceanic and Atmospheric Administration 
PA  Public Assistance 
PDA  Preliminary Damage Assessment 
PW  Project Worksheet 
SBA  Small Business Administration 
SECBL  Steel, Engineered Commercial Building, Low-Rise 
SECBM  Steel, Engineered Commercial Building, Mid-Rise 
SECBH  Steel, Engineered Commercial Building, High-Rise 
TO  Task Order 





FEMA HMTAP 440  HURRICANE WIND MODEL VALIDATION STUDY 

CONTENTS 

ABBREVIATIONS 
................................................................................
........................................ iii
EXECUTIVE SUMMARY 
................................................................................
............................ vii
INTRODUCTION 
................................................................................
..........................................1
Validation Study Objectives 
.............................................................................. 2 
Overview of HAZUS-MH 
................................................................................
... 2 


1.0 
METHODOLOGY.....................................................................
...............................................4


2.0 DATA 
COLLECTED.......................................................................
.......................................17


3.0 ANALYSES RESULTS AND OBSERVATIONS 
...................................................................20


3.1 HAZUS-MH Macro Level Results for Aggregate Inventory at the County 
Level 
................................................................................
............................... 21 

3.2 HAZUS-MH Micro Level Results for Site Specific Critical Facilities 
.......... 37 


3.3 HAZUS-MH Loss of Functionality for 
Hospitals......................................... 46 


4.0 CONCLUSION AND RECOMMENDATIONS 
.......................................................................48


4.1 Conclusions 
................................................................................
.............. 48 


4.2 Post-disaster Data Collection Recommendations 
..................................... 49 


4.3 Model Improvement 
Recommendations.................................................... 50 


4.4 Software Functionality Enhancement 
Recommendations.........................51 





FEMA HMTAP 440  HURRICANE WIND MODEL VALIDATION STUDY 

TABLES 

1a. HAZUS-MH and Observed Data Agreement by Hurricane, County, and Category 
.............ix 
1b. Prior Validation Study - Summary of Initial ISO Insured Loss 
Estimates................................x 


2. List of Hurricanes Considered in TO 
440.............................................................................
.....7 
3a. Charlotte County Building Exposure 
................................................................................
....14 
3b. Escambia and Santa Rosa Counties Building Exposure 
.....................................................14 


4. Summary of Data Received and Used for Validation Study 
...................................................18 


5. Summary of Data Received for Validation 
Study....................................................................19 
6a. Hurricane Charley Residential Damage - HAZUS-MH Wind Results 
...................................22 
6b. Hurricane Ivan Residential Damage - HAZUS-MH Wind 
Results.........................................24 
7a. Hurricane Charley Shelter Demand - HAZUS-MH Wind 
Results..........................................25 
7b. Hurricane Ivan Shelter Demand - HAZUS-MH Wind Results 
...............................................27 
8a. Hurricane Charley Residential Economic Loss (FDOIR)  HAZUS-MH Wind 
Results.........28 
8b. Hurricane Ivan Residential Economic Loss (FDOIR)  HAZUS-MH Wind 
Results...............30 
8c. Hurricane Charley Residential Economic Loss (ISO)  HAZUS-MH Wind Results 
..............30 
8d. Hurricane Ivan Residential Economic Loss (ISO)  HAZUS-MH Wind 
Results....................32 
9a. Hurricane Charley Commercial and Industrial Economic Loss (ISO) - HAZUS-MH 
Wind 
Results.........................................................................
..............................................................32 
9b. Hurricane Ivan Commercial and Industrial Economic Loss (ISO) - HAZUS-MH Wind 
Results 
................................................................................
......................................................34 
10a. Hurricane Charley Damage - HAZUS-MH Site Specific Hurricane Wind 
Results...............37 
10b. Hurricane Ivan Damage - HAZUS-MH Site Specific Hurricane Wind Results 
....................37 
11a. Hurricane Charley Damage - Comparison of Observed Site Specific Critical 
Facility 
Damage and HAZUS-MH Wind Damage 
Curve.........................................................................40 
11b. Hurricane Ivan Damage - Comparison of Observed Site Specific Critical 
Facility Damage 
and HAZUS-MH Wind Damage 
Curve...........................................................................
.............41 


12. Hurricane Ivan Economic Loss - Comparison of Observed Site Specific Critical 
Facility 
Economic Loss and Loss Ratios and HAZUS-MH Wind Loss Curve..44 

13a. Hurricane Charley Hospital Loss of Functionality - HAZUS-MH Site Specific 
Hurricane 
Wind 
Results.........................................................................
.....................................................46 

13b. Hurricane Ivan Hospital Loss of Functionality - HAZUS-MH Site Specific 
Hurricane Wind 
Results.........................................................................
..............................................................47 

April 2007 




FEMA HMTAP 440  HURRICANE WIND MODEL VALIDATION STUDY 

FIGURES 

1. Hurricane Charley Study Region 
................................................................................
..............5 


2. Hurricane Ivan Study Region 
................................................................................
...................6 
3a. Hurricane Charley HAZUS-MR2 Peak Gust Wind Speeds 
....................................................7 
3b. Hurricane Ivan HAZUS-MR2 Peak Gust Wind Speeds 
..........................................................8 


4. Hurricane Charley Windfield 
................................................................................
....................9 


5. Hurricane Ivan Windfield 
................................................................................
........................10 


6. HAZUS Validation Results Range 
................................................................................
..........13 


7. Default Mapping Schemes in HAZUS-
MH..............................................................................
15 


8. Hurricane Charley Residential Damage (Total Damaged Building Count) - HAZUS-
MH Wind 
Results.........................................................................
..............................................................23 

9. Hurricane Charley Shelter Demand - HAZUS-MH Wind 
Results............................................26 


10. Hurricane Charley Residential Economic Loss (FLDOIR) - HAZUS-MH Wind 
Results........29 


11. Hurricane Charley Residential Economic Loss (ISO) - HAZUS-MH Wind 
Results...............31 


12. Hurricane Charley Commercial and Industrial Economic Loss (ISO) - HAZUS-MH 
Wind 
Results.........................................................................
..............................................................33 

13. Hurricane Charley Damage - HAZUS-MH Site Specific Wind Results 
.................................38 


14. Hurricane Ivan Damage - HAZUS-MH Site Specific Wind 
Results.......................................39 


15. Hurricane Charley Damage - Comparison of Observed Site Specific Critical 
Facility Damage 
and HAZUS-MH Wind Damage 
Curve...........................................................................
.............42 

16. Hurricane Ivan Damage - Comparison of Observed Site Specific Critical 
Facility Damage 
and HAZUS-MH Wind Damage 
Curve...........................................................................
.............43 

17. Hurricane Ivan Economic Loss - Comparison of Observed Site Specific Critical 
Facility Loss 
and HAZUS-MH Wind Loss 
Curve...........................................................................
...................45 

APPENDICES 

A. Templates for Collecting Damaged 
Data............................................................................
.. A-1 


B. Detailed Raw Data Obtained for Observed 
Damage............................................................ B-1 


C. ARA Prior HAZUS Validation 
Report..........................................................................
.......... C-1 


D. Acknowledgements: Contact 
Log.............................................................................
............D-1 


E. H*Wind Speeds and ARA Modeled Wind 
Speeds................................................................ E-1 


F. 
Glossary........................................................................
....................................................... F-1


April 2007 




FEMA HMTAP 440  HURRICANE WIND MODEL VALIDATION STUDY 

EXECUTIVE SUMMARY 

This study was performed for FEMA by URS Corporation and PBS&J, as Task Order 
440, under 
HMTAP contract EMW-2000-CO-0247. This report presents the findings of a FEMA 
validation 
study of the HAZUS-MH MR-2 (Build 45) Hurricane Wind Model. The validation study 
involved the comparison of HAZUS-MH modeled results with observed hurricane wind 
hazards 
and impact data. To make comparisons to the HAZUS-MH modeled results from 
observed data, 
data collection activities were conducted from November 2005 (more than a year 
after the 
storms) through August 2006. Data were collected from local, state, and federal 
agencies and the 
private sector after Hurricane Charley in Charlotte, DeSoto, Hardee, Lee, 
Orange, Osceola, and 
Polk Counties; and Hurricane Ivan in Escambia County in the State of Florida. In 
addition, data 
were also used from prior HAZUS validation studies conducted in 2004 for 
Hurricanes Charley 
(HMTAP TO  332) and Ivan (HMTAP TO  348). 

The purpose of this project was to benchmark the best modeled runs of HAZUS-MH 
(MR2 
version) for wind and compare those runs to the observed and recorded damage and 
loss in 
various counties and jurisdictions in Florida. A primary goal was to test run 
HAZUS-MHs 
functionality and utility against real world historical field data to support 
disaster operations. 
A secondary goal was to develop standardized data collection process and 
analysis for HAZUSMH for long-term recovery operations. This report describes 
data collection, modeling 
improvement, and software functionality enhancement recommendations for future 
HAZUS-MH 
applications, including validation studies. 

This validation study was intended to help provide a systematic assessment of 
how well the 
model performed in several categories compared with readily available observed 
data from these 
specific events. However, some comparisons could not be made due to limitations 
with existing 
data or a lack of data. For example, some readily available observed data sets 
did not distinguish 
damage caused by wind versus flood; and commercial, industrial and most critical 
facility 
qualitative damage count data are not collected. 

HAZUS-MH modeled results for residential, commercial, and industrial occupancy 
classes 
compared well with the observed data for the Hurricane Charley study region. 
Overall, HAZUSMH modeled results were in good agreement with observed data for 
residential qualitative 
damage and economic loss. This good agreement can be attributed to the updated 
and improved 
residential building stock in the MR2 version HAZUS-MH default inventory. Even 
with the 
uncertainties and limitations with the national default inventory for commercial 
and industrial 
occupancy classes, HAZUS-MH economic loss estimates compared well with the 
observed data 
from both insurance data sources -- FDOIR and ISO -- for the Hurricane Charley 
study region. 
Also, HAZUS-MH estimates for shelter demand were in good agreement for southwest 
Florida. 

Prior validation studies conducted for FEMA by Applied Research Associates (ARA) 
indicated 
that HAZUS-MH estimates compared well with preliminary ISO losses for Hurricanes 
Charley, 
and Jeanne. 

HAZUS-MH consistently and significantly underestimated economic loss for public 
and critical 
facilities. However, this may be attributed to the age and source of the 
critical facilities default 
inventory in HAZUS-MH, which is not as current as the general building stock, 
specifically the 
residential building stock. The general building stock square footage for 
schools (EDU1-2) and 
government essential facilities (GOV1-2) are not well-represented in the 
commercial real estate 
datasets purchased for HAZUS. The residential building stock valuations are 
current as of 2005, 
while the square footage information is based on the 2000 census, and the 
critical facilities data 
is current as of 2001. The critical facilities default data were collected from 
national and state 
data providers and most likely do not reflect the most current data (i.e., 
number and location of 
facilities) that are available from local governments. 

The level of agreement between HAZUS-MH and observed data for site specific 
critical facilities 
varied. HAZUS-MH estimates for critical facility damage states were in good 
agreement with 
more than half of the observed data for site specific facilities. The HAZUS-MH 
wind damage 
curve more accurately predicted the type of damage, the closer the facility was 
located to the 
hurricane track in nearly all cases for Hurricane Charley. However, the accuracy 
of the damage 
curve varied for facilities that were located farther away from the hurricane 
path, as seen for 
Hurricane Ivan. The wind damage curve estimates were in good agreement for more 
than half of 
the sites for Hurricane Charley, but underestimated the damage for Hurricane 
Ivan for all but one 
site. The HAZUS-MH wind loss curve underestimated economic loss and loss ratio 
for all four 
of the site specific critical facilities in Escambia County for Hurricane Ivan. 

The summary of the comparisons of HAZUS-MH modeled results and observed data and 
observations about these comparisons are listed in Table 1. Overall, there was 
better agreement 
in the Hurricane Charley study region than there was in the Hurricane Ivan study 
region. This is 
to be expected, as HAZUS-MH estimates are most accurate when used at a multi-
county regional 
level such as Hurricane Charley. The accuracy decreases when the HAZUS-MH 
modeled results 
are compared with observed data at a smaller geographic region, such as the one 
county study 
region of Escambia County for Hurricane Ivan. Additionally, HAZUS-MH estimates 
compared 
the best with observed data for the Hurricane Charley study region, as Charley 
caused 
predominantly wind damage and loss. Therefore hurricane wind modeled results 
generated by 
HAZUS-MH could be more accurately compared with observed data for predominantly 
a wind 
event. This correlation was not as strong for Hurricane Ivan since it was both a 
wind and flood 
event. Based on a comparison of the economic loss data received for Ivan, it 
appears that 35 
percent was attributed to flood damage. 



Table 1a. HAZUS-MH and Observed Data Agreement by Hurricane, County, and 
Category 

Hurricane County 
Agreement of HAZUS-MHEstimate With Observed 
Data 
Charley (Residential Damage) Hurricane Charley Study Region Agreement at low end 
Ivan (Residential Damage) Escambia 
HAZUS appears to underestimate 
damage. However, this is not the 
case since Ivan caused flood 
damage that is not accounted for in 
the Hurricane Model. Therefore, 
direct comparisons were not able to 
be made as the observed data 
included both wind and flood 
damage. 
Charley (Shelter Demand) Hurricane Charley Study Region Good agreement 
Ivan (Shelter Demand) Escambia Underestimates 
Charley (Residential Economic Loss)  FDOIR data Hurricane Charley Study Region 
Good agreement 
Ivan (Residential Economic Loss)  FDOIR data Escambia Underestimates wind loss 
Charley (Residential Economic Loss)  ISO data Hurricane Charley Study Region 
Good agreement 
Ivan (Residential Economic Loss)  ISO data Escambia Agreement at the high end 
Charley (Commercial and Industrial Economic Loss)  
ISO data Hurricane Charley Study Region Good agreement 
Ivan (Commercial and Industrial Economic Loss)  ISO 
data Escambia Underestimates wind loss 
Charley (Public Facilities Economic Loss) Hurricane Charley Study Region HAZUS 
underestimates wind loss. 
This is likely due to the 
generalizations made about the 
building inventory, size (sf), and 
replacement value for these types of 
facilities. 
Ivan (Public Facilities Economic Loss) Escambia 
Charley (Hospital Economic Loss) Charlotte, DeSoto, and Orange 
Ivan (Hospital Economic Loss) Escambia 
Ivan (School Economic Loss) Escambia 
Charley (Site Specific Critical Facility Damage) Hardee, Osceola, and Polk 80% 
of Sites in Good Agreement 
or Agreement at Low End 
Ivan (Site Specific Critical Facility Damage) Escambia 50% of Sites in Good 
Agreement 
Charley (Wind Damage Curve) Hardee, Osceola, and Polk 60% of Sites in Agreement 
at Low 
End 
Ivan (Wind Damage Curve) Escambia 88% of Sites were Underestimated 
Ivan (Wind Loss Curve) Escambia Underestimates 



Previous research has shown that the HAZUS-MH Wind Model results have compared 
well with 
observed data. A prior validation study of the HAZUS-MH Wind Model was conducted 
in 
November 2004 by ARA for FEMA. Initial estimates of industry-wide insured losses 
released by 
ISO for Hurricanes Charley, Frances, Ivan, and Jeanne were used in comparison 
with HAZUSMH modeled economic loss estimates. ARA performed hurricane wind field 
modeling and 
hurricane wind loss estimates before, during, and after each of the four 
hurricanes. 

The modeled results compared well with insurance economic loss data for 
Hurricanes Charley 
and Jeanne, but there were significant differences in the estimates for 
Hurricanes Frances and 
Ivan. More recent runs for Hurricane Frances have produced results between $3.0B 
and $5.8B, 
through updates in wind model parameters brought about model updates that 
accounted for 
FCMP tower wind speeds for terrain effects. The comparison of the modeled 
results and the 
observed data is shown in Table 1b and Appendix C. 

Table 1b. Prior Validation Study - Summary of Initial ISO Insured Loss Estimates 

Hurricane 
Landfall 
Date 
ISO Press 
Release Date 
Initial ISO 
Insured Loss 
Estimate ($B) 
HAZUS-MH 
Estimate Based 
on Final ARA 
Tracks from 
Tables 3-6 ($B) 
States 
Included 
Charley 8/13/04 8/25/04 6.7 7.1 FL 
Frances* 9/5/04 9/23/04 4.1 1.8 FL 
Ivan 9/16/04 10/14/04 5.3 1.6 FL, AL, GA 
Jeanne 9/26/04 10/26/04 2.8 2.8 FL 
2004 Total 18.9 13.3 

Source: ARA

 * More recent runs for Hurricane Frances have produced results between $3.0B 
and $5.8B, through updates in 
wind model parameters brought about through correction of the FCMP tower wind 
speeds for terrain effects 
The specific conclusions from this report include: 

 The observed data for the Hurricane Charley study region compared well with 
HAZUS-MH (1) residential qualitative damage; (2) residential, commercial, and 
industrial economic loss; and (3) short-term shelter demand estimates. There was 
better agreement at the regional level, as seen in the Hurricane Charley study 
region 
versus the results for one county, Escambia County, in the Hurricane Ivan study 
region. Hurricane Charley was predominantly a wind event. Therefore, it is more 
appropriate to compare HAZUS-MH wind results with observed wind data for 
Charley, than it is for Ivan that was both a wind and flood event. 
 HAZUS-MH public and critical facilities qualitative damage (i.e., for schools) 
and 
economic loss estimates did not compare well with observed data. HAZUS-MH 
consistently and significantly underestimated economic loss for public and 
critical 
facilities. This is most likely because the HAZUS-MH default inventory for 
public 
and critical facilities was collected at the national level in 2001, and does 
not include 
the most current number and location of facilities. 

 HAZUS-MH site specific qualitative damage estimates were in good agreement for 
80 percent of the sites for Hurricane Charley and 50 percent of the sites for 
Hurricane 
Ivan. Considering that HAZUS-MH was designed to be used at a larger scale (i.e., 
region, county), it appears that the analysis showed that HAZUS-MH estimates 
compared reasonably well with the observed damage at the site specific level. 
However, site specific economic loss estimates were underestimated by HAZUS-MH. 
 HAZUS-MH wind damage curve estimates were in good agreement at the low end 
for 60 percent of the sites for Hurricane Charley, but underestimated 88 percent 
of the 
sites for Hurricane Ivan. 
 HAZUS-MH wind loss curve estimates underestimated for all sites for Hurricane 
Ivan. 
 HAZUS-MH hospital loss of functionality estimates did not compare well with 
the 
observed data. The model significantly overestimated the loss of functionality 
(i.e., 
number of days). However, it is important to consider that HAZUS-MH estimates 
loss of functionality based on building damage. It is possible that a facility 
can be 
operational, if key parts of the building are not damaged. 
The lessons learned and next steps for FEMA include understanding the 
challenges, and 
recommendations and benefits for HAZUS-MH. This information is organized by: (1) 
data 
collection, (2) modeling capabilities, and (3) software functionality. Some of 
the lessons learned 
and next steps are listed below. All lessons learned and next steps are included 
in Section 4. 

HAZUS-MH Data Collection 

Challenges: 

 Modeled estimates for a variety of categories such as: displaced households; 
debris; 
damage and loss at the jurisdictional level; damage to site specific facilities; 
and 
vulnerability reduction measures through mitigation options were not compared 
with 
observed data because the observed data was incomplete, unavailable, or 
incomparable. 
 Data for comparison with HAZUS-MH estimates are not always separated by 
hazard; 
and damage data are not collected for commercial, industrial, public, and 
critical 
facilities. 
 Currently there is no coordination with counties prior to hurricane season to 
determine what data are already being collected that are useful for future 
HAZUSMH validation studies and to determine which additional data are required 
for the 
local level. There is limited staff available to collect detailed HAZUS-MH data 
after 
an event. 
Recommendations and Benefits: 

 It is recommended that FEMA convene a focus group to determine the appropriate 
data to collect to enhance the default inventory and to document observed 
disaster 
impacts. The focus group should develop a pre-disaster local data gap analysis 
and 
data gathering coordination groups for enhanced planning and mitigation efforts. 





 It is recommended that FEMA encourage state and local jurisdictions to use the 
data 
collection template (refer to Appendix A) developed for this project to help 
consistently collect data. This data collection template should be examined by 
the 
focus group to ensure that all necessary data are being requested. 
 It is recommended that future validation studies should be conducted at a 
regional 
level for counties that only experienced wind damage, and should include data 
for 
only wind impacts. Focused data collection efforts enhance the value, and 
usefulness 
of HAZUS-MH validation. 
 It is recommended that FEMA prepare prewritten rapid response HMTAP task 
orders 
for the types of HAZUS-MH data needed to be collected so deployment to collect 
the 
data is executed shortly after the event. 
 It is recommended that coordination occur with counties to determine which 
data sets 
are available for comparison with HAZUS-MH, and which data sets need to be 
collected. It might be necessary to provide assistance to counties to collect 
data that 
is not typically collected (e.g., commercial, industrial and public facilities 
damage). 
HAZUS-MH Modeling Capabilities 

Challenges: 

 When the study regions were exported, all of the results were not 
automatically 
exported with the study region. As such, sharing the results between HAZUS-MH 
users involved exporting individual results tables. 
 The damaged building count is generated from the general building stock for 
general 
occupancy classes, but not specific occupancy classes. Damage counts are not 
provided for critical facilities in HAZUS-MH. 
 The default general building stock for the grade school occupancy class (i.e., 
EDU1) 
does not include all of the schools that are in the critical facilities default 
inventory. 
Recommendations and Benefits: 

 Develop the capability to retain the results that have been run in HAZUS-MH 
when it 
is exported to allow for user sharing. 
 Develop the capability to provide damage counts for critical facilities. 
 Develop a process to permit local governments to submit updated building stock 
inventory for use with HAZUS-MH. 
Software Functionality 

Challenges: 

 Damage and loss estimates are not provided at the zip code, jurisdictional, or 
site 
specific levels by HAZUS-MH; this would improve the usefulness of the model for 
comparative analysis. 


 HAZUS-MH analysis parameters revert to the default parameters after an 
analysis is 
run. Therefore, it can not determine which parameters were selected for analysis 
after 
the analysis has been run. Also, when all parameters are not selected for 
analysis, 
HAZUS-MH does not always generate all the results specified. 
 Damaged building counts are not provided for specific occupancy classes or 
critical 
facilities. 
Recommendations and Benefits: 

 Develop the capability in HAZUS-MH to produce results at the zip code, 
jurisdictional, and site specific levels. Develop capability to select zip code 
or 
jurisdiction level data attributes from the map view. This functionality will 
allow for 
more detailed comparisons and validations of results. 
 Develop the capability in HAZUS-MH to identify which analysis parameters have 
been run, instead of the analysis screen reverting back to default analysis 
parameter 
settings. Also, develop the capability within HAZUS-MH to run individual 
analysis 
parameters. Although it appears that this can be done, sometimes the results are 
not 
provided consistently. 
 Develop the functionality to provide the damaged building count for specific 
occupancy classes and critical facilities in the wind model. This functionality 
will 
allow for more detailed comparisons and validations of results. 


INTRODUCTION 

This validation study examines the impacts caused by Hurricane Charley in 
Charlotte, DeSoto, 
Hardee, Lee, Orange, Osceola, and Polk Counties, and Hurricane Ivan in Escambia 
County using 
HAZUS-MH. These two devastating hurricanes provided an unprecedented opportunity 
to 
validate the HAZUS-MH Hurricane Wind Model. According to the National Climatic 
Data 
Center (NCDC), the general impacts caused by Hurricanes Charley and Ivan are as 
follows: 

 Hurricane Charley made landfall in Southwest Florida on August 13, 2004, as a 
Category 4 storm, causing over $15 billion in losses and at least 34 deaths in 
Florida, 
South Carolina, and North Carolina. 
 Hurricane Ivan made landfall near Gulf Shores, Alabama, on September 16, 2004, 
as 
a Category 3 storm, causing over $14 billion in losses and at least 57 deaths in 
the 
eastern United States. Ivan was the most destructive hurricane to impact the 
area in 
over 100 years. 
Two other significantly damaging hurricanes occurred in Florida during 2004 and 
2005: 

 Hurricane Jeanne made landfall in East-Central Florida on September 26, 2004, 
as a 
Category 3 storm, causing over $6.9 billion in losses and at least 28 deaths in 
the 
Eastern U.S. and Puerto Rico. 
 Hurricane Dennis made landfall near Pensacola, Florida on July 10, 2005, also 
as a 
Category 3 storm, causing $2 billion in losses and at least 12 deaths in the 
Florida 
Panhandle and eastern United States. Losses from Hurricane Dennis were 
substantially less than Ivan, even though both were Category 3 hurricanes. This 
difference was due to four factors: intensity, size, speed and location. 
Hurricane 
Dennis made landfall east of Pensacola, Florida where there was less property 
than 
the area that was impacted by Hurricane Ivan. 
A prior validation study that was conducted for FEMA by ARA in November 2004 
showed that 
the HAZUS-MH Wind Model results compared well with economic loss data for 
Hurricanes 
Charley, and Jeanne; as shown in full copy in Appendix C. 

HAZUS-MH is a risk assessment model developed by FEMA to estimate damage and 
loss from 
natural and man-made hazards. Readily available as public domain software on 
FEMAs 
website, HAZUS-MH software is the most frequently downloaded content on the 
website. The 
analyses and results are used to help make decisions for disaster preparedness, 
response, 
recovery, and mitigation. HAZUS-MH is used to estimate affected populations and 
infrastructure 
damage and economic loss, to target response resources, and to evaluate the 
savings from 
implementing hazard mitigation measures to reduce impacts from natural hazards. 
These 
measures include but are not limited to land use planning, zoning, structural 
projects, and 
building code enhancements. 

In this study, damage is defined as qualitative damage (e.g., minor) and 
economic loss is defined 
as the cost to repair or replace structures and contents. The results of this 
report will be used to 
understand how well HAZUS-MH estimates impacts for a given event. This 
understanding will 
provide a foundation to potentially improve the HAZUS-MH model, and to enhance 
future data 
collection efforts for future validation studies. These efforts will support 
communities who use 
this loss estimation tool to help identify and prioritize mitigation measures, 
and perform more 
accurate analyses of avoided damages resulting from the implementation of 
mitigation 
techniques. 

Validation Study Objectives 

The objectives of the validation study were to: 

 Compare HAZUS-MH-modeled estimates of wind damage and loss with actual 
damage and loss for the general building stock and critical facilities at the 
county 
level. 
 Compare HAZUS-MH-modeled estimates of wind impacts such as displaced 
populations and debris generated at the county level with observed data. 
 Compare HAZUS-MH-modeled damage and loss estimates for critical facilities at 
the 
site level with observed data. 
 Compare HAZUS-MH-modeled damage states and resultant loss of functionality 
(loss of use in days) of hospitals at the site level with actual impacts. 
 Explore documented vulnerability reduction measures and the potential to 
mitigate 
these measures in HAZUS-MH. 
 Validate the existing HAZUS-MH damage and wind loss curves. 
 Provide recommendations, as appropriate, to improve the HAZUS-MH Hurricane 
Wind Model. 
 Provide recommendations to enhance data collection for future HAZUS-MH 
validations. 
Overview of HAZUS-MH 

HAZUS-MH is a standardized loss estimation software program built upon an 
integrated 
Geographic Information System (GIS) platform. HAZUS-MH includes a wide range of 
inventory data (e.g., demographics, building stock, critical facility, 
transportation, and utility 
lifelines) and three models to estimate potential losses from earthquakes, 
hurricanes, and floods. 
The system was developed by the Department of Homeland Securitys FEMA to 
support 
improved risk assessments to address mitigation, emergency planning, and 
response. 

The HAZUS-MH Hurricane Wind Model provides users the ability to estimate 
potential 
economic damage and loss to residential, commercial, and industrial buildings. 
It also allows 
users to estimate direct economic loss, post-storm shelter needs, and building 
debris. The model 
addresses wind pressure, windborne debris, surge and waves, atmospheric pressure 
change, 
duration or fatigue, and rain. HAZUS-MH is flexible and can be used in 
conjunction with third-
party models and other hazard and building inventory data to support a range of 
hazard-related 
analyses, such as. The results are displayed in a series of tabular reports and 
maps. 

It includes the following features: 

 A building classification system that depends on the characteristics of the 
building 
envelope and building frame. 
 The capability to compute damage based on building classes and the effects of 
rain 
and progressive failure. 
 The capability to compute damage to contents and building interior. 
 The capability to estimate tree blow-down and structure debris quantities. 
 Loss estimates that include direct and indirect economic loss, shelter 
requirements, 
and casualties. 
 Modules that facilitate future assessment of mitigation, benefit-cost, and 
building 
code issues. 
Model Releases MR-1 and MR-2 

Initially, MR-1 was to be used to run the analyses as it was available at the 
onset of this project. 
However, MR-2 was ultimately used, since this version of the model had enhanced 
functionality 
as it included the latest modifications to the data and methodology. The 
following updates have 
been incorporated in MR-2: 

 The default inventory data has been updated with 2005 valuation data for all 
occupancy classes. 
 Valuation data for single-family residential housing and manufactured housing 
has 
been updated and validated based on comparisons with other national databases. 
 Zeros have been substituted for any negative values calculated for the 
daytime, 
nighttime, working commercial, working industrial and commuting populations. 


1.0 METHODOLOGY 
The methodology for this comparative validation study includes conducting two 
analyses: 

. Macro-analyses by county of observed data versus HAZUS-MH estimates 
 Building damage count for residential structures and critical facilities 
 Shelter population requirements 
 Economic loss to residential, commercial, industrial, public, and critical 
facilities 
 Wind speed sensitivity analysis 

. Micro-analysis by jurisdiction of observed data versus HAZUS-MH estimates 
 Critical facilities (only damage) 
 Hospital loss of functionality 
 Comparison of observed data for site specific critical facilities damage and 

economic loss with HAZUS wind damage and wind loss curves 
 Wind speed sensitivity analysis 

Hurricane wind study regions were created for Hurricane Charley for Charlotte, 
DeSoto, Hardee, 
Lee, Orange, Osceola, and Polk Counties, and for Hurricane Ivan hurricane for 
Escambia 
County. These two study regions are provided in Figure 1 and Figure 2, 
respectively. 



Figure 1. Hurricane Charley Study Region 


Figure 2. Hurricane Ivan Study Region 


Hazard Modeling 

Landfall conditions for Hurricanes Charley and Ivan that were considered in this 
study are 
summarized in Table 2. Two types of data were obtained for these hurricanes: (1) 
Applied 
Research Associates (ARA) best estimate of the hurricane intensity parameters 
based on data 
from the following sources: (i) National Hurricane Center (NHC) forecast 
advisories, (ii) 
H*Wind surface wind analysis output provided by Hurricane Research Division 
(HRD), and (iii) 
surface level wind and pressure observations to determine the values of the 
profile parameter and 
radius of maximum winds; and (2) H*Wind swaths which were downloaded directly 
from the 
Internet. Comparisons were made for sustained wind because H*Wind data and ARA 
windfield 
data both included sustained wind speeds. However, sustained winds were 
converted to peak 
gust wind speeds for HAZUS-MH modeling purposes. 



Table 2. List of Hurricanes Considered in TO 440 

Hurricane 
Landfall Conditions 
Location Date NHC Saffir-
Simpson Category Sustained Wind Speed 
NHC 1-minute 
(mph) 
Charley Charlotte County, FL 8/13/04 4 145 
Ivan Baldwin County, AL 9/16/04 3 130 

Figures 3a and 3b shows the resulting peak gust wind speeds at the census tract 
level using the 
ARA modeled tracks in HAZUS MR-2 for the two hurricanes included in this study. 

Figure 3a. Hurricane Charley HAZUS-MR2 Peak Gust Wind Speeds 





Figure 3b. Hurricane Ivan HAZUS-MR2 Peak Gust Wind Speeds 


Figures 4 and 5 illustrate the comparison of the sustained wind speeds between 
ARA and 
H*Wind data for all the jurisdictions within the counties considered in this 
study, and Appendix 
E lists this comparison in tabular format. This comparison shows in general 
small variations in 
the predicted wind speeds between the two data sources. It seems from the plot 
of both storms 
that right along the track the ARA data was very consistent with H wind, but 
less accurate away 
from the track. In comparison to ARA windfield data, H*Wind sustained wind 
speeds were: 

 10 percent lower for Charley 
 10 percent higher for Ivan 


Due to the 10-percent difference in the wind speeds, three hazard scenarios were 
created to 
account for a 10-percent margin of error for the wind speeds: 

1) Default HAZUS-MH using ARA windfield data, which is referred to in the 
results 
section as the Optimum HAZUS Results. 

2) HAZUS-MH using ARA windfield data decreased by 10 percent, which is referred 
to in 
the results section as the Lower End Results. 

3) HAZUS-MH using ARA Windfield data increased by 10 percent, which is referred 
to in 
the results section as the Higher End Results. 

Figure 4. Hurricane Charley Windfield 

Figure 5. Hurricane Ivan Windfield 

Hazard Modeling Assumptions and Limitations 

This analysis used the best available wind speed data. As is the case with all 
models, there are 
modeling uncertainties that result from incomplete scientific knowledge about 
hurricanes and 
their effects on structures. For example, it is difficult to predict all types 
of flying debris that 
could damage a structure. 

HAZUS-MH modeled peak gust wind speeds were converted from sustained wind speeds 
for the 
analysis. However, it is always likely that micro-bursts could occur, which 
would not be 
represented in the peak gust wind speeds. Or, the location of large buildings in 
an area could 
reduce wind speeds in a given area. These wind speed anomalies would not be 
accounted for and 
cause uncertainty in modeling the hazard. 


Study Approach 

Observed damage and loss data were compared with HAZUS-MH modeled estimates at 
the 
county-level, and observed damage data were compared at the site-specific level. 
Hospital loss-
of-functionality-observed data were also compared with HAZUS-MH estimates. 
HAZUS-MH 
estimates were generated using three hazard scenarios for the purpose of a 
sensitivity analysis, 
which is described in greater detail following this study approach section. 
These three scenarios 
included running HAZUS-MH with a hazard scenario using wind speeds provided by 
ARA, and 
running two scenarios to account for a +/-10 percent variance in wind speed. 
HAZUS-MH 
estimates were compared with the observed data that were collected from a 
variety of sources 
that are summarized in Section 2.0, and discussed in detail and provided in 
tabular format in 
Appendix B. 

A county-level (macro-level) analysis was performed to compare HAZUS-MH 
estimates of the 
following with observed data: 

 The number of people requiring short-term shelter. 
 Qualitative damage (i.e., minor and moderate damage, severe and destroyed) 
counts 
for the residential building stock, and public and critical facilities. 
 Economic loss to the residential building stock, and public and critical 
facilities. 
Debris quantities were not received for either county; consequently, debris 
estimates could not be 
compared with observed data. 

Damage and economic loss to structures were modeled in HAZUS-MH based on the 
vulnerability of the structure. This vulnerability was based on building 
characteristics such as the 
type of building materials and roof type. Short-term shelter demand was modeled 
in HAZUSMH based on a percentage of the population that would require sheltering 
because their homes 
were damaged. 

A detailed (micro-level) analysis was performed to compare HAZUS-MH of the 
following with 
observed data: 

 Qualitative damage (i.e., minor and moderate damage, severe and destroyed) for 
site 
specific critical facilities. 
 Economic loss to the site specific critical facilities (Escambia only). 
 Wind damage curve for site specific critical facilities. 
 Wind loss curve for site specific critical facilities. 
A micro level analysis was intended to be performed at the jurisdictional level 
to compare 
HAZUS-MH damage and loss estimates with observed data. However, this could not 
be 
completed because observed data were not available for this analysis at the 
jurisdictional level. 
Data received were aggregated at the county level. 

An analysis was also performed using HAZUS-MH to estimate hospital loss of 
functionality 
(i.e., loss of use in days). Observed data were compared with HAZUS-MH 
estimates. 


Approach Assumptions and Limitations 

Most of the observed data included damage and economic loss from both wind and 
flood hazard 
for Escambia County, and could not be separated. As such, comparing HAZUS-MH 
results with 
these data sets did not usually result in good agreement. 

Comparisons were attempted for the damaged building count for critical 
facilities such as 
schools, fires stations, and shelters. The damaged building count is generated 
from the general 
building stock for general occupancy classes, but not specific occupancy 
classes. Damage counts 
are not provided for critical facilities in HAZUS-MH 

Sensitivity Analysis 

Given the nature of uncertainty in HAZUS, it is more practical to compare 
observed data with a 
range of HAZUS-MH results instead of simply comparing the observed data with 
just HAZUS 
results. This uncertainty may be due to uncertainty in the hazard modeling, the 
way the inventory 
is modeled, the valuation in the damage model, or a combination of some or all 
three of these 
parameters. The comparison to ARA windfield data with the H*Wind sustained wind 
speeds 
were 10 percent lower for Charley and 10 percent higher for Ivan. 

A range of +/- 10 percent of wind speeds is believed to have the highest impact 
on results and 
would cover the valuation in results if the other parameters are varied. The 
scale shown in 
Figure 6 provides a consistent and statistically based scale for deriving 
quantitatively based 
conclusions as to how well HAZUS-MH estimates compared with observed data. The 
scale 
consists of five categories: 


HAZUS-MH underestimates impacts in comparison with observed data. The 
observed data are greater than the upper-end results. 

Observed data agrees with HAZUS low-end results. The range includes numbers 
that fall within the bounds between the low-end results, and the midpoint of the 
low-end results and the optimum results. 

Observed data has good agreement. The range includes numbers that fall within 
the bounds between the midpoint of the low-end results and the optimum results, 
and the midpoint of the optimum results and the upper-end results. 

Observed data agrees with HAZUS upper-end results. This data includes the 
numbers that fall within the bounds between the midpoint of the optimum results 
and the high-end results, and the upper-end results. 


HAZUS-MH overestimates impacts in comparison with observed data. The 
observed data are less than the lower-end results. 

These icons, as illustrated, are used as a visual guide to represent the level 
of agreement in the 
Results and Observation Section of this report. These icons are correlated with 
the levels of 
agreement in Figure 6. 

Figure 6. HAZUS Validation Results Range 
Lower-end 
Results (LR) 
Optimum (Default) 
Results (OR) 
Good 
Agreement (A) 
Agreement at 
the low end (AL) 
Agreement at 
the high end (AH) 
HAZUS 
Underestimates 
Losses (U) 
HAZUS 
Overestimates 
Losses (O) 
Upper-end 
Results (UR) 
(LR + OR)/2 (OR + UR)/2 
Inventory 

HAZUS-MH MR-2 Build 45 default inventory obtained from national level sources 
was used for 
this study. There are five general building types (e.g., wood, masonry, 
concrete, steel, and 
manufactured homes). The general building stock data are in aggregate form, and 
percentages of 
building types are assumed for each census tract or block based on average 
characteristics of the 
geographic region. 

The HAZUS-MH default building inventory is based on U.S. Census (2000) and Dun & 
Bradstreet (D&B) (2002) data. The Census and D&B data provide a range of the 
year of 
construction at the census block , beginning with pre-1939 structures and 
includes each decade 
up to 1990, as well as structures built during 1998 to 2002 (these are referred 
to in HAZUS-MH 
as post-1998 construction). The default general building stock data are further 
classified into 39 
specific building types and 33 occupancy classes, which includes building square 
footage and 
building value. General building stock data are grouped by occupancy class 
(e.g., residential, 
commercial, industrial, and governmental facilities), critical facilities data 
(e.g., emergency 
operation centers, hospitals, police and fire departments, and schools), and 
population 
characteristics. Buildings are further categorized by characteristics such as 
roof shape, roof 
covering, and opening protection. 


Default inventory data were used, meaning that no data modifications were made 
to the general 
building stock percentage distribution to reflect local building inventory 
characteristics (e.g., 
building count, replacement value, building type, roof type). Local building 
inventory was 
considered, though none was received for this validation study that was 
applicable for modifying 
the general building stock exposure, occupancy class, or detailed building 
characteristics. 

Table 3a lists the building exposure values for counties within the Hurricane 
Charley study 
region, and Table 3b lists the building exposure for the county within the 
Hurricane Ivan study 
region, for HAZUS-MH MR-1 and MR-2. Building valuations for MR-2 are current as 
of 2005. 
These tables are provided to illustrate the difference in building valuation 
between MR-1 and 
MR-2. 

Table 3a. Hurricane Charley Study Region - County Building Exposure 

Counties Residential 
($B) 
Commercial 
($B) 
Industrial 
($B) 
Other 
Occupancies($B) 
Total 
HAZUS-MR2 
($B) 
Total 
HAZUS-MH 
MR1 ($B) 
Charlotte 8.9 0.8 0.1 0.1 9.8 8.7 
Desoto 1.1 0.1 0 0 1.3 1.3 
Hardee 0.9 0.1 0 0 1 0.9 
Lee 26.9 3.2 0.5 0.3 30.9 26.4 
Orange 47 8.9 1.4 1.3 58.6 51.1 
Osceola 8.3 1.1 0.1 0.2 9.7 8.3 
Polk 22.3 3 0.9 0.6 26.8 22.9 
Total for Region 115.4 17.2 3 2.5 138.1 119.6 
Table 3b. Hurricane Ivan Study Region - County Building Exposure 

Counties 
Affected 
Total 
HAZUS-MR1 
($ Billion) 


Total 
HAZUS-MR2 
($ Billion) 
16.2 
Residential 
($ Billion) 
Other 
Occupancies 
($ Billion) 
Industrial 
($ Billion) 
Commercial 
($ Billion) 
Escambia 

13.4 

2.1 

0.3 

0.4 

The distribution of the exposure data, building count, and square footage into 
hurricane-specific 
building types was done using the default mapping schemes of the hurricane 
model. Figure 7 
illustrates the allocation of these default schemes for the different areas in 
Florida. 





Figure 7. Default Mapping Schemes in HAZUS-MH 
These schemes generally reflect the different building codes requirements where 
southeast 
Florida has historically stronger wind provisions while the northern portion of 
the state reflects 
more wood frame construction. Southeast Florida is typically exposed to higher 
wind speeds and 
buildings are designed to higher wind pressures. 
Default Inventory Assumptions and Limitations 
General building stock data are in aggregate form by census block and tract. The 
percentages of 
building types are assumed for each census tract or block based on average 
characteristics of the 
geographic region. As noted in the HAZUS-MH MR-2 User Manual, the model can be 
used to 
estimate losses for a group of similar buildings. However, nominally similar 
buildings have 
experienced greatly different damages and losses during a hurricane. This could 
be due to factors 
such as the structural quality of construction, enforcement of building codes, 
and lack of 
maintenance. Where construction quality is known to be different from the 
defined norms in the 
HAZUS-MH model, larger uncertainties in loss projections can occur. 
Default data were obtained at the national level, and did not include recently 
constructed 
building stock and critical facilities after 2000, or contain actual replacement 
cost values for the 
building stock. Estimates used are based on a replacement value established with 
R.S. Means, 
per the HAZUS-MH Hurricane Wind Technical Manual. This does not reflect actual 
repair costs 
that can fluctuate with the economy at the time of the disaster. 
Local Inventory Data 
Local inventory data were considered to update the general building stock and 
critical facilities 
databases, but minimal data were received. Sample general building stock 
characteristics and 
damage data were collected in prior validation studies for Hurricanes Charley 
(TO  332) and 
Ivan (TO  348) for residential and commercial structures and public and 
critical facilities. 
Region I. Southeast Florida 
Region II. South Florida 
Region III. Middle Florida 
Region IV. North Florida 
Region I. Southeast Florida 
Region II. South Florida 
Region III. Middle Florida 
Region IV. North Florida 




Local Inventory Data Assumptions and Limitations 

Sufficient data were not received to update the general building stock or 
critical facilities 
inventory. Prior validation study site assessments did not represent 
statistically significant 
samples, for which general building stock conditions could be extrapolated at 
the jurisdictional 
or county level. There were not enough building characteristic data to fully 
update the site 
specific critical facilities for the micro analysis. No critical facilities data 
were collected for 
Charlotte County after Hurricane Charley. 

There is subjectivity in characterizing damage during field observations. 
Efforts were made to 
reduce this subjectivity, by providing descriptions of damage states to field 
inspectors. However, 
uncertainty is always possible. 


2.0 DATA COLLECTED 
Per the scope of work, this validation study was to include Hurricane Ivan for 
Escambia County 
and Hurricane Charley for Charlotte County. To illustrate the regional 
comparison of HAZUSMH estimates with observed data for Hurricane Charley, 
results were included for these 
additional six counties: DeSoto, Hardee, Lee, Polk, Orange, and Osceola 
Counties. 

Data were also collected but not presented in this report include six additional 
counties for 
Hurricane Frances (i.e., Brevard, Indian River, Martin, Okeechobee, Palm Beach, 
and St. Lucie 
Counties), and two counties for Hurricane Jeanne (i.e., Martin and St. Lucie 
Counties), and one 
county for Hurricane Ivan (i.e., Santa Rosa). 

Data were collected for Hurricane Dennis for Escambia County, per the scope of 
work. Data 
collected for Hurricane Dennis was not useable as much of the structures were 
damaged during 
Ivan and not all had been repaired when Dennis impacted these same counties. 
Therefore, the 
default HAZUS-MH building stock inventory would still include the structures as 
they were 
initially constructed. Nor, would the default HAZUS-MH building stock inventory 
have been 
updated to include structural repairs or reconstruction to current code. 
Therefore, it was prudent 
to compare the HAZUS-MH results with observed data in Escambia County for 
Hurricane Ivan 
to reduce the margin of error. 

Data aggregated to the county, jurisdictional, and site levels were requested 
from various sources 
for this study for Hurricanes Charley and Ivan. Data were requested from local, 
state, and federal 
agencies and organizations during November 2005 through March 2006. Readily 
available data 
were received by August 2006. Data collected included hazards data, physical 
damage to 
structures and contents, social impacts, and economic loss. Data were also 
retrieved from prior 
HAZUS validation studies conducted in 2004 for Hurricanes Charley (TO  332) and 
Ivan (TO  
348). The data collection form that was sent to local governments is provided in 
Appendix A. 

At the local level, data were provided by DeSoto, Escambia, and Hardee Counties. 
At the state 
level, data were provided by the Florida Department of Insurance Regulation 
(FDOIR) and the 
Florida Hospital Association (FHA). At the federal level, data were compiled 
from the American 
Red Cross (ARC), FEMA HMTAP task orders such as local and regional HAZUS 
validations, 
and from the FEMA Public Assistance (PA) program. Private sector level data for 
the hurricane 
windfield were provided by ARA. 

Data used in this validation study includes Charlotte, DeSoto, Hardee, Lee, 
Polk, Orange, and 
Osceola Counties for Hurricane Charley, and Escambia County for Hurricane Ivan. 
Table 4 
summarizes which data have been received, its source, identifies the gaps, and 
summarizes the 
usefulness of the data for this validation study. Therefore, only the categories 
with green cells 
would allow any comparisons. Appendix B includes all data that were collected, 
and describes 
the observed data, data source(s), limitations, and modeling usefulness of data 
that was collected 
for this study, as summarized in Table 5. 


Table 4. Summary of Data Received and Used for Validation Study 

Data Received and 
Charley Ivan 
Charlotte 
DeSotoHardeeLeeOrange 
OsceolaPolkEscambiaSource Usefulness for Validation 
Residential Qualitative Damage: 
American Red Cross (ARC) 
Preliminary Damage Assessment 
(PDA) 
Yes, but includes flood damage for 
Ivan. 
County Damage Assessment -------
No, data are very similar to ARC 
data, which is used instead. 
Critical Facility Damage: 
County Damage Assessment ------Yes for schools. 
Post-disaster Short-term Shelter Population 
ARC Yes. 
Residential Economic Loss: 
Florida Department of Insurance 
Regulation claims (FDOIR) Yes. 
Residential, Commercial, and Industrial Economic Loss: 
Insurance Services Office, Inc. 
(ISO) Yes. 
Public Building Economic Loss: 
FEMA Public Assistance (PA) No,includes flood damage for Ivan. 
Hospital Economic Loss: 
Florida Hospital Association (FHA) ----Yes. 
School Economic Loss: 
County -------Yes. 
Debris Generated 
County Estimate ------No, estimates are not final. 
FEMA PA --------Not available. 
Site Specific Damage: 
HAZUS Validations ----Yes. 
Site Specific Economic Loss: 
Escambia County School Board ----Yes. 
Hospital Loss of Functionality: 
FHA ---Yes. 


= Data Received 



Table 5. Summary of Data Received for Validation Study 

Data Received and Source 
Charley Dennis Frances Ivan Jeanne 
CharlotteDeSotoHardeeLeeOrangeOsceolaPolkEscambia 
Santa RosaBrevardIndian River 
MartinOkeechobee 
Palm Beach 
St. LucieEscambia 
Santa RosaMartinSt. Lucie 
Residential Qualitative Damage 
ARC PDA --------
County ------------------
Commercial Qualitative Damage -------------------
Industrial Qualitative Damage -------------------
Critical Facility Qualitative Damage 
Medical Care Facilities -------------------
Schools (County) ------------------
Fire Stations (County) -----------------
Police Stations ------------------
Shelters (County) -----------------
Critical Facility Site Damage 
(Prior HAZUS Validations) ------------
Critical Facility Loss of Function 
Medical Care Facilities (FHA) -------------
Schools -------------------
Fire Stations -------------------
Police Stations -------------------
Shelters -------------------
Displaced Households (ARC) 
Residential Economic Loss 
Wind (FL DOIR claims) 
Commercial and Industrial 
Economic Loss (ISO) 
Critical Facility Economic Loss 
Medical Care Facilities (FHA) -------------
Schools (county) ------------------
Fire Stations -------------------
Police Stations -------------------
Shelters (county) ------------------
Public Building Economic Loss 
(FEMA PA) 
Debris Generated (County) ----------------
Injuries and Deaths (County) ------------------
Road Damage (FDOT) -----------------


= Data Collected 


FEMA HMTAP 440  HURRICANE WIND MODEL VALIDATION STUDY 

3.0 ANALYSES RESULTS AND OBSERVATIONS 
HAZUS-MH results for the macro level are presented by: 

1. residential damage 
2. critical facilities damage 
3. shelter populations 
4. residential, commercial, and industrial economic loss 
5. public and critical facilities economic loss 
HAZUS-MH results for the micro level are provided for: 

1. Site specific critical facilities damage 
2. Site specific critical facilities economic loss 
3. Wind damage curve for site specific critical facilities. 
4. Wind loss curve for site specific critical facilities. 
HAZUS-MH results are also provided for hospital loss of functionality. 

Each of these categories of results presents observations about the level of 
agreement between 
the HAZUS-MH results and the observed data. 

Observed data were compared with three HAZUS-MH estimates. 

1. The ARA wind speeds were decreased by 10 percent to model the lower-end HAZUS 
estimate. 
2. The ARA wind speeds were used to model the optimum HAZUS estimate. 
3. The ARA wind speeds were increased by 10 percent to model the upper-end HAZUS 
estimate. 
The icons below are used as a visual guide to represent the level of agreement 
in this section. 


HAZUS-MH underestimates impacts in comparison with observed data. The 
observed data are greater than the upper-end results. 

Observed data agrees with HAZUS low-end results. The range includes numbers 
that fall within the bounds between the low-end results, and the midpoint of the 
low-end results and the optimum results. 


Observed data has good agreement. The range includes numbers that fall within 
the bounds between the midpoint of the low-end results and the optimum results, 
and the midpoint of the optimum results and the upper-end results. 

April 2007 




FEMA HMTAP 440  HURRICANE WIND MODEL VALIDATION STUDY 


Observed data agrees with HAZUS upper-end results. This data includes the 
numbers that fall within the bounds between the midpoint of the optimum results 
and the high-end results, and the upper-end results. 


HAZUS-MH overestimates impacts in comparison with observed data. The 
observed data are less than the lower-end results. 

3.1 HAZUS-MH Macro Level Results for Aggregate Inventory at the County Level 
Hurricane Charley and Ivan Results and Observations 

Residential Damage 

HAZUS-MH was used to obtain wind-damaged building-count estimates for 
residential 
structures (i.e., all residential general building stock occupancy classes in 
HAZUS-MH). 
HAZUS-MH results were compared with ARC (American Red Cross) Preliminary Damage 
Assessment (PDA) data, as this data set included qualitative damage estimates. 
HAZUS-MH 
estimated damage counts for each qualitative damage state (e.g., minor, 
moderate, severe, and 
destroyed) and were compared with ARC data. ARC data were the best available 
data that was 
received for the study region. ARC PDAs are collected by windshield survey 
performed by Red 
Cross volunteers for the purpose of estimating shelter needs and are not 
detailed site inspections. 
PDA data might under- or over-estimate the damage counts for Hurricanes Charley 
and Ivan. 
Ivan damage was due to hurricane wind and coastal flooding, which is not 
differentiated in the 
PDA data. The following number of structures was inaccessible during the PDA 
data collection 
process: Charlotte  200, Escambia  684, Hardee  461, and Polk  5,000. 
Residential damage 
comparisons are provided in Table 6a for the Hurricane Charley Counties and 
Table 6b for 
Escambia County. In addition, not all County ARC estimates included estimates 
for buildings 
that had minor damage; therefore, the total damaged structures for those 
Counties will not be 
readily comparable for the minor damage state or total damage. In HAZUS-MH, 
lower wind 
speeds resulted in more minor damage and fewer moderate, severe, and destroyed 
structures. 
Higher wind speeds resulted in fewer structures estimated to have minor damage 
and more 
moderate, severe, and destroyed structures. 

ARC PDA qualitative damage definitions are very comparable to those in HAZUS-MH. 
Detailed 
ARC PDA qualitative damage definitions are included in Appendix B. For reporting 
purposes, 
the following correlation between damage states has been established for 
comparison of 
HAZUS-MH estimates with observed ARC PDA data: 

ARC PDA HAZUS-MH Damage State 

Affected = Minor Damage 
Minor Damage = Moderate Damage 
Major Damage = Severe Damage 
Destroyed = Destruction 

April 2007 




FEMA HMTAP 440  HURRICANE WIND MODEL VALIDATION STUDY 

Table 6a. Hurricane Charley Residential Damage - HAZUS-MH Wind Results 

HAZUS Damage HAZUS-MH Validation 
County DamageState 
Lower-
End 
Optimum 
HAZUS 
Estimate 
Upper-
End 
ARC PDA 
(wind and 
flood) 
Minor 20,499* 19,027* 12,963* 8,373 x 
Moderate 10,128 17,478 18,240 12,457 x 
Severe 2,031 7,644 15,004 12,006 x 
Destroyed 722 3,109 8,274 5,013 x 
Charlotte 
Total 33,380 47,258 54,481 37,849 x 
Minor 2,922 2,792 2,024 0** x 
Moderate 1,578 2,826 2,999 2,020 x 
Severe 262 1,108 2,213 2671 x 
Destroyed 130 593 1,489 3644 x 
DeSoto 
Total 4,892 7,319 8,725 8,335 x 
Minor 1,627 2,424 2,473 1,578 x 
Moderate 493 1,305 2,093 2,488 x 
Severe 63 331 877 1,052 x 
Destroyed 40 225 682 367 x 
Hardee 
Total 2,223 4,285 6,125 5,485 x 
Minor 8,174 14,067 21,187 9,648 x 
Moderate 2,898 5,634 9,111 6,817 x 
Severe 691 2,090 4,017 654 x 
Destroyed 230 908 2,377 331 x 
Lee 
Total 11,993 22,699 36,692 17,450 x 
Minor 9,593 26,495 48,698 0** x 
Moderate 1,185 5,096 14,366 2,036 x 
Severe 23 197 1,268 177 x 
Destroyed 4 80 501 2 x 
Orange 
Total 10,805 31,868 64,833 2,215 x 
Minor 2,840 7,435 12,923 0** x 
Moderate 361 1,549 4,158 149 x 
Severe 9 85 491 487 x 
Destroyed 3 46 250 137 x 
Osceola 
Total 3,213 9,115 17822 773 x 
Minor 7,209 15,136 22,223 0** x 
Moderate 1,393 4,825 10,454 1,700 x 
Severe 102 681 2,401 1,782 x 
Destroyed 146 751 2,380 2,012 x 
Polk 
Total 8,850 21,393 37,458 5,494 x 
Minor 52,864 87,376 122,491 19,599 x 
Moderate 18,036 38,713 61,421 27,667 x 
Severe 3,181 12,136 26,271 18,829 x 
Destroyed 1,275 5,712 15,953 11,506 xTotal for 
Region Total 75,356 143,937 226,136 77,601 x 
*Lower wind speeds resulted in more minor damage and fewer moderate, severe, and 
destroyed structures. Higher wind speeds 
resulted in fewer minor damage and more moderate, severe, and destroyed 
structures. 
**DeSoto, Orange, Osceola, and Polk County ARC estimates did not include 
estimates for buildings that had minor damage. 


April 2007 




FEMA HMTAP 440  HURRICANE WIND MODEL VALIDATION STUDY 

Figure 8 illustrates the geographic trend of the comparison of the modeled and 
observed results. 
HAZUS-MH predicted the damage type that actually occurred, more accurately in 
counties that 
were located closer to the point of the landfalling hurricane. 

Figure 8. Hurricane Charley Residential Damage (Total Damaged Building Count) 
HAZUS-MH Wind Results 


April 2007 




FEMA HMTAP 440  HURRICANE WIND MODEL VALIDATION STUDY 

Table 6b. Hurricane Ivan Residential Damage - HAZUS-MH Wind Results 

HAZUS Wind Damage HAZUS-MH Validation 
County Damage 
State 
Lower-
End 
Optimum 
HAZUS 
Estimate 
Upper-
End 
ARC PDA 
(wind and 
flood 
damage) 
Minor 8,702 21,131 33,765 29,898 x 
Moderate 1,073 4,385 11,539 22,926 x 
Severe 22 196 1,204 9,385 x 
Destroyed 2 50 336 5,224 x 
Escambia 
Total 9,799 25,762 46,844 67,433 x 
Observations: 

 Minor damage data was not collected for DeSoto, Orange, Osceola, and Polk 
Counties. The HAZUS-MH estimates were in good agreement for Charlotte, Hardee, 
and Lee Counties. 
 The observed damage data, which are based on windshield surveys performed by 
Red 
Cross volunteers, might under- or overestimate the damage states through study 
region. 
 There were 5,000 inaccessible structures in Polk County, which is nearly equal 
to the 
number of structures that were reported to be damaged. It is likely that this 
data 
limitation tainted the comparison for Polk County, but it did not seem to affect 
the 
level of agreement for the study region. The study region level of agreement was 
computed without Polk County and there was still a 60 percent level of good 
agreement. 
 It appears that HAZUS-MH significantly underestimated damage in Escambia 
County. This is due to the fact that Hurricane Ivan caused coastal flood damage, 
and 
this model only estimates wind damage. Based on a comparison of ARC PDA data 
and National Flood Insurance Program (NFIP) data, it appears that about 10 
percent 
of the damage was attributed to flood damage. However, NFIP data only includes 
claims for insured properties. 
 It looks like HAZUS also did a better job of predicting losses when the damage 
is 
severe or destroyed and was not as good at damage predicting when the loss was 
minor. However, it is important to consider that not all County ARC estimates 
included estimates for buildings that had minor damage. Therefore, it is not 
appropriate to directly compare the HAZUS-MH estimated number of structures with 
minor damage with the ARC data for minor damage. 
 It appears that the model did a better job closer to the landfall spot then it 
did inland 
as the windfield decays. 
April 2007 




FEMA HMTAP 440  HURRICANE WIND MODEL VALIDATION STUDY 

Critical Facility Damage 

HAZUS-MH was used to obtain hurricane wind damage building count estimates for 
Escambia 
County schools. Comparisons were attempted for damage for Escambia County 
schools, fires 
stations, and shelters. However, HAZUS-MH does not provide damaged building 
counts by 
qualitative damage state for critical facilities. The damaged building count is 
generated from the 
general building stock for general occupancy classes, but not specific occupancy 
classes. For that 
reason, results could not be generated for fire stations and shelters. 

Results were provided for the damaged building count for schools, using the 
estimates provided 
for the general occupancy for schools (EDU). It was determined that the schools 
included in the 
EDU general occupancy class only contained grade schools (EDU1). Therefore the 
observed 
data, which was of the same occupancy class, was suitable for comparison. 

Observations: 

 The HAZUS-MH model consistently and significantly underestimated the damage to 
the schools. This underestimate is due to the underestimated educational (EDU) 
building stock. The HAZUS-MH default inventory contains 25 schools in Escambia 
County. There were 72 schools for which damage was reported for by the county. 
The total number of schools in the county was not reported. 
Shelter Population 

HAZUS-MH was used to obtain hurricane wind estimates for short term post-
landfall shelter 
population and compared with ARC data collected at the hurricane shelters. Pre-
landfall shelter 
population is included to show the perception of the evacuees needs to evacuate. 
Post-landfall 
shelter population comparisons are provided in Table 7a for the Hurricane 
Charley Counties and 
Table 7b for Escambia County. 

Table 7a. Hurricane Charley Shelter Demand - HAZUS-MH Wind Results 

County 
Shelter Needs 
HAZUS Short Term 
ARC Pre-
landfall 
Shelter 
Population 
ARC Post-
landfall 
Shelter 
Population 
Shelter 
Capacity 
HAZUS-MH Validation 
Lower-
End 
Optimum 
HAZUS 
Estimate 
Upper-
End 
Charlotte 652 2,475 5,617 425 425 1,500 x 
DeSoto 102 453 1,081 1,374 1,400 4,245 x 
Hardee 26 140 415 537 537 1,267 x 
Lee 142 474 1,044 8,129 1,191 17,768 x 
Orange 104 406 1,112 727 727 6,320 x 
Osceola 29 109 321 1,714 102 10,284 x 
Polk 64 327 1,072 3,390 14 11,172 x 
Total for 
Region 1,055 4,057 9,590 12,906 4,382 41,384 x 

April 2007 




FEMA HMTAP 440  HURRICANE WIND MODEL VALIDATION STUDY 

Figure 9 illustrates the geographic trend of the comparison of the modeled and 
observed results. 
HAZUS-MH predicted the shelter requirements more accurately for the inland 
counties. 

Figure 9. Hurricane Charley Shelter Demand - HAZUS-MH Wind Results 


April 2007 




FEMA HMTAP 440  HURRICANE WIND MODEL VALIDATION STUDY 

Table 7b. Hurricane Ivan Shelter Demand - HAZUS-MH Wind Results 

County 
Shelter Needs 
HAZUS Short Term 
ARC Pre-
landfall 
Shelter 
Population 
ARC Post-
landfall 
Shelter 
Population 
Shelter 
Capacity 
HAZUS-MH Validation 
Lower-
End 
Optimum 
HAZUS 
Estimate 
Upper-
End 
Escambia 72 252 698 6,707 978 16,827 x 

Observations: 

 HAZUS-MH short-term shelter estimates were in very good agreement at the 
regional 
level and for Orange and Osceola Counties in the Hurricane Charley Study region. 
Shelter needs were underestimated for DeSoto, Hardee, and Lee Counties, and 
overestimated for Charlotte and Polk Counties. It is possible that people 
evacuated 
away from Charlotte County to DeSoto, Hardee, and Lee because they were located 
farther away from where Charley was predicted to make impact. 
 The pre-landfall and the post-landfall population for Charlotte County are the 
same, 
and the shelter was operating at less than 30 percent of its capacity. It is 
important to 
note that the observed data for shelter use was low in comparison to shelter 
estimates 
developed in the Hurricane Evacuation Studies (HES). As stated in the FEMA 2004 
Hurricane Assessments Executive Summary, low shelter use was due to low 
evacuation participation. It is also likely that people needing shelter in one 
county 
may have sought shelter in adjacent counties, or stayed with family or friends, 
or at a 
hotel. 
 It appears that HAZUS-MH underestimated the short term shelter needs in 
Escambia 
County. Observed shelter data could include transient or tourist populations, 
which 
are not reflected in the HAZUS-MH default inventory, although they can be added 
where the data are available. People may have stayed in shelters after landfall 
because 
they could not return to their homes due to electric, water, and sewage outages. 
This 
could explain why HAZUS-MH underestimated the shelter need for Escambia 
County. This could also be related to the fact that Ivan was also a coastal 
flood event. 
 HAZUS-MH estimates short term shelter needs based on damaged building stock. 
It 
is also possible that the estimates did not agree well with the observed data 
due to 
uncertainties in the default building characteristic data. 
 The behavior of whether people who chose to evacuate could have been caused by 
their perception of the need to evacuate based on experience with prior 
hurricanes. 
For example, people in Escambia County had possibly experienced hurricane 
impacts 
during the 1990s (i.e., Erin and Opal), and were more likely to evacuate. 
April 2007 




FEMA HMTAP 440  HURRICANE WIND MODEL VALIDATION STUDY 

Residential Economic Loss 

HAZUS-MH was used to obtain wind economic loss estimates for residential 
structures found in 
the general building stock occupancy classes given in HAZUS-MH. HAZUS-MH 
estimated 
economic loss was compared with FDOIR as well as ISO wind insurance claim data. 
These 
insured-loss estimates provide a useful benchmark for the HAZUS-MH wind loss 
estimate 
comparisons. 

However, FDOIR and ISO data cannot be directly compared with HAZUS-MH estimates. 
FDOIR and ISO insurance claims data includes losses for automobiles, boats, and 
appurtenant 
structures, and additional living expenses, yet do not include deductibles or 
uninsured properties. 
The FDOIR and ISO data includes fewer inventories (i.e., fewer structures) than 
HAZUS, as it 
only includes insured properties. However, FDOIR and ISO data include more 
property (e.g., 
automobiles and boats) than is included in HAZUS. In addition, the ISO claims 
data do not 
include manufactured housing. As such, the total economic loss for the 
residential building stock 
less the manufactured housing loss was compared with the ISO data. 

Raw FDOIR data were compared with HAZUS-MH estimates, as the penetration rate 
(i.e., the 
percent of structures that are insured) was not provided. However, since the 
penetration rate was 
provided for ISO data, the ISO raw data were converted for comparison. The 
penetration rate for 
the ISO data was 45 percent. Therefore the ISO losses were converted to 100 
percent by 
multiplying the ISO raw losses by 2.22. 

Residential loss comparisons using FDOIR data are provided in Table 8a for the 
Hurricane 
Charley Counties and Table 8b for Escambia County. Residential loss comparisons 
using ISO 
data are provided in Table 8c for the Hurricane Charley Counties and Table 8d 
for Escambia 
County. 

Table 8a. Hurricane Charley Residential Economic Loss (FDOIR) - HAZUS-MH Wind 
Results 

County 
HAZUS Residential Loss ($M) 
FDOIR Data 
($M) 
HAZUS-MH Validation 
Lower-End 
Optimum 
HAZUS 
Estimate 
Upper-End 
Charlotte 884 2,322 4,317 2,561 x 
DeSoto 109 300 555 283 x 
Hardee 42 130 285 138 x 
Lee 362 874 1657 1,014 x 
Orange 311 716 1597 991 x 
Osceola 77 183 425 560 x 
Polk 150 430 1045 554 x 
Total for 
Region 1,935 4,955 9,881 6,101 x 

Note: HAZUS-MH inventory includes more buildings. FDOIR claims include fewer 
buildings, but also include 
losses for automobiles, boats and appurtenant structures, and additional living 
expenses. 

April 2007 




FEMA HMTAP 440  HURRICANE WIND MODEL VALIDATION STUDY 

Figure 10 illustrates the geographic trend of the comparison of the modeled and 
observed 
results. HAZUS-MH modeled residential economic loss results were in good 
agreement with 
FLDOIR observed data for six of the seven counties. 

Figure 10. Hurricane Charley Residential Economic Loss (FLDOIR) - HAZUS-MH Wind 
Results 


April 2007 




FEMA HMTAP 440  HURRICANE WIND MODEL VALIDATION STUDY 

Table 8b. Hurricane Ivan Residential Economic Loss (FDOIR)  HAZUS-MH Wind 
Results 

County 
HAZUS Residential Loss ($M) 
FDOIR Data 
($M) 
HAZUS-MH Validation 
Lower-End 
Optimum 
HAZUS 
Estimate 
Upper-End 
Escambia 205 448 968 1,698 x 

Note: HAZUS-MH inventory includes more buildings than insurance data. FDOIR 
claims include fewer buildings, 
but also include losses for automobiles, boats and appurtenant structures, and 
additional living expenses. 

Table 8c. Hurricane Charley Residential Economic Loss (ISO)  HAZUS-MH Wind 
Results 

County 
HAZUS Residential Loss ($M) 
ISO Data 
($M) 
HAZUS-MH Validation 
Lower-End 
Optimum 
HAZUS 
Estimate 
Upper-End 
Charlotte 809 2,139 4,020 1,763 x 
DeSoto 88 239 448 87 x 
Hardee 37 113 244 53 x 
Lee 340 823 1,562 384 x 
Orange 308 709 1,579 591 x 
Osceola 74 174 401 226 x 
Polk 121 341 835 226 x 
Total for 
Region 1,777 4,538 9,089 3,330 x 

Note: HAZUS-MH inventory includes more buildings. ISO claims include fewer 
buildings, but also include losses 
for automobiles, boats and appurtenant structures, and additional living 
expenses. 

April 2007 




FEMA HMTAP 440  HURRICANE WIND MODEL VALIDATION STUDY 

Figure 11 illustrates the geographic trend of the comparison of the modeled and 
observed 
results. HAZUS-MH modeled residential economic loss results were in good 
agreement with 
ISO observed data for six of the seven counties. 

Figure 11. Hurricane Charley Residential Economic Loss (ISO) - HAZUS-MH Wind 
Results 


April 2007 




FEMA HMTAP 440  HURRICANE WIND MODEL VALIDATION STUDY 

Table 8d. Hurricane Ivan Residential Economic Loss (ISO)  HAZUS-MH Wind Results 

County 
HAZUS Residential Loss ($M) 
ISO Data 
($M) 
HAZUS-MH Validation 
Lower-End 
Optimum 
HAZUS 
Estimate 
Upper-End 
Escambia 200 435 933 895 x 

Note: HAZUS-MH inventory includes more buildings. ISO claims include fewer 
buildings, but also include losses 
for automobiles, boats and appurtenant structures, and additional living 
expenses. 

 Observations: 

 It appears that HAZUS-MH estimates were in very good agreement with the 
observed 
residential economic loss data for Charlotte County. 
 It appears that HAZUS-MH significantly underestimated residential economic 
loss 
for Escambia County. It is likely that the underestimation occurred, because 
Escambia County experienced economic loss from both hurricane wind and coastal 
flood. It is also possible that the estimates did not agree well with the 
observed data 
due to uncertainties in the default inventory and building characteristic data. 
Commercial and Industrial Economic Loss 

HAZUS-MH was used to obtain wind economic loss estimates for commercial and 
industrial 
structures found in the general building stock occupancy classes in HAZUS-MH. 
HAZUS-MH 
estimated economic loss was compared with ISO wind insurance claim data. These 
insured-loss 
estimates provide a useful benchmark for the HAZUS-MH wind loss estimates. The 
commercial 
and industrial losses are combined for comparison with ISO data, as provided in 
Table 9a for the 
Hurricane Charley Counties and Table 9b for Escambia County. 

Table 9a. Hurricane Charley Commercial and Industrial Economic Loss (ISO) - 
HAZUSMH Wind Results 

County 
HAZUS Commercial and Industrial Loss ($M) 
ISO Data 
($M) 
HAZUS-MH Validation 
Lower-End 
Optimum 
HAZUS 
Estimate 
Upper-End 
Charlotte 118 340 609 202 x 
DeSoto 20 60 107 24 x 
Hardee 6 20 42 18 x 
Lee 25 71 145 64 x 
Orange 26 115 367 131 x 
Osceola 6 23 78 47 x 
Polk 18 75 208 67 x 
Total for 
Region 219 704 1,556 553 x 

April 2007 




FEMA HMTAP 440  HURRICANE WIND MODEL VALIDATION STUDY 

Figure 12 illustrates the geographic trend of the comparison of the modeled and 
observed 
results. HAZUS-MH modeled commercial and industrial economic loss results were 
in good 
agreement with ISO observed data for all seven counties. 

Figure 12. Hurricane Charley Commercial and Industrial Economic Loss (ISO) - 
HAZUSMH Wind Results 


April 2007 




FEMA HMTAP 440  HURRICANE WIND MODEL VALIDATION STUDY 

Table 9b. Hurricane Ivan Commercial and Industrial Economic Loss (ISO) - HAZUS-
MH 
Wind Results 

County 
HAZUS Commercial and Industrial Loss ($M) 
ISO Data 
($M) 
HAZUS-MH Validation 
Lower-End 
Optimum 
HAZUS 
Estimate 
Upper-End 
Escambia 13 53 163 311 x 

Observations: 

 HAZUS-MH is in good agreement for every county in the Hurricane Charley study 
region. However, HAZUS-MH underestimated loss for Escambia County. 
 Perhaps the reason the model appears to underestimate the results for Escambia 
County is that the building inventory is not as accurate as it is in southwest 
Florida. 
Public and Critical Facilities Economic Loss 

Public Facilities Economic Loss 

HAZUS-MH was used to obtain economic loss estimates for public and critical 
facilities. 
HAZUS-MH results for government buildings, hospitals, and schools (i.e., GOV1, 
GOV2, 
COM6, and EDU1 in the general building stock) were compared with a summary of PA 
funds 
that were aggregated at the county level. PA funding was generated from NEMIS 
for Category 
E Public Facilities for structure and content loss. 

Category E covers uninsurable losses to repair or restore publicly owned and 
maintained 
structures, equipment (e.g., electrical, mechanical, telecommunications, etc.), 
and contents (e.g., 
furniture, books, computers, etc.). PA loss amounts could be overestimated or 
underestimated, as 
insurance claims were still being settled by local governments at the time that 
this report was 
prepared. PA loss amounts might also include associated debris removal and mold 
remediation 
costs, or costs for code and standard upgrades. 

Observations: 

 It appears that HAZUS-MH underestimated the economic loss of public and 
critical 
facilities in comparison to the PA data for most of the Hurricane Charley study 
region 
and for Escambia County. 
 This underestimation appears to be due to the fact that Hurricane Ivan caused 
both 
hurricane wind and coastal flood economic loss. In comparing the optimum HAZUSMH 
results with the observed PA data, the Hurricane Charley study region results 
were underestimated by 62 percent, whereas, Escambia County results were 
underestimated by 800 percent. 
April 2007 




FEMA HMTAP 440  HURRICANE WIND MODEL VALIDATION STUDY 

 This underestimation appears to be caused by the fact that the default general 
building 
stock is incomplete. For example, the grade school occupancy class (i.e., EDU1) 
does 
not include all of the schools that are in the critical facilities default 
inventory. It is 
assumed that the default inventory is also inaccurate for the governmental and 
hospital structures. 
 It is also plausible that the underestimation occurred because of an 
underestimation of 
content value. For example, hospitals are considered commercial structures, for 
which 
HAZUS-MH estimates the content value to be between 100 to 150 percent of the 
replacement value of the structure. As such, if the default inventory is not 
accurate for 
replacement value for commercial structures, HAZUS-MH would not be able to 
accurately calculate the content replacement cost. 
Hospital Economic Loss 

HAZUS-MH was used to obtain hurricane wind economic loss estimates for 
hospitals. HAZUSMH results for hospitals (i.e., COM6 in the general building 
stock) were compared with losses 
that were reported by the FHA, which included loss to structures, equipment, 
contents, and 
other damages as listed in the following observations. 

Observations: 

 It appears that HAZUS-MH underestimated economic loss for hospitals for both 
Charlotte and Escambia Counties. 
 It is possible that the estimates did not agree well with the observed data, 
as it 
included losses for items not modeled in HAZUS-MH. The FHA-observed data 
included costs for non-structural damage, referred to as other damages (e.g., 
debris 
removal, signage, landscaping, fencing, screens, canopies and awnings, and 
compressors). FHA reported that of $67.4 million in losses for all 2004 
hurricanes, 
$10.9 million or 16 percent of economic loss was attributed to other damages. 
 It is possible that the estimates did not agree well with the observed data 
due to 
uncertainties associated with missing facilities or the assumptions made for the 
default building characteristic data in the general building stock for 
commercial 
structures. 
 It is also plausible that the underestimation occurred because of an 
underestimation of 
content value. Hospitals are considered commercial structures, for which HAZUSMH 
estimates the content value to be between 100 to 150 percent. As such, if the 
default inventory is not accurate for commercial structures, HAZUS-MH would not 
be able to accurately calculate the content replacement cost. 
School Economic Loss 

HAZUS-MH was used to obtain hurricane wind economic loss estimates for schools. 
HAZUSMH results for schools (i.e., EDU1 in the general building stock) were 
compared with losses that 

April 2007 




FEMA HMTAP 440  HURRICANE WIND MODEL VALIDATION STUDY 

were reported by Escambia County. No data were received for southwestern 
counties that were 
impacted by Hurricane Charley. 

Observations: 

 It appears that HAZUS-MH significantly underestimated economic loss for 
schools 
for Escambia County. 
 It is possible that the estimates did not agree well with the observed data 
due to 
uncertainties associated with missing facilities or the assumptions made for the 
default building characteristic data in the general building stock. 
April 2007 




FEMA HMTAP 440  HURRICANE WIND MODEL VALIDATION STUDY 

3.2 HAZUS-MH Micro Level Results for Site Specific Critical Facilities 
Site Specific Damage 

HAZUS-MH was used to obtain hurricane wind damage estimates for site specific 
critical 
facilities in Escambia County. Data were not received for critical facility 
damage in Charlotte 
County. Site specific comparisons are provided in Table 10a for Hardee, Osceola 
and Polk 
Counties, and Table 10b for Escambia County. 

Table 10a. Hurricane Charley Damage - HAZUS-MH Site Specific Hurricane Wind 
Results 

Damage State HAZUS-MH Validation 
County Type of 
Facility Facility Name Lower-
End 
Optimum 
HAZUS 
Estimate 
Upper-
End 
Observed 
Wind 
Damage 
Hardee School 
Hardee High 
School Minor Severe Severe Minor x 
Hardee School 
Wauchula 
Elementary Minor Severe Severe Minor x 
Osceola School 
Poinciana High 
School Minor Minor Minor Moderate x 
Osceola School 
Thacker 
Elementary Minor Minor Minor Minor x 
Polk 
Fire 
Station 
Haines City Fire 
Dept. Minor Minor Moderate Minor x 
Table 10b. Hurricane Ivan Damage - HAZUS-MH Site Specific Hurricane Wind Results 

Damage State HAZUS-MH Validation 
County Type ofFacility Facility Name Lower-
End 
Optimum 
HAZUS 
Estimate 
Upper-
End 
Observed 
Wind 
Damage 
Escambia Hospital Baptist Hospital Minor Minor Minor Minor x 
Escambia Hospital 
Naval Hospital 
Pensacola Minor Minor Minor Minor x 
Escambia Hospital 
Sacred Heart 
Health System Minor Minor Minor Minor x 
Escambia Hospital 
West Florida 
Hospital Minor Minor Minor Severe x 
Escambia School 
Bellview 
Elementary 
School Minor Minor Minor Minor x 
Escambia School 
Longleaf 
Elementary Minor Minor Minor Severe x 
Escambia School 
Pine Forest High 
School Minor Minor Minor Moderate x 
Escambia School 
West FL High 
School/Beggs 
Educational 
Center Minor Minor Minor Moderate x 
April 2007 




FEMA HMTAP 440  HURRICANE WIND MODEL VALIDATION STUDY 

Figures 13 and 14 illustrates the geographic trend of the comparison of the 
modeled and 
observed results for the Hurricane Charley and Hurricane Ivan study regions, 
respectively. The 
maps show the levels of agreement of HAZUS-MH damage estimates with the observed 
damage 
estimates for the site specific critical facilities. 

Figure 13. Hurricane Charley Damage - HAZUS-MH Site Specific Wind Results 
April 2007 




FEMA HMTAP 440  HURRICANE WIND MODEL VALIDATION STUDY 

Figure 14. Hurricane Ivan Damage - HAZUS-MH Site Specific Wind Results 


Observations: 

 HAZUS-MH estimates were in good agreement with 80 percent of the observed 
damage states for the counties in the Hurricane Charley study region, and were 
in 
good agreement with 50 percent of the observed damage to the critical facilities 
in 
Escambia County even though, HAZUS-MH was not intended to be used for site-
specific assessments. 
 HAZUS-MH modeled damage results were generally in good agreement with the 
observed damage for critical facility sites that were located along the path of 
the 
hurricane track. However, damage to facilities that were located farther away 
from 
the hurricane track were also compared for Hurricane Ivan to determine if there 
is any 
fluctuation in the modeling accuracy, depending on the proximity of the 
facilities to 
April 2007 




FEMA HMTAP 440  HURRICANE WIND MODEL VALIDATION STUDY 

the hurricane track. There was a lot more variance in the agreement of the 
HAZUSMH estimates in comparison with the observed data for the Hurricane Ivan 
study 
region, than there was for the Hurricane Charley study region. 

 All three hazard scenarios produced the same level of damage for all sites in 
Escambia County. It appears that varying the wind speed did not have an effect 
on the 
damage states at the site-level, as it did at the aggregate level. As such, it 
does not 
appear that HAZUS-MH is valid for site-specific analysis. 
Wind Damage Curve Comparison for Site Specific Critical Facilities 

The type of observed critical facility damage (e.g., minor, moderate, etc.) 
provided by the 
Escambia County School Board was compared with the probability of that same of 
type of 
damage predicted by the HAZUS-MH wind damage curve. The damage curve shows the 
likelihood of the type of qualitative damage (e.g., minor) associated with a 
particular wind speed 
for each model building type, expressed as a percentage. These model building 
types are spelled 
out in the Abbreviations List at the beginning of this report. 

Tables 11a and 11b show the observed damage compared with the probability of 
that predicted 
type of damage for 13 critical facilities in Hardee, Osceola, Polk, and Escambia 
Counties. For 
example, the first data record in Table 12a shows that HAZUS-MH estimated there 
to be a 60 
percent chance that Hardee High School would experience minor damage. 

Table 11a. Hurricane Charley Damage - Comparison of Observed Site Specific 
Critical 
Facility Damage and HAZUS-MH Wind Damage Curve 

County 
Type of 
Facility Facility Name 
HAZUSMH Wind 
Class 
Peak 
Gust 
Observed 
Wind 
Damage 
HAZUS-MH Wind 
Damage Curve 
Estimated Percent 
of Type of 
Building Damage 
Level of Agreement 
Hardee School Hardee High School MECBL 133 Minor 60% - minor 
Agreement at Low 
End 
Hardee School 
Wauchula 
Elementary SECBL 133 Minor 60% - minor 
Agreement at Low 
End 
Osceola School 
Poinciana High 
School MLR1 107 Moderate 55% - moderate 
Agreement at Low 
End 
Osceola School Thacker Elementary MECBL 104 Minor 19% - minor Underestimates 
Polk 
Fire 
Station 
Haines City Fire 
Dept. MECBL 107 Minor 20% - minor Underestimates 

April 2007 




FEMA HMTAP 440  HURRICANE WIND MODEL VALIDATION STUDY 

Table 11b. Hurricane Ivan Damage - Comparison of Observed Site Specific Critical 
Facility Damage and HAZUS-MH Wind Damage Curve 

HAZUS-MH Wind 
Damage Curve Level of 
Agreement 
Agreement at Low 
End 
County 
Escambia 
Type ofFacility 
Hospital 
Facility Name 
Baptist Hospital 
HAZUSMH Wind 
Class 
MECBM 
Peak 
Gust 
109 
Observed 
Wind 
Damage 
Minor 
Estimated Percent of 
Type of Building 
Damage 
50% - minor 
Escambia Hospital 
Naval Hospital 
Pensacola CECBM 96 Minor 20% - minor Underestimates 
Escambia Hospital 
Sacred Heart 
Health System SECBM 106 Minor 40% - minor Underestimates 
Escambia Hospital 
West Florida 
Hospital SECBH 106 Severe 0% - severe Underestimates 
Escambia School 
Bellview 
Elementary School MECBL 109 Minor 25% - minor Underestimates 
Escambia School 
Longleaf 
Elementary CECBL 108 Severe 0% - severe Underestimates 
Escambia School 
Pine Forest High 
School CECBL 113 Moderate 12% - moderate Underestimates 
Escambia School 
West FL High 
School/Beggs 
Educational 
Center CECBL 113 Moderate 12% - moderate Underestimates 

April 2007 




FEMA HMTAP 440  HURRICANE WIND MODEL VALIDATION STUDY 

Figures 15 and 16 illustrate the geographic trend of the comparison of the 
modeled and observed 
results for the Hurricane Charley and Hurricane Ivan study regions, 
respectively. The maps show 
the percent chance of the HAZUS-MH damage curve predicting the type of observed 
damage for 
each of the site specific critical facilities. 

Figure 15. Hurricane Charley Damage - Comparison of Observed Site Specific 
Critical 
Facility Damage and HAZUS-MH Wind Damage Curve 


April 2007 




FEMA HMTAP 440  HURRICANE WIND MODEL VALIDATION STUDY 

Figure 16. Hurricane Ivan Damage - Comparison of Observed Site Specific Critical 
Facility Damage and HAZUS-MH Wind Damage Curve 


Observations: 

 The HAZUS-MH wind damage curve more accurately predicted the type of damage, 
the closer the facility was located to the hurricane track in nearly all cases 
for 
Hurricane Charley. However, this was not the case for Hurricane Ivan. In 
Escambia 
County, the critical facilities were located 30 to 50 miles east of the 
hurricane track. 
There did not appear to be a correlation between accuracy of damage type 
estimates 
for facilities located closer to the path of hurricane. Critical facility damage 
that was 
farther away from the path was predicted with less accuracy than damage to 
facilities 
that were closer to the track. It is recommended that this be studied for future 
analysis, collecting more data for damage to critical facilities throughout the 
county. 
April 2007 




FEMA HMTAP 440  HURRICANE WIND MODEL VALIDATION STUDY 

HAZUS-MH wind damage curve estimates were in good agreement at the low end 
for 60 percent of the sites for Hurricane Charley, but underestimated 88 percent 
of the 
sites for Hurricane Ivan. 
Wind Loss Curve Comparison for Site Specific Critical Facilities 
The economic loss and economic loss ratios provided by the Escambia County 
School Board 
were also compared with the HAZUS-MH wind loss curve. The wind loss curve shows 
the 
percentage of economic loss associated with a particular wind speed for each 
modeled building 
type, expressed as a percentage. The economic loss ratio percentages generated 
by HAZUS-MH 
were multiplied by the replacement costs (provided by the School Board) to 
calculate the 
economic loss values. Table 12 shows the observed economic loss and loss ratios 
compared with 
HAZUS-MH estimates. 
Table 12. Hurricane Ivan Economic Loss - Comparison of Observed Site Specific 
Critical 
Facility Economic Loss and Loss Ratios and HAZUS-MH Wind Loss Curve 
Observed 
Economic Loss 
Reported by School 
Board ($) 
HAZUS-MH Wind Loss 
Curve 
County 
Typeof 
Facility Facility Name 
HAZUS-
MH 
Wind 
Class 
Peak 
Gust 
Total 
Building 
Value 
(Structure 
and 
Contents) 
($) Loss ($) 
Loss 
Ratio 
Estimated 
Building 
Economic 
Loss ($) 
Estimated 
Building 
Economic 
Loss Ratio 
Bellview 
Escambia 


School 

Elementary School 

MECBL 

109 

4,700,096 

500,000 

0.1064 

58,751 

0.0125 
Longleaf 
Escambia 

Elementary

School 

CECBL 

108 

15,454,013 

10,000,000 

0.6471 

6,439 

0.0004 
Pine Forest High 
Escambia 

School 

School 

CECBL 

113 

18,755,925 

1,000,000 

0.0533 

15,630 

0.0008 

West FL High 
School/Beggs 
Escambia 


School 

Educational Center 

CECBL 

113 

34,710,840 

6,000,000 

0.1729 

28,926 

0.0008 

Observations: 

 HAZUS-MH wind loss curve estimates underestimated for all sites for Hurricane 
Ivan. 
April 2007 

44 




FEMA HMTAP 440  HURRICANE WIND MODEL VALIDATION STUDY 

Figure 17. Hurricane Ivan Economic Loss - Comparison of Observed Site Specific 
Critical 
Facility Loss and HAZUS-MH Wind Loss Curve 


Vulnerability Reduction Measures for Mitigation Options 

Of the 13 site-specific critical facilities for which data were collected, two 
of them reportedly 
had shutters, and two had hurricane strapping. These were the only two 
vulnerability reduction 
measures that were provided in the available data. These facilities are 
commercial facilities. 
HAZUS-MH currently allows users to select two mitigation options for commercial 
facilities 
which include shutters, and the use of superior metal deck attachment for roofs. 

April 2007 




FEMA HMTAP 440  HURRICANE WIND MODEL VALIDATION STUDY 

3.3 HAZUS-MH Loss of Functionality for Hospitals 
HAZUS-MH was run to obtain hurricane wind induced loss of functionality 
estimates for 
hospitals for Hurricane Charley in Charlotte, DeSoto, Hardee, and Orange 
Counties and for 
Hurricane Ivan in Escambia County. An attempt was made to compare HAZUS-MH 
estimates 
with FHA data. The FHA provided loss of functionality by county, not by 
individual hospital. 
These are not direct comparisons since the observed data were aggregated by 
county and 
HAZUS-MH estimates were provided by hospital (e.g., FL000091). Comparisons are 
provided 
for Charlotte, DeSoto, Hardee, and Orange Counties in Table 13a and for Escambia 
County in 
Table 13b. 

Table 13a. Hurricane Charley Hospital Loss of Functionality - HAZUS-MH Site 
Specific 
Hurricane Wind Results 

County 
HAZUSLower-End 
EUpper-End 
Closed 
FHA - Days 
Closed 
HAZUS-MH Validation 
Optimum 
HAZUS 
Estimate 
stimated Days 
Charlotte 
10* 
x 
FL000091 161 71 257 
FL000195 96 31 175 
FL000196 96 31 175 
DeSoto 
2* 
x 
FL000104 43 118 200 
FL000108 52 138 223 
Hardee 0 0 0 0 x 
Orange 
0 
x 
FL000071 1 5 20 
FL000072 1 5 15 
FL000074 1 7 27 
FL000131 1 5 19 
FL000138 1 2 7 
FL000219 0 0 0 
FL000229 0 0 0 

* One hospital closed for each period of time. 
April 2007 




FEMA HMTAP 440  HURRICANE WIND MODEL VALIDATION STUDY 

Table 13b. Hurricane Ivan Hospital Loss of Functionality - HAZUS-MH Site 
Specific 
Hurricane Wind Results 

County 
HAZUSLower-End 
EUpper-End 
Closed 
FHA - Days 
Closed 
HAZUS-MH Validation 
Optimum 
HAZUS 
Estimate 
stimated Days 
Escambia 0 x 
FL000082 2 8 24 
FL000183 1 5 13 
FL000184 1 7 20 

Observations: 

 Even at the aggregate level, HAZUS-MH appears to have overestimated the loss 
of 
functionality for hospitals. 
 Although there were several hospitals that did close, overall the hospitals 
remained 
functional. This is due in part to good building performance, operational 
capability 
from auxiliary power, and site design of a hospital to protect key functional 
areas 
from wind damage. 
 HAZUS-MH estimates loss of functionality based on building damage, such as 
damage to the roof and building openings. This is not a clear indication of 
complete 
loss of function, as part of the building could be damaged and the hospital 
could still 
be operational. The FHA reported that there was $46.3 million in economic loss 
to 
hospitals in the Hurricane Charley study region and Escambia County. Despite the 
significant economic losses that occurred, only two hospitals closed for brief 
periods 
of time. One hospital in Charlotte County closed for 10 days and one hospital in 
DeSoto County closed for two days. 
April 2007 




FEMA HMTAP 440  HURRICANE WIND MODEL VALIDATION STUDY 

4.0 CONCLUSION AND RECOMMENDATIONS 
The conclusions and recommendations are based on the evaluation of how the 
HAZUS-MH 
default building inventory was used to provide results and the observations 
about the comparison 
of HAZUS-MH estimates with observed historical data from past hurricane events. 
This section 
presents general and detailed conclusions and recommendations for post-disaster 
data collection, 
model improvements, and software functionality enhancements. To provide 
potential future 
HAZUS-MH improvements, the recommendations are relatively ranked and listed in 
order of 
importance under the data collection, modeling, and software functionality 
headers below. 

4.1 Conclusions 
To achieve better agreement, the HAZUS-MH default inventory can be improved with 
more 
current and accurate data, especially for public and critical facilities, such 
as schools and 
government facilities. Also, the some of the observed data received for this 
study did not allow 
for direct comparisons with HAZUS-MH results, as data that were collected for 
past disasters did 
not differentiate between wind versus flood damage, or the data attributes 
needed for comparison 
with HAZUS-MH results were not always presented in a manner for direct 
comparisons. For 
example, ARC PDA data included damage from both wind and flood for Hurricane 
Ivan, and 
commercial qualitative damage data did not appear to have been collected. HAZUS-
MH does not 
generate results that can be compared with the attributes of some of the damage 
and loss data 
that it is currently collected. For example, the damaged building count by 
qualitative damage 
states are not generated for all critical facilities, only those which are 
represented in the general 
occupancy class such as educational facilities (EDU). 

The observed data for the Hurricane Charley study region compared well with 
HAZUS-MH (1) 
residential qualitative damage; (2) residential, commercial, and industrial 
economic loss; and (3) 
short-term shelter demand estimates. There was better agreement at the regional 
level, as seen in 
the Hurricane Charley study region versus the results for one county, Escambia 
County, in the 
Hurricane Ivan study region. This appears to be due to several factors. It 
appears that the 
HAZUS-MH results consistently underestimated for Escambia County since Hurricane 
Ivan 
caused both flood and wind damage and loss. Another possible explanation, could 
be that the 
there is better default inventory data for the southwest Florida region versus 
the northwest 
Florida region. 

HAZUS-MH public and critical facilities qualitative damage (i.e., for schools) 
and economic loss 
estimates did not compare well with observed data. HAZUS-MH consistently and 
fairly 
significantly underestimated economic loss for public and critical facilities. 
This appears to be 
due to several factors. The HAZUS-MH underestimates may be attributed to the age 
and source 
of the critical facilities default inventory in HAZUS-MH, which is not as 
current as the general 
building stock, specifically the residential building stock. The residential 
building stock 
valuations are current as of 2005. However, the critical facilities data are 
current as of 2001. The 
critical facilities default data were collected from national and state data 
providers and likely 
does not reflect more accurate observed data that is available from local 
governments. Also, the 
HAZUS-MH general national building stock inventory is not current or accurate 
for those 
specific occupancy classes that were used for the critical facility comparisons 
(i.e., EDU for 
schools, GOV for public facilities, and COM6 for hospitals). Observed data such 
as FEMA PA 
data can include costs to repair the structure beyond its pre-disaster 
condition. For example, 

April 2007 




FEMA HMTAP 440  HURRICANE WIND MODEL VALIDATION STUDY 

additional funding can be provided to upgrade public facilities using 406 hazard 
mitigation, or 
upgrade to current building codes and design standards. Therefore, the public 
assistance 
economic losses will be greater than the HAZUS-MH estimates. 

The level of agreement between HAZUS-MH and observed data for site specific 
critical facilities 
varied. HAZUS-MH site specific qualitative damage estimates were in good 
agreement for 80 
percent of the sites for Hurricane Charley and 50 percent of the sites for 
Hurricane Ivan. 
Considering that HAZUS-MH was designed to be used at a larger scale (i.e., 
region, county), it 
appears that the analysis showed that HAZUS-MH estimates compared reasonably 
well with the 
observed damage at the site specific level. However, site specific economic loss 
estimates were 
underestimated by HAZUS-MH. HAZUS-MH wind damage curve estimates were in good 
agreement at the low end for 60 percent of the sites for Hurricane Charley, but 
underestimated 88 
percent of the sites for Hurricane Ivan. HAZUS-MH wind loss curve estimates 
underestimated 
for all sites for Hurricane Ivan. 

HAZUS-MH hospital loss of functionality estimates did not compare well with the 
observed 
data. The model significantly overestimated the loss of functionality (i.e., 
number of days). 

4.2 Post-disaster Data Collection Recommendations 
Below are the challenges, recommendations, and benefits for future validation 
studies. 

The specific data collection challenges included: 

 Modeled estimates for a variety of categories such as: displaced households; 
debris; 
damage and loss at the jurisdictional level; damage to site specific facilities; 
and 
vulnerability reduction measures through mitigation options were not compared 
with 
observed data because the observed data was incomplete, unavailable, or 
incomparable. 
 Data for comparison with HAZUS-MH estimates are not always separated by 
hazard; 
and damage data are not collected for commercial, industrial, public, and 
critical 
facilities. 
 Vulnerability reduction measures were reviewed and data were insufficient to 
explore 
further vulnerability measures for the mitigation options in HAZUS-MH. Further 
research could be conducted to determine what measures are being used to reduce 
vulnerability. For example, post-disaster damage assessment could be conducted 
for 
structures that have been mitigation through the Hazard Mitigation Grant 
Program. 
 Currently there is no coordination with counties prior to hurricane season to 
determine what data are already being collected that are useful for future 
HAZUSMH validation studies and to determine which additional data are required 
for the 
local level. There is limited staff available to collect detailed HAZUS-MH data 
after 
an event. 
 After a disaster event, limited staff is available to collect detailed HAZUS-
MH data, 
because of numerous response and recovery activities. As such, it was a strain 
on 
LTRO staff resources to compile the data needed for this validation study. If a 
county 
is severely impacted, it is suggested to send support staff to assist with data 
collection 
April 2007 




FEMA HMTAP 440  HURRICANE WIND MODEL VALIDATION STUDY 

efforts, as several of the counties contacted during this study had a limited 
number of 
staff that was very involved with response and recovery activities. 

Specific data collection and analysis recommendations include: 

 It is recommended that FEMA convene a focus group to determine the appropriate 
data to collect to enhance the default inventory and to document observed 
disaster 
impacts. The focus group should develop a pre-disaster local data gap analysis 
and 
data gathering coordination groups for enhanced planning and mitigation efforts. 
 It is recommended that FEMA encourage state and local jurisdictions to use the 
data 
collection template (refer to Appendix A) developed for this project to help 
consistently collect data. This data collection template should be examined by 
the 
focus group to ensure that all necessary data are being requested. 
 The value and usefulness of these validation studies requires more than 
readily data 
and could be focused for further study. Many data sets were incomplete, not 
available 
or too specific to be used to compare with an aggregate model. It is recommended 
that future validation studies should be conducted at a regional level, for 
counties that 
only experienced wind damage, and should include data for only wind impacts. 
 It is recommended that FEMA prepare prewritten rapid response HMTAP task 
orders 
for the types of HAZUS-MH data needed to be collected so deployment to collect 
the 
data is executed shortly after the event. 
 The benefit for future studies is that better data collection enhances the 
value and 
usefulness of HAZUS-MH to support all areas of disaster operations. 
4.3 Model Improvement Recommendations 
The following were the model challenges for this validation: 

 When the study regions were exported, all of the results were not 
automatically 
exported with the study region. As such, sharing the results between HAZUS-MH 
users involved exporting individual results tables. 
 The damaged building count is generated from the general building stock for 
general 
occupancy classes, but not specific occupancy classes. Damage counts are not 
provided for critical facilities in HAZUS-MH. 
 The default general building stock for the grade school occupancy class (i.e., 
EDU1) 
does not include all of the schools that are in the critical facilities default 
inventory. 
 For the site-specific critical facilities analysis, all three hazard scenarios 
produced the 
same level of damage for all sites. It appears that varying the wind speed did 
not have 
an effect on the damage states at the site-level, as it did at the aggregate 
level. As 
such, further analysis should be conducted to determine whether HAZUS-MH is 
valid 
for site-specific analysis. 
Below are the recommendations for model improvements for future validation 
studies: 

April 2007 




FEMA HMTAP 440  HURRICANE WIND MODEL VALIDATION STUDY 

 Develop the capability to retain the results that have been run in HAZUS-MH 
when it 
is exported. 
 Develop the capability to provide damage counts for critical facilities, and 
economic 
loss for critical facilities at the site specific level. 
 Develop a process to permit local governments to submit updated building stock 
inventory for use with HAZUS-MH. 
 When possible, develop the functionality in HAZUS-MH models to display and 
export results the same way for all models. 
4.4 Software Functionality Enhancement Recommendations 
The following were the software challenges for this validation: 

 Damage and loss estimates are not provided at the zip code, jurisdictional, or 
site 
specific levels by HAZUS-MH; this would improve the usefulness of the model for 
comparative analysis. 
 HAZUS-MH analysis parameters revert to the default parameters after an 
analysis is 
run. Therefore, it can not determine which parameters were selected for analysis 
after 
the analysis has been run. Also, when all parameters are not selected for 
analysis, 
HAZUS-MH does not always generate all the results specified. 
 Damaged building counts are not provided for specific occupancy classes or 
critical 
facilities. 
Below are the recommendations for future validation studies: 

 Develop the capability in HAZUS-MH to produce results at the zip code, 
jurisdictional, and site specific levels. Develop capability to select zip code 
or 
jurisdiction level data attributes from the map view. This functionality will 
allow for 
more detailed comparisons and validations of results. 
 Develop the capability in HAZUS-MH to identify which analysis parameters have 
been run, instead of the analysis screen reverting back to default analysis 
parameter 
settings. Also, develop the capability within HAZUS-MH to run individual 
analysis 
parameters. Although it appears that this can be done, sometimes the results are 
not 
provided consistently. 
 Develop the functionality to provide the damaged building count for specific 
occupancy classes and critical facilities in the wind model. This functionality 
will 
allow for more detailed comparisons and validations of results. 
 Develop the capability to increase or decrease wind speeds by a percentage, 
instead of 
having to enter this data manually, and have the option to modify the maximum 
radius winds and central pressure that corresponds with the wind speed 
modification. 
Automate this process instead of having to enter this data by census block or 
tract or 
through sequel server. 
April 2007 




FEMA HMTAP 440  HURRICANE WIND MODEL VALIDATION STUDY 

 In the wind speed map legend that is generated by HAZUS-MH, develop breaks in 
the range that do not overlap. For example, instead of having the range of wind 
speeds overlap (<50, 50  65, 65  80, etc.), do not have them overlap (<50, 51 
 65, 
66  80, etc.). Currently when the wind speed maps are created with the wind 
speed 
labels listed over the census tracts on the map, there are cases where different 
colored 
tracts show up with the same wind speed label. 
 Allow more standard ArcGIS features in HAZUS-MH such as ability to sum columns 
in attribute tables, without first having to export as a data layer. 
April 2007 




FEMA HMTAP 440  HURRICANE WIND MODEL VALIDATION STUDY 

5.0 SOURCES 
Federal Emergency Management Agency (FEMA), 2006. HAZUS-MH Models. April 6, 
2006. 
<www.fema.gov/plan/prevent/hazus/hz_models.shtm>. Downloaded 4/19/06. 

National Climatic Data Center (NCDC), 2006. Billion Dollar U.S. Weather 
Disasters. March 15, 
2006. <www.ncdc.noaa.gov/oa/reports/billionz.html#chron>. Downloaded 3/17/06. 

NCDC, 2004. Climate of 2004 Atlantic Hurricane Season. December 13, 2004. 
<www.ncdc.noaa.gov/oa/climate/research/2004/hurricanes04.html>. Downloaded 
3/20/2006. 

NCDC, 2004. Climate of 2005 Atlantic Hurricane Season. January 13, 2006. 
<www.ncdc.noaa.gov/oa/climate/research/2005/hurricanes05.html>. Downloaded 
3/20/2006. 

April 2007 




TASK ORDER 440  HURRICANE WIND MODEL VALIDATION STUDY 

APPENDIX A 
Templates for Collecting Damaged Data 


Appendix A provides data templates that were used to collect data. Data were 
collected for 
damages and losses to residential, commercial, and industrial structures and 
critical facilities. 

A-1. County-Level Data Collection Form 

Data were requested from Escambia and Santa Rosa Counties for Hurricanes Dennis 
and Ivan; 
and Charlotte, Hardee, DeSoto, and Orange for Hurricanes Charley, Frances, and 
Jeanne, using 
the following data collection form: 

Local Government Damage Survey Data Collection 

__________ County
Hurricane ___________: Flood 


An analysis is being conducted to compare HAZUS-MH estimates with known damages 
and 
losses caused during the 2004 and 2005 hurricane seasons. Results will be used 
to improve the 
models accuracy. 

Please provide as much data that is available in the tables below, or in your 
spreadsheet format 
(if already available). Please list separately by residential, commercial, and 
industrial, as data 
are available. 

Also, please send Residential Substantial Damage Estimates and local National 
Flood 
Insurance Claims Payments data for flood damages for county and municipal 
damages. 

Please contact <insert staff> at <insert phone number & e-mail> for more 
information. 

Thank you. 

General Occupancy Damage and Loss 

The following clarifications pertain to residential, commercial, and industrial 
tables: 

(1) Total Replacement Value is the assessed value of structures in the county. 
(2) Minor includes minor damage to roofs, siding, decking, few broken windows, 
etc. 
(3) Major requires substantial repairs to house before it is safe for use. 
Repairs will take a few 
weeks. 
(4) Destroyed means total loss/must be demolished. 
April 2007 A-1 




TASK ORDER 440  HURRICANE WIND MODEL VALIDATION STUDY 

(5) Business interruption loss refers to disaster impacts such as revenue 
loss. 
1.0 RESIDENTIAL STRUCTURES 
Counties 
Affected 
Total # of 
Residential 
Structures 
Total 
Replacement 
Value of 
Residential 
Structures 
(1) 
Total # 
Residential 
Structures 
Damaged 
Total # 
Residential 
Structures 
with Minor 
Damage (2) 
Total # 
Residential 
Structures 
with MajorDamage (3) 
Total # 
Residential 
Structures 
Destroyed 
(4) 
Economic 
Loss to 
Residential 
Buildings 
(repair cost) 
Unincorporated 
Add Cities, Towns 
Countywide Total 
2.0 COMMERCIAL STRUCTURES
Counties 
Affected 
Unincorporated 
Total # of 
Commercial 
Structures 
Total 
Replacement 
Value (1) 
Total # 
Commercial 
Structures 
Damaged 
Total # 
Commercial 
Structures 
with Minor 
Damage 
Total # 
Commercial 
Structures 
with Major 
Damage 
Total # 
Commercial 
Structures 
Destroyed 
(4) 
Economic Loss to 
Commercial Buildings 
Building 
& 
Content 
Damage 
Business 
Interruption 
Loss (5) 
Add Cities, Towns 
Countywide Total 

3.0 INDUSTRIAL 
Counties 
Affected 
Unincorporated 
Total # of 
Industrial 
Structures 
Total 
Replacement 
Value (1) 
Total # 
Industrial 
Structures 
Damaged 
Total # 
Industrial 
Structures 
with Minor 
Damage 
Total # 
Industrial 
Structures 
with Major 
Damage 
Total # 
Industrial 
Structures 
Destroyed 
(4) 
Economic Loss to 
Industrial Buildings 
Building & 
Content 
Damage 
Business 
Interruption 
Loss (5) 
Add Cities, Towns 
Countywide Total 

April 2007 A-2 




TASK ORDER 440  HURRICANE WIND MODEL VALIDATION STUDY 

4.0 INJURIES AND DEATHS 
Counties 
Affected Residential 
Injuries 
Commercial Industrial Total Residential 
Deaths 
Commercial Industrial Total 
Unincorporated 
Add Cities, Towns 
Countywide Total 

5.0 ALL OCCUPANCIES (COMBINED) 
ONLY fill this out if data are not available by occupancy classes in the 
previous tables. 

Counties 
Affected 
Total # of 
Structures 
Total 
Replacement 
Value (1) 
Total # 
Residential 
Structures 
Damaged 
Total # 
Residential 
Structures 
with Minor 
Damage 
Total # 
Residential 
Structures 
with Major 
Damage 
Total # 
Residential 
Structures 
Destroyed (4) 
Economic 
Loss to 
Residential 
Buildings 
(estimated or 
actual repaircost) 
Unincorporated 
Add Cities, Towns 
Countywide Total 

Critical Facilities Damage and Loss 

The following clarifications pertain to critical facilities tables: 

(1) Total Replacement Value = Assessed value of structures in the county. 
(2) Minor  repairs can be made in 1-2 week(s); school back in use within 3-4 
weeks. 
(3) Major  repairs take 90 days or more. 
(4) Destroyed  needs to be rebuilt. 
(5) Estimated Cost of Repairs  include repair or replacement costs, as 
applicable. 
April 2007 A-3 




TASK ORDER 440  HURRICANE WIND MODEL VALIDATION STUDY 

6.0 CRITICAL FACILITIES  SCHOOLS 
Counties 
Affected 
Total # 
of 
Public 
Schools 
Total ReplaceSchoBuilding 
ment Value of 
ols (1) 
Content 
Total # 
Schools 
Damaged 
Total # 
Schools 
with 
Minor 
Damage 
(2) 
Total # 
Schools 
with 
Major 
Damage 
(3) 
Total # 
Schools 
Destroyed 
(4) 
EstimateRepaEconomic Loss: 
d Cost of 
irs (5) 
Building Content 
Unincorporated 
Add Cities, 
Towns 
Countywide Total 

7.0 CRITICAL FACILITIES  MEDICAL CARE FACILITIES 
(hospitals and nursing homes, etc.) 
Counties 
Affected 
Total # of 
Hospitals 
Total Replaof HospBuilding 
cement Cost 
itals (1) 
Content 
Total # 
Hospitals 
Damaged 
Total # 
Hospitals 
with 
Minor 
Damage 
(2) 
Total # 
Hospitals 
with 
MajorDamage 
(3) 
Total # 
Hospitals 
Destroyed 
(4) 
EconomEstimateRepaic Loss: 
d Cost of 
irs (5) 
Building Content 
Unincorporated 
Add Cities, Towns 
Countywide Total 

8.0 CRITICAL FACILITIES  FIRE STATIONS (FS) 
Total Replacement Cost of 
FS (1) 
Economic Loss: 
Estimated Cost of 
Repairs (5) Counties 
Affected 
Total 
# of 
FS 
Building Content 
Total # 
FS 
Damaged 
Total # 
FS with 
Minor 
Damage 
(2) 
Total # 
FS with 
Major 
Damage 
(3) 
Total # FS 
Destroyed 
(4) 
Building Content 
Unincorporated 
Add Cities, Towns 
Countywide Total 
April 2007 A-4 




TASK ORDER 440  HURRICANE WIND MODEL VALIDATION STUDY 

9.0 CRITICAL FACILITIES  POLICE STATIONS (PS) 
Counties 
Affected 
Total 
# of 
PS 
Total ReplacePSBuilding 
ment Cost of 
(1) 
Content 
Total # 
PS 
Damaged 
from 
Wind 
Total # 
PS with 
Minor 
Damage 
(2) 
Total # 
PS with 
MajorDamage 
(3) 
Total # PS 
Destroyed 
(4) 
Estimated Co(5) 
Economic Loss: 
st of Repairs 
Building Content 
Unincorporated 
Add Cities, Towns 
Countywide Total 

10.0 CRITICAL FACILITIES  SHELTERS 
(primarily schools, churches, civic centers, senior centers, etc.) 
Counties 
Affected 
Total # 
of 
Shelters 
ToCost of SBuilding 
tal Rephelter (1) 
Content 
lacement 
Total # 
Shelters 
Damaged 
from 
Wind 
Total # 
Shelters 
with 
Minor 
Damage(2) 
Total # 
Shelters 
with 
Major 
Damage(3) 
Total # 
Shelters 
Destroyed 
(4) 
Estimated Co(5) 
Economic Loss: 
st of Repairs 
Building Content 
Unincorporated 
Add Cities, Towns 
Countywide Total 

11.0 DEBRIS ESTIMATES 
Please provide estimates or known quantities (volume  cubic yard, weight  
tons) for occupancy-class generated debris and or total debris (whichever is 
available): 

Counties 
Affected ResidVegetative 
ential 
C&D 
CommVegetative 
Debrercial 
C&D 
is GeInduVegetative 
nerated 
strial 
C&D 
ToVegetative 
tal 
C&D 
Unincorporated 
Add Cities, Towns 
Countywide Total 

April 2007 A-5 




TASK ORDER 440  HURRICANE WIND MODEL VALIDATION STUDY 

APPENDIX B 

Detailed Raw Data Obtained for Observed Damage 

The data included in Appendix B was collected from local, state, and federal 
government 
agencies and national programs and organizations. Data were provided for damage 
and economic 
loss for Hurricanes Ivan and Dennis in Escambia and Santa Rosa Counties; 
Hurricane Charley in 
Charlotte, DeSoto, Hardee, Lee, Polk, Orange, and Osceola Counties; Hurricane 
Frances in 
Brevard, Indian River, Martin, Okeechobee, Palm Beach, and St. Lucie Counties; 
and Hurricane 
Jeanne in Martin and St. Lucie Counties. 

B-1. Qualitative Damage Estimates 

B-1.1 Residential Damage 

Residential damage data were requested from the American Red Cross (ARC), the 
Florida 
Department of Insurance Regulation (FLDOIR), and from the counties that are 
being assessed 
for this study. 

Data: PDA 
Source: ARC 
Usefulness: Provided qualitative damage estimates of minor and major damage and 
destruction 
to residential buildings (e.g., single-family dwelling, apartment/multi-family 
unit, and 
manufactured homes). 
Limitations: These data were collected through windshield survey, not a physical 
inspection of 
the damaged structures. Qualitative damage types are not the same as HAZUS 
damage types; 
however, an analysis can be performed of the damage descriptions to match the 
ARCs damage 
types with HAZUS damage types. Damages are not separated for wind versus flood 
damage. 
Hurricanes/County(ies): Hurricanes Ivan in Escambia and Santa Rosa Counties; 
Hurricane 
Charley in Charlotte, DeSoto, Hardee, Lee, Polk, Orange, and Osceola Counties; 
and Hurricane 
Frances in Brevard, Indian River, Martin, Okeechobee, and Palm Beach Counties. 

Note: PDA data were not received for Dennis or Jeanne. 

Whenever damage states are 0, it is assumed that this data were not available. 

Table 1. Preliminary Damage Estimates - Hurricane Charley  Charlotte County 

Dwelling Type Destroyed Major Minor Affected Inaccessible Total 
Single Family Dwelling 5,013 12,006 12,457 8,373 200 38,049 
Apartment/Multi-Family Unit 0 0 0 0 0 0 
Mobile Home 0 0 0 0 0 0 
Unknown Dwelling Type 0 0 0 0 0 0 
Sub-Total 5,013 12,006 12,457 8,373 200 38,049 

April 2007 B-1 




TASK ORDER 440  HURRICANE WIND MODEL VALIDATION STUDY 

Table 2. Preliminary Damage Estimates - Hurricane Charley  DeSoto County 

Dwelling Type Destroyed Major Minor Affected Inaccessible Total 
Single Family Dwelling 149 968 981 0 0 2,098 
Apartment/Multi-Family Unit 21 96 70 0 0 187 
Mobile Home 3,474 1,607 969 0 0 6,050 
Unknown Dwelling Type 0 0 0 0 0 0 
Sub-Total 3,644 2,671 2,020 0 0 8,335 

Table 3. Preliminary Damage Estimates - Hurricane Charley  Hardee County 

Dwelling Type Destroyed Major Minor Affected Inaccessible Total 
Single Family Dwelling 122 775 1,791 1,054 153 3,895 
Apartment/Multi-Family Unit 0 0 84 0 0 84 
Mobile Home 245 277 613 524 308 1,967 
Unknown Dwelling Type 0 0 0 0 0 0 
Sub-Total 367 1,052 2,488 1,578 461 5,946 

Table 4. Preliminary Damage Estimates - Hurricane Charley  Lee County 

Dwelling Type Destroyed Major Minor Affected Inaccessible Total 
Single Family Dwelling 326 630 6,321 8,665 0 15,942 
Apartment/Multi-Family Unit 0 3 158 780 0 941 
Mobile Home 5 21 338 203 0 567 
Unknown Dwelling Type 0 0 0 0 0 0 
Sub-Total 331 654 6,817 9,648 0 17,450 

Table 5. Preliminary Damage Estimates - Hurricane Charley  Orange County 

Dwelling Type Destroyed Major Minor Affected Inaccessible Total 
Single Family Dwelling 2 177 2,036 o o 2,215 
Apartment/Multi-Family Unit 0 0 0 0 0 0 
Mobile Home 0 0 0 o o 0 
Unknown Dwelling Type 0 0 0 0 0 0 
Sub-Total 2 177 2,036 0 0 2,215 

Table 6. Preliminary Damage Estimates - Hurricane Charley  Osceola County 

Dwelling Type Destroyed Major Minor Affected Inaccessible Total 
Single Family Dwelling 137 487 149 0 5,000 5,773 
Apartment/Multi-Family Unit 0 0 0 0 0 0 
Mobile Home 0 0 0 0 0 0 
Unknown Dwelling Type 0 0 0 0 0 0 
Sub-Total 137 487 149 0 5,000 5,773 

April 2007 B-2 




TASK ORDER 440  HURRICANE WIND MODEL VALIDATION STUDY 

Table 7. Preliminary Damage Estimates - Hurricane Charley  Polk County 

Dwelling Type Destroyed Major Minor Affected Inaccessible Total 
Single Family Dwelling 2,012 1,782 1,700 0 0 5,494 
Apartment/Multi-Family Unit 0 0 0 0 0 0 
Mobile Home 0 0 0 0 0 0 
Unknown Dwelling Type 0 0 0 0 0 0 
Sub-Total 2,012 1,782 1,700 0 0 5,494 

Table 8. Preliminary Damage Estimates - Hurricane Ivan  Escambia County 

Dwelling Type Destroyed Major Minor Affected Inaccessible Total 
Single Family Dwelling 2,699 6,084 17,280 23,973 684 50,720 
Apartment/Multi-Family Unit 2,217 2,373 3,212 3,259 0 11,061 
Mobile Home 308 928 2,434 2,666 0 6,336 
Unknown Dwelling Type 0 0 0 0 0 0 
Sub-Total 5,224 9,385 22,926 29,898 684 68,117 

Table 9. Preliminary Damage Estimates - Hurricane Ivan  Santa Rosa County 

Dwelling Type Destroyed Major Minor Affected Inaccessible Total 
Single Family Dwelling 796 2,469 5,641 10,054 245 19,205 
Apartment/Multi-Family Unit 33 261 414 445 0 1,153 
Mobile Home 109 296 959 1,106 44 2,514 
Unknown Dwelling Type 0 0 0 0 0 0 
Sub-Total 938 3,026 7,014 11,605 289 22,872 

Table 10. Preliminary Damage Estimates - Hurricane Jeanne  Martin County 

Dwelling Type Destroyed Major Minor Affected Inaccessible Total 
Single Family Dwelling 32 762 1,639 1,662 38 4,133 
Apartment/Multi-Family Unit 8 192 531 130 1 862 
Mobile Home 141 389 540 462 3 1,535 
Unknown Dwelling Type 0 0 0 0 0 0 
Sub-Total 181 1,343 2,710 2,254 42 6,530 

Table 11. Preliminary Damage Estimates - Hurricane Jeanne  St. Lucie County 

Dwelling Type Destroyed Major Minor Affected Inaccessible Total 
Single Family Dwelling 263 781 2,019 12,031 0 15,094 
Apartment/Multi-Family Unit 37 56 0 0 0 93 
Mobile Home 428 1,031 1,206 631 0 3,296 
Unknown Dwelling Type 0 0 0 0 0 0 
Sub-Total 728 1,868 3,225 12,662 0 18,483 

April 2007 B-3 




TASK ORDER 440  HURRICANE WIND MODEL VALIDATION STUDY 

Background information regarding PDA qualitative damage states versus HAZUS 
damage states: 

Table 12. PDA Damage State Definitions 

Destroyed  Structure is a total loss or damaged to such an extent that repairs 
are not economically feasible. 
Any one of the following may constitute a status of destroyed: 

. Repair of structure is not economically feasible. 
. Structure is permanently uninhabitable. 
. There is a complete failure of major structural components (collapse of walls 
or roof). 
. Unaffected structure will be required to be removed or demolished due to 
ordinance (e.g., beachfront 
homes removed due to severe beach erosion). 
Major  Structure has sustained structural or significant damage, is 
uninhabitable, and requires extensive 
repairs. Any of the following may constitute major damage: 

. Substantial failures to structural elements of the residence (e.g., walls, 
floors, foundations). 
. Damage to the structure exceeds the Disaster Housing Program, Home Repair 
Grant maximum 
($10,000). 
. General exterior property damage exceeds the Disaster Housing Program Home 
Repair Grant 
maximum (e.g., roads and bridges, wells, earth movement) and has more than 50 
percent damage to 
the structure. 
. Damage will take more than 30 days to repair. 
Minor  Structure is damaged and uninhabitable, but may be made habitable in a 
short period of time with 
home repairs. Any of the following may constitute minor damage: 

. Structure can be repaired within 30 days. 
. Structure has more than $100 of eligible habitability items through the 
Disaster Housing Program, 
Home Repair Grant; or has less than $10,000 of eligible habitability items 
through the Disaster Repair 
Program, Home Repair Grant. 
. Damage repair costs are less than 50 percent of total value of house. 
Affected  Sustained some damage to structure and contents, but is habitable 
without repairs, and damage to 
habitability items is less than Disaster Housing Program, Home Repair Grant 
minimum. 

The PDA definitions are very comparable to the ones in HAZUS. For reporting 
purposes, the 
following alignment provides the appropriate mapping between the two: 

Human Services PDA HAZUS-MH Damage States 

Affected = Minor Damage 
Minor Damage = Moderate Damage 
Major = Severe 
Destroyed = Destruction 

Data: Damaged Residential Structures 
Source: DeSoto County 
Usefulness: Provided qualitative damage estimates of minor and major damage and 
destruction 
to residential buildings. 
Limitations: These data were indicative of what has been reported as of January 
2006. DeSoto 
County provided qualitative damage estimates for residential structures that 
were slightly lower 
than the PDA data. 
Hurricanes/County(ies): Hurricane Charley in DeSoto County. 

April 2007 B-4 




TASK ORDER 440  HURRICANE WIND MODEL VALIDATION STUDY 

Table 13. Residential Damage - Hurricane Charley  DeSoto County 

County Total # of 
Buildings 
Total # 
Damaged 
Buildings 
Minor Damage Major Damage Destroyed 
DeSoto 10,700 9,672 *3,587 2,589 3,496 

* Minor 2,095 plus affected 1,492 
Data: Wind Loss Insurance Claims Data 
Source: FLDOIR 
Usefulness: Provided insurance data for total claims and total losses (i.e., 
destroyed) for 
residential properties. 
Limitations: These data only reflect insured property losses, but includes 
damage to appurtenant 
structures and automobiles, which are not modeled in HAZUS-MH. 
Hurricanes/County(ies): All hurricanes and all counties involved in this study. 

Table 14. Residential Damage - Hurricane Charley, Charlotte County and 
Hurricane Ivan, Escambia County (FLDOIR) 

County Disaster # of Claims Reported # of Claims Total Loss 
Escambia Ivan 101,715 3,859 
Santa Rosa Ivan 43,785 2,025 
Escambia Dennis 14,330 57,416,279 
Santa Rosa Dennis 21,648 127,077,866 
Charlotte Charley 83,085 8,601 
DeSoto Charley 13,209 1,714 
Hardee Charley 8,101 638 
Lee Charley 78,317 1,945 
Orange Charley 99,164 1,017 
Osceola Charley 42,204 843 
Polk Charley 49,595 1,857 
Brevard Frances 61,321 2,838 
Indian River Frances 31,627 1,730 
Martin Frances 24,064 1,288 
Okeechobee Frances 6,783 587 
Palm Beach Frances 107,926 2,534 
St. Lucie Frances 56,722 4,053 
Martin Jeanne 20,966 626 
St. Lucie Jeanne 31,866 960 

* Claims payments for Dennis only reflect those made as of 10/7/2005. 
Modeling Usefulness: PDA data for qualitative residential damage estimates 
(e.g., number of 
structures with minor or major damage, or destroyed) were compared with the 
damage estimates 
generated by the HAZUS-MH Hurricane Wind Model. Although it is widely known that 
PDA 
data are based on windshield survey and estimates can be somewhat subjective, 
this appears to 
be the best available data set for comparison purposes for this validation 
study. Insurance claims 
were much higher than the HAZUS-MH estimates for total damage and the PDA 
estimates, as 

April 2007 B-5 




TASK ORDER 440  HURRICANE WIND MODEL VALIDATION STUDY 

the FDOIR data included claims paid for items not modeled in HAZUS-MH such as 
boats and 
cars. Therefore the number of claims is not suitable for comparison with HAZUS-
MH estimates. 

General Limitations: Residential damage data likely contains damage caused by 
both flood and 
wind, which are not able to be separated. This does not appear to be a concern 
for counties that 
were impacted by Hurricane Charley, as this was predominantly a wind event. 
However, this is a 
concern for counties that were impacted by Frances, Ivan, Jeanne, and Dennis. In 
addition, it will 
be difficult to determine which damages occurred from Ivan versus Dennis or 
Jeanne versus 
Frances, since these storms hit the same areas. As such, all prior damages had 
not yet been 
repaired from the first hurricane when the second hurricane caused damage. 

B-1.2 Commercial and Industrial Damage 

Commercial and industrial damage data were requested from the ARC, the Insurance 
Services 
Office, Inc. (ISO), and from the counties that are being assessed for this 
study. No data were 
received. ISO data were received for residential, commercial, and industrial 
damage. These data 
are not reproduced per licensing agreement requirement. 

B-1.3 Critical Facilities Damage 

Aggregate Critical Facilities Damage 

Critical facilities damage data were requested for medical care facilities, 
schools, fire stations, 
police stations, and shelters. Data were requested from the Florida Hospital 
Association and from 
the counties that are being assessed for this study. 

Data: Damaged Critical Facilities 
Sources: Escambia County School Board Risk Manager, Escambia County, and DeSoto 
County. 
Usefulness: Provided qualitative damage estimates of minor, moderate, and major 
damage to 
critical facilities from wind. 
Limitations: Qualitative damage types are not the same as HAZUS damage types; 
however, an 
analysis can be performed of the detailed damage descriptions to match the 
countys damage 
types with HAZUS damage types. The shelter data are not a comprehensive list of 
all shelter 
damage as it only includes schools that served as shelters. 
Hurricanes/County(ies): Qualitative damage were provided for Escambia County 
schools, fire 
stations, and shelters for Hurricane Ivan, and for DeSoto County fire stations 
and shelters for 
Hurricane Charley. 

April 2007 B-6 




TASK ORDER 440  HURRICANE WIND MODEL VALIDATION STUDY 

Table 15. Critical Facilities Damage  Hurricanes Ivan and Charley 

County Disaster Critical 
Facility Type 
Total # of 
Facilities 
Total # of 
Facilities 
Damaged 
Minor Moderate Major Destroyed 
Escambia Ivan Schools n/a n/a 25 17 30 
Escambia Ivan Fire Stations 23 23 21 0 2 
Escambia Ivan Police Stations n/a n/a n/a n/a n/a 
Escambia Ivan Shelters 10 n/a 0 3 2 
DeSoto Charley Fire Stations 2 2 2 0 0 
DeSoto Cities Total Charley Fire Stations 2 2 1 1 
DeSoto Charley Shelters 1 1 

n/a = not available 

Schools Damage 

Table 16. Detailed Qualitative Damage Estimates  Hurricane Ivan  Escambia 
County Schools 

School Name Major Moderate Minor 
Jim Allen Portables-Major damage; 
Minor roof leaks over 
kitchen. 
Bellview Elementary Roof missing; major water 
damage; siding & facial; 
portables-minor damage. 
Beulah Damaged bus canopy; roof 
damage; major water 
damage. 
Bibbs, Spencer Siding 100-200 rooms; front 
entrance awnings; awnings. 
Blue Angels, (Shelter) Major damage east side 
structural; minor damage 
throughout; portables 
damaged. 
Bratt Roof blown off; major water 
damage; siding/awnings; 
bldg. 3-severe water 
damage. 
Brentwood Elementary Roof missing main hallways; 
extensive water damage all 
spaces. 
Byrneville Portables - tree damage 
Hellen Caro Roof leaks-major in some 
areas; broken windows 615; 
portables A & D-windows. 
Century/Carver 2 trees on new building; 1 
tree on #2 portable. 

April 2007 B-7 




TASK ORDER 440  HURRICANE WIND MODEL VALIDATION STUDY 

School Name Major Moderate Minor 
Cook, N. B. Main hall skylight gone; 
major water damage; roof 
damage; second floor-
extensive damage; trap door 
(over stage) open; kitchen 
exhaust could fall. 
Cordova Park Main bldg.-ceiling; A/C 
damaged by walkway; rms. 
111,114-water damage. 
Edgewater Major roof damage - bldg. 
#2; windows; rms. 
138,135,130 extensive. 
Ensley Roof is good; awnings; trees; 
*power line 
Ferry Pass Elementary No assessment 
Hallmark Minor leaks; many trees 
down. 
Holm Minor roof damage; kitchen, 
rm. 325; awnings. 
Lincoln Park Roof leaks throughout; 
awnings; walkways. 
Lipscomb (Shelter) Lift station over flowing; 
classroom roof leaks all 
wings; windows & siding on 
portables. 
Longleaf Major roof damage; water 
damage all spaces except 
new wing. 
McArthur Roof damage-819; siding743; bldg. 99; roof leaks-
various minor; awnings. 
Molino Park (Shelter) Awnings; roof leaks; major 
water damage (admin. 
spaces). 
Montclair Trees; portables; roof vents. 
Myrtle Grove Extensive roof damage; 
shingles; leaks in most 
spaces; trees on portables. 
Navy Point Major walls rms. 30-33; 
cafeteria-pine tree; 
walkways; 2nd floor 
Windows in 2 Classrooms. 
Oakcrest Roof damage all spaces; 
awnings; portables-major 
damage. 
Pine Meadow Major trees down; roof 
damage minor-104, 101, 
204, 105, 642, 256; water 
damage; office-major 
damage. 
Pleasant Grove Building 3-roof damage; 
portables electrical lines. 

April 2007 B-8 




TASK ORDER 440  HURRICANE WIND MODEL VALIDATION STUDY 

School Name Major Moderate Minor 
Scenic Heights 300 bldg.-tree on roof; 
awnings; vent fan off. 
Semmes Siding-portable; bldg 1 & 
bldg. 9-awning, siding; 
media center-major damage; 
backflow preventer box-
structural damage. 
Sherwood Bldg. 3-major roof damage; 
bldg. 9-no roof; major water 
damage. 
Suter, A.K. Roof-118, 112, 119, 100D, 
111, 108, 124, 100F, 107, 
107C, 205, 203, 125A, 125B; 
media addition; #99 
portable-roof sagging; 8 
trees down. 
Warrington Elementary Roof gone; ceilings; floors; 
trees; awnings; covered 
walks. 
Weis, C. A. Extensive water damage all 
spaces; awnings; roof 
damage all spaces. 
West Pensacola Extensive debris clean-up. 
Yniestra, Allie Gutters; portables settled. 
Molino (Vacant) Walkways bldgs. 2 & 3 
Bailey (Shelter) Roof system severe; 
windows out; portable 
damage. 
Bellview Middle Cafeteria/kitchen damage; 
portable damage; water 
damage; ceiling tiles & water 
all over; ese/admin. 
windows, flooding. 
Brentwood Middle Siding; roof peeling on 
trailers; minor water damage. 
Brown Barge Roof damaged over 
mechanical room; major roof 
damage-108,109,106,104, 
063,cafeteria 208, library, 
314,317; awnings, portables. 
Brownsville Gas leak-secure 14:30; 
minor water damage; 
portables-siding & windows; 
large tree on main building. 
Ferry Pass Middle Main building-shingles; 
skylights; awnings; siding; 
office flooded; major water 
damage-618, 619, 500, 501, 
516, kitchen, cafeteria, 201, 
202, bookkeepers' office. 

April 2007 B-9 




TASK ORDER 440  HURRICANE WIND MODEL VALIDATION STUDY 

School Name Major Moderate Minor 
Ransom Portables siding damaged-
all; windows portables-all; 
roof-admin.; library-roof, 
windows; trees total school; 
water main 
Ernest Ward Minor roof damage; 
walkways & awnings. 
Warrington Middle Major roof damage bldgs. 
1,2,4,5; windows on 
portables; extensive water 
damage. 
Wedgewood Major flooding; library; major 
roofing damage. 
Workman Main building-roof; library-
water damage; all awnings; 
girls' p.e.-water damage; 
cafeteria-water damage; 
cafeteria-windows; 200 wing-
roof. 
Escambia Gym major roof damage; 
major leaks; awnings; 
broken windows. 
Northview Roof leaks all spaces; minor 
water damage. 
Pensacola High Bleachers destroyed; gym 
destroyed; awnings/ramps; 
water damage; surveillance 
cameras stolen. 
Pine Forest Roof damage; cooling tower 
destroyed. 
Tate Lift station down; major 
water damage-main bldg.; 
windows-main bldg.; all 
walkways; cafeteria, gym. 
Washington Music/choral/band roof 
damage and water damage; 
minor roof damage over 
atrium; water damage-north 
classrooms. 
Woodham Cafeteria; media center 
destroyed; major roof 
damage; water damage. 
West Florida (Shelter) Major debris clean-up; major 
roof damage; bldg. 7-lost 
roof; awnings/gutters. 
Hall Center 
McDaniel Administration 2nd floor windows w/ 
extensive water damage; 
supt. office-water damage. 
Data Center Perimeter offices water 
damage; computer room dry. 
End User Support Roof gone; major water 
damage. 

April 2007 B-10 




TASK ORDER 440  HURRICANE WIND MODEL VALIDATION STUDY 

School Name Major Moderate Minor 
Walnut Hill Bus Garage Shingles; tree damage. 
Transportation Main Garage Major roof damage; 2 roll up 
doors; water leaks all over 
wash rack and electronic 
equipment. 
Andrews, Judy Broken windows; no roof or 
water damage. 
Dixon 3 broken windows. 
Clubbs, A. V. 3 trees w/ roof penetration; 
major roof leaks; water 
damage all spaces. 
Escambia Westgate Roof missing - 505, 506; 
hallway leaks @ new bldg. 
seam; cafeteria-broken 
windows, water damage. 
ESEAL Quansit huts gone; tool shed 
gone. 
Sid Nelson Roof damage all buildings, 
cafeteria kitchen-300, 500, 
400, 200; all awnings. 
McMillan/Title I Portables - heavy damage; 
minor leaks 
Petree Pre-K Wiring down; shingles blew 
off roof; tree on covered 
walkway; tree on fence. 
Pickens Windows-board covers 
missing. 
Environmental Center Many Trees on Roof. 
Warehouse Freezer up; 1 unit down; roof 
over 70 degree room. 
George Stone 
SEE WFHSAT 
Awnings/gutters; bldg. 3flooded; paint booth 
destroyed; portable 99destroyed; portable 17-blown 
off foundation; administration 
wing-roof destroyed. 
Gibson No assessment 
Old Carver No assessment 
Totals 30 17 25 

April 2007 B-11 




TASK ORDER 440  HURRICANE WIND MODEL VALIDATION STUDY 

Shelter Damage 

Table 17. Shelter Detailed Qualitative Damage Estimates  Hurricane Ivan  
Escambia County 

School Name Major Moderate Minor 
Blue Angels (Shelter) Major damage east side 
structural; minor damage 
throughout; portables 
damaged. 
Lipscomb (Shelter) Lift station over flowing; 
classroom roof leaks all 
wings; windows & siding on 
portables. 
Molino Park (Shelter) Awnings; roof leaks; major 
water damage (admin. 
spaces). 
Bailey (Shelter) Roof system severe; 
windows out; portable 
damage. 
West Florida (Shelter) Major debris clean-up; major 
roof damage; bldg. 7-lost 
roof; awnings/gutters. 
Totals 2 3 0 

Site Specific Critical Facilities Damage 

Critical facility qualitative damage data were provided from prior HAZUS-MH 
validation 
studies conducted in 2004 for Hurricanes Charley , Ivan, and Dennis. 

Data: Damaged Critical Facilities 
Sources: Hurricanes Charley (TO  332), Ivan (TO  348), and Dennis (TO  406). 
Usefulness: Provided qualitative damage estimates of minor, moderate, and major 
damage to 
critical facilities. 
Limitations: Observed damage states collected in the field can be subjective. 
Hurricanes/County(ies): Qualitative damage were provided for Osceola and Hardee 
schools, 
and a Polk County fire station; and for Escambia County schools, fire stations, 
and shelters for 
Hurricane Ivan. 

Table 18. Prior HAZUS Validation  Hurricane Charley 

County 
Type of 
Facility Facility Name Latitude Longitude 
HAZUS 
Wind 
Class 
Observed 
Damage 
Osceola EDU1 Poinciana High School 28.239121 -81.486760 SECBL Moderate 
Hardee EDU1 Wauchula Elementary 27.542352 -81.817834 MLR1 Minor 
Hardee EDU1 Hardee High School 27.529024 -81.834071 MECBL Minor 
Osceola EDU1 Thacker Elementary 28.289289 -81.424193 MECBL Minor 
Polk Fire Station Haines City Fire Dept. 28.108560 -81.622275 MECBL Minor 

April 2007 B-12 




TASK ORDER 440  HURRICANE WIND MODEL VALIDATION STUDY 

Table 19. Prior HAZUS Validation  Hurricane Ivan 

HAZUS Observed Observed 
County 
Type of 
Facility Facility Name Latitude Longitude 
Wind 
Class 
Wind 
Damage 
Flood 
Damage 
-
Escambia EDU1 Longleaf Elementary 30.486689 87.291219 CECBL Severe None 
-
Escambia EDU1 Pine Forest High School 30.293898 87.452102 CECBL Moderate None 
Escambia EDU1 
West FL High School/Beggs 
Educational Center 30.293879 
-
87.451672 CECBL Moderate None 
Santa Rosa Fire Station Navarre Beach VFD #18 30.379285 
-
86.879664 SECBL Minor Minor 
Santa Rosa EDU1 Gulf Breeze Middle School 30.370639 
-
87.176129 MECBL Minor None 
Santa Rosa Hospital 
The Friary of Baptist Health 
Care Center 30.3976 -87.03941 WMUH1 Minor None 
Santa Rosa Hospital Gulf Breeze Hospital 30.36104 -87.15677 MECBL Moderate None 
Escambia Hospital Sacred Heart Health System 30.4753 -87.21374 SECBM Minor None 
Escambia Hospital Baptist Hospital 30.43083 -87.23097 MECBM Minor None 
Escambia Hospital West Florida Hospital 30.51439 -87.21784 SECBH Major None 

Table 20. Prior HAZUS Validation  Hurricane Dennis 

HAZUS Observed Observed 
County 
Type ofFacility Facility Name Longitude Latitude 
Wind 
Class 
Wind 
Damage 
Flood 
Damage 
Escambia Fire Station Pensacola Beach FD 30.33813 87.11544 CECBL Moderate 
Moderate 
Santa Rosa EDU2 
Pensacola Jr College - Milton 
Campus 30.60474 -87.07495 MECBM 
very 
minor none 
Santa Rosa EDU1 Hobbs Middle School 30.62813 -87.06482 MECBL moderate none 
Santa Rosa COM6 Santa Rosa Medical Center 30.63452 -87.06714 MECBM 
very 
minor none 
Santa Rosa EDU1 Pearidge Elementary School 30.60832 -87.11166 MECBL moderate 
none 
Santa Rosa Fire Station 
Holley-Navarre Vol. Fire 
Dept. 30.43208 86.87494 S5 None none 
Santa Rosa EDU1 West Navarre Elem School 30.40642 86.93256 MLR1 
Very 
Minor none 
Santa Rosa EDU1 T.R. Jackson Pre-K 30.61542 87.04339 MLR1 None none 
Santa Rosa COM1 School of Readiness 30.62382 87.0368 MECBL None none 
Santa Rosa Fire Station Fire Station 18 30.37941 86.87975 MECBL Severe Moderate 
Santa Rosa COM6 Gulf Breeze Hospital 30.47532 87.21374 SECBM Minor Minor 

B-1.4 County Structures Damage 

Data: Damaged County Structures 

April 2007 B-13 




TASK ORDER 440  HURRICANE WIND MODEL VALIDATION STUDY 

Source: Escambia County 
Usefulness: Provided number of county structures damaged. 
Limitations: Data are not separated by occupancy class, or by hazard (e.g., wind 
versus flood). 
Hurricane/County: Hurricane Ivan for Escambia County 


Modeling Usefulness: There was not enough information known about which building 
occupancy classes were included in the observed data. These data were not 
suitable for 
comparison with damage estimates generated by the HAZUS-MH Hurricane Wind Model. 


Table 21. County Structure Damage  Hurricane Ivan  Escambia County 

Community 
Total # of 
Buildings 
(county 
structures) 
Total # of 
Buildings 
Damaged in 
Hurricane Ivan 
Total # of 
Buildings 
Sampled in 
Survey 
Total # of 
Buildings 
Surveyed that 
Sustained at 
Least Minor 
Damage 
Total # of 
Buildings 
Surveyed that 
Sustained at 
Least Moderate 
Damage 
Escambia County 240 192* n/a n/a n/a 

n/a = not available 
County structures damaged (this does not include parks and recreation) 


Data: Damaged Structures (countywide) 
Source: Santa Rosa County Property Appraiser 
Usefulness: Providec qualitative damage estimates of minor and major damage for 
all buildings 
(occupancies). 
Limitations: County data were not useable as they were not separated by 
occupancy class, or by 
hazard (i.e., wind versus flood). As such, ARC PDA data were used for analysis 
purposes, so that 
all qualitative damage estimates for residential units will come from one source 
to be consistent. 
Hurricanes/County(ies): Hurricane Dennis in Santa Rosa County 

Table 22. Countywide Damaged Structures - Hurricane Dennis  Santa Rosa 
County 

Community 
Total # of 
Buildings 
(all occupancies) 
Total # of 
Buildings 
Damaged in 
Hurricane 
Dennis 
Total # of 
Buildings 
Sampled in 
Survey 
Total # of 
Buildings 
Surveyed that 
Sustained at 
Least Minor 
Damage 
Total # of 
Buildings 
Surveyed that 
Sustained at 
Least Moderate 
Damage 
Navarre 11,024 200 41 10 2 
Bagdad 2,400 29 9 9 7 
Milton 3,246 28 164 69 27 
Navarre Beach 1,729 0 156 126 36 
Pensacola Beach 0 0 5 0 0 
Gulf Breeze 2,850 0 12 0 0 
Totals 21,249 257 387 214 72 

April 2007 B-14 




TASK ORDER 440  HURRICANE WIND MODEL VALIDATION STUDY 

Loss of Function 

B-2.1 Critical Facilities 

2.3.1.6 Critical Facilities Loss of Function 
Critical facilities loss of function data (e.g., loss of use in days) was 
requested from the FHA and 
the counties included in this study. 

Data: Hospital closures 
Source: Florida Hospital Association 
Usefulness: Providec economic loss estimates by county. 
Limitations: Does not indicate whether closures were due to physical damage or 
loss of 
power/water. Does not list damage for each hospital, only by county. 
Hurricanes/County(ies): Hurricane Ivan for Escambia County, and for Charlotte, 
Hardee, 
DeSoto, and Orange Counties for Hurricane Charley. 

Modeling Usefulness: All relevant available data for loss of functionality for 
critical facilities 
(e.g., loss of use in days) were compared with the loss of functionality 
estimates generated by the 
HAZUS-MH Hurricane Wind Model. 

B-2.1.1 Medical Care Facilities 

Table 23. Hospital Loss of Function  Hurricane Ivan and Charley 

County Disaster 
Number of 
Hospitals in County 
(Acute/Other) 
Number 
Responding to 
FHA survey Closures Days Closed 
Escambia Ivan 3/3 2 0 0 
Santa Rosa Ivan 3/1 DNR DNR DNR 
Charlotte Charley 3 2 1 10 
DeSoto Charley 1 1 1 2 
Hardee Charley 1 1 0 0 
Orange Charley 10/3 6 0 0 

DNR = Did not report 

B-2. Shelter Demand 

B-3.1 Shelter Population 

Pre- and post-disaster shelter population data were requested from the ARC. 

Data: Pre- and post-disaster shelter counts  Hurricanes Ivan and Dennis 
Source: Ivan: ARC; Dennis: FL Division of Emergency Management (FDEM) 
Usefulness: Provided shelter population, from which to derive the number of 
displaced 
households by county. 

April 2007 B-15 




TASK ORDER 440  HURRICANE WIND MODEL VALIDATION STUDY 

Limitations: Does not include population that evacuated outside the county. This 
data might 
include population that were displaced from their homes short-term due to 
electric and 
water/sewage damage, not structural damage. Data does not indicate which 
populations sought 
shelter to avoid coastal flooding versus wind impacts. 
Hurricanes/County(ies): All hurricanes and all counties involved in this study. 

Modeling Usefulness: Post-disaster shelter population counts were compared with 
the shelter 
population estimates generated by the HAZUS-MH Hurricane Wind Model. 

Table 24. Shelter Populations - Hurricanes Charley, Dennis, Frances, Ivan, and 
Jeanne  All Counties 

County Disaster Pre-Landfall Shelter Population Post-Landfall Shelter 
Population* 
Escambia** Ivan 7,692 978 
Santa Rosa** Ivan 636 349 
Escambia*** Dennis 2,472 100 
Santa Rosa*** Dennis 878 35 
Charlotte Charley 425 425 
DeSoto Charley 1,374 1,400 
Hardee Charley 537 537 
Lee Charley 8,129 1,191 
Orange Charley 727 70 
Osceola Charley 1,714 102 
Polk Charley 3,390 14 
Brevard Frances 9,701 10,654 
Indian River Frances 4,055 1,205 
Martin Frances 5,558 275 
Okeechobee Frances 1,595 250 
Palm Beach Frances 4,348 17,585 
St. Lucie Frances 4,896 2,745 
Martin Jeanne 1,873 118 
St. Lucie Jeanne 2,118 536 

* Data includes post-land fall shelter population the night after the hurricane 
made landfall. 
** Total provided by FDEM.
*** Total provided by the ARC, estimated on post-landfall population.
B-4 Economic Loss 

B-4.1 Residential Economic Loss 

Residential economic loss data were requested from the FLDOIR, FEMAs Individual 
Assistance (IA) Program, ISO, and the counties included in this study. 

FDOIR and ISO data were received for residential damage. This data are not 
reproduced per 
licensing agreement requirement. 

Data: Wind Loss Insurance Claims Data; value of repair or replacement cost 
Source: FLDOIR 

April 2007 B-16 




TASK ORDER 440  HURRICANE WIND MODEL VALIDATION STUDY 

Usefulness: Provided insurance claims for residential properties. 
Limitations: These data only reflect insured property losses. 
Hurricanes/County(ies): All hurricanes and all counties involved in this study. 

Modeling Usefulness: Insurance losses were compared with loss estimates 
generated by the 
HAZUS-MH Hurricane Wind Model. It did not appear that IA data were be useful for 
this 
validation study, as the program provides loans to homeowners for $5,200 for 
property loss, 
which typically represents only a portion of the loss. 

Table 25. Department of Insurance Regulation Wind Loss Claims Payments  
Hurricanes Charley, Dennis, Frances, Ivan, and Jeanne  All Counties 

County Disaster Claims Payments* 
Escambia Ivan 1,698,123,360 
Santa Rosa Ivan 668,074,159 
Escambia Dennis 38,336,681 
Santa Rosa Dennis 56,755,861 
Charlotte Charley 2,561,459,188 
DeSoto Charley 283,494,623 
Hardee Charley 138,479,116 
Lee Charley 1,014,468,730 
Orange Charley 990,660,827 
Osceola Charley 560,179,016 
Polk Charley 554,140,262 
Brevard Frances 988,571,302 
Indian River Frances 766,420,342 
Martin Frances 423,664,555 
Okeechobee Frances 94,125,899 
Palm Beach Frances 1,165,038,320 
St. Lucie Frances 1,127,106,791 
Martin Jeanne 201,448,046 
St. Lucie Jeanne 246,490,019 

* Claims payments for Dennis only reflect those made as of 10/7/2005. 

B-4.2 Commercial and Industrial Economic Loss 

No observed data were used for this study 

Commercial economic loss data were requested from the Small Business 
Administration (SBA), 
ISO, and the counties included in this study. It was decided that SBA data would 
not reflect 
accurate economic loss, as SBA provides business loans to those who apply. 

Commercial and industrial damage data were requested from the ARC, the Insurance 
Services 
Office, Inc. (ISO), and from the counties that are being assessed for this 
study. No data were 
received. ISO data were received for residential, commercial, and industrial 
damage. This data 
are not reproduced per licensing agreement requirement. 

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TASK ORDER 440  HURRICANE WIND MODEL VALIDATION STUDY 

B-4.3 Critical Facilities Economic Loss 

B-4.3.1 Medical Care Facilities 

Critical facilities economic loss data requested from the FHA and the counties 
included in this 
study. 

Data: Hospital Economic Loss 
Source: Florida Hospital Association 
Usefulness: Provided economic loss estimates for hospitals by county. 
Limitations: The data does not list economic loss for each hospital; only by 
county. 
Hurricanes/County(ies): Economic loss data were provided for Hurricane Ivan for 
Escambia 
County, and for Charlotte, Hardee, DeSoto, and Orange Counties, for Hurricane 
Charley. 

Table 26. Hospital Economic Loss - Hurricanes Ivan and Charley 

Actual Cost 
Number of Estimates 
County Hurricane 
Hospitals in 
County 
(Acute/Other) 
Number 
Responding 
to FHA survey 
Total Cost 
for Repairs 
Related to 
Patient 
Care 
Staffing 
Costs 
Escambia Ivan 3/3 2 $20,335,000 $4,600,000 $1,040,800 
Santa Rosa Ivan 3/1 DNR DNR DNR DNR 
Charlotte Charley 3 2 $23,000,000 DNR DNR 
DeSoto Charley 1 1 $2,501,000 DNR DNR 
Hardee Charley 1 1 DNR DNR DNR 
Orange Charley 10/3 6 $284,978 $571,500 $874,666 

DNR = Did not report 

B-4.3.2 Schools 

Data: School Economic Loss 
Source: Escambia County School Board Risk Manager 
Usefulness: Provided economic losses to school buildings and educational 
facilities. 
Limitations: Economic losses are not separated by building and contents. 
Hurricane/County: Economic loss data were provided for Hurricane Ivan for 
Escambia County. 


Table 27. School Economic Loss  Hurricane Ivan  Escambia County 

Location Name Building Value Contents Value Est. Damages 
Jim Allen Elementary 4,841,890 718,190 250,000 
Judy Andrews Pre-K 995,805 149,371 20,000 
Bailey Middle 12,818,250 1,922,738 6,000,000 
Bellview Elementary 4,087,040 613,056 500,000 
Bellview Middle 8,999,640 1,349,946 450,000 
Beulah Elementary 5,042,480 756,372 750,000 
Spencer Bibbs Elementary 3,848,720 577,308 150,000 
Blue Angels Elementary 8,858,407 1,763,000 400,000 

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Location Name Building Value Contents Value Est. Damages 
Brentwood Elementary 3,756,400 563,460 700,000 
Brentwood Middle 6,766,740 1,015,011 250,000 
Brown Barge Middle 4,067,990 610,199 1,000,000 
Brownsville Middle 8,117,820 1,217,673 150,000 
Byrneville Elementary 673,840 101,076 75,000 
Hellen Caro Elementary w Cafeteria Expansion 6,491,506 966,580 400,000 
Carver Century K-8 5,253,120 911,482 100,000 
Carver (Middle Old) 6,109,809 916,471 no data 
Cook Elementary - AV Clubbs Ctr. 2,149,440 322,416 300,000 
N.B. Cook Elementary 11,000,000 1,650,000 1,000,000 
Cordova Park Elementary 3,800,400 570,060 100,000 
Dixon Elementary 3,093,680 464,052 50,000 
Edgewater Elementary 3,513,120 526,968 200,000 
Ensley Elementary 3,945,286 636,560 200,000 
Escambia High w Elevator 19,025,048 2,825,690 1,000,000 
Escambia Westgate Center & Snoezelen Bldg. 6,101,580 1,093,965 1,500,000 
Ferry Pass Elementary 4,012,400 601,860 200,000 
Ferry Pass Middle 7,110,540 1,066,581 400,000 
McMillan Learning Ctr. Goulding (Pre-K) 2,256,072 338,411 100,000 
Hallmark Elementary 2,517,200 377,580 75,000 
Holm Elementary 10,447,800 2,134,818 150,000 
Lincoln Park Elementary 3,503,600 525,540 150,000 
RC Lipscomb Elementary 6,726,320 1,008,948 150,000 
Lakeview Center no data no data no data 
Longleaf Elementary 14,587,950 866,063 10,000,000 
McArthur Elementary 4,305,760 645,864 150,000 
Molino Elementary 1,758,080 263,712 50,000 
Molino Park Elementary 10,861,556 1,200,000 200,000 
Montclair Elementary 3,445,840 516,876 75,000 
Myrtle Grove Elementary 3,869,760 580,464 300,000 
Navy Point Elementary 4,287,440 643,116 500,000 
Northview High 11,017,400 1,652,610 450,000 
Oakcrest Elementary & Media Center 4,420,534 695,348 500,000 
Pensacola High School 21,210,800 3,181,620 1,500,000 
Petree Pre-K 940,969 141,145 75,000 
Pine Forest High 16,309,500 2,446,425 1,000,000 
Pine Meadow Elementary 4,731,280 709,692 250,000 
Pleasant Grove Elementary 3,420,640 513,096 150,000 
Ransom Middle 11,023,200 1,653,480 50,000 
Scenic Heights Elementary 4,902,720 735,408 100,000 
Semmes Elementary & Media Center 4,065,964 665,762 350,000 
Sherwood Elementary 4,671,517 701,606 1,500,000 
Sidney W. Nelson Pre-K 3,658,052 548,708 300,000 
George Stone Vocational (AKA) West FL High/Beggs 
Educational Center 29,918,122 4,792,718 6,000,000 
Suter Elementary 2,800,320 405,019 400,000 
Tate High 25,367,100 3,805,065 8,000,000 

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Location Name Building Value Contents Value Est. Damages 
Ernest Ward Middle & Pump House 6,337,530 950,630 100,000 
Ernest Ward Bus Facility no data no data no data 
Warrington Elementary 3,821,680 573,252 5,000,000 
Warrington Middle 7,102,620 1,065,393 1,000,000 
Washington High 19,319,100 2,897,865 450,000 
Wedgewood Middle 6,552,270 982,841 4,000,000 
Weis Elementary 6,325,760 948,864 2,000,000 
West Pensacola Elementary 4,524,400 678,660 350,000 
Woodham High School 17,401,200 2,610,180 4,000,000 
Workman Middle & ESE Suite 9,146,601 1,363,132 4,000,000 
Yniestra Elementary 2,692,800 403,920 100,000 
Administrative Office 2,359,474 353,921 150,000 
Gibson School Federal Project 470,015 70,502 no data 
Maintenance and Transportation 1,727,933 259,190 no data 
Kirskey Warehouse 344,677 51,702 n/a 
Administrative Bldg. (End-User) 208,440 31,266 400,000 
Data Center 1,252,493 187,874 100,000 
Central Warehouse 3,408,912 511,337 75,000 
Transportation Facility 2,626,683 394,002 550,000 
Roy L. Hyatt Environmental Studios 175,805 26,371 200,000 
J.E. Hall Center-Support Svc. Facilities Planning 9,131,215 1,369,682 500,000 
Storage - Pickens Book Dept. 1,645,050 246,758 50,000 
ESEAL 1,263,731 189,560 100,000 
Bratt Elementary 3,162,640 474,396 800,000 
Occupied by Plumbers & Pipefitters 137,000 no data no data 
Occupied by USO 1,800,000 no data 200,000 
Occupied by Property Appraiser 372,000 no data no data 
Same as above Building 2 135,000 no data no data 
Data Center-EDP Only no data no data no data 
Portables to be added effective 7/1/04 no data no data no data 
Freezer Food-All Schools no data no data 1,500,000 
Vehicles-Various: no data no data no data 
Trucks no data no data 100,000 
Cars no data no data no data 
Buses no data no data 250,000 
Total $484,951,476 $72,300,474 $74,645,000 

B-4.3.3 Fire Stations 

Data: Fire Station Replacement Cost 
Source: Escambia County 
Usefulness: Provided replacement cost of fire stations, but does not break the 
replacement costs 
down by fire station, which would have been useful to update the HAZUS default 
inventory. So, 
this data are not useful by itself. 
Limitations: Economic loss was not provided, which is necessary to determine the 
loss ratio. 

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TASK ORDER 440  HURRICANE WIND MODEL VALIDATION STUDY 

Hurricane/County: Replacement cost data were provided for Hurricane Ivan for 
Escambia 
County. 

Table 28. Fire Station Replacement Cost  Hurricane Ivan  Escambia County 

Disaster County Total Replacement Cost of Fire Stations 
Ivan Escambia $10,414,593 
B-4.3.4 Police Stations 

No observed data were received. 

B-4.3.5 Shelters 

Data: Shelter (schools) economic loss 
Source: Escambia County School Board Risk Manager 
Usefulness: Provided economic loss to shelter buildings. 
Limitations: This was not a comprehensive list of all shelter damage as it only 
included schools 
that served as shelters. 
Hurricane/County: Shelter economic loss was provided for Hurricane Ivan for 
Escambia 
County. 

Table 29. Shelter Economic Loss  Hurricane Ivan  Escambia County 

School Name Cost of Reconstruction or Remediation 
Blue Angels, (Shelter) $400,000 
Lipscomb (Shelter) $150,000 
Molino Park (Shelter) $200,000 
Bailey (Shelter) $6,000,000 
West Florida (Shelter) $6,000,000 
Totals $12,750,000 

B-4.3.6 County Structures 

Data: County structures total replacement value 
Source: Escambia County 
Usefulness: Provides replacement cost of all county structures (except for parks 
and recreation), 
but does not break the replacement costs down by structure, which would have 
been useful to 
update the HAZUS default inventory. So, this data are not useful by itself. 
Limitations: Economic loss was not provided, which is necessary to determine the 
loss ratio. 
Hurricane/County: Replacement cost data were provided for Hurricane Ivan for 
Escambia 
County. 


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TASK ORDER 440  HURRICANE WIND MODEL VALIDATION STUDY 

Table 30. County Structures Replacement Cost  Hurricane Ivan  Escambia 
County 

Disaster County Total Replacement Cost of County 
Structures* 
Ivan Escambia $175,648,373 
* This does not include Parks and Recreation. 
Data: Public Assistance Category E (Buildings) Summary Data 
Source: FEMA Public Assistance Program 
Usefulness: This data provided the total PA costs for buildings, prepared as 
Category E project 
worksheets (PWs). 

Costs Covered By PA: Category E PWs cover losses not covered by insurance for 
the repair or 
restoration or publicly owned and maintained structures, equipment (e.g., 
electrical, mechanical, 
telecommunications), and contents (e.g., furniture, books, computers). Sometimes 
vegetative or 
construction and demolition (C&D) debris removal costs, or mold remediation 
costs are included 
in Category E PWs, and sometimes these costs are covered under Categories A or B 
PWs. As 
such, it is indiscernible as to whether the PA summary data includes these costs 
and to what 
degree (e.g., none, some, or all). This information could be identified by 
perusing each Category 
E PW, which is a very time consuming process. 

As stated on FEMA.gov, FEMA may pay for upgrades that are required by certain 
codes and 
standards, such as roof bracing installed following a hurricane, and upgrades to 
meet standards 
regarding use by the disabled. For repairs, upgrades are limited to damaged 
elements only. If a 
structure must be replaced, the new facility must comply with all applicable 
codes and standards 
regardless of the level of FEMA funding. If a damaged building must be replaced, 
FEMA has the 
authority to pay for a building with the same capacity as the original 
structure. However, if the 
standard for space per occupant has changed since the original structure was 
built, FEMA may 
pay for an increase in size to comply with that standard while maintaining the 
same occupant 
capacity. A federal or state agency or statute must mandate the increase in 
space; it cannot be 
based only on design practices for an industry or profession. 

Limitations: This data does not reflect costs that were covered by insurance or 
insurance 
deductibles, which can grossly underestimate building repair/replacement costs. 
This data does 
reflect economic loss to critical facilities, but includes costs for most public 
buildings. 
Sometimes building economic loss can be covered under other PA categories. For 
example 
temporary repairs/mold remediation could be covered under Category B  Emergency 
Protective 
Measures, or repairs could be covered under Category G, Parks, Recreation, and 
Other. This data 
includes both wind and flood loss, which does not allow for direct comparison, 
but is a useful 
benchmark. 
Hurricanes/County(ies): All hurricanes and all counties involved in this study. 

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TASK ORDER 440  HURRICANE WIND MODEL VALIDATION STUDY 

Table 31. Public Buildings Economic Loss  FEMA Public Assistance 

County Hurricane Total Obligated 
Charlotte Charley $25,156,724 
DeSoto Charley $3,557,181 
Hardee Charley $1,989,438 
Lee Charley $4,546,783 
Orange Charley $10,398,422 
Osceola Charley $4,931,922 
Polk Charley $2,946,422 
Brevard Frances $5,324,603 
Indian River Frances $3,192,071 
Martin Frances $10,889,585 
Okeechobee Frances $1,044,711 
Palm Beach Frances $12,765,742 
St. Lucie Frances $20,342,900 
Escambia Ivan $38,761,211 
Santa Rosa Ivan $4,797,993 
Martin Jeanne $4,182,316 
St. Lucie Jeanne $5,523,031 
Escambia Dennis $2,741,666 
Santa Rosa Dennis $2,181,305 

Modeling Usefulness: FHA, Escambia County schools, and PA economic loss data 
could not be 
directly compared with the loss estimates for critical facilities, generated by 
the HAZUS-MH. 
Hurricane Wind Model. Shelter loss cannot be compared with HAZUS-MH estimates 
because 
there are no building characteristic data (e.g., building type) to run a user 
defined scenario. 

B-4 Debris Generated 

Debris quantities for both vegetative and construction and demolition debris 
types were 
requested from the FEMA Public Assistance Program and the counties included in 
this study. 
Some data were received from the jurisdictions. 

Data: Debris quantities data for Santa Rosa County for Hurricane Dennis; and 
DeSoto and 
Hardee Counties, and Hardee cities for Hurricane Charley. 
Source(s): Santa Rosa, DeSoto, and Hardee Counties 
Usefulness: Provides vegetative debris estimates as of January 2006. This is an 
incremental 
quantification of debris, and this quantity will very likely increase in the 
future. This data set 
does not appear to be accurate for comparison with HAZUS-MH debris quantity 
estimates. 
Limitations: Debris quantities are not separated by wind versus flood hazard. 
Does not include 
concrete and demolition debris estimates. Debris operations are still underway 
in some of these 

April 2007 B-23 




TASK ORDER 440  HURRICANE WIND MODEL VALIDATION STUDY 

counties; therefore, these estimates will not reflect the total quantity once 
all debris has been 
removed from eligible areas. It was difficult to determine the amount of debris 
generated for 
Orange County, as not all of the debris had been collected for Hurricane Charley 
when Hurricane 
Frances made landfall. 
Hurricane(s)/County(ies): Debris quantity estimates were provided by Santa Rosa 
County for 
Hurricane Dennis, and DeSoto and Hardee Counties for Hurricane Charley. 

Modeling Usefulness: Debris quantities provided by these counties were not 
compared with the 
debris quantity estimates generated by the HAZUS-MH Hurricane Wind Model, as the 
observed 
quantities appear to be incremental. Comparing this observed data with HAZUS-MH 
would not 
result in a fair comparison. 

Table 32. Debris Estimates  Hurricanes Ivan, Dennis and Charley  Escambia, 
Santa Rosa, DeSoto, and Hardee Counties 

County Disaster Total C&D Veg 
Escambia Ivan NDR NDR NDR 
Santa Rosa Ivan NDR NDR NDR 
Escambia Dennis NDR NDR NDR 
Santa Rosa Dennis 17M TN NDR NDR 
DeSoto Charley 95K CY NDR 95K CY 
Hardee Charley 11K CY NDR 11K CY 
Hardee Cities Charley 165K CY NDR 165K CY 

NDR = No data received. 

Note: It was difficult to determine the amount of debris generated for Orange 
County, as not all 
of the debris had been collected for Charley when Frances made landfall. 

B-5 Injuries and Deaths 

Table 33. County Structures Replacement Cost  Hurricane Charley  DeSoto 
County 

Disaster County Injuries (Residential) Deaths (Residential) 
Charley DeSoto 12 1 

April 2007 B-24 




APPENDIX C 
ARA Prior HAZUS Validation Report 


DRAFT Final Report  November 22, 2004 

HAZUS-MH Support for Hurricanes Charley, Frances, Ivan and Jeanne 


Prepared for: 


Federal Emergency Management Agency 
Mitigation Division 
500 C Street, SW 
Washington, DC 20472 


Prepared by: 


Applied Research Associates, Inc. 
8540 Colonnade Center Drive, Suite 307 
Raleigh, NC 27615 


November 2004 


ARA 16567 

April 2007 C-1 




TASK ORDER 440  HURRICANE WIND MODEL VALIDATION STUDY 

1. Introduction 
In August and September of 2004, four hurricanes made landfall in Florida and 
Alabama (see 
Table 1). In response to a request for support from the Federal Emergency 
Management 
Agency (FEMA) Response Division, FEMAs Mitigation Division contracted with 
Applied 
Research Associates, Inc. (ARA) to provide near real-time loss estimation 
support using 
HAZUS-MH. ARA performed hurricane wind field modeling and hurricane wind loss 
estimates 
before, during, and after each of the four hurricanes. In addition, ABS 
Consulting performed 
coastal and inland flooding loss estimates for the first three hurricanes under 
a subcontract to 
ARA. 

Table 1. Summary of 2004 Hurricanes Assessed Using HAZUS-MH 

Hurricane 
Landfall Conditions 
Location Date 
NHC Saffir-
Simpson 
Category 
NHC 1-minute 
Sustained Wind 
Speed (mph) 
Charley Charlotte County, FL 8/13/04 4 145 
Frances Martin County, FL 9/5/04 2 105 
Ivan Baldwin County, AL 9/16/04 3 130 
Jeanne Martin County, FL 9/26/04 3 115 

This report summarizes the analyses performed by ARA and ABS and lessons 
learned. The 
hurricane wind loss estimates performed by ARA are discussed in Section 2. The 
coastal and 
inland flooding loss estimates performed by ABS Consulting are discussed in 
Section 3. 

April 2007 C-2 




TASK ORDER 440  HURRICANE WIND MODEL VALIDATION STUDY 

2. HAZUS-MH Hurricane Wind Loss Estimates 
This section summarizes the approach and lessons learned by ARA in developing 
hurricane 
storm tracks, wind fields, and wind loss estimates for Hurricanes Charley, 
Frances, Ivan, and 
Jeanne. 

At the time of the analyses, two versions of the HAZUS-MH hurricane model were 
available: 

1. HAZUS-MH version 1.0 (Build 31)  the first and only official FEMA release of 
HAZUS-MH to date. 
2. HAZUS-MH Build 36A  a developmental version of HAZUS-MH Maintenance Release 
1 (MR1). The completed version of HAZUS-MH MR1 is expected to be delivered to 
FEMA in December 2004. 
With respect to near real-time estimates of hurricane wind losses, there are 
three key 
differences between Build 31 and Build 36A: 

1. Build 36A includes an updated database of general building stock valuations. 
The 
baseline building valuations in the southeastern United States are approximately 
18% 
lower than the valuations given in Build 31.1 In this report, the baseline 
building 
valuation data in Builds 31 and 36A are referred to as the old and new 
valuations, 
respectively. 
2. Build 36A includes a new capability to automatically download and import 
National 
Hurricane Center (NHC) Forecast/Advisories (F/A) from the HurrEvac ftp site. In 
Build 
31 this information had to be manually input into the HAZUS hurricane scenario 
wizard. 
3. Build 36A includes a new capability to accept radii to 50 knot or 34 knot 
winds at points 
on the storm track where the maximum wind speeds (either observed or forecast) 
are less 
than hurricane force (i.e., the radius to 64 knot winds is undefined). This 
improvement 
allows better modeling of damage and loss as the system intensifies from a 
tropical storm 
to a hurricane and as it weakens again after landfall. 
Initially during Hurricane Charley, loss estimates were computed using both 
Build 31 and Build 
36A and using both the old and the new building valuation data. Since Builds 31 
and 36A 
produced essentially the same loss estimates given the same hurricane scenario 
definition and 
the same building inventory data, we decided to stop using Build 31 after 
Forecast/Advisory 17 
(FA17) on August 13, 2004.2 Furthermore, because the new valuation data 
provide a better 
represent the true replacement value of the building stock in Florida and its 
neighboring states, 
we also decided to stop using the old valuation data after FA19 on August 13, 
2004. 
In general, three categories of hurricane track models were evaluated for each 
storm: 

1. FA. The Forecast/Advisory tracks directly model the information provided in 
the NHC 
Forecast/Advisory. These tracks were generated by mapping the FA parameters 
(i.e., 
central pressure, maximum sustained wind speed, and maximum radius to 64, 50, or 
34 
knot winds) to the fundamental HAZUS hurricane storm track parameters (i.e., 
central 
1 The changes in general building stock valuation between Build 31 and Build36A 
are the result of a more refined 

implementation of the RSMeans regional cost multipliers. 
2 See, for example, Hurricane Charley runs 3 and 5 in Table 3. 

April 2007 C-3 




TASK ORDER 440  HURRICANE WIND MODEL VALIDATION STUDY 

pressure, Holland profile parameter (B), and radius to maximum winds). This 
mapping 
was accomplished either by: (a) directly using the HurrEvac download and 
validation 
option in Build 36A, or (b) running the mapping algorithms off-line and manually 
inputting the resulting parameters into the HAZUS hurricane scenario wizard. 

2. ARA. The ARA tracks use the NHC FA represent our best estimate of the 
hurricane 
intensity parameters based on data from the following sources: (1) NHC 
Forecast/Advisories, (2) H*Wind surface wind analysis output provided by 
Hurricane 
Research Division (HRD), and (3) surface level wind and pressure observations to 
determine the values of the profile parameter and radius to maximum winds. 
3. Hwind. H*Wind tracks (actually peak 3-second wind gust estimates by census 
tract 
centroid) were provided to ARA as research products by the HRD. Two swaths were 
made available for each of the four storms: an initial estimate made near the 
time of 
landfall and a final estimate generally made within 2-4 days after landfall. 
Each of the three track models used the actual or forecast position and time 
information from the 
latest official NHC Forecast/Advisory. In a few cases, minor adjustments to the 
overland track 
positions were made in the ARA tracks to provide a smoother track because the 
storm 
coordinates in the Forecast/Advisories are rounded to tenths of degrees. 

The evolutions of HAZUS wind loss estimates for Hurricanes Charley, Frances, 
Ivan, and 
Jeanne are summarized in Tables 3-6.3 
The following paragraphs highlight several observations and recommendations 
resulting from 
our analysis: 

Loss Estimates Based on NHC Tracks 
As with Hurricane Isabel in 2003, the general trend seen in Tables 3-6 is that 
the tracks based 
directly on NHC Forecast/Advisories tend to produce wind speeds and loss 
estimates that are 
significantly higher than the ARA or H*Wind tracks. 

Recommendation: Although the Forecast/Advisory import capability provides a 
simple and 
efficient method for estimating losses for an approaching hurricane, emergency 
managers 
should not rely solely on the direct use of NHC Forecast/Advisories for response 
planning and 
mobilization decisions. 

Loss Estimates Based on H*Wind Swaths 
With the exception of Hurricane Charley, the final loss estimates based on 
H*Wind swaths 
provided by HRD are very comparable to the loss estimates from the ARA tracks. 
To a large 
degree, this is to be expected because the ARA tracks rely heavily on the same 
off-shore and 
coastal observations used by H*Wind. However, the models do tend to diverge 
inland from the 
coast, primarily because of differences in the two filling models. This 
difference was most 
pronounced in Hurricane Charley. 

Recommendation: FEMA should continue to encourage the NHC to operationalize the 
H*Wind 
surface wind analysis capability as a needed decision support tool for response 
planning. 

3 Complete sets of summary reports (quick, global, economic, shelter, and 
debris) and peak gust wind swath maps 
have been archived for each run and can be furnished to FEMA in electronic 
format upon request. 

April 2007 C-4 




TASK ORDER 440  HURRICANE WIND MODEL VALIDATION STUDY 

Loss Estimates Based on ARA Tracks 
Over the course of the four hurricanes, ARA developed a repeatable process for 
extracting the 
storm track information from multiple sources, mapping this information into the 
format required 
by the HAZUS hurricane model, generating peak gust estimates, and iterating once 
or twice, if 
necessary. This process was generally carried out during the first 30 minutes or 
so following the 
release of each Forecast/Advisory and the results were immediately transmitted 
to FEMA via e-
mail or ftp. Once the track was finalized and sent to FEMA, loss calculations 
were performed by 
ARA using the HAZUS-MH tool. In general, copies of five summary reports (quick, 
global, 
economic, shelter, and debris) and a map of the peak gust wind swath were 
archived for each 
run. The remaining time between advisories was used to perform comparisons of 
modeled wind 
speed traces, wind direction traces, and atmospheric pressure traces to 
observations at as 
many locations a possible. This process was repeated through landfall. A final 
best estimate 
ARA track was generally available within 2-3 days of landfall. 

Recommendation: The process for estimating the track parameters should be 
automated as 
much as practical in order to ensure timely, accurate, and cost-effective loss 
estimates for future 
hurricanes. However, we should not attempt to fully automate this process. 
Expert knowledge 
and judgment will always be necessary to identify and address errant 
observational data and 
other unusual situations. 

Storm Track Time Step 
Hurricane Charley was an intense, small, and fast-moving storm. The current time 
interval used 
in HAZUS-MH for evaluating peak gust wind speeds is once every 15 minutes. This 
time step 
was too large for Hurricane Charley and resulted in an underestimate of peak 
gust wind speeds 
in some census tracts. This was confirmed in Run #31, which was a re-analysis of 
Run #29 
using a 3-minute time step instead of a 15-minute time step. The loss estimate 
for Run #31 
increased by over 75%. 

Recommendation: An algorithm should be developed and implemented in HAZUS-MH to 
allow 
a variable time step based on the forward translation speed, size, and intensity 
of the storm. 

Set-up, Analysis, and Post-Processing Times 
The time required to create the run the analysis and process the results after 
the release of a 
new Forecast/Advisory and the development of the new storm track parameters was 
generally 
on the order of 20-40 minutes (depending on the size of the study region) on a 3 
GHz Pentium 4 
with 1 GB of RAM. When the forecast track shifted outside the bounds of the 
previous study 
region, an additional time of 10-20 minutes was usually required to generate a 
new study 
region. Although these times were generally viewed to be acceptable, the 
following 
opportunities for reducing the turnaround time have been identified. 

Recommendation: A tool should be added to the hurricane model to automatically 
generate a 
pre-selected set of reports and maps at the end of a scenario analysis. This 
task has been 
funded under the current HAZUS contract and will be implemented in MR2. 
Recommendation: A tool should be added to the hurricane model to automatically 
generate the 
list of counties needed to encompasses a user-specified storm track. There 
should be an option 
to review this information and pass it directly to the HAZUS shell to create a 
new study region. 

Loss Estimates 
Initial estimates of industry-wide insured losses have been released by ISO for 
each of the four 
hurricanes. The ISO estimates cannot be directly compared to the estimates 
produced by 
HAZUS-MH because the ISO estimates include losses for automobiles and boats, 
appurtenant 

April 2007 C-5 




TASK ORDER 440  HURRICANE WIND MODEL VALIDATION STUDY 

structure losses, and additional living expenses, yet do not include deductibles 
or uninsured 
properties. In spite of these differences, insured loss estimates do provide a 
useful benchmark 
for the HAZUS-MH wind loss estimates. 

Table 2. Summary of Initial ISO Insured Loss Estimates 

Hurricane 
Landfall 
Date 
ISO Press 
Release 
Date 
Initial ISO 
Insured Loss 
Estimate 
($B) 
HAZUS-MH 
Estimate Based 
on Final ARA 
Tracks from 
Tables 3-6 ($B) 
States 
Included 
Charley 8/13/04 8/25/04 6.7 7.1 FL 
Frances 9/5/04 9/23/04 4.1 1.8 FL 
Ivan 9/16/04 10/14/04 5.3 1.6 FL, AL, GA 
Jeanne 9/26/04 10/26/04 2.8 2.8 FL 
2004 Total 18.9 13.3 

Also shown in Table 2 are the HAZUS-MH loss estimates based on the final ARA 
storm tracks. 
It can be seen that the ISO and HAZUS-MH estimates for Hurricanes Charley and 
Jeanne are 
very similar, but there are significant differences in the estimates for 
Hurricanes Frances and 
Ivan. Further investigation is required to better understand these differences.4 

Recommendation: A follow-on task should be planned to perform a more detailed 
analysis of 
the HAZUS-MH loss estimates relative to the final ISO loss estimates for all 
four hurricanes. 

4 Upon further review of the Hurricane Frances results, it now appears that the 
final ARA track model developed for 
Run #31 may have given too much weight to the surface level wind speeds measured 
at the Florida Coastal 
Monitoring Program towers. Therefore, the previous ARA best track estimate 
developed for Run #29 may be a 
better representation of the actual storm track than the track that was 
subsequently developed for Run #31. The 
resulting loss estimate from Run #29 was $3.2B. More recent runs for Hurricane 
Frances have produced results 
between $3.0B and $5.8B, through updates in wind model parameters brought about 
through correction of the 
FCMP tower wind speeds for terrain effects. Major updates to the Ivan estimates 
due to hurricane modeling issues 
are not expected 

April 2007 C-6 




TASK ORDER 440  HURRICANE WIND MODEL VALIDATION STUDY 

Table 3. Wind Loss Estimates for Hurricane Charley 
Peak Displaced 
Gust Households 
Run Date Build Valuation Study Region Scenario Advisory Track (mph) Loss ($B) 
(1,000's) 
1 13-Aug 31 Old Charley_FL31Cnty_B31_OldVal Charley_B31_16_90pct_PJV 16 ARA 119 
5.0 9.9 
2 13-Aug 36A New Charley_FL31Cnty_B36A_NewVal Charley_2004_160 16 FA 126 12.0 
33.8 
3 13-Aug 31 Old Charley_FL31Cnty_B31_OldVal 
Charley_FLCnty31_B31_OldVal_CliffFA16 16 FA 122 11.4 25.5 
4 13-Aug 36A Old Florida26 Charley_Cliff_FA16_Try2 16 FA 122 11.3 26.6 
5 13-Aug 36A Old Florida31 Charley_Cliff_FA16_Try2 16 FA 122 11.4 26.6 
6 14-Aug 31 Old Charley_16 Charley_16 16 ARA100 127 13.9 33.3 
7 13-Aug 36A Old Florida26 Charley_2004_160 16 FA 126 15.7 37.4 
8 13-Aug 36A Old Florida31 Charley_2004_160 16 FA 126 15.7 37.4 
9 13-Aug 36A New Charley_FL31Cnty_B36A_NewVal C_FL31Cnty_B36A_New_AF17_90_PJV 17 
ARA 118 4.3 8.5 
10 13-Aug 36A Old Florida31 Charley_2004_170 17 FA 134 32.1 89.4 
11 13-Aug 36A New Charley_FL31Cnty_B36A_NewVal C_PJV_Adv17 17 ARA 131 24.6 73.5 
12 13-Aug 31 Old Charley_FL31Cnty_B31_OldVal C_FL31Cnty_B31_old_AF17Cliff 17 FA 
125 27.5 64.4 
13 13-Aug 36A New Charley_FL28Cnty_B36A_NewVal Charley_2004_180 18 FA 160 17.1 
68.1 
14 13-Aug 36A New Charley_FL28Cnty_B36A_NewVal Adv18_Reduced_B 18 ARA 142 9.7 
28.6 
15 13-Aug 36A New Charley_FL28Cnty_B36A_NewVal Charley_2004_190 19 FA 163 10.9 
44.1 
16 13-Aug 36A New Charley_FL28Cnty_B36A_NewVal Charley_AF19_Reduced_Rmax 19 ARA 
147 7.9 24.9 
17 13-Aug 36A New Charley_FL31Cnty_B31_NewVal Charley_2004_180 18? FA 134 24.4 
79.2 
18 13-Aug 36A Old Florida31 Charley_FA17_Cliff 17 FA 125 27.5 66.2 
19 13-Aug 36A Old Florida28 Charley_2004_180 18 FA 160 21.1 72.0 
20 13-Aug 36A New Charley_FL26Cnty Charley_2004_160 16 FA 126 12.0 33.8 
21 13-Aug 36A Old Florida26 Charley_2004_160 16 FA 126 15.7 37.4 
22 13-Aug 36A Old Florida26 HWIND_FA17 17 Hwind 136 8.4 21.2 
26 13-Aug 36A Old Florida28 Charley FA19 Cliff 19 FA 167 16.6 58.5 

27 14-Aug 36A New Charley_FL28_B36A_NewVal Charley_2004_211 21 FA 161 5.2 20.0 
28 14-Aug 36A New Charley_FL28_B36A_NewVal Charley_2004_211Corrected 21 FA 161 
5.2 20.0 
29 14-Aug 36A New Charley_FL28_B36A_NewVal FA22_ARA4 22 ARA 146 6.4 18.0 
30 14-Aug 36A New Charley_FL28_B36A_NewVal hwind_5pmSat_charley 22 Hwind 159 3.2 
12.3 
31 23-Aug 36A New Charley_FL28_B36A_NewVal FA22_ARA4 22 ARA 146 11.4 34.6 
32 27-Aug 36A New Charley_FL28_B36A_NewVal hwind_charley_27aug04 22 Hwind 147 
2.7 7.7 
33 30-Aug 36A New Charley_B36A_NewVal_FL28Cnty Charley_ARA_30Aug2004 22 ARA 150 
7.1 22.3 

Landfall at ~5pm EDT (2100Z) on 8/13 (same time as FA19) Advisory FA ARA Hwind 
FA18 17.1 9.7 

Hurricane Charley Loss Estimates FA19 10.9 7.9 

FA21 5.2 

FA22 6.4 3.2 

FA22-3min 11.4 

18 

FA18 FA19 FA21 FA22 FA22-3min Final 
Advisory 
Final 7.1 2.7 

FA
16


ARA 
Hwind 

14 

12 

10 

8 

6 

4 

2 

0 

Landfall at FA19 


Direct Economic Loss ($B) 

April 2007 C-7 




TASK ORDER 440  HURRICANE WIND MODEL VALIDATION STUDY 

Table 4. Wind Loss Estimates for Hurricane Frances 

Displaced 
Peak Gust Households 
Run Date Build Valuation Study Region Scenario Advisory Track (mph) Loss ($B) 
(1,000's) 

1 1-Sep 36A New Frances_B36A_NewData_15WF_FL Frances_PJV_20040901_1718_32_Alt 32 
ARA 147 31.7 114.9 
2 1-Sep 36A New Frances_B36A_NewData_15WF_FL Frances_PJV_20040901_1718_32_FA 32 
FA 164 61.9 273.1 

3 2-Sep 36A New Frances_B36A_NewData_15WF_FL Frances_PJV_2004_340 
4 2-Sep 36A New Frances_B36A_NewData_15WF_FL Frances_PJV_34_Alt 
5 2-Sep 36A New Frances_B36A_NewVal_20040902_1004_49CnFrances_PJV_Adv35Alt 
6 2-Sep 36A New Frances_B36A_NewVal_20040902_1004_49CnFrances_PJV_Adv35 
7 2-Sep 36A New Frances_B36A_NewVal_20040902_1004_49CnFrances_2004_350 
8 3-Sep 36A New Frances_B36A_NewVal_20040902_1004_49CnFrances_2004_380 
9 3-Sep 36A New Frances_B36A_NewVal_20040902_1004_49CnFrances_PJV_38 

10 3-Sep 36A New Frances_B36A_NewVal_20040902_1004_49CnFrances_PJV_38_ALT 
11 3-Sep 36A New Frances_B36A_NewVal_20040902_1004_49CnFrances_PJV_39_ALT 
12 3-Sep 36A New Frances_B36A_NewVal_20040902_1004_49CnFrances_Hurrevac_39 
13 3-Sep 36A New Frances_B36A_NewVal_20040902_1004_49CnFrances_PJV_39 
14 3-Sep 36A New Frances_B36A_NewVal_20040902_1004_49CnFrancesHwind39 
15 3-Sep 36A New Frances_B36A_NewVal_20040902_1004_49CnFrances_2004_400 
16 3-Sep 36A New Frances_B36A_NewVal_20040902_1004_49CnFrances_PJV_40 
17 3-Sep 36A New Frances_B36A_NewVal_20040902_1004_49CnFrances_PJV_40_ALT 
18 4-Sep 36A New Frances_B36A_NewVal_20040902_1004_49CnFrances_PJV_42_ALT 
19 4-Sep 36A New Frances_B36A_NewVal_20040902_1004_49CnFrances_PJV_42 
20 4-Sep 36A New Frances_B36A_NewVal_20040902_1004_49CnFrances_2004_421 
21 4-Sep 36A New Frances_B36A_NewVal_20040904_57Cnty Frances_PJV_43_Alt 
22 4-Sep 36A New Frances_B36A_NewVal_20040904_57Cnty Frances_2004_430 
26 4-Sep 36A New Frances_B36A_NewVal_20040904_57Cnty Frances_PJV_43 
27 5-Sep 36A New Frances_B36A_NewVal_20040904_57Cnty Frances_PJV_47_Alt 
28 6-Sep 36A New Frances_B36A_NewVal_20040902_1004_49CnFrances_PJV_50_Alt2 
29 6-Sep 36A New Frances_B36A_NewVal_FL_Final Frances_PJV_51_Alt 
30 10-Sep 36A New Frances_B36A_NewVal_FL_Final Frances_HWIND_091004 
31 28-Sep 36A New Frances_B36A_NewVal_20040904_57Cnty Frances_28Sep04_ARA 

Landfall at ~1am EDT (0500Z) on 9/5 (between FA45 at 0300Z and FA46 at 0900Z) 

Direct Economic Loss ($B) 

120 
100 
80 
60 
40 
20 
0 

Hurricane Frances Loss Estimates 

FA 
ARA 
Hwind 
34 FA 169 116.1 501.0 
34 ARA 151 41.1 160.6 
35 ARA 148 24.3 89.4 
35 FA 167 78.1 340.7 
35 FA 170 79.7 339.9 
38 FA 159 65.7 251.7 
38 FA 162 69.7 287.4 
38 ARA 148 21.3 79.4 
39 ARA 120 4.3 8.6 
39 FA 136 18.1 52.2 
39 FA 137 16.8 49.8 
39 Hwind 114 2.6 4.4 
40 FA 142 22.7 80.7 
40 FA 145 29.5 102.9 
40 ARA 129 10.2 25.0 
42 ARA 118 6.7 13.2 
42 FA 131 17.2 45.7 
42 FA 125 18.8 46.0 
43 ARA 117 6.4 12.3 
43 FA 125 9.2 21.4 
43 FA 131 17.8 48.7 
47 ARA 115 3.1 5.9 
50 ARA 115 3.4 6.1 
51 ARA 115 3.2 6.0 
Final Hwind 111 2.7 5.6 
Final ARA 106 1.8 2.3 
Advisory FA ARA Hwind 
FA32 61.9 31.7 
FA34 116.1 41.1 
FA35 79.7 24.3 
FA38 65.7 21.3 
FA39 18.1 4.3 2.6 
FA40 22.7 10.2 
FA42 18.8 6.7 
FA43 9.2 6.4 
FA47 3.1 
FA50 3.4 
FA51 3.2 
Final 1.8 2.7 

FA32 FA34 FA35 FA38 FA39 FA40 FA42 FA43 FA47 FA50 FA51 Final 

Advisory 


Landfall between FA45 and FA46 

April 2007 C-8 




TASK ORDER 440  HURRICANE WIND MODEL VALIDATION STUDY 

Table 5. Wind Loss Estimates for Hurricane Ivan 

Displaced 
Peak Gust Households 
Run Date Build Valuation Study Region Scenario Advisory Track (mph) Loss ($B) 
(1,000's) 

1 14-Sep 36A New Ivan_FL_GA_AL_MS_LA_Expanded Ivan_2004_480 48 FA 165 48.6 227.8 
2 14-Sep 36A New Ivan_FL_GA_AL_MS_LA_Expanded Ivan_PJV_Adv48 48 FA 164 38.1 
174.6 
3 14-Sep 36A New Ivan_FL_GA_AL_MS_LA Ivan_PJV_Adv48_Alt 48 ARA 146 11.6 40.2 
4 14-Sep 36A New Ivan_FL_GA_AL_MS_LA_Expanded Ivan_PJV_Adv49 49 FA 159 37.8 
184.3 
5 14-Sep 36A New Ivan_FL_GA_AL_MS_LA Ivan_PJV_Adv49_Alt 49 ARA 149 21.2 97.8 
6 15-Sep 36A New Ivan_FL_GA_AL_MS_LA_Expanded Ivan_2004_520 52 FA 147 13.6 52.6 
7 15-Sep 36A New Ivan_FL_GA_AL_MS_LA_Expanded Ivan_PJV_Adv52 52 FA 156 22.1 98.3 
8 15-Sep 36A New Ivan_FL_GA_AL_MS_LA Ivan_PJV_Adv52_Adj 52 ARA 137 7.8 25.1 
9 15-Sep 36A New Ivan_FL_GA_AL_MS_LA_Expanded Ivan_PJV_Adv53 53 FA 152 30.2 
131.7 
10 15-Sep 36A New Ivan_FL_GA_AL_MS_LA Ivan_PJV_Adv53_Alt 53 ARA 142 11.4 42.9 
11 16-Sep 36A New Ivan_FL_GA_AL_MS_LA_Expanded Ivan_2004_560 56 FA 149 19.6 72.5 
12 16-Sep 36A New Ivan_FL_GA_AL_MS_LA Ivan_PJV_Adv56_Adj 56 ARA 118 1.4 2.0 
13 16-Sep 36A New Ivan_FL_GA_AL_MS_LA_Expanded Ivan_HWND_091604_1107am 56 Hwind 
121 1.7 2.9 
14 16-Sep 36A New Ivan_FL_GA_AL_MS_LA Ivan_PJV_Adv57_Adj 57 ARA 118 1.6 2.6 
15 17-Sep 36A New Ivan_FL_GA_AL_MS_LA Ivan_PJV_Adv60_Adj 59 ARA 118 1.6 2.6 
16 17-Sep 36A New Ivan_FL_GA_AL_MS_LA_Expanded Ivan_HWND_091704_1352 59 Hwind 
121 1.7 2.9 

Landfall at ~2am CDT (0700Z) on 9/16 (between FA55 at 0300Z and FA56 at 0900Z) 

Advisory FA ARA Hwind 
FA48 38.1 11.6 

Hurricane Ivan Loss Estimates FA49 37.8 21.2 
FA52 22.1 7.8 
FA53 30.2 11.4 
FA56 19.6 1.4 1.7 
FA57 1.6 
Final 1.6 1.7 

FA48 FA49 FA52 FA53 FA56 FA57 Final 

Advisory 


Landfall just before FA56 

0 
5 
10 
15 
20 
25 
30 
35 
40 
Direct Economic Loss ($B) 
FA 
ARA 
Hwind 
April 2007 C-9 




TASK ORDER 440  HURRICANE WIND MODEL VALIDATION STUDY 

Table 6. Wind Loss Estimates for Hurricane Jeanne 

Displaced 
Peak Gust Households 
Run Date Build Valuation Study Region Scenario Advisory Track (mph) Loss ($B) 
(1,000's) 

1 24-Sep 36A New Jeanne_FL_NoPanhandle Jeanne_PJV_adv44_Adj 44 ARA 85 0.3 0.1 
2 24-Sep 36A New Jeanne_FL_NoPanhandle Jeanne_PJV_adv48 44 ARA 115 3.8 7.0 
3 25-Sep 36A New Jeanne48 Jeanne_48_ARA 48 ARA* 130 12.6 33.5 
4 25-Sep 36A New Jeanne48 Jeanne_48_ARA_NoIntensification 48 ARA 117 5.1 9.6 
5 25-Sep 36A New Jeanne48 Jeanne_49_HRD_Based 49 ARA* 140 25.1 77.7 
6 25-Sep 36A New Jeanne48 Jeanne_49_ARA 49 ARA 134 17.1 46.3 
7 25-Sep 36A New Jeanne48 Jeanne_49_Hwind 49 Hwind 123 2.6 5.1 

8 26-Sep 36A New Jeanne_51 
9 27-Sep 36A New Jeanne_51 
10 27-Sep 36A New Jeanne_51 
11 28-Sep 36A New Jeanne_51 
12 28-Sep 36A New Jeanne_51 

Jeanne_51_ARA2 
Jeanne_52_Hwind 
Jeanne_56a_ARA 
Jeanne_56a_ARA2 
Jeanne_28Sep04_Hwind 

Landfall just after 11pm EDT on 9/25 (i.e., just after FA50 at 0300Z on 9/26) 

Direct Economic Loss ($B) 

18 
16 
14 
12 
10 
8 
6 
4 
2 
0 

Hurricane Jeanne Loss Estimates 

FA44 FA48 FA49 FA51 FA56 Final 
Advisory 
FA 
ARA 
Hwind 
Landfall at FA50 

51 ARA 110 2.5 4.1 
52 Hwind 105 0.8 0.9 
56 ARA 105 1.5 1.9 
56 ARA 113 2.9 5.1 
56 Hwind 114 2.7 4.2 

Advisory FA ARA Hwind 
FA44 3.8 0.3 
FA48 5.1 
FA49 17.1 2.6 
FA51 2.5 
FA56 1.5 
Final 2.9 2.7 

April 2007 C-10 




TASK ORDER 440  HURRICANE WIND MODEL VALIDATION STUDY 

3. HAZUS-MH Flood Loss Estimates 
3.1 Hurricane Charley 
This section documents the work performed by ABS Consulting using the HAZUS-MH 
floodmodel for Hurricane Charley. The work began on August 13, the day Charley 
made 
landfall on the western side of Florida. HAZUS flood model study regions were 
constructed for 
the coastal counties predicted to be in Charleys path. 
Inputs 
Using the results of SLOSH runs posted on the National Hurricane Center (NHC) 
website, the 
coastal flood hazard was computed based on the SLOSH storm surge elevations. By 
the time 
the hurricane made landfall, the actual storm track was south of the predicted 
track. As such, 
additional flood model study regions were built, and the coastal hazard was 
recomputed based 
on the most recent SLOSH results. The DEM for each county was downloaded from 
the 
National Elevation Dataset (NED) on the USGS website. Following are pertinent 
details 
regarding the flood model runs: 

Charley hurricane advisory 18 
HAZUS build number 31 
HAZUS DVD data version Build 36 
Counties analyzed Florida: Charlotte, Collier, Lee, Monroe, Sarasota 

Approach/Methodology 
We ran each county with the storm surge elevation that we received from the NHC. 
The 
projected surge elevations showed little variation across each county, so a 
uniform surge 
elevation was applied to each county. In HAZUS, only General Building Stock 
(Damage and 
Loss) and Essential Facilities analyses were run in the interest of time. 

Results 
The projected surge elevations and flood discharges for each county is shown in 
the table 
below. The resulting loss estimates are also shown below. 

County 
Surge Elevation 
(ft) 
Building Loss 
($) 
Processing Time 
Hazard Analysis 
Charlotte 4.5 471,520 3 min 9 hours 
Collier 8 6,318,000 3 min 12 hours 
Lee 13.5 48,472,000 6 min 18 hours 
Monroe 5.5 2,537,000 2 hr 10 min 9 hours 
Sarasota 3 704,000 10 min 3 hours 

Lessons Learned 
The exercise of applying the HAZUS flood model for coastal flooding has brought 
forth some 
lessons that can be applied to future hurricanes: 

. Study Region Size  The number of census blocks impacted over a county due to 
a large 
coastal flood can be quite high. As such, its recommended to build single 
county study 
regions for coastal analysis. 
. Storm Track  In the final hurricane advisories prior to landfall, the storm 
track 
changed rather significantly. Due to this, the affected counties and associated 
storm 

April 2007 C-11 




TASK ORDER 440  HURRICANE WIND MODEL VALIDATION STUDY 

surges can also change in a short period of time. Hence, it is recommended to 
delay 
running of the coastal model until the true storm track is known. 

. Validation  For future hurricanes, it would be useful to acquire measured 
storm surge 
elevations and apply them to the coastal model, in order to help validate the 
accuracy of 
the model results. At this time it is not clear if the surge elevations computed 
in the final 
SLOSH runs mimicked actual surge elevations. 
. Computer Requirements  At this time, coastal analysis in the HAZUS flood 
model is 
much more computer-intensive than riverine analysis. It is important to use the 
most 
powerful machine available in terms of processor speed and RAM. 
. Relative Impact  For Hurricanes Charley, Frances, and Ivan, hurricane winds 
and 
riverine flooding had a greater impact on damage than coastal storm surge. 
Figure 1. NHC Storm Surge Map 

3.2 Hurricane Frances 
This section documents the work performed by ABS Consulting using the HAZUS-MH 
flood 
model for Hurricane Frances. The work began with a flood analysis of a swath of 
counties in 
Central Florida. As the storm moved west, another group of counties in and 
around the Florida 
panhandle were run through the flood model. Later the storm moved north and 
stalled in the 
region along the North Carolina  Tennessee border. A third group of counties 
were run for this 
area. This was the first effort to use the HAZUS Flood Model over a large area 
in a real-time 
April 2007 C-12 




TASK ORDER 440  HURRICANE WIND MODEL VALIDATION STUDY 

setting. In an effort to reduce processing times, these first three groups of 
study regions were 
analyzed for dollar exposure, not for damage and loss. 
Later in the process, discharge values for selected streams were obtained by 
FEMA for two 
counties in central Florida, and a few areas in western North Carolina. Study 
regions for these 
areas were also constructed. For these study regions, dollar losses were 
computed. 

Inputs 
A map of the area to be analyzed was posted on the National Hurricane Center 
(NHC) website 
and is attached at the end of this report. John Ingargiola, of the Department of 
Homeland 
Security, provided a document with the peak discharges from gages of the 
affected area. The 
DEM for each county was downloaded from the National Elevation Dataset (NED) on 
the USGS 
website. Following are pertinent details regarding the flood model runs: 

Frances hurricane advisory 50 
HAZUS build number 37+ (Build 37 with updated flood software) 
HAZUS DVD data version Build 36 
Counties analyzed Florida: Hillsborough, Pasco 
SR1-Brevard, Indian River, St. Lucie (all in FL) 
SR2-Seminole, Orange, Osceola, Okeechobee (all in FL) 
SR3-Polk, Hardee, DeSoto, Highlands (all in FL) 
SR4-Pinellas, Hillsborough, Manatee, Sarasota (all in FL) 
SR5-Hernando, Pasco, Sumter, Lake (all in FL) 
SR6-Calhoun, Gulf, Liberty, Franklin (all in FL) 
SR7-Gadsen, Leon, Wakulla (all in FL) 
SR8-Walton, Holmes, Washington, Bay (all in FL) 
SR9-Geneva, AL, Houston, AL, Jackson, FL 
SR10-Early, Miller, Seminole, Decatur (all in GA) 
SR11-Graham, Swain, Jackson, Haywood (all in NC) 
SR12-Blount, Sevier, Jefferson, Cocke (all in TN) 
SR13-Hambler, Greene, Washington, Unicoi (all in TN) 
SR14-Madison, Buncombe, Yancey, McDowell (all in NC) 
SR15-Mitchell, Avery, Burke, Caldwell (all in NC) 
Florida: Manatee, Pasco, Hillsborough 
North Carolina: Buncombe, Madison, Transylvania, Gaston, Stanly 

Approach/Methodology 
Given the relatively low storm surges predicted for Hurricane Frances, the focus 
was on riverine 
flooding. The 15 study regions in Florida were analyzed for dollar exposure, not 
for damage 
and loss. The selection of specific counties to be analyzed was based on the 
flood outlook from 
NOAAs Southeast River Forecast Center, obtained through a link on the FEMA 
website. Areas 
identified on the map as Significant River Flooding Likely were included in 
flood study regions. 
The maps are shown as Figures 2 and 3. 
Due to the large amount of inventory data and cell size of the depth grids, the 
flood model is 
ideally run with one county per region. To cut down analysis time, river 
networks were defined 
based on a 25-square mile drainage area, resulting in fairly large streams. Peak 
rainfall 
estimates were received but those data were difficult to apply to HAZUS because 
the flood 
model does not perform rainfall-runoff modeling. 
The flood hazard was based on current/peak discharges at gages provided by FEMA. 
The 
gages had to be located in each study region, in which the information was 
provided or was 

April 2007 C-13 




TASK ORDER 440  HURRICANE WIND MODEL VALIDATION STUDY 

found on NOAAs website. In HAZUS, the hydrologic analysis was skipped because 
the 
discharges were already provided. The discharges were distributed downstream of 
each 
respective gage. 
For study regions in which damage analyses were performed, the results include 
the General 
Building Stock (Damage and Loss) and Essential Facilities. The results also 
contain exposure 
estimates for each of the analysis areas, in terms of full replacement costs and 
depreciated 
costs for residential and government occupancies, along with population totals. 
The totals 
include any census blocks that intersect the floodplain boundary. Losses were 
only computed 
for the Florida and North Carolina single county regions. 

Results 

The projected flood discharges for each county are shown in the following table. 
The resulting 
loss estimates are not available due to problems in the analysis portion of the 
program. 

April 2007 C-14 




TASK ORDER 440  HURRICANE WIND MODEL VALIDATION STUDY 

County Flood Discharge River Name 
Processing Time 
Hazard Analysis 
SR1 100-yr Entire SR NA 
SR2 100-yr Entire SR 9 hrs NA 
SR3 100-yr Entire SR NA 
SR4 100-yr Entire SR 6.5 hrs NA 
SR5 100-yr Entire SR 9 hrs NA 
SR6 100-yr Entire SR 6.5 hrs NA 
SR7 100-yr Entire SR 4 hrs NA 
SR8 100-yr Entire SR NA 
SR9 100-yr Entire SR NA 
SR10 100-yr Entire SR 6 hrs NA 
SR11 100-yr Entire SR 6.5 hrs NA 
SR12 100-yr Entire SR 10 hrs NA 
SR13 100-yr Entire SR NA 
SR14 100-yr Entire SR NA 
SR15 100-yr Entire SR 7.5 hrs NA 
Hillsborough, FL 5,000, 7,000, 11,500 cfs Hillsborough, Alafia, & 
Little Manatee 
4 hrs 1.5 hrs 
Pasco, FL 3,000 cfs Anclote 4 hrs 30 min 
Buncombe, NC 50,000 cfs French Broad 1.5 hrs 20min 
Madison, NC 43,000 and 64,500 cfs French Broad 1.5 hrs 20min 
Transylvania, NC 22,000 cfs French Broad 1 hr 25 min 
Gaston, NC 10,200 cfs South Fork Catawba 1 hr 20 min 
Stanly, NC 52,500 cfs Rocky 12 min 20 min 

Lessons Learned 
Through this analysis, several lessons were learned that can be applied to the 
use of the 
HAZUS flood model during future hurricanes: 

. Computers  There is a 4-county limit on the size of flood model study regions 
due to the 
system limits on the size of study region databases. As such, in order to 
analyze a large 
area, it is necessary to build multiple study regions. With time crunches 
typical with 
emergency response, one of the most important factors in the analysis for these 
study 
regions was the availability of multiple computers, which allowed for 
simultaneous 
analysis. 
. Study Region Creation  Based on the river to be analyzed, care should be 
exercised 
during study region creation. If a river to be analyzed forms a border between 
counties, 
the study region should include the counties on both sides of the river. 
Otherwise, the 
synthetic stream network may not be continuous. 
. Processing Time  The computers used for this exercise had 3.2 GHz processors, 
with 2 
GB of RAM, and tens of gigabytes of hard drive space. For HAZUS flood analysis, 
the 
processor speed and RAM are very important. The total processing times are 
listed in 
the tables above. 

April 2007 C-15 




TASK ORDER 440  HURRICANE WIND MODEL VALIDATION STUDY 


Figure 2. Florida Forecasted Affected Areas 

April 2007 C-16 




TASK ORDER 440  HURRICANE WIND MODEL VALIDATION STUDY 


Figure 4. North Carolina Forecasted Affected Areas 


Figure 5. Central Florida Counties Analyzed 

April 2007 C-17 




TASK ORDER 440  HURRICANE WIND MODEL VALIDATION STUDY 


Figure 6. Florida Panhandle Counties Analyzed 


Figure 7. North Carolina  Tennessee Border Counties Analyzed 

April 2007 C-18 




TASK ORDER 440  HURRICANE WIND MODEL VALIDATION STUDY 


Figure 8. North Carolina Study Regions 

3.3 Hurricane Ivan 
This section documents the work performed by ABS Consulting using the HAZUS-MH 
flood 
model for Hurricane Ivan. 
Inputs 
Using the results of SLOSH runs posted on the National Hurricane Center (NHC) 
website, the 
coastal flood hazard was computed based on the SLOSH storm surge elevations, as 
seen in 
Figure 9. For riverine analyses, John Ingargiola, of the Department of Homeland 
Security, 
provided documents with the peak discharges from gages of the affected area. The 
DEM for 
each county was downloaded from the National Elevation Dataset (NED) on the USGS 
website. 

April 2007 C-19 




TASK ORDER 440  HURRICANE WIND MODEL VALIDATION STUDY 

Following are pertinent details regarding the flood model runs: 

Ivan hurricane advisory 56 
HAZUS build # 37+ (Build 37 with updated flood software) 
HAZUS DVD data version Build 36 
Counties analyzed Coastal: Baldwin, AL, Escambia, FL, Santa Rosa, FL 
Riverine: 
SR1-Buncombe, NC, Madison, NC 
SR2-Transylvania, NC, Henderson, NC 
SR3-Graham, NC, Swain, NC 
SR4-Allegheny, PA, Beaver, PA 
SR5-Allegheny, PA, Westmoreland, PA, Armstrong, PA 
SR6-Jefferson, OH, Brooke, WV, Hancock, WV 
SR7-Ohio, WV, Marshall, WV, Belmont, OH, Monroe, OH 

Approach/Methodology 
In order to run the coastal analysis, the storm surge elevation for each 
countys coast was 
estimated from the SLOSH results. The projected surge elevations showed little 
variation 
across each county, so a uniform surge elevation was applied to each county. In 
HAZUS, the 
General Building Stock (Damage & Loss) and Essential Facilities analyses were 
computed. 
For the riverine analysis, aggregating flood study regions in the flood model 
only allows for a 
maximum of 4 counties per region, so five study regions were constructed for the 
exposure 
analysis. The riverine analysis consisted of multi-county study regions, seen 
below in Figures 
10 through 12. Specific study region maps are included in the zip file. Due to 
the large amount 
of inventory data and cell size of the depth grids, the flood model is ideally 
run with one county 
per region. To cut down analysis time, river networks were defined based on a 
25-square mile 
drainage area, resulting in fairly large streams. 
River discharges were computed using hydrologic regression analysis. As such, 
floodplains 
were determined assuming a 100-year return period flood. The flood hazard was 
based on 
current/peak discharges at gages provided by FEMA. The gages had to be located 
in each 
study region, in which the information was provided or was found on NOAAs 
website. In 
HAZUS, the hydrological analysis was skipped because the discharges were already 
provided. 
The discharges were distributed downstream of each respective gage. The analyses 
ran in 
HAZUS for each study region includes the General Building Stock (Damage and 
Loss) and 
Essential Facilities. Exposure estimates for each of the analysis areas were 
computed, in terms 
of full replacement costs and depreciated costs for residential and government 
occupancies, 
along with population totals. The totals include any census blocks that 
intersect the floodplain 
boundary. The global summary reports were produced for each of the study 
regions. The 
exceptions are study regions 6 and 7 along the Ohio River. There were technical 
difficulties 
with the flood model that didnt allow for loss computations at the time of this 
report. 

Results 
The projected surge elevations and flood discharges for each county is shown in 
the table 
below. The resulting loss estimates are not available due to problems in the 
analysis portion of 
the program. 

April 2007 C-20 




TASK ORDER 440  HURRICANE WIND MODEL VALIDATION STUDY 

Coastal Analysis 

County Surge Elevation (ft) Coast Name 
Processing Time 
Hazard Analysis 
Baldwin, AL 7 Baldwin 5 min 7 hr 41min 
Escambia, FL 7 Escambia 7 min 3 hr 8 min 
Santa Rosa, FL 7.5 and 3.6 Santa Rosa 2 min 6 hr 2 min 

Riverine Analysis 

County Flood Discharge (cfs) River Name 
Processing Time 
Hazard Analysis 
Buncombe, NC 30,300 French Broad 1 hr 32 min 16 min 
Madison, NC 48,000 and 74,600 French Broad 1 hr 32 min 16 min 
Transylvania, NC 19,400 French Broad 45 min 14 min 
Graham, NC 30,750 Tuskasegee 58 min 22 min 
Swain, NC 30,750 Tuskasegee 58 min 22 min 
Henderson, NC 19,400 French Broad 45 min 14 min 
Allegheny, PA 197,000 and 193,000 and 
178,000 and 325,000 
Allegheny, 
Ohio 
1 hr 23 min, 
1 hr 25 min 
1 hr 31min, 
47 hr 46min 
Westmoreland, PA 194,000 Allegheny 1 hr 23min 1 hr 31min 
Armstrong, PA 174,000 Allegheny 1hr 23min 1 hr 31min 
Beaver, PA 361,000 Ohio 1 hr 25min 47 hr 46min 
Jefferson, OH 356,000 and 351,000 and 
346,000 
Ohio 36 min 4 min 
Belmont, OH 353,000 Ohio 1 hr 32 min 8 min 
Monroe, OH 346,000 Ohio 1 hr 32 min 8 min 
Brooke, WV 368,000 Ohio 36 min 4 min 
Hancock, WV 368,000 Ohio 36 min 4 min 
Ohio, WV 353,000 Ohio 1 hr 32min 8 min 
Marshall, WV 357,000 Ohio 1 hr 32min 8 min 

Lessons Learned 
Through this analysis, several lessons were learned that can be applied to the 
use of the 
HAZUS flood model during future hurricanes: 

. Computers  There is a 4-county limit on the size of flood model study regions 
due to the 
system limits on the size of study region databases. As such, in order to 
analyze a large 
area, it is necessary to build multiple study regions. With time crunches 
typical with 
emergency response, one of the most important factors in the analysis for these 
study 
regions was the availability of multiple computers, which allowed for 
simultaneous 
analysis. 
. Processing Time  The computers used for this exercise had 3.2 GHz processors, 
with 2 
GB of RAM, and tens of gigabytes of hard drive space. For HAZUS flood analysis, 
the 
processor speed and RAM are very important. The total processing times are 
listed in 
the tables above. 

April 2007 C-21 




TASK ORDER 440  HURRICANE WIND MODEL VALIDATION STUDY 

. Riverine Study Region Creation  Based on the river to be analyzed, care 
should be 
exercised during study region creation. If a river to be analyzed forms a border 
between 
counties, the study region should include the counties on both sides of the 
river. 
Otherwise, the synthetic stream network may not be continuous. 
. Coastal Study Region Creation  At this time it is recommended to build a 
single 
county study region for coastal analysis. The number of census blocks impacted 
over a 
county due to a large coastal flood can be quite high, resulting in long general 
building 
stock processing times. 
. Storm Track  In the final hurricane advisories prior to landfall, the storm 
track 
changed rather significantly. Due to this, the affected counties and associated 
storm 
surges can also change in a short period of time. Hence, it is recommended to 
delay 
running of the coastal model until the final storm track is known. 
. Validation  For future hurricanes, it would be useful to acquire measured 
storm surge 
elevations and apply them to the coastal model, in order to help validate the 
accuracy of 
the model results. At this time it is not clear if the surge elevations computed 
in the final 
SLOSH runs mimicked actual surge elevations. 
Figure 9. SLOSH Output Map 

April 2007 C-22 




TASK ORDER 440  HURRICANE WIND MODEL VALIDATION STUDY 


Figure 10. Alabama-Florida Study Regions 


Figure 11. North Carolina Study Regions 

April 2007 C-23 




TASK ORDER 440  HURRICANE WIND MODEL VALIDATION STUDY 


Figure 12. Ohio-Pennsylvania-West Virginia Study Regions 

April 2007 C-24 




TASK ORDER 440  HURRICANE WIND MODEL VALIDATION STUDY 

APPENDIX D 
ACKNOWLEDGEMENTS: CONTACT LOG 

Caller Contact Name Agency Or 
Organization 
County Data 
Requested 
For 
Requested 
Date Of 
Phone Or 
Email Contact 
Date Data 
Received Comments 
Stacy 
Robinson 
Rick Burgess Charlotte 
County GIS 
Charlotte Follow-up on 
Tom's request 
for county 
damage data 
12/2/2005; 
12/6/05 
No response 
Stacy 
Robinson 
Ron Peacock Charlotte Co 
Schools 
Charlotte Follow-up on 
Tom's request 
for school 
damage data 
12/2/2005 No response 
Stacy 
Robinson 
Danny 
Killcollins 
FDEM ALL Statewide 
shelter info for 
Charley, Ivan, 
and Dennis 
12/2/2005 Referred to 
Barbara 
Bratcher 
Stacy 
Robinson 
Barbara 
Bratcher 
FDEM ALL Statewide 
shelter info for 
Charley, Ivan, 
Dennis, 
Francis, and 
Jeanne 
12/2/2005 Referred to 
Michael 
Whitehead 
Stacy 
Robinson 
Michael 
Whitehead 
FLDBPR ALL Statewide 
shelter info for 
Charley, Ivan, 
and Dennis 
12/5/2005; 
1/11/2006 
12/5/2005 and 
ongoing 
Rec'd pre- and 
post-landfall 
shelter 
estimates for 
all counties for 
Charley, Ivan, 
Dennis, 
Jeanne, and 
Frances. 
Stacy 
Robinson 
AC Castello Florida 
Hospital Assoc 
ALL Statewide 
hospital 
damage info 
for Charley, 
Ivan, and 
Dennis 
12/2/2005 Sent a report 
and was 
referred to 
Debbie 
Hegerty 
Stacy 
Robinson 
Debbie 
Heggerty 
Florida 
Hospital Assoc 
ALL Statewide 
hospital 
damage info 
for Charley, 
Ivan, and 
Dennis 
12/2/2005 Rec'd what 
limited data 
they had 
Stacy 
Robinson 
Karen 
Thornhill 
Santa Rosa 
Co 
Santa Rosa Follow-up on 
Tom's request 
for county 
damage data 
12/2/2005 Rec'd limited 
data 
(apparently 
what Tom 
asked for) 

April 2007 D-1 




TASK ORDER 440  HURRICANE WIND MODEL VALIDATION STUDY 

Caller Contact Name Agency OrOrganization 
County DataRequested 
For 
Requested 
Date Of 
Phone Or 
Email Contact 
Date Data 
Received Comments 
Lisa Flax Karen 
Thornhill 
Santa Rosa 
Co 
Santa Rosa Sent e-mail to 
request data 
per revised 
collection 
forms 
1/17/2006 
Lisa Flax Karen 
Thornhill 
Santa Rosa 
Co 
Santa Rosa 1/18/2006 Left follow-up 
message 
Lisa Flax Karen 
Thornhill 
Santa Rosa 
Co 
Santa Rosa 1/19/2006 Left follow-up 
message 
Lisa Flax Gregg Cotton Santa Rosa 
Co PW 
Santa Rosa Debris 
estimate 
1/18/06 1/18/06 17.M TN for 
Dennis 
Stacy 
Robinson 
NW FL Red 
Cross 
NW FL Red 
Cross 
Escambia & 
Santa Rosa 
Shelter counts, 
damaged 
housing counts 
and displaced 
HH 
12/2/2005; 
1/12/06 
No response Dent follow-up 
email 1/12 
Stacy 
Robinson 
Charlotte Co 
Red Cross 
(mike Lee) 
Charlotte Co 
Red Cross 
Charlotte Shelter counts, 
damaged 
housing 
counts, and 
displaced HH 
12/2/2005; 
1/12/06 
No response Sent follow-up 
email 1/12 
Stacy 
Robinson 
Becky Sebren Central FL 
Red Cross 
Orange Shelter counts, 
damaged 
housing 
counts, and 
displaced HH 
12/2/2005; 
1/11/2006 
12/5; awaiting 
another 
response 
Referred to 
Orange 
County EM for 
damaged 
housing counts 
Stacy 
Robinson 
Luke Wood Manatee Red 
Cross 
Hardee Shelter counts, 
damaged 
housing 
counts, and 
displaced HH 
12/2/2005; 
1/11/2006 
12/5/2006 Requested 
Frances and 
Jeanne 
1/11/2006 
Stacy 
Robinson 
SW FL Red 
Cross 
SW FL Red 
Cross 
DeSoto Shelter counts, 
damaged 
housing 
counts, and 
displaced HH 
12/2/2005 No response Sent follow-up 
email 1/12 to 
Bill Sullivan 
(see contact 
below) 
Stacy 
Robinson 
Pam Miner PBS&J 
Chipley 
Help ID 
FDOT3 
contacts 
12/1/2005 12/5/2006 
Stacy 
Robinson 
Denny Wood FDOT3 Escambia & 
Santa Rosa 
12/5/2005 12/5/2006 Referred to 
Tina Hegan 
Stacy 
Robinson 
John Locke FDOT3 Escambia & 
Santa Rosa 
12/5/2005 12/5/2005 
Stacy 
Robinson 
Tina Hegan FDOT3 Escambia & 
Santa Rosa 
12/5/2005 12/7/2005 Rec'd 
spreadsheets 
of all road 
repair and 
DOT debris 
removal costs 

April 2007 D-2 




TASK ORDER 440  HURRICANE WIND MODEL VALIDATION STUDY 

Caller Contact Name Agency OrOrganization 
County DataRequested 
For 
Requested 
Date Of 
Phone Or 
Email Contact 
Date Data 
Received Comments 
Stacy 
Robinson 
Mike Healy FDOT3 Orange Debris 
estimates 
12/5/2005 12/5/2005 
Stacy 
Robinson 
Bill Sullivan DeSoto 
County Red 
Cross 
DeSoto Displaced HH 
and damaged 
housing counts 
1/12/2006 1/17/2006 
Lisa Flax Rick Burgess City of Punta 
Gorda 
Charlotte County 
damage and 
loss data 
1/17/2006 Left message 
about data 
collection. 
Lisa Flax Rick Burgess City of Punta 
Gorda 
Charlotte 1/18/2006 Asked about 
status of data 
collection; he 
will check and 
call tomorrow 
Lisa Flax Angela 
Whitehead 
Charlotte 
County / Solid 
Waste 
Charlotte Debris 
estimates 
1/18/2006 She will e-mail 
available data 
Lisa Flax Greg Colton Santa Rosa 
Co / Public 
Works 
Santa Rosa Debris 
estimates 
1/18/2006 He will e-mail 
available data 
Lisa Flax Sherry 
Babcock 
Escambia 
County 
Escambia County 
damage and 
loss data 
1/17/2006 
Lisa Flax Sherry 
Babcock 
Escambia 
County 
Escambia 1/18/2006 Left message 
about data 
collection 
Lisa Flax Sherry 
Babcock 
Escambia 
County 
Escambia 1/19/2006 Spoke with 
Sherry and her 
Director. They 
are very busy 
with hurricane 
recovery effort 
and it wlll take 
a lot of effort 
and time to 
provide 
requested 
data. Will see 
if they can 
work on it. 
Lisa Flax Susan Holt Escambia 
County/Solid 
Waste 
Escambia 1/18/2006 Left message 
about debris 
data request 

April 2007 D-3 




TASK ORDER 440  HURRICANE WIND MODEL VALIDATION STUDY 

Caller Contact Name Agency OrOrganization 
County DataRequested 
For 
Requested 
Date Of 
Phone Or 
Email Contact 
Date Data 
Received Comments 
Stacy 
Robinson 
Steve Detwiler 
and John 
Witcher 
Orange 
County EM 
and Orange 
Co. Property 
Appraiser 
Orange Emailed data 
survey 
1/17/2006 Referred to 
them by Becky 
Sebren. 
Received 
damage totals 
by damage 
state for 
Charley, 
Frances, and 
Jeanne on 
1/23. OCEM is 
working on 
other info 
requested. 
Stacy 
Robinson and 
Nathan 
Slaughter 
Don Daniels St. Lucie EM St. Lucie Stacy emailed 
data survey, 
Nathan 
followed up 
with phone call 
1/19/2006 Spoke to Mr. 
Daniels - he 
indicated that 
he was very 
busy but would 
try to make 
time for this. 
Did not give an 
indication of 
when he would 
have data 
ready. 
Stacy 
Robinson 
Keith Holman Martin EM Martin Emailed data 
survey 
1/17/2006 1/18-he is 
gathering the 
data 
Stacy 
Robinson and 
Darrin 
Punchard 
Catherine Furr, 
Dawn Ballard, 
Larry Hilton, 
Adrian Cline 
DeSoto 
County--EM, 
Chamber of 
Commerce, 
Bldg&Zoning, 
School 
superintendent 
DeSoto Stacy emailed 
data survey; 
Darrin followed 
up with phone 
calls 
1/17/2006 1/24/2006 
(schools) 
Received 
schools data 
from Florence 
Gobble. 
Catherine Furr 
currently 
collecting other 
data. 
Stacy 
Robinson and 
Darrin 
Punchard 
Rich Shepard, 
Janet Gilliard, 
Kathy 
Crawford, Park 
Winter, Betty 
Croy, Joann 
McCray, and 
County 
Chamber of 
Commerce 
Hardee 
County--EM, 
Community 
Development, 
Property 
Appraiser, 
Economic 
Development, 
School 
Superintenden 
t's office, and 
chamber of 
commerce 
Hardee Stacy emailed 
data survey; 
Darrin followed 
up with phone 
calls 
1/17/2006 1/19/06 
(partial) 
Received 
updated critical 
facility listing 
from Rich S. 
Damage 
survey data 
are still being 
gathered by 
others. 

April 2007 D-4 




TASK ORDER 440  HURRICANE WIND MODEL VALIDATION STUDY 

Appendix E 
H*Wind Wind Speeds and ARA Modeled Wind Speeds 


This appendix shows the variation between the Hwind and ARA-modeled tracks in 
sustained 
wind speed prediction at the jurisdiction level for each of the Hurricanes 
considered in this 
report. 

(1) Hurricane Charley 
Hwind ARA 
City/Place County MIN MAX MEAN MIN MAX MEAN 
Charlotte Harbor Charlotte 119 126 122 105 108 106 
Charlotte Park Charlotte 93 122 108 104 111 108 
Cleveland Charlotte 85 117 102 65 116 99 
Englewood Charlotte 42 55 47 54 75 65 
Grove City Charlotte 44 44 44 59 106 80 
Harbour Heights Charlotte 105 122 113 108 108 108 
Manasota Key Charlotte 44 56 52 59 59 59 
Port Charlotte Charlotte 94 126 112 87 108 104 
Punta Gorda Charlotte 76 137 112 104 111 108 
Rotonda Charlotte 58 79 67 75 106 91 
Solana Charlotte 117 117 117 104 116 110 
Arcadia Desoto 75 102 85 72 106 97 
Southeast Arcadia Desoto 73 91 80 72 105 94 
Bowling Green Hardee 86 105 96 89 100 95 
Wauchula Hardee 104 113 110 87 105 93 
Zolfo Springs Hardee 103 110 107 87 105 96 
Alva Lee 43 48 45 0 56 22 
Bokeelia Lee 73 137 94 118 118 118 
Bonita Springs Lee 40 63 52 0 56 32 
Buckingham Lee 46 49 48 0 57 33 
Burnt Store Marina Lee 65 89 77 91 108 101 
Cape Coral Lee 48 89 57 69 108 78 
Charleston Park Lee 43 43 43 0 0 0 
Cypress Lake Lee 48 48 48 62 67 65 
East Dunbar Lee 48 48 48 60 62 61 
Estero Lee 43 62 48 0 56 41 
Fort Myers Lee 48 49 48 0 69 59 
Fort Myers Shores Lee 48 49 49 53 56 55 
Gateway Lee 46 48 47 0 51 17 
Harlem Heights Lee 48 48 48 66 66 66 
Iona Lee 48 49 48 65 74 69 
Lehigh Acres Lee 40 49 45 0 54 7 
Lochmoor Waterway 
Estates Lee 48 48 48 69 73 71 

April 2007 E-1 




TASK ORDER 440  HURRICANE WIND MODEL VALIDATION STUDY 

Hwind ARA 
City/Place County MIN MAX MEAN MIN MAX MEAN 
Matlacha Lee 65 65 65 118 118 118 
McGregor Lee 48 48 48 64 70 66 
North Fort Myers Lee 48 52 49 53 104 71 
Olga Lee 47 48 48 0 56 22 
Page Park Lee 48 48 48 62 62 62 
Palmona Park Lee 48 48 48 68 72 70 
Pine Island Center Lee 71 101 81 100 118 109 
Pine Manor Lee 48 48 48 62 64 63 
Pineland Lee 92 101 96 118 118 118 
San Carlos Park Lee 46 48 47 54 59 56 
Sanibel Lee 51 99 73 81 109 95 
St. James City Lee 59 97 76 100 118 109 
Suncoast Estates Lee 48 48 48 66 72 68 
Three Oaks Lee 46 47 47 0 56 41 
Tice Lee 48 48 48 59 62 60 
Villas Lee 48 48 48 56 65 61 
Whiskey Creek Lee 48 48 48 62 66 64 
Apopka Orange 26 40 30 0 55 28 
Azalea Park Orange 63 88 77 77 80 79 
Bay Hill Orange 38 50 44 63 73 68 
Bay Lake Orange 33 52 39 62 72 67 
Belle Isle Orange 83 91 87 79 81 80 
Bithlo Orange 37 43 40 59 71 64 
Christmas Orange 33 34 33 59 59 59 
Citrus Ridge Orange 30 35 32 0 72 45 
Conway Orange 82 88 84 79 81 80 
Doctor Phillips Orange 43 50 47 67 73 69 
Eatonville Orange 57 71 64 64 71 68 
Edgewood Orange 72 88 80 79 81 81 
Fairview Shores Orange 57 76 66 64 74 70 
Goldenrod Orange 74 87 80 78 79 79 
Gotha Orange 33 37 35 59 65 62 
Holden Heights Orange 66 88 75 78 81 80 
Hunters Creek Orange 81 93 87 78 84 82 
Lake Buena Vista Orange 38 58 47 62 73 67 
Lake Butter Orange 31 43 35 50 67 61 
Lake Hart Orange 48 50 49 58 73 66 
Lockhart Orange 44 57 51 55 67 62 
Maitland Orange 57 71 65 65 76 71 
Meadow Woods Orange 63 93 81 70 83 78 
Oak Ridge Orange 58 79 69 76 81 78 
Oakland Orange 27 28 28 0 50 17 
Ocoee Orange 29 34 31 0 62 48 
Orlando Orange 37 91 64 58 81 75 
Orlovista Orange 37 52 46 63 68 66 
Paradise Heights Orange 30 30 30 0 51 17 

April 2007 E-2 




TASK ORDER 440  HURRICANE WIND MODEL VALIDATION STUDY 

Hwind ARA 
City/Place County MIN MAX MEAN MIN MAX MEAN 
Pine Castle Orange 72 91 82 81 82 81 
Pine Hills Orange 32 48 40 59 69 64 
Sky Lake Orange 72 79 75 79 82 81 
South Apopka Orange 30 32 31 0 55 39 
Southchase Orange 88 89 88 78 82 81 
Taft Orange 91 91 91 73 82 79 
Tangelo Park Orange 65 65 65 77 77 77 
Tangerine Orange 24 24 24 0 0 0 
Tildenville Orange 28 31 29 50 50 50 
Union Park Orange 53 68 58 71 78 76 
Wedgefield Orange 34 42 36 59 71 65 
Williamsburg Orange 65 72 68 77 82 79 
Windermere Orange 33 43 37 50 63 57 
Winter Garden Orange 27 33 30 0 55 30 
Winter Park Orange 71 88 80 71 80 76 
Zellwood Orange 24 29 27 0 0 0 
Campbell Osceola 85 95 90 80 84 82 
Celebration Osceola 39 70 53 72 72 72 
Citrus Ridge Osceola 31 55 40 52 74 65 
Kissimmee Osceola 57 95 85 70 84 80 
Poinciana Osceola 50 98 79 72 85 80 
St. Cloud Osceola 44 55 50 62 72 66 
Yeehaw Junction Osceola 63 88 77 70 81 77 
Auburndale Osceola 32 42 36 54 70 63 
Babson Park Osceola 50 59 56 73 84 80 
Bartow Osceola 36 61 47 55 90 73 
Citrus Ridge Osceola 31 41 37 0 74 52 
Combee Settlement Osceola 28 31 29 0 52 10 
Crooked Lake Park Osceola 63 63 63 84 84 84 
Crystal Lake Osceola 28 31 30 0 52 21 
Cypress Gardens Osceola 67 83 75 78 89 83 
Davenport Osceola 59 85 72 74 85 80 
Dundee Osceola 90 100 95 89 91 90 
Eagle Lake Osceola 51 60 55 76 81 79 
Fort Meade Osceola 78 106 90 90 100 94 
Frostproof Osceola 52 54 53 57 73 65 
Fussels Corner Osceola 29 34 32 0 61 46 
Gibsonia Osceola 27 27 27 0 0 0 
Haines City Osceola 54 95 74 67 89 81 
Highland City Osceola 35 35 35 55 59 57 
Highland Park Osceola 59 69 64 82 87 85 
Hillcrest Heights Osceola 57 57 57 73 84 79 
Inwood Osceola 42 51 46 68 75 71 
Jan Phyl Village Osceola 38 51 43 59 76 69 
Kathleen Osceola 26 27 26 0 0 0 
Lake Alfred Osceola 40 58 46 65 78 71 

April 2007 E-3 




TASK ORDER 440  HURRICANE WIND MODEL VALIDATION STUDY 

Hwind ARA 
City/Place County MIN MAX MEAN MIN MAX MEAN 
Lake Hamilton Osceola 90 90 90 89 91 90 
Lake Wales Osceola 62 103 89 78 94 87 
Lakeland Osceola 27 32 28 0 55 4 
Lakeland Highlands Osceola 29 32 30 0 58 27 
Loughman Osceola 55 79 69 72 85 79 
Medulla Osceola 28 29 29 0 0 0 
Mulberry Osceola 31 34 33 0 58 38 
Polk City Osceola 28 31 29 0 52 17 
Wahneta Osceola 60 77 68 81 84 83 
Waverly Osceola 83 103 96 87 94 90 
Willow Oak Osceola 28 29 29 0 55 11 
Winston Osceola 27 28 28 0 0 0 
Winter Haven Osceola 40 100 63 65 91 78 

(2) Hurricane Ivan 
Hwind ARA 
City/Place County MIN MAX MEAN MIN MAX MEAN 
Bellview Escambia 89 91 90 84 87 85 
Brent Escambia 87 90 88 83 85 84 
Century Escambia 89 90 89 73 77 75 
Ensley Escambia 87 91 89 83 84 84 
Ferry Pass Escambia 86 90 88 82 84 83 
Gonzalez Escambia 90 92 91 82 84 83 
Goulding Escambia 86 87 87 84 85 85 
Molino Escambia 92 93 92 80 81 81 
Myrtle Grove Escambia 88 91 89 85 87 86 
Pensacola Escambia 84 103 92 82 87 84 
Warrington Escambia 88 90 89 86 89 87 
West Pensacola Escambia 88 90 89 85 87 86 
Bagdad 
Santa 
Rosa 80 83 82 69 78 75 
Gulf Breeze 
Santa 
Rosa 83 103 97 79 82 81 
Jay 
Santa 
Rosa 85 87 86 77 77 77 
Milton 
Santa 
Rosa 82 84 83 69 76 74 
Pace 
Santa 
Rosa 85 88 87 78 82 80 

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APPENDIX F 
Glossary 


At Risk  Exposure values that include the entire building inventory or 
population value in a 
census block or tract that lie within, or bordering the inundation areas or any 
area potentially 
exposed to a hazard based on location. 

Building  A structure that is walled and roofed, principally above ground and 
permanently 
fixed to a site. The term includes a manufactured home on a permanent foundation 
on which the 
wheels and axles carry no weight. 

Census Block A subdivision of a census tract (or, prior to 2000, a block 
numbering area), a 
block is the smallest geographic unit for which the U.S. Census Bureau tabulates 
100-percent 
data. Many blocks correspond to individual city blocks bounded by streets, but 
blocks  
especially in rural areas  may include many square miles and may have some 
boundaries that 
are not streets. 

Census Tract  A small, relatively permanent statistical subdivision of a county 
delineated by a 
local committee of census data users for the purpose of presenting data. Census 
tract boundaries 
normally follow visible features, but may follow governmental unit boundaries 
and other non-
visible features in some instances; they always nest within counties. Designed 
to be relatively 
homogeneous units with respect to population characteristics, economic status, 
and living 
conditions at the time of establishment, census tracts average about 4,000 
inhabitants. They may 
be split by any sub-county geographic entity. 

Critical Facility  Facilities that are critical to the health and welfare of 
the population and that 
are especially important following a hazard. Critical facilities include 
essential facilities, 
transportation systems, lifeline utility systems, high-potential loss 
facilities, and hazardous 
materials sites. As defined for the Portland risk assessment, this category 
includes: schools, 
hospitals, fire stations, police stations, and hazardous materials sites. 

Content Value  The value of a buildings content include all the items in a 
building, excluding 
the structure itself. The values are estimated to be 50 percent of the 
residential structural value 
and 100 percent of the commercial building replacement value. 

Duration  The length of time a hazard occurs. 

Erosion  Wearing away of the land surface by detachment and movement of soil 
and rock 
fragments, during a flood or storm or over a period of years, through the action 
of wind, water, or 
other geologic processes. 

Exposure  The number and dollar value of assets that are considered to be at 
risk during the 
occurrence of a specific hazard. 

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Extent  The size of an area affected by a hazard or the occurrence of a hazard. 

Federal Emergency Management Agency (FEMA)  Independent agency (now part of the 
Department of Homeland Security) created in 1978 to provide a single point of 
accountability for 
all federal activities related to disaster mitigation and emergency 
preparedness, response, and 
recovery. 

Geographic Information Systems (GIS)  A computer software application that 
relates data 
regarding physical and other features on the earth to a database to be used for 
mapping and 
analysis. 

Hazard  A source of potential danger or an adverse condition that can cause 
harm to people or 
cause property damage. For this risk assessment, priority hazards were 
identified and selected for 
the pilot project effort. A natural hazard is a hazard that occurs naturally 
(such as flood, wind, 
and earthquake). A man-made hazard is one that is caused by humans (for example, 
a terrorist 
act or a hazardous material spill). Hazards are of concern if they have the 
potential to harm 
people or property. 

Hazard Mitigation  Sustained actions taken to reduce or eliminate the long-term 
risk and 
effects that can result from the occurrence of a specific hazard. For example, 
building a retaining 
wall can mitigate potential hazards. 

HAZUS (Hazards U.S.)  A GIS-based nationally standardized earthquake loss 
estimation tool 
developed by FEMA. HAZUS was replaced by HAZUS-MH (see below) in 2003. 

HAZUS-MH (Hazards U.S. - Multi-Hazard)  A GIS-based nationally standardized 
earthquake, flood, and wind loss estimation tool developed by FEMA. The purpose 
of this pilot 
project is to demonstrate and implement the use of HAZUS-MH to support risk 
assessments. 

HAZUS-MH Provided Data  The databases included in the HAZUS-MH software that 
allow 
users to run a preliminary analysis without collecting or using local data. 

HAZUS-MH Risk Assessment Methodology  This analysis uses the HAZUS-MH modules 
(earthquake, wind [hurricane] and flood) to analyze potential damages and 
losses. For this pilot 
project risk assessment the earthquake and flood hazards were evaluated using 
this methodology. 

HAZUS-MH Supported Risk Assessment Methodology  This analysis involves using 
inventory data in HAZUS-MH combined with knowledge such as (1) information about 
potentially exposed areas, (2) expected impacts, and (3) data regarding 
likelihood of occurrence 
for hazards. 

Infrastructure  The public services of a community that have a direct impact on 
the quality of 
life. Infrastructure includes communication technology such as phone lines or 
Internet access, 
vital services such as public water supplies and sewer treatment facilities, and 
transportation 
systems (such as airports, heliports, highways, bridges, tunnels, roadbeds, 
overpasses, railways, 

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TASK ORDER 440  HURRICANE WIND MODEL VALIDATION STUDY 

bridges, rail yards, depots; and waterways, canals, locks, seaports, ferries, 
harbors, dry docks, 
piers, and regional dams). 

Intensity  A measure of the effects of a hazard occurring at a particular 
place. 

Inventory  The assets identified in a study region. The inventory assessment 
addresses what 
can be lost when a disaster occurs, that is, what community resources are at 
risk. Assets include 
people, buildings, transportation, and other valued community resources. 

Lifelines  Critical facilities that include utility systems (potable water, 
wastewater, oil, natural 
gas, electric power facilities, and communication systems) and transportation 
systems (airways, 
bridges, roads, tunnels, and waterways). 

Loss Estimation  The process of assigning hazard-related damage and loss 
estimates to 
inventory, infrastructure, lifelines, and population data. HAZUS-MH can estimate 
the economic 
and social loss for specific hazard occurrences. Loss estimation is essential to 
decision making at 
all levels of government and provides a basis for developing mitigation plans 
and policies. It also 
supports planning for emergency preparedness, response, and recovery. 

Occupancy Classes  Categories of buildings used by HAZUS-MH (for example, 
commercial, 
residential, industrial, government, and other). 

Replacement Value  The cost of rebuilding or repairing a structure. This cost 
is usually 
expressed in terms of cost per square foot and reflects the present-day cost of 
labor and materials 
to construct a building of a particular size, type, and quality. 

Risk  The estimated impact that a hazard would have on people, services, 
facilities, and 
structures in a community; the likelihood of a hazard occurring and resulting in 
an adverse 
condition that causes injury or damage. Risk is often expressed in relative 
terms such as a high, 
moderate, or low likelihood of sustaining damage above a particular threshold 
due to occurrence 
of a specific type of hazard. Risk also can be expressed in terms of potential 
monetary losses 
associated with the intensity of the hazard. 

Risk Assessment  A methodology used to assess potential exposure and estimated 
losses 
associated with priority hazards. The risk assessment process includes four 
steps: (1) identifying 
hazards, (2) profiling hazards, (3) conducting an inventory of assets, and (4) 
estimating losses. 
This pilot project report documents this process for selected hazards addressed 
as part of the pilot 
project. 

Scale  A proportion used in determining a dimensional relationship; the ratio 
of the distance 
between two points on a map, and the actual distance between the two points on 
the earths 
surface. 

Structure  Something constructed (for example, a residential or commercial 
building). 

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TASK ORDER 440  HURRICANE WIND MODEL VALIDATION STUDY 

Substantial Damage  Damage of any origin sustained by a structure in a SFHA, 
for which the 
cost of restoring the structure to its pre-hazard event condition would equal or 
exceed 50 percent 
of its pre-hazard event market value. 

Vulnerability  Description of how exposed or susceptible an asset is to damage. 
This term 
depends on an assets construction, contents, and the economic value of its 
functions. Like 
indirect damages, the vulnerability of one element of the community is often 
related to the 
vulnerability of another. For example, many businesses depend on uninterrupted 
electrical 
power. If an electric substation is flooded, it will affect not only the 
substation itself, but a 
number of businesses as well. Often, indirect effects can be much more 
widespread and 
damaging than direct ones. 

Vulnerability Assessment  Evaluation of the extent of injury and damage that 
may result from 
a hazard event of a given intensity in a given area. The vulnerability 
assessment should address 
impacts of hazard occurrences on the existing and future built environment. 

April 2007 F-4