Managing Future Development Conditions
in the National Flood Insurance Program

Neil C. Blais, Y-Co Nguyen,
Eric Tate, Fatih Dogan,
Laura Samant, Edward Mifflin,
and Chris Jones

ABSG Consulting Inc.

October 2006

Prepared under subcontract to the American Institutes for Research
as part of the 20012006 Evaluation of the National Flood Insurance Program


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Managing Future Development Conditions in the 
National Flood Insurance Program (NFIP) 

Prepared as part of the 2001-2006 Evaluation of the National Flood Insurance Program 
(NFIP) 

Neil C. Blais 
Y-Co Nguyen 
Eric Tate 
Fatih Dogan 
Andrew Petrow
Laura Samant 
Edward Mifflin 
Chris Jones 

ABSG Consulting Inc. 
300 Commerce Drive, Suite 200 
Irvine, CA 92602 

October 2006 


REPORTS IN THE EVALUATION OF THE NATIONAL FLOOD 
INSURANCE PROGRAM (NFIP) 

This Evaluation consists of a series of reports assessing questions identified and prioritized by a 
steering committee about the National Flood Insurance Program (NFIP). The reports of the 
Evaluation will be posted on the FEMA website as they are finalized. The website URL is 
http://www.fema.gov/business/nfip/nfipeval.shtm. The reports in the Evaluation are: 

The Evaluation of the National Flood Insurance Program  Final Report 

American Institutes for Research and NFIP Evaluation Working Group 

Assessing the Adequacy of the National Flood 
Insurance Program's 1 Percent Flood Standard. 

Galloway, Baecher, Plasencia, Coulton, 
Louthain, and Bagha, Water Policy 
Collaborative, University of Maryland. 

Assessing the National Flood Insurance 
Programs Actuarial Soundness. Bingham, 
Charron, Kirschner, Messick, and Sabade, 
Deloitte Consulting. 

Costs and Consequences of Flooding and 
the Impact of the National Flood Insurance 
Program. Sarmiento and Miller, Pacific 
Institute of Research and Evaluation. 

Developmental and Environmental Impacts of 
the National Flood Insurance Program: A 
Review of Literature. Rosenbaum, American 
Institutes for Research. 

The Developmental and Environmental Impact 
of the National Flood Insurance Program: A 
Summary Research Report. Rosenbaum and 
Boulware, American Institutes for Research. 

An Evaluation of Compliance with the National 
Flood Insurance Program Part A: Achieving 
Community Compliance. Monday, Grill, 
Esformes, Eng, Kinney, and Shapiro, American 
Institutes for Research. 

An Evaluation of Compliance with the National 
Flood Insurance Program Part B: 
Are Minimum Building Requirements Being 
Met? Mathis and Nicholson, Dewberry.

Evaluation of the National Flood Insurance 
Programs Building Standards. Jones, 
Coulbourne, Marshall, and Rogers, Christopher 
Jones and Associates. 

Managing Future Development Conditions in 
the National Flood Insurance Program. 

Blais, Nguyen, Tate, Dogan, Petrow, ABSG 
Consulting; and Mifflin and Jones. 

The National Flood Insurance Programs 
Environmental Reviews: An Assessment of 
FEMAs Implementation of NEPA and Executive 
Order 11988. Rosenbaum, American Institutes 
for Research. 

The National Flood Insurance Programs 
Mandatory Purchase Requirement: Policies, 
Processes and Stakeholders. Tobin and Calfee, 
American Institutes for Research. 

The National Flood Insurance Programs 
Market Penetration Rate: Estimates and Policy 
Implications. Dixon, Clancy, Seabury, and 
Overton, RAND Corporation. 

Performance Assessment and Evaluation 
Measures for Periodic Use by the National 
Flood Insurance Program. Miller, Langston, 
and Nelkin, Pacific Institute of Research and 
Evaluation. 

State Roles and Responsibilities in the National 
Flood Insurance Program. Mittler, Morgan, 
Shapiro, and Grill, American Institutes for 
Research. 

Evaluation of the National Flood Insurance Program 

Managing Future Development Conditions in the National Flood Insurance Program 


i 

Managing Future Development Conditions in the 
National Flood Insurance Program (NFIP) 

Prepared as part of the 2001-2006 Evaluation of the 
National Flood Insurance Program (NFIP) 

The research described in this report was funded with Federal funds from the Federal Emergency 
Management Agency (FEMA) under contract # 282-98-0029 and under subcontract to the 
American Institutes for Research (AIR). The content of this publication does not necessarily 
reflect the views or policies of FEMA, nor does mention of trade names, commercial products, or 
organizations imply endorsement by the U.S. Government. 

ABS Consulting is the worlds premier risk consulting engineering company. The company has 
activities in 32 countries, with 15 major U.S. offices. Its employees make up a diversified 
workforce of over 1100 people globally. Part of the ABS Group of Companies, ABS Consulting 
provides an enterprise-wide approach to deliver rational solutions that blend effective 
management controls, state-of-the-art engineering analyses, practical loss-control measures and 
innovative risk-transfer options. ABS Consulting personnel have considerable experience in all-
hazards risk analysis and mitigation planning and emergency response and recovery planning. 

Established in 1946, with headquarters in Washington, D.C., the AIR is an independent, 
nonpartisan not-for-profit organization that conducts behavioral and social science research on 
important social issues and delivers technical assistance both domestically and internationally in 
the areas of health, education, and workforce productivity. 

Evaluation of the National Flood Insurance Program 

Managing Future Development Conditions in the National Flood Insurance Program 


ii 

BIOGRAPHICAL INFORMATION ON REPORT AUTHORS 

Neil Blais is the Director of the Hazards Research and Planning Solutions (HaRPS) Division within ABS 
Consulting. He participates in wide ranging projects related to emergency management and risk and 
vulnerability assessments. Mr. Blais is the Project Manager for the HAZUSMH (Hazards-United 
States, Multi-Hazard) Flood Loss Estimation Model. His professional affiliations include the Association 
of State Floodplain Managers, and the International Association of Emergency Managers. 

Y-Co Nguyen has three years of experience in the areas of Geographical Information Systems (GIS) as 
an engineer in Hazards Research and Planning Solutions (HaRPS) at ABS Consulting. Ms. Nguyen has a 
Bachelor of Arts in Environmental Analysis & Design from the University of California, Irvine. 

Eric C. Tate was a Project Engineer with ABS Consulting active in the areas of geographic information 
systems (GIS), water resources engineering, and environmental engineering for eight years. His areas of 
expertise include digital floodplain mapping and GIS application development. Mr. Tate holds a 
Bachelors Degree in Civil (Environmental) Engineering from Rice University and a Masters Degree in 
Environmental and Water Resources Engineering from the University of Texas. 

Fatih Dogan is a Project Engineer in Hazards Research and Planning Solutions (HaRPS) at ABS 
Consulting. Mr. Dogan is the primary developer of the coastal hazard module in HAZUSMH . He holds a 
bachelors degree in Environmental Engineering and a Master of Business Administration degree with an 
option in Information Systems. 

Andrew Petrow is a member of ABS Consultings Hazard Research and Planning Solutions (HaRPS) 
group where he is a senior planner working in emergency management and hazard mitigation. Prior to 
joining ABS Consulting he was employed by the Governors Office of Emergency Services (OES) for 
eleven years where he was the Deputy State Hazard Mitigation Officer. One of his primary 
responsibilities was to manage the Hazard Mitigation Grant Program (HMGP) in Southern California. 

Laura Samant formerly a project engineer at ABS Consulting during the development of this project. 
She has since left ABS Consulting. She has seven years of experience in assessing natural disaster risk 
and developing community-based risk management strategies. Laura has a Bachelors of Science in Civil 
Engineering and a Masters of Science in Structural Engineering from Stanford University. 

Chris Jones is a recognized expert on coastal building code and floodplain management issues. He is 
currently a consultant to FEMA and FEMA Contractors on a number of related projects. As part of the 
NFIP review, he is performing the Evaluation of NFIP Building Standards where he is evaluating the 
costs, benefits and effectiveness of the National Flood Insurance Programs minimum design and 
construction requirements for riverine and coastal flood hazard areas. He also was the primary technical 
leader in development of the coastal flood hazard in the HAZUS Flood Model. Mr. Jones has a Bachelor 
of Science and a Masters of Science from the University of Florida, Gainesville, Florida. 

Ed Mifflin has a rich base of historical program knowledge from 25 years involvement and engineering 
management with the NFIP. He has managed an engineering department and conducted special studies 
for FEMA. He has extensive experience in complex hydraulics and hydrology (H&H), including surface 
water hydrology, open- and closed-channel hydraulics, alluvial systems, riverine engineering, coastal 
hydrodynamics, storm climatology, wave dynamics, floodplain mapping, and data collection through both 
field investigations and literature searches. Mr. Mifflin was the primary technical leader on the 
development of the riverine hazard methodology within the HAZUS Flood Model. 

Evaluation of the National Flood Insurance Program 

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TABLE OF CONTENTS 

Acknowledgements ...................................................................................................................... vi
EXECUTIVE SUMMARY ......................................................................................................... ix
1. INTRODUCTION................................................................................................................... 1

1.1 Nation Flood Insurance Program (NFIP)........................................................................ 1 

1.2 Special Flood Hazard Areas (SFHAs) ............................................................................ 1 

1.3 Managing Future Development Conditions Project ....................................................... 3 

1.4 Managing Future Development Definition..................................................................... 3 

1.5 Managing Future Development for Coastal.................................................................... 4 

1.5.1 Coastal Erosion ....................................................................................................... 5 

1.5.2 Topography............................................................................................................. 6 

1.5.3 Buildings and Development.................................................................................... 6 

1.5.4 Sea Level Rise......................................................................................................... 6 

2. METHODOLOGY .................................................................................................................. 9

2.1 Overview........................................................................................................................ 9 

2.2 Community Selection Process ...................................................................................... 11 

2.3 Interview Process (Qualitative Assessment)................................................................. 11 

2.4 Computational Process (Quantitative Assessment) ...................................................... 12 

3. COMMUNITY SELECTION PROCESS SUMMARY..................................................... 15

4. INTERVIEW PROCESS RESULTS (QUALITATIVE ASSESSMENT) ....................... 17

4.1 Overview....................................................................................................................... 17 

4.2 Community Officials .................................................................................................... 18 

4.3 Developers .................................................................................................................... 18 

4.4 Floodplain Managers .................................................................................................... 19 

4.5 Insurance Agents........................................................................................................... 20 

4.6 Lenders......................................................................................................................... 20 

4.7 State Officials................................................................................................................ 20 

4.8 Summary....................................................................................................................... 21 

5. COMPUTATIONAL PROCESS RESULTS (QUANTITATIVE ASSESSMENT)........ 23

5.1 Overview....................................................................................................................... 23 

5.2 Mecklenburg County, North Carolina .......................................................................... 25 

5.2.1 Flood Data............................................................................................................. 26 

5.2.2 Inventory Data ...................................................................................................... 29 

5.3 Larimer County (Fort Collins), Colorado ..................................................................... 35 

5.3.1 Flood Data............................................................................................................. 36 

5.3.2 Inventory Data ...................................................................................................... 39 

5.4 Harris County, Texas .................................................................................................... 43 

5.4.1 Flood Data............................................................................................................. 44 

5.4.2 Inventory Data ...................................................................................................... 47 

5.5 DuPage County, Illinois................................................................................................ 51 

5.5.1 Flood Data............................................................................................................. 52 

5.5.2 Inventory Data ...................................................................................................... 55 

6. CONCLUSIONS AND RECOMMENDATIONS............................................................... 61

6.1 Conclusions................................................................................................................... 61 

6.1.1 Interviews.............................................................................................................. 61 

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6.1.2 Computational Analysis........................................................................................ 62 

6.2 Recommendations......................................................................................................... 62 

7. APPENDICES....................................................................................................................... 67

Appendix 1: Community Data................................................................................................. 67 

1.1 DuPage County, Illinois............................................................................................ 67 

1.2 Escambia County, Florida......................................................................................... 67 

1.3 Grand Forks County, North Dakota.......................................................................... 68 

1.4 Harris County, Texas ................................................................................................ 68 

1.5 Larimer County, Colorado........................................................................................ 68 

1.6 Mecklenburg County, North Carolina ...................................................................... 69 
Appendix 2: Interview Discussion Guide................................................................................ 70 
Appendix 3: HAZUSMH Flood Model Distribution Tables ................................................... 71 

8. DATA DICTIONARY........................................................................................................... 77

8.1 Inventory Data .............................................................................................................. 77 

8.2 Hazard Data .................................................................................................................. 93 

9. ACRONYMS......................................................................................................................... 99

10. REFERENCES................................................................................................................... 101

List of Tables 

TABLE 1. Future Coastal Flood Conditions Parameters and Their Influence on Hazard Maps... 7 
TABLE 2. Case Study Communities and Selection Criteria Characteristics .............................. 15 
TABLE 3: Number of Interviews Performed By Occupation ..................................................... 17 
TABLE 4. Mecklenburg County Structure Dollar Damage by HAZUS Occupancy Type by 

Floodplain Analysis .............................................................................................................. 33 
TABLE 5. Mecklenburg County Contents Dollar Damage by HAZUS Occupancy Type by 

Floodplain Analysis .............................................................................................................. 34 
TABLE 6. Larimer County (Fort Collins) Structure Dollar Damage by HAZUS Occupancy 

Type ..................................................................................................................................... 41 
TABLE 7. Larimer County (Fort Collins) Contents Dollar Damage by HAZUS Occupancy 

Type ..................................................................................................................................... 42 
TABLE 8. Harris County Structure Dollar Damage by HAZUS Occupancy Type .................... 49 
TABLE 9. Harris County Contents Dollar Damage by HAZUS Occupancy Type..................... 50 
TABLE 10. DuPage County Structure Dollar Damage by HAZUS Occupancy Type ............... 57 
TABLE 11. DuPage County Contents Dollar Damage by HAZUS Occupancy Type................ 58 

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List of Figures 

FIGURE 1. Existing Conditions Flood Depth Grid for Mecklenburg County ........................ 28 
FIGURE 2. Future Build-out Flood Conditions for Mecklenburg County ........................ 28 
FIGURE 3. Flood Depth Comparison (feet) Between Future and Existing Flood Grids for 
Mecklenburg County ............................................................................................................ 29 
FIGURE 4. Sample of Mecklenburg County Data Used in This Analysis.................................. 31 
FIGURE 5. Comparison of Flood Losses at Build-Out vs Flood Losses for the Existing 
Inventory, Mecklenburg County........................................................................................... 35 
FIGURE 6. Existing Conditions Flood Depth Grid for Fort Collins ....................................... 37 
FIGURE 7. Future Build-out Flood Conditions for Fort Collins ....................................... 37 
FIGURE 8. Flood Depth Comparison (feet) Between Future and Existing Flood Grids for Fort 
Collins.................................................................................................................................. 38 
FIGURE 9. Example of Larimer County Data Used in This Analysis........................................ 40 
FIGURE 10. Comparison of Flood Losses at Build-out vs Flood Losses With the Existing 
Inventory, Fort Collins.......................................................................................................... 43 
FIGURE 11. Existing Conditions Flood Depth Grid for Harris County ................................. 45 
FIGURE 12. Future Build-out Flood Conditions for Harris County ................................. 45 
FIGURE 13. Flood Depth Comparison (feet) Between Future and Existing Flood Grids for 
Harris County........................................................................................................................ 46 
FIGURE 14. Example of Harris County Data Used in This Analysis......................................... 48 
FIGURE 15. Comparison of Flood Losses at Build-out vs Flood Losses With the Existing 
Inventory, Harris County ...................................................................................................... 51 
FIGURE 16. Existing Conditions Flood Depth Grid for DuPage County ............................... 53 
FIGURE 17. Future Build-out Flood Conditions for DuPage County ............................... 53 
FIGURE 18. Flood Depth Comparison (feet) Between Future and Existing Flood Grids for 
DuPage County ..................................................................................................................... 54 
FIGURE 19. Example of DuPage County Data Used in This Analysis ...................................... 56 
FIGURE 20. Comparison of Flood Losses at Build-out vs Flood Losses With the Existing 
Inventory, DuPage County.................................................................................................... 59 

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vi 

Acknowledgements 

Mecklenburg County, North Carolina 

Mr. Philip Letsinger, NFIP Coordinator, North Carolina State Emergency Management 
The Honorable Ruth Samuelson, Mecklenburg County Commissioner 
Mr. Marvin Bethune, County Attorney, Ruff, Bond, Cobb, Wade, and Bethune, LLP 
Mr. Dave Canaan, Director of Land and Water Resources, Mecklenburg County 
Mr. David McDuffie, Independent Insurance Sales 
Ms. Kathy Peppard, Independent Mortgage Lender 
Mr. Marc Houle, Real-Estate Developer, Yarbrough-Williams & Houle, Inc. 
Mr. Shay Baird, Independent Real-Estate Developer 
Ms. Tammy Lawing, Allen Tate Insurance 
Ms. Vallie McMahon (Trepp), Real-Estate Developer, Hayden McMahon Development, Inc. 

Larimer County, Colorado 

Mr. Kevin Houck, NFIP Coordinator, State of Colorado 
The Honorable Ray Martinez, Mayor, City of Fort Collins 
Mr. John Fischbach, City Manager, City of Fort Collins 
Ms. Marsha Hilmes-Robinson, Floodplain Manager, City of Fort Collins 
The Honorable Gregory Koch, Professional Engineer, City of Fort Collins Flood Review Board 
Mr. David Dilley, Independent Insurance Sales 
Mr. Terry Jones, Independent Insurance Sales 

Harris County and Houston, Texas 

Mr. Mike Howard, NFIP Coordinator, State of Texas 
The Honorable Mark Ellis, Councilman, City of Houston, Texas 
Mr. Mike Loomis, Floodplain Manager, City of Houston 
Ms. Amy Matthews, Real-Estate Developer, GSL Industrial Partners, L.P. 
Mr. Warren Sloan, Independent Insurance Sales 
Mr. Welcome Wilson, Sr., Real-Estate Developer, GSL Industrial Partners, L.P. 

Pensacola, Florida 

Mr. Charles Speights, NFIP Coordinator, State of Florida 
Mr. Monte Blews, General Manager, Pensacola Beach Santa Rosa Island Authority 
Mr. Brad Hattaway, Planning Manager, Escambia County Department of Public Safety 
Mr. Joe Pace, Independent Insurance Sales 
Multiple Participants, Independent Insurance Sales, Molten Allen and Williams of Pensacola, 
LLC 

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Grand Forks, North Dakota 

Mr. Jeffrey Klein, NFIP Coordinator, State of North Dakota 
The Honorable Eliot Glassheim, Councilman, City of Grand Forks City 
Mr. Al Grasser, City Engineer, City of Grand Forks 
Mr. Keith Lund, Community Development Manager, City of Grand Forks 
Ms. Bev Collings, Floodplain Manager, City of Grand Forks 
Ms. Clair Thompson & Mr. Ray Pollard, Independent Insurance Sales 
Mr. Mark Nelson, Independent Mortgage Lender 

DuPage County, Illinois 

Mr. Paul Osman, NFIP Coordinator State of Illinois 
Mr. Bruce Maki, DuPage County Regulatory Services 
Mr. Bill Brown, DuPage County Stormwater Management 
Mr. Don Dressel, PE, Independent Real-Estate Developer 
Mr. Louis DiMaggio, Independent Insurance Sales 
Mr. Nick Textor, PE, Independent Real-Estate Developer 

Association of State Floodplain Managers 

Mr. Doug Plasencia, Association President (2003) 
Mr. Larry Larson, Executive Director 

Federal Officials 

Mr. Joseph Coughlin, Department of Homeland Security 
Mr. Mike Robinson, Department of Homeland Security 

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EXECUTIVE SUMMARY 

As floodplains are developed and permeable surfaces are replaced with non-permeable 
surfaces, there is an increased chance of flooding. Additionally, the incorporation of storm water 
management systems typically means that large quantities of water are collected, increasing 
discharges, and potentially creating a higher flood stage elevation downstream. Because of these 
changing conditions it has put an emphasis on the relationship between development, the 
floodplain, and floodplain management. 

In 1999, the Federal Emergency Management Agency (FEMA) convened a committee to 
establish a framework for conducting the first comprehensive evaluation of the National Flood 
Insurance Program (NFIP). The NFIP, created under the National Flood Insurance Act of 1968, 
is administered by the Department of Homeland Security (DHS) through the Mitigation 
Division. The committee produced a set of questions to guide an evaluation that would assess 
the NFIPs effectiveness and efficiency. 

One question raised was whether current floodplain management techniques typically 
applied by communities to manage the NFIP properly address changing conditions within a 
given watershed, and what effect they have on decreasing future costs and consequences of 
flooding  one of the primary goals of the NFIP. 

In 2000, FEMA contracted with the American Institutes for Research (AIR) to perform a 
comprehensive evaluation of the NFIP. As part of the evaluation, AIR subcontracted with 
ABSG Consulting to conduct a sub-study that would analyze practices of managing future 
conditions in the floodplain. The objective of the study is to analyze the consequences (cost and 
benefits) of managing future conditions. 

To analyze this issue, the sub-study would use both a qualitative (interviews) and 
quantitative (computational analysis) assessment. The interviews will be used to identify the 
cost, while, computational analysis will be used to identify the benefits (avoided losses). To best 
identify the costs associated with floodplain management, it was determined to select 
communities that are known to be using floodplain management regulations that exceed the 
NFIP minimum standards. By analyzing these communities the project team could identify the 
additional costs associated with implementing and enforcing floodplain management programs. 

The project team visited each of the selected communities and arranged interviews with a 
cross section of people affected by floodplain regulations. The purpose of the interviews was to 
establish a qualitative understanding of the communities perspectives on how their floodplain 
management regulations, especially those exceeding normal standards, may have changed 
development trends, the costs of development, and the ability to meet development demands 
within the community. The interviews were semi-structured for free flowing discussion. The 
interviewer was free to ask pointed questions in response to the interviewees answers. The 
interview process also provided the opportunity to work with the local staff to identify available 
flood data for our computational analysis effort. 

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To perform the computational analysis, the project team incorporated the local data into 
the HAZUS-MH1 Flood Model (hereafter referred to as HAZUS) developed by the National 
Institute for Building Sciences (NIBS) for FEMA. The benefits (avoided losses) will be 
identified by comparing the current and future inventory against the current and future 100-year 
floodplain. While working with annualized losses would provide a more accurate assessment of 
the losses avoided, many communities do not have the necessary data to perform this assessment. 
Three (3) return intervals are required in order to properly extrapolate and estimate annualized 
losses. Based on this fact, the project team opted to perform the comparative analysis for the 
100-year flood. 

There were five (5) initial communities; they included: Mecklenburg County, North 
Carolina; Escambia County (specifically Pensacola), Florida; Harris County (specifically 
Houston), Texas; Larimer County (specifically Fort Collins), Colorado; and Grand Forks County 
(specifically Grand Forks), North Dakota. After the initial interviews were completed some 
communities were eliminated from the list. For various reason discussed in Chapter 4, Escambia 
County (Pensacola), Florida, and, Grand Forks, North Dakota were removed from the study. In 
an effort to maintain a satisfactory number of communities in the study the County of DuPage, 
Illinois was added to the list. The results from the interviews (qualitative) and computational 
(quantitative) process can be found in Chapters 5 and 6, respectively. 

Based on the results from the interviews and computational analysis, the project team has 
drawn the conclusion that costs associated with managing future conditions are negligible. Many 
of the interviewees believe that there are other factors that have more of a significant impact on 
costs. Additionally, the computational assessment determined that managing to future conditions 
provide substantial avoided losses. This was largely due to the fact that many of the existing 
structures in the floodplain were greatly impacted by anticipated development. 

The analysis was performed three times using a variation of the analysis inputs so the 
increased impact caused by development within the watershed could be seen more clearly. 

 Current Inventory with Current Flood Conditions: The first analysis looked at the 
potential existing conditions losses. For this study, potential existing conditions losses 
are defined as the 100-year flood losses for the buildings that currently exist in the 
community and the flood conditions for the 100-year flood used as the design condition 
flood for the community. 
 Current Inventory with Future Flood Conditions: The second analysis examined the 100year 
flood loses for the buildings that currently exist in the community, but the 100-year 
flood is what is anticipated at build-out assuming no additional management of the 
floodplain (such as on-site storage) as the development progresses. (On-site storage is 
assumed to prevent any change in the 100-year flood conditions). 
 Future Inventory with Future Flood Conditions: The final analysis explores the 100-year 
flood losses for the built-out conditions (current plus zoned future development) against 
the build out 100-year flood conditions used in the second analysis. 
1 Hazards United States or HAZUS is a trademark of the Federal Emergency Management Agency. 

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When comparing the results, the difference between the first and second analysis 
represents the impact created by the changing flood conditions on the existing inventory, that is, 
the losses created on existing structures by unmanaged development within the watershed. The 
difference between the second analysis and the third analysis represents the losses attributed to 
the development of structures within harms way, that is, the new structures placed in the areas 
that are flooded during the 100-year event. Finally, comparing the first analysis to the third 
analysis represents the total losses that could potentially be avoided with managing the 
floodplain to the future conditions. 

In Mecklenburg County, for example, the building and content damage resulting from 
future conditions flooding represents a nearly 440% increase from the damage associated with 
existing conditions flooding. Even in Fort Collins (Larimer County), where the increased 
flooding is relatively minor, the future conditions flooding may result in a 37% increase in flood 
damages from the 100-year event. To existing structures, the future conditions flood can mean 
that more structures are impacted by flooding (as seen by the addition of some occupancies in the 
analysis tables that were not there before) and those that were impacted before, have increased 
levels of damage. For example, in Harris County, the future conditions flood could create a 
1,300% increase in flood losses in the existing inventory alone. In fact, with the exception of 
Mecklenburg County, the existing inventory represented the majority of the additional losses 
caused by the future conditions flood. 

Based on the results from the interviews and computational assessment, the project team 
also made some recommendations. The primary recommendation is that communities should be 
encouraged to manage future conditions in the watershed. As identified in the study, there are 
two (2) approaches to managing future conditions: 1) managing the watershed to the anticipated 
100-year build-out flood conditions; and, 2) attempting to avoid or delay the flood waters from 
entering the stream system thereby reducing or preventing an increase in the hydrograph. This 
study is not making a recommendation or distinction between the two (2) approaches; just the 
recommendation that communities should manage future conditions in the watershed. The 
communities should select the approach that best suits their needs. 

The study also provided other recommendations/observations on: flood maps; 
establishing and maintaining flood studies; floodplain management programs; data collection 
guidelines; lack of knowledge; public awareness; special flood hazard areas (SFHA); and local 
economics. 

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1. INTRODUCTION 
1.1 Nation Flood Insurance Program (NFIP) 
The National Flood Insurance Program (NFIP) was created under the National Flood 
Insurance Act of 1968 and is currently administered by the Mitigation Directorate, as part of the 
Department of Homeland Security through the Mitigation Division. The goals of the NFIP2 can 
be stated as follows: 1) decrease the risk of flood losses; 2) reduce the costs and adverse 
consequences of flooding; 3) reduce demands and expectations for federal disaster assistance 
after floods; and 4) restore and preserve the natural and beneficial values of floodplains. 

The regulatory standards of the NFIP are minimum standards in that they are generally 
applicable everywhere within participating communities and provide a basic measure of 
protection. Results from previous NFIP studies suggest that buildings built to the minimum 
NFIP regulations experience 80 percent less damage through reduced frequency and severity of 
losses.3 However, they are still subject to damages due to changes in the watershed, larger 
storms, filling of the floodplain, incomplete understanding of the system hydrology and 
hydraulics (H&H) used to define the regulatory floodplain, and development within the entire 
watershed. 

As of 2006, the NFIP membership exceeds 20,000 communities with more five (5) 
million policy holders paying annual premiums whose total exceeds $2.2 billion. 

1.2 Special Flood Hazard Areas (SFHAs) 
As part of the NFIP, and in cooperation with state and local agencies, communities, and 
mapping contractors, FEMA has produced Flood Insurance Rate Maps (FIRMs) depicting 
Special Flood Hazard Areas (SFHAs) for over 20,000 communities nationwide. The SFHA (also 
referred to as the 100-year flood zone) represents the flood that has a one percent (1%) annual 
chance of occurring in any given year, sometimes referred to as the base flood. The base flood 
is the national standard used by the NFIP and all federal agencies for the purposes of regulating 
development and requiring the purchase of flood insurance. 

As discussed in Assessing the Adequacy of the National Flood Insurance Programs 1 
Percent Flood Standard (Galloway et al., 2006), the 100-year flood, also known as the 1 
percent annual chance flood, is a statistical construct: it is a flood that has a certain discharge 
that produces a specific flood elevation, and has a 1% chance of occurrence in any one year. In 

2 These ultimate outcome goals were reached by a consensus of FEMA and the NFIP Evaluation team in the 2002 
Design for the Evaluation of the National Flood Insurance Program. The fourth goal is not included in statute, 
however. The primary legislatively stated purposes of the 1968 Act creating the NFIP were to Through insurance, 
better indemnify individuals for flood losses that created personal hardships and economic distress; reduce future 
flood damages through State and community floodplain management regulations; and reduce Federal expenditures 
for disaster assistance and flood control (42 USC 4001). 
3 Source: Implementation of Community Floodplain Management Ordinances during Post-Disaster Reconstruction 
and Federal Agency compliance with Executive Order 11988-Floodplain Management, FEMA Memorandum to 
Regional Directors, September 22, 2005 

Evaluation of the National Flood Insurance Program 

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reality, the 100-year flood represents a range of discharge and elevation values because of the 
uncertainties in the information available for its computation and the resulting use of probability 
distributions to portray the possibilities. Development may take place within the SFHA provided 
that the development complies with local floodplain management ordinances, which must meet 
the minimum federal requirements.4 

The 100-year flood zones on the FIRMs reflect varying degrees of analysis. The 100year 
flood zones in communities with limited existing or potential development are typically 
mapped by approximate methods. In contrast, other 100-year flood zones are mapped by 
detailed methods reflecting complex hydrologic and hydraulic studies that analyze flood 
duration, drainage area, structures, the amount of impermeable surface, and other factors that 
affect flood hazards. These studies assign statistical probabilities to different size floods. This is 
done to understand what might be a common or ordinary flood for a particular area versus a less 
likely or severe flood for that same river or coastline. Flood studies developed using detailed 
methods include the computed elevation to which floodwaters are anticipated to rise during the 
base flood. Detailed flood studies are a vital part of a floodplain management program and 
provide the necessary flood elevations, flood velocities, wave action areas, and floodway 
dimensions to ensure newly constructed buildings are built to minimize flood damage. 

The accuracy of the FIRMs and the 100-year flood zones are dependent on the accuracy 
of the computation of the 1% flood and the accuracy of the topographic information available for 
the area being mapped. The myriad of factors involved in generating the flood map make it 
difficult to ascribe to the level of accuracy that is frequently given to it by communities. Many 
interpret the 1% flood line as an assurance that development above that elevation or outside that 
line are guaranteed to be safe from the 1% flood. 

The 100-year flood zones also only reflect existing conditions; they do not incorporate 
future development. Following the passage of the Flood Disaster Protection Act of 1973, a 
decision was made that property owners, under certain circumstances, were mandated to 
purchase flood insurance. Since future development was often driven by local political, social, 
and economic conditions and attitudes, the property owners could not be mandated to purchase 
flood insurance based upon development that may never occur. 

Another issue effecting the 100-year flood zone is that, as floodplains are developed and 
more of the ground surfaces are paved (e.g., made impervious or non-absorbing), the risks and 
expected elevations of flooding increase. The predicted elevation of the base flood increases, 
and the 100-year flood zone subsequently spreads beyond their mapped boundaries. FEMAs 
present policy of mapping 100-year flood zones based on current development conditions means, 
among other things, that, in rapidly developing areas and areas downstream, FIRMs may become 
outdated shortly after their completion (FEMA, 2000). As a result, some development could be 
constructed without proper protection from the flood hazard it may face throughout its life span. 

This may also mean that some development is either uninsured or paying rates that do not 
accurately reflect their risk. Flood insurance rates are based on the buildings risk of flooding. 

4 Communities participating in the NFIP must adopt legally enforceable floodplain management measures that are 
compliant with 44 CFR 60.3 of the NFIP regulations. 

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Most insurance rates are determined based on the elevation of the lowest floor of the building in 
relation to the base flood elevation. Enforcement of floodplain management regulations by 
communities and incorporation of updated estimates of base flood elevations on FIRMs are 
critical for the NFIP to achieve its objectives of protecting lives and property and making flood 
insurance available within participating communities at affordable insurance premium rates. 

Additionally, as floodplains are developed and more surfaces are paved, expected 
discharges and subsequent frequency and elevations of flooding are increased. While asphalt or 
concrete efficiently transports water to a storm drain, where it is then conveyed via pipe or 
culvert into a swale, stream, river, or other receiving water, this effectiveness means that large 
quantity of water enter into out-falls very quickly, thereby rapidly increasing discharges and 
potentially creating a higher flood stage elevation. 

1.3 Managing Future Development Conditions Project 
In 1999, the Federal Emergency Management Agency (FEMA) convened a committee to 
establish a framework for conducting the first comprehensive evaluation of the National Flood 
Insurance Program (NFIP). The outcome of the committees sessions was a set of questions to 
guide an evaluation that would assess the NFIPs effectiveness and efficiency. One of the 
questions raised was whether current floodplain management techniques typically applied by 
communities to manage the NFIP properly addresses changing conditions within a given 
watershed and what effect they had on decreasing future costs and consequences of flooding  
one of the primary goals of the NFIP. 

In 2000, FEMA contracted with the American Institutes for Research (AIR) to perform a 
comprehensive evaluation of the NFIP. As part of the evaluation, AIR subcontracted with 
ABSG Consulting to conduct a sub-study that would assess the consequences (cost and benefits) 
of managing future development. To understand this issue, this sub-study identifies several 
communities that are known to be using floodplain management regulations that exceed the 
NFIP minimum standards, specifically managing their anticipated future flood conditions. 
Because of the intensity of modeling and data collection required for the qualitative analysis, it 
was decided by FEMA, AIR, and the project team to minimize the number of communities in the 
study. 

Additionally, this project initially tried to attempt to examine both riverine and coastal 
flood conditions. However, based on discussions with subject matter experts, future conditions 
in the coastal area is more highly dependent on meteorological and geographic conditions other 
than the development of impervious surfaces or an increase in the number of habitable structures. 
For that reason, this project focused its attention on the riverine flood areas. Section 1.5 provides 
further discussions of the reasoning for excluding coastal flooding from this study. 

1.4 Managing Future Development Definition 
Managing Future Development, for this study, refers to actions taken to ensure that 
conditions of the watershed are maintained or improved to the predetermined condition. 
Typically, development modifies the percentage of the land area that is considered impervious to 

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rainfall, which subsequently leads to modified discharges. There are typically two (2) 
approaches to managing the watershed: 1) managing to the anticipated 100-year build-out flood 
conditions; and, 2) attempting to avoid or delay the flood waters from entering the stream system 
thereby reducing or preventing an increase in the hydrograph. 

The second approach is generally accomplished through the use of temporary or 
permanent on-site storage, with limits on the rate at which the water can be discharged into the 
storm water system, and subsequently, the watershed. The approach is to require all subsequent 
development to retain the increased runoff (up to 100-year flows) on site and discharge the 
runoff over a period of several days. 

Many communities across the country have recognized the impact of new development 
on the 100-year flood zone and have implemented programs to mitigate potential increased base 
flood elevation or floodplain limits. These programs are designed to promote sustainable 
development; development that incorporates measures to lessen the impacts of their effects on 
the watershed. This effort is consistent with the watershed management program espoused by 
the Association of Floodplain Mangers (ASFPM) No Adverse Impact program. As defined in 
an ASFPM white paper5, a No Adverse Impact refers to: 

an action taken by one property owner that does not adversely impact the rights 

of other property owners, as measured by increased flood peaks, flood stage, 

flood velocity, and erosion and sedimentation. 

1.5 Managing Future Development for Coastal 
The following discussion is intended to clarify reasons for not including coastal flooding 
in the future conditions project. The methodology for the identification of the 100-year flood 
zone for coastal areas is done slightly different than for riverine. NFIP coastal flood hazard 
mapping methods (as outlined in the April 2003 Guidelines and Specifications for Flood Hazard 
Mapping Partners, Appendix D  Guidance for Coastal Flooding Analysis and Mapping) 
produce maps that reflect existing conditions, i.e., those known or postulated to be in existence at 
the time the flood insurance study is conducted. These methods consider the following 
parameters associated with the establishment of the base flood or 100-year flood: 

 stillwater elevations -- due to ambient sea level, astronomical tide and/or storm surge 
conditions; 
 wave effects such as shoreline wave lengths and wave heights, wave setup, wave 
propagation over flooded land, wave run-up and overtopping (run-up is the 
phenomenon in which a wave crest runs up along a slope to a level that is higher than 
the original wave crest due to the momentum of the wave), and if required, tsunami 
wave run-up; 
 topography, modified to account for expected storm-induced erosion under base 
flood conditions; and, 
5 ASFPM White Paper on No Adverse Impact published in 2004 of the ASFPM website 
http://www.floods.org/NoAdverseImpact/whitepaper.asp 

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 land cover such as the presence of vegetation, dunes, buildings, or other 
obstructions and unobstructed areas, all of which modify wave effects. 
Note that climate change is not explicitly included above. This study recognizes the fact 
that climate change can alter storm meteorology (e.g., frequency, intensity, track, etc.) and flood 
conditions over time, but assumes the effects will be included, implicitly, through consideration 
of the stillwater elevation and wave effects parameters. 

The typical coastal flood study draws analysis transects perpendicular to the shoreline, 
and considers each of the above parameters along each transect. One or more models (e.g., 
erosion assessment, WHAFIS (Wave Height Analysis for Flood Insurance Studies), RUNUP 
(wave run-up computation)) are used to determine base flood conditions along each transect, 
depending upon the nature of the flooding and terrain at a given transect. Base flood conditions 
are interpolated between transects by the study contractor, and a FIRM is created. 

Future conditions can potentially modify the parameters and flood hazard zones shown in 
the FIRM, thereby affecting the accuracy of FIRMs mapped on the basis of existing conditions. 
The future conditions considered by this study are described below, followed by a discussion of 
how they may affect flood hazard zones and Base Flood Elevations (BFEs), and a summary table 
at the end of this section. 

1.5.1 Coastal Erosion 
Coastal erosion is usually taken to mean long-term erosion, where the shoreline retreats at 
or around an average rate over a period of decades. However, shorelines (and dunes and bluffs) 
can also retreat during a single storm. FEMAs current flood hazard mapping procedures for 
coastal areas incorporate storm-induced coastal erosion, but do not incorporate long-term coastal 
erosion. This discussion distinguishes between long-term coastal erosion and storm-induced 
coastal erosion. 

FEMAs current flood hazard mapping procedures for coastal areas incorporate storm-
induced erosion, but do not incorporate long-term erosion. 

Storm-induced Coastal Erosion 

FEMA coastal flood mapping methods account for base flood induced erosion of dunes 
and bluffs by elevated water levels and waves. Storm-induced erosion should not be considered 
a future conditions parameter that will alter flood hazard mapping results. While future changes 
in the base flood water level and wave conditions may affect the outcome of storm-induced 
erosion calculations, water levels and wave conditions are each addressed separately as future 
conditions parameters. 

Long-term Coastal Erosion 

Long-term erosion at a shoreline is the net result of a variety of factors (e.g., sediment 
losses from storms, tidal inlets, submarine canyons, longshore transport gradients, sea level rise, 

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dredging or mining, etc.) averaged over several decades. Shorelines rarely recede at a constant 
rate; instead, erosion occurs in a cyclic fashion, with seasonal and episodic influences resulting 
in periods of erosion, periods of stability and periods of accretion. On shorelines suffering from 
long-term erosion, the periods of erosion outweigh or overwhelm the periods of stability and 
accretion. 

1.5.2 Topography 
The net result of long-term erosion is a horizontal recession of the beach/dune/bluff 
profile or the net loss of land over time. However, since the recession profile is dynamic, and 
responds to a variety of forces, the shape of the profile at a given site can vary substantially over 
time. As the profile recedes beaches can narrow or widen; the dune can be created or destroyed, 
and upland elevations can increase or decrease. 

The NFIP flood hazard mapping results are sensitive to the topography (profile shape) at 
the time the calculations are made. Thus, flood hazard zones and BFEs can vary at a given site, 
depending upon when the flood hazards are mapped. Fortunately, NFIP coastal mapping 
methods account for some of this variability by ignoring any dune whose cross-sectional width is 
sufficient to prevent its loss during the base flood. Moreover, the most dynamic changes usually 
occur at the shoreline itself, seaward of most development. The landward portion of the profile 
(where most development exists) does not fluctuate as readily as the shoreline. 

1.5.3 Buildings and Development 
Buildings and development can affect local topography as the number/distribution of 
buildings change, and the exposure grows. Topographic alterations typical of development can 
affect the inland extent of flooding, and the magnitude of wave effects and buildings can alter the 
obstruction coefficients used in the Wave Height Analysis for Flood Insurance Studies 
(WHAFIS) models. Generally, in wave velocity zones (V zones) or those zones where waves 
and the surge pressure can increase damage to buildings, the effects of topographical alterations 
are minimal  compliant buildings are elevated on open foundations and will have minimally 
disturbed the topography (due to lack of a structural fill), resulting in a minimal effect on flood 
extents and wave transmission. A zones, areas subject to inundation by the 100-year flood 
source without the additional velocity hazard, cause larger topographic alternations since 
buildings can be elevated on fill and with solid foundations. However, barring large-scale 
alterations of coastal upland areas, the effect of future A zone construction on flood hazards 
should be localized and minimal as well. 

1.5.4 Sea Level Rise 
The primary effect of rises in the sea level is to increase ambient water levels and to 
provide a higher platform upon which storm surge and wave effects can propagate (inundation). 
The wave effects will increase as water depths increase. Currently the effects of sea level rises 
are handled through postulated adjustment and landward recession of beach profiles in response 
to higher water levels (e.g., Bruun rule). However, there is no consensus, on the magnitude of 

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the rise in sea level. While some believe the recession is over than 100 times greater than the 
vertical rise in water level, others (e.g., Galvin6) dispute this. 

Table 1 provides a listing of the future conditions parameters known to exist within the 
coastal flood hazard areas. The parameter could either have a primary influence on the 
subsequent hazard or have a secondary influence. For example, long-term erosion will have a 
primary influence on wave effects (by reducing dune height or reservoir and reducing distance to 
the structures), and the topography (by literally removing terrain). Long-term erosion has a 
secondary effect on land cover, as lost soil reduces the amount of vegetation. Long-term erosion 
has little or no impact on the stillwater elevation because it changes only the terrain impacted by 
the hazard, and not the flood hazard itself. 

TABLE 1. Future Coastal Flood Conditions Parameters and Their Influence on Hazard Maps 

Future Conditions Effect of Future Conditions Parameter on: 

Parameter Stillwater Elevation Wave Effects Topography Land Cover 
Long-Term Erosion P P S 
Flood Protection 
Structure 
P P 
Beach/Dune 
Nourishment 
S P S 
Buildings and 
Development 
S S P 
Sea Level Rise P S S 

P = primary or principal influence of future conditions parameter on flood hazard mapping results 
S = secondary or minor influence 
-- = no influence or negligible influence 

6 Galvin, C. 1983. Sea Level Rise and Shoreline Recession. in ASCE Proceedings of Coastal Zone '83. pp. 2684

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2. METHODOLOGY 
2.1 Overview 
The first step in the methodology was to determine which communities would be 
included in this study. Working with both FEMA and AIR, the project team developed 
community selection criteria and identified communities that have adopted higher floodplain 
management regulations. After assessing the communities against the criteria, the final selection 
was completed. 

After the selection of the communities, a methodology was developed to achieve the 
projects goal (identifying the cost and benefits of implementing watershed management 
programs to address management of future conditions). It was determined that the project team 
would perform both a qualitative and quantitative approach. A series of interviews with key 
local personnel would aid in the qualitative approach; while the project team would utilize 
HAZUS-type methodologies to complete the quantitative approach. 

The interviews provided the project team with detailed background information that 
helped understand the flood history of the community, the current management methods, and the 
amount of development that has occurred since the NFIP was implemented within the 
community (costs). The interviews also tried to identify the ramifications (i.e., political 
consequences, law suits, takings) from the enforcement of NFIP and increased floodplain 
management standards. These interviews attempted to capture the perspectives of a broad cross-
section of stakeholders in the land-use and risk management process, while remaining 
sufficiently limited so OMB approval was not required. 

HAZUS and HAZUS-type methodologies were utilized to complete the quantitative 
approach. With this approach the project team would have to collect community-specific GIS-
based data. The community-specific GIS-based data included parcel level data from the County 
Tax Assessor, shape files of the county parcel boundaries, and flood hazard data. During the 
data collection process, the project team discovered that the flood hazard data varied among the 
communities; one modified the flood maps to reflect future development, while others enforced 
floodplain management programs (requiring land owners to retain water) and thus did not revise 
the flood maps for new development. This process aided in the identification of the benefits 
from the implementation of managing future conditions. 

After the communities have been identified and the necessary data collected or processed, 
the project team followed the below methodology: 

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Create a Study Region for Each Community 

The primary purpose of the study region is to provide the analysis foundation for the 
project. The analysis would be done using local inventory data and local flood data 
thereby bypassing the Level 17 analysis possible within HAZUS. 

Duplicate the Study Region 

Two (2) study regions are required to perform the analysis. Because the project team was 
using point level data, it is necessary to dedicate study regions to the selected inventory. 
The first is dedicated to the Existing Condition inventory although the flood conditions 
would vary from the existing 100-year flood (study case 1) and the future conditions 100year 
flood (study case 2). The second study region would be dedicated to the Future 
Condition inventory (build out) and the future 100-year flood. 

Generate Two (2) Building Inventory Tables 

The project team processed the assessor data for import into the User Defined Facilities 
table in HAZUS. Two tables were generated. One (1) table will have the existing 
building inventory (those where the assessor data file defines an improvement on the 
parcel) and one with the future building inventory. Again the future inventory includes 
both the current structures and a HAZUS baseline building at the centroid of all vacant 
parcels. Each of the database tables had the specific information needed by the flood 
model to perform the loss estimation analysis. 

Create Flood Depth Grid and 100-year Return Intervals 

The flood depth grid for the existing and future 100-year flood conditions were 
developed using the Flood Information Tool (FIT). The FIT uses the cross section 
information, attributed with the flood elevations, a bounding floodplain polygon, and a 
digital elevation model to develop an estimated grid with the depth of flooding. This 
information is in a format that is readily imported into HAZUS for use in the damage 
assessment. 

Select Flood Depth Grid and 100-year Return Intervals 

The flood depth grid developed from the FIT was imported into HAZUS and the 100year 
return interval selected for the analysis. The first study region (the region with the 
existing building inventory) would have two (2) study cases, the first with the current 
flood FIT data and a second using the future conditions FIT data. The second study 
region (the region with the future building inventory) would have only the future 
conditions FIT data. 

7 A Level I analysis refers to the analysis that can be performed within HAZUS entirely using the baseline data 
supplied with the model. This data is based on the US Census and Dunn & Bradstreet data and is performed at the 
census block level. This level of analysis was not sufficiently detailed enough for this study and therefore local data 
at the point level was used. 

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Process Data in HAZUS 

On the Analysis menu, the project team selected the User Defined Analysis and the flood 

model was allowed to process the loss estimates. In the duplicate region, the analysis 

was repeated. The results from each analysis was extracted from the SQL Server 

database and brought into Excel to format into charts and tables as seen in the report. 

2.2 Community Selection Process 
The selection of the communities followed a three (3) step process: 1) identify 
communities known to have adopted higher floodplain management regulations; 2) ensure that, 
in totality, the communities represented a cross section of flood communities; and, 3) that each 
community possessed adequate GIS-based data to perform the quantitative analysis. 

In collaboration with FEMA and AIR, the project team created a list of communities 
located in flood areas known to have implemented floodplain management regulations that 
include management of the future flood conditions. It was agreed that analyzing these 
communities would provide valuable information which could be used to meet the projects 
goals. Communities were selected purposively in a way that allowed geographic diversity to 
better relate the results to other communities. The project team tried to select communities that 
may have experienced a range of legal, institutional, and other barriers to the adoption and 
implementation of higher floodplain standards. 

2.3 Interview Process (Qualitative Assessment) 
The goal of the interviews was to establish a qualitative understanding of the 
communities perspectives on how the implementation of floodplain management, especially 
those who have adopted higher than normal standards, may have changed development trends 
and costs within the community. The sampling pool and the loose format of the interviews were 
designed to ensure that we were not seeking duplicate information from more than 10 people 
within each given community or from our sampling as a whole. Although the interviews were 
not formally structured, the project team tried to cover the following topics with each stakeholder 
regarding the communitys floodplain management and flooding history. The main topics 
include: 

 overall history and impact of floods on the community; 

 impact of implementing the NFIP on development trends and costs; 
 impact of implementing future condition management on development trends and 
costs; 
 impact on building standards; and, 
 impact on development trends. 
The subjects of floodplain management and flooding history were covered to examine if 
the respondents felt the flood risk was significant and warranted increased regulation. The 
discussion of community impacts of the NFIP included community perceptions, impacts on land 

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use, environmental impacts, financial impacts, and social impacts. These questions provided the 
background information for each community regarding the status and direction of their flood 
program. 

Implementation of the NFIP was discussed to discover whether the communities were 
participants in the Community Rating System (CRS) and what issues they encountered 
implementing the program. Additionally, the project team developed questions to identify any 
legal issues the community may have encountered. This provided further information about how 
some community residents felt about the NFIP implementation. 

A stratified purposive sample was used to select individuals to interview within each 
selected community. The sample frame was stratified by community and job category. Within 
each community individuals who were contacted to participate in the study included community 
officials, developers, insurance agents, and lenders. Each person was contacted by telephone 
with an explanation of the study and request to participate. If the person accepted the request to 
participate, then in-person or telephone interviews were scheduled. 

The interviews were done in a semi-structured, open-ended interview format, but the 
general topics were discussed in the interview. This allowed the project team to be flexible if a 
topic of particular interest arose and for the respondent to expand in areas that they were 
particularly knowledgeable. Each interview was approximately one (1) hour in length. 

2.4 Computational Process (Quantitative Assessment) 
The computational analysis focused on the losses avoided for the 100-year flood. While 
working with annualized losses would provide a more accurate assessment of the losses avoided, 
existing data is limited. Three (3) return intervals are required in order to properly extrapolate 
and estimate annualized losses. The project team opted to perform the comparative analysis for 
the 100-year flood. To perform the analysis, the project team used the HAZUS-MH8 Flood 
Model (hereafter referred to as HAZUS) developed by the National Institute for Building 
Sciences (NIBS) for FEMA. The flood model provided a consistent scientific analysis tool for 
comparisons. 

The computational analysis for this project was performed three (3) times for each 
community using a variation of the inputs so the increased impact caused by development within 
the watershed could be seen more clearly. The analyses can be identified as: 1) Current 
Inventory with Current Flood Conditions; 2) Current Inventory with Future Flood Conditions; 
and, 3) Anticipated Inventory with Future Flood Conditions. 

The first analysis looked at the potential existing conditions losses. This analysis 
examined the current 100-year flood impacts against current building inventory. The second 
analysis examined the 100-year flood impacts for existing buildings, but analyzed it against the 
anticipated 100-year flood which assumes build-out with no additional floodplain management 
(such as on-site storage). As the development progresses, on-site storage is assumed to prevent a 

8 Hazards United States or HAZUS is a trademark of the Federal Emergency Management Agency. 

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change in the 100-year flood conditions. The third and final analysis considered the anticipated 
built-out conditions (current inventory plus zoned future development) against the build-out 100year 
flood conditions. 

Using HAZUS, the depth of flooding from the depth grid cell can be determined at each 
buildings location (latitude and longitude). This depth of flooding is used to define the depth of 
the water at the structure. The foundation type is then used to define the height of the first floor 
in the structure. The subtraction of the two defines the depth of the water within the structure. 
This is input into the default depth/damage functions to determine the percentage of replacement 
cost of the structure that has been inundated and damaged. 

For single family residential structures, the default damage functions were derived from 
the Federal Insurance Administrations credibility weighted loss functions modified to account 
for policy exclusions (See the HAZUS Technical Manual for detailed discussions of all of the 
depth damage functions within HAZUS). For the other occupancies, a variety of U.S. Army 
Corp of Engineer depth-damage functions have been selected as the default functions because of 
their general applicability throughout the United States. Default functions were used since the 
functions have been reviewed and approved by the HAZUS Flood Model Oversight Committee 
for their applicability throughout the nation. Using consistent damage functions in all of the 
analyses allows for more direct comparison of the changing flood conditions by removing the 
variable of damage function selections. 

The results of the HAZUS analysis are essentially an estimation of loss associated with 
the inundation based on the percentage of the replacement cost that has been damaged (classified 
as benefits for this study). Likewise, functions within HAZUS allow for the estimation of loss to 
the structure contents and (for selected occupancies) inventory that is sold from the location. 
Results are in terms of dollars and can be aggregated to represent the total losses for the flood 
being examined. For this report, contents and inventory are being aggregated together to provide 
a representation of the total non-building related damage. 

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3. COMMUNITY SELECTION PROCESS SUMMARY 
There were five (5) initial communities identified to be part of this study; they included: 
Mecklenburg County, North Carolina; Escambia County (specifically Pensacola), Florida; Harris 
County (specifically Houston), Texas; Larimer County (specifically Fort Collins), Colorado; and 
Grand Forks County (specifically Grand Forks), North Dakota. 

Working with FEMA and AIR, the project team, developed a list of criteria for the 
community selection. As stated previously, the purpose of the criteria is to ensure that the 
selected communities represent a good cross section of characteristics found within flood 
communities. Below is a list of the initial selection criteria: 

 A community that has mapped existing future condition hydrology for the riverine or 
coastal flood hazard; 
 A community that has not mapped future conditions hydrology (at least one); 
 A community where either coastal or riverine flooding could be defined as the 
predominant flood threat; 
 A community located in large watersheds; 
 A community located in small watersheds; 
 A community that has attempted to manage flooding and runoff on-site; and 
 A community experiencing both low and high development rates9 
Utilizing the list of communities, the project team assessed whether or not all of the 
criteria was met. The communities and the project teams identification of relevant 
characteristics are summarized in Table 2. 

TABLE 2. Case Study Communities and Selection Criteria Characteristics 
Communities Characteristics 

Mecklenburg County,  Riverine flooding conditions predominate 
North Carolina  Future conditions hydrology has already been mapped 

 Floodplain management policies already considered future conditions hydrology 
 High growth rate 
DuPage County, Illinois  Riverine flooding conditions predominate 
 Future conditions hydrology has already been mapped 
 Future conditions is considered in floodplain management policies 
 Low to medium growth rate 
Houston, Texas  Riverine flooding conditions predominate 
 Future conditions hydrology have not been mapped 
 Floodplain management policies already considered future conditions hydrology 
 Medium-High growth rate 
Fort Collins, Colorado  Riverine flooding conditions predominate 
 Future conditions hydrology has already been mapped 
 Floodplain management policies already considered future conditions hydrology 
 High growth rate 
After the project team completed site visit and/or conducted the interviews some communities 
were eliminated from the list. Escambia County (Pensacola), Florida was eliminated because 

9 Community growth rates established through a comparison of total population in the 2000 Census vs the total 
population in the 1990 Census. Verification was made onsite through discussions with community leaders. 

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other research has shown that development within coastal areas will not have a direct impact on 
the flood conditions in terms of the area of inundation. As discussed later in this report, 
development will have some impact on the flood conditions outside of the scope of this project. 
Likewise, Grand Forks, North Dakota was removed as their future conditions planning assumed 
protection from their soon to be completed levee. The project review committee agreed that 
assuming levee failure was outside the scope of this project. In an effort to maintain a 
satisfactory number of communities in the study the County of DuPage, Illinois was added to the 
list. With the addition of DuPage County, the total number of communities in the study was four 
(4). 

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4. INTERVIEW PROCESS RESULTS (QUALITATIVE ASSESSMENT) 
4.1 Overview 
As previously stated, the communities selected for this study are all known to have 
incorporated higher floodplain management practices than what is required under the NFIP. 
Because of this the interview questions were designed to elicit responses which would illustrate 
the challenges (costs) of the increased regulations. The objective was to determine if the 
implementation of watershed management programs created additional issues or compliance 
problems within the community. 

For this study, the project team interviewed community officials, developers, floodplain 
managers, insurance agents, mortgage lenders, federal officials, and state officials. Table 3 
shows the number of people interviewed within each county by occupation. Again, the 
interviews were intended to familiarize the project team with the community and their 
perspective on the relative success and cost associated with the floodplain management strategy 
chosen. These interviews are primarily to support the technical analysis of this project. Another 
research team is performing more detailed interviews to support this requirement. 

TABLE 3: Number of Interviews Performed By Occupation 

County/City Community 
Official Developer Floodplain 
Manager 
Insurance 
Agent 
Mortgage 
Lender 
State 
Official Total 
Mecklenburg 2 3 1 2 1 1 10 
Larimer 3 0 1 2 0 1 7 
Harris 1 2 1 1 0 1 6 
Escambia 1 0 2 2 0 1 6 
Grand Forks 2 0 2 2 1 1 8 
DuPage 1 2 1 1 0 1 6 
Total 10 7 8 10 2 6 43 

NOTE: Because federal officials are not considered part on any one county, the numbers are not reflective in this table. For a 
summary of the results refer to section 4.8. Interviews with Escambia and Grand Forks County were conducted prior to the 
determination to drop them from the study. Interviews for DuPage County were conducted after inclusion into the study. 

The majority of the interviews were conducted in person but a few interviews (i.e., 
federal officials, state officials) were conducted via telephone. Prior to performing the 
interviews, each interviewee was provided with a background of the project describing the effort 
to determine the costs and benefits resulting from management of future conditions. Appendix 2 
provides the base interview guide used in this study. The interviews were semi-structured and 
were unique to the conditions of each community. 

After all the interviews were completed, qualitative analysis methods searched for 
patterns and themes in the interview data. The data was analyzed using a coding scheme based 
on specific topics of interest and the interview guide topic. Data within each code category, data 
was analyzed for common threads or themes. Then, consensus-coding methods were used to 
increase the validity of the findings. Consensus-coding is an approach where multiple reviewers 
will meet and reach a consensus regarding the meaning of the interview subjects responses as 
they are tabulated for use in the analysis. 

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4.2 Community Officials 
The community officials interviewed were selected based on their roles in land use 
management. The officials, who are locally elected or appointed, include city managers, city 
council members, county administrators, and mayors. Each plays a role that can make or change 
flooding regulations and practices. Their decisions affect the local floodplain and watershed 
management. In the interviews with the community officials, the questions were focused on 
community impacts and history of flooding. 

In response to our areas of focus, the interviewed officials were aware of the flood history 
of their communities and even those areas where flooding occurred most often. Generally, the 
agreement was that the future conditions regulations were implemented without a heavy political 
fight, but community officials still receive occasional threats of lawsuits and the development 
community will occasionally try to make the regulation of the building industry an issue during 
elections. When asked about specific lawsuits, only two communities could recall lawsuits. In 
one city the suit was settled when the city bought the land; and the other city lost the suite and 
the home was built. Ironically, in the city that settled, a later suit was filed that claimed that the 
city did not do enough to protect the residents. The outcome of that suit was not discussed as the 
interviewee went on to other topics. It can be inferred that most of the cities had relatively little 
legal trouble related to their floodplain regulations. 

When asked about their perception of the impact the regulations had on development, the 
officials commented that restrictions in the floodplain reduced the value of existing structures in 
the controlled area, but new home values continued to increase. Nearly all stated that floodplain 
management was not a major factor in the increasing development costs. The officials cited 
other land use and environmental regulations, site location, local traffic, and overall mobility as 
factors that have a greater influence on the value of a home. All officials felt that development 
was strong in their communities and that using the floodplain wisely (such as greenways and 
recreation) have added aesthetic value to their city even if some residents felt that property 
values may have decreased. 

4.3 Developers 
The developers that were interviewed are land developers and contractors in the local 
areas specializing in both residential and commercial development. The land development 
companies ranged from small -home offices to large corporations. Each developer has 
experience in building inside and outside the SFHA and has developed to a range of regulatory 
requirements. Their interviews focused on community regulations and the effects on the permit 
process, the cost of doing business, and the sale of their products. 

Developers who have worked in the community for long periods of time were aware of 
the flood history and areas prone to flooding. When asked their general opinion of the NFIP, 
developers tended to look at the regulatory aspects, which they view negatively, mainly because 
it affects what and how they can or cannot build. They indicated that it has affected how they 

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develop the land and has had added some increased costs to development in the floodplain. 
However, several were quick to point out (similar to the community officials) that other land use 
and environmental regulations had a greater impact on development and costs more than the 
floodplain regulations. 

Interestingly, half of the developers interviewed believed that the NFIP enhanced the 
value of the land adjacent to greenways and this allowed them to sell homes near the open space 
at a higher price. This same group of developers also believed it has helped to preserve natural 
habitats. The other half of the developers thought the NFIP had little or no impact on their cost 
and the sale value of the structures, nor they did not know of any environmental improvement or 
impact. 

Legally, the developers did not have any direct issues with the NFIP and the associated 
regulations except to state that there is a perception of takings. A taking occurs when a 
government agency has significantly reduced the economic value of land or significantly restricts 
the entitled use on the land. In most states, if a taking has occurred, the government entity is 
required to reimburse the land owner the fair market value of the land. One developer related an 
example, stating that farmers are paying property tax based on assessed value of their property, 
but if a portion of their land within the floodplain then the regulations has effectively reduced the 
market value of the land because it has reduced development opportunities. This can be 
perceived as a taking from the farmers perspective. 

Aside from restrictions on development within the floodway and the floodplain, the 
developers did not identify specific changes in development trends that could be attributed to the 
future conditions floodplain management. Generally they agreed that development trends are 
driven by the market and other intangibles such as views, location, etc. This also reflected 
comments made by the community officials. 

4.4 Floodplain Managers 
The floodplain manager of each community was the project teams main point of contact. 
The floodplain manager is the person in charge of enforcing the flood regulations. Each has 
been with his or her community for at least seven (7) years, and they are all certified floodplain 
managers, indicating their expertise in the field. In their interviews, they all provided detailed 
information on the community impacts and history, program implementation, and building 
standards. 

The floodplain managers general opinion of the NFIP, based on the programs they have 
implemented, is positive, but they expressed concern about repetitive loss structures. The 
floodplain managers are the only stakeholders who knew how much of the land in their 
community was built after entering the NFIP. It ranged from 15-70%. Discussions of their land 
use practice revealed that there is no development in the floodway, except in Harris County, 
Texas, where floodway development has to meet Base Flood Elevation (BFE) plus eighteen (18) 
inches and cannot increase the flood elevations above 1-foot. 

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Floodplain managers believe that the NFIP has increased the natural value of floodplains 
due to the programs that are being administered citing that it has increased the use of floodplains 
for natural recreation in their communities. They all expressed the opinion that they have seen 
an increase in the cost of homes in or near the SFHA, but the availability has not been affected 
and they did not attribute increasing housing costs solely to their programs. They see the 
Community Rating System (CRS) program as a beneficial program because it helps them exceed 
the minimum NFIP standards and has given floodplain managers a tool to implement regulations. 
However, they expressed frustration with the required paperwork, the program benefiting 
individuals rather than the community, and their perception that it promotes development by 
reducing premiums. 

None of the floodplain managers interviewed could remember any major political or legal 
issues related to the floodplain management regulations in their community beyond the 
posturing of developers and landowners about the reducing the value of their land and takings. 
However, a few of the floodplain managers have used the legal system to force compliance with 
their regulations. 

4.5 Insurance Agents 
The 11 insurance agents interviewed were all local agents identified using the Internet 
yellow pages. Insurance agents have the most interaction with property owners and are one of 
the major sources of flood insurance information for them. The number of flood policies written 
per year varied among agencies  some wrote two (2) policies per year, while others wrote 
dozens of policies per year. Not all of the agents were equally knowledgeable about flood issues, 
but they all answered the questions to the best of their knowledge. 

Many of the insurance agents had a detailed knowledge of their communities flood 
history and areas with the tendency to flood. None of the insurance agents felt they could 
provide input into whether the NFIP and the future conditions management had any impact on 
building costs or changes in development standards and trends. They commented that they did 
not see any changes in development after their community had entered the NFIP. However, 
there was an observation by some agents that regulations have increased the setback zones in 
their communities. 

4.6 Lenders 
Lenders are a part of the banking industry. They are the ones who inform property 
owners that flood insurance is required during the loan processing. The lenders interviewed 
were all identified using the Internet yellow pages. The majority of lenders declined to be 
interviewed. For this reason, there are no results to report here. 

4.7 State Officials 
The state officials that were interviewed are all State NFIP Coordinators. Their job is to 
perform a variety of activities that support implementation of the NFIP in their states; and in 
many states, they are the principle point of contact between FEMA and participating 

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communities. They are the liaisons between the local floodplain managers and FEMA. A 
number of the NFIP Coordinators are also certified floodplain managers. Their interviews were 
conducted by phone. 

Most of the NFIP Coordinators knew about the flood history of the communities in our 
study. They felt that the NFIP has been widely accepted in their state and these communities in 
particular. Some mentioned that they feel that the regulations are forced upon the communities 
and the rural communities do not like it. However, the residents see the need for the flood 
insurance. The state officials had a wide range of answers regarding the question on land use 
practices and most maintained support of local decision making authority. 

The majority of the state officials said that there was no impact on the cost and 
availability of housing in or near the SFHA. In terms of the CRS, the NFIP Coordinators felt the 
communities have reacted positively to the program. They cited positive outcomes that included 
the reduction in insurance premiums, reduction in flood losses (in the communities of this study), 
and a public perception that something is being done about floods. 

4.8 Summary 
In summary, the interviews demonstrated that most of the stakeholders had a fairly good 
understanding of the history of flooding in their community and the areas in their community 
with the greatest risk. In the communities addressed in our study, the cost of development in the 
community may have increased, but this increase is negligible when compared to cost increases 
caused by market forces and other regulations. There is agreement that the floodplain can 
become an amenity and has value, but there is disagreement as to whether this value plays any 
role in development trends, increased cost, or housing supply. 

There have been some legal challenges to the regulations but not as much as conventional 
wisdom might have indicated. Litigation has included developers suing to develop property and 
people suing stating that the community has not done enough to protect the residents. Almost all 
of the lawsuits have occurred in one community and the others report little or no activity. 

Politically, it appears that the regulations may be used as a political issue from time to 
time. It does appear that members of the community will complain more than they will actually 
try to create a political movement out of the issue. 

The sampling is obviously not statistically relevant, but these are communities that have 
been leaders in implementing floodplain regulations dealing with future flood conditions. These 
communities have effectively tested the waters and did so successfully. 

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5. COMPUTATIONAL PROCESS RESULTS (QUANTITATIVE 
ASSESSMENT) 
5.1 Overview 
The primary objective of the computational process was to aid in the identification of 
benefits for the study. Whenever possible, the computational analysis was completed using the 
HAZUS. As stated previously, the project team collected data from the selected communities 
including up-to-date parcel level tax assessors data. This data allowed the project team to view 
the current land use designations of the parcels, and the current value of the improvements (if 
any). The collected data allowed the project team to assign the HAZUS occupancy 
classifications to the parcel based on the occupancy identified by the assessor. The value of the 
contents was determined using a direct relationship between the occupancy class and building 
value and a content value multiplier (e.g., for residential structures, content value is 
approximately 50% of structure value) used in the HAZUS model. 

Using the user defined facilities capabilities within HAZUS and the default damage 
functions; it is possible to perform an analysis that will define the losses that are created by the 
changing flood conditions. This is determined by comparing the current inventory losses from 
the current 100-year flood and the future 100-year flood. It is also possible to compare the 
impact of the changing inventory. This is determined by comparing the existing inventory losses 
with the future 100-year flood and the future inventory losses caused by the future 100-year 
flood. 

Because anticipated build-out inventory and build-out 100-year flood hazard data 
typically do not exist, some data sets needed to be created. Using the current inventory and 100year 
flood conditions as a basis, the data was process to create the needed data sets. Below is the 
methodology followed to create the required data sets: 

Current Inventory Data 

This includes the tax assessor data and GIS-based parcel data within the study area. 
Using the unique parcel number included in the GIS-based parcel data and the tax 
assessor data the data sets can be combined. This combination allows analysis of key 
information to be processed at specific geographic locations. 

In most cases, the exact location of the building within the parcel was unknown. 
Accordingly, the project team needed to assume a location for the building; therefore the 
building or structure was assumed to be located at the centroid of the parcel for each 
analysis. Since the building was not moved when performing the comparative analysis, 
this is not likely to affect the results; however, it does introduce some uncertainty, as 
flood depths and associated damage will vary especially across the larger parcels. 

The tax assessor data is typically a very accurate representation of the built environment 
with respect to all revenue generating properties; however, in some instances (i.e., tax-
exempted buildings) some of the data is incomplete. The project team processed the tax 

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assessor data with supplemental information to provide the necessary missing 
information. The supplemental information was typically first floor height, foundation 
type, and other detailed parameters with which the assessor typically is not concerned. 
When this data was incomplete and required supplementing, the project team used the 
HAZUS national distributions, as discussed below. As this data is constant throughout 
the analysis, it is assumed to have little effect on the variations in results. 

 Foundation type was generated by semi-randomly assigning a foundation type 
to the parcel based on the occupancy classification and the distribution used in 
HAZUS (Table 1 in Appendix 3). The assignment is considered semi-random 
since some effort was taken to maintain the HAZUS distributions over the 
entire study area. All non-residential development was assumed to be slab on 
grade. 
 First floor heights were determined from the foundation types assigned in the 
previous step. Table 2 in Appendix 3 shows the assumed first floor heights 
used in the analysis. Since the project team did not know if the parcel was 
within the SFHA or not, every structure was assumed to be built before the 
FIRM was issued, or pre-FIRM, for the purpose of assigning the damage 
function and the first floor height. This approach is taken because the analysis 
is not measuring site specific results, but is comparing the change in damage 
resulting from changes in the height of the flood inundation between the 
existing 100-year flood surface and the future 100-year flood surface. 
Based on input from the tax assessors, the project team attempted to adjust the assessors 
valuation to represent engineering replacement value of the structures. In other words, 
the project team adjusted the value to more closely reflect the estimated cost of building 
the exact same building today. If this was not possible, the project team used the HAZUS 
baseline values per square foot by occupancy to develop a representative replacement 
value. The HAZUS valuation parameters are shown in Table 3 in Appendix 3. 

Anticipated Inventory Data 

Within the assessors data, vacant property is usually clearly defined by a parcel use 
code, and in some cases, the zoning is identified. Future development is assumed to 
occur with the current approved occupancy. The total floor area and valuation of the 
projected development is assumed to match that of the typical HAZUS values, which 
vary by occupancy or use, developed from RSMeans Square Foot Costs (2002), an 
industry standard engineering cost estimation methodology document. The HAZUS 
valuation model is summarized in Table 3 in Appendix 3. 

The GIS data would occasionally display artifacts of the digitizing process; rights of way 
and other small areas of vacant land were ignored, as no development was likely to occur 
on these small slivers of land. 

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Current Flood Conditions (100-year Flood Area) 

This data was often provided in the format of GIS data layers with attribute information. 

Most common was flood areas/zones (polylines) with flood elevations and discharges. 

The project team processed the data using the FIT or the FIT-like methodologies to 

develop depth grids that provided sufficient data for the analysis. 

Future Flood Conditions (100-year Flood Area) 

This data was often provided in the format of GIS data layers with attribute information. 

Most common was flood areas/zones (polylines) with flood elevations and discharges. 

The project team processed the data using the FIT or the FIT-like methodologies to 

develop depth grids that provided sufficient data for the analysis. 

The damage analysis is performed for buildings and contents. For this project, the 
contents results shown in the figures also includes business inventory (those items for sale by a 
business entity). The analysis of buildings is based on the depth damage curves by occupancy 
available within HAZUS. These depth damage curves are presented in the HAZUS Flood Model 
Technical Manual (2006). Damage to both buildings and contents are provided to give a more 
accurate representation of the potential savings resulting from the floodplain management 
strategy the community has chosen. 

When selecting the river reaches, the availability of data drove the number of reaches and 
the area within the community analyzed. Although this makes it difficult to generalize the 
results to other watersheds in the nation, the project team believes that the analysis process itself 
is valid and demonstrates the cost and benefits of future conditions management in a measurable 
way. 

The subsequent subsections will provide more detail about the analysis effort within each 
community. Specific information about the data provided by each community, the communities 
approach to managing future conditions, and the net results of the analyses are provided. For 
each community, the project team worked with the local community to identify a river reach or 
several reaches where there was still opportunity for future development. The community 
confirmed that these parcels were vacant and entitled for development. 

5.2 Mecklenburg County, North Carolina 
The project team supplemented this study for Mecklenburg County with a study which 
was performed using a tabular approach similar to the methodology that was eventually 
incorporated into HAZUS. This Mecklenburg County study was completed before the 
completion of HAZUS. Likewise, while the Flood Information Tool (FIT), designed to facilitate 
import of user-defined flood hazard data into HAZUS, was still in development, a FIT-like 
methodology was used to develop the flood depth grids for analysis for Mecklenburg County. 
The project team re-analyzed the Mecklenburg County study data within HAZUS to show that 
there was no measurable difference using the HAZUS software. 

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Mecklenburg County is unique among the communities selected for this project in several 
ways. The county has taken significant steps to understand their watershed, since most the 
watersheds originate within the county and drain out of the county. The county believes that 
through aggressive mitigation efforts, such as buy-out programs, such as those they have 
performed in McAlpine Creek, the overall risk and exposure to flooding will continue to 
decrease over time rather than increase. 

Mecklenburg County has taken the approach of estimating the total discharge for each 
watershed assuming build-out based on the currently approved land use. With the assistance of 
high resolution (Light Detection and Ranging; or Laser Imaging Detection and Ranging 
(LIDAR) based centimeter scale) Digital Elevation Model (DEM) data, the county resurveyed 
each of the watersheds and performed new hydrology and hydraulic (H&H) modeling. The 
county also used historic data to generate electronic models of their flood studies that were 
conducted in 1975. To simulate build-out conditions that were anticipated to occur around 2023, 
the county modified the percentage of the soil that is considered impervious to rainfall, which 
subsequently modified the discharges. The result of their efforts are new Flood Insurance Rate 
Maps (FIRM) that have the effective flood elevations of the 100-year flood that is likely to occur 
when the area becomes developed to its current approved zoning. 

The county continues to allow development to occur without on-site storage of runoff, 
but does require development to provide buffer area to slow and filter the flush of water into 
the river channel. The flood data provided by Mecklenburg County accounted for these buffer 
requirements and any potential reduction in flood timing. Their development standards allow for 
a general assumption of the total impervious area created when a parcel is developed. The 
floodplain standards require new development to be constructed to the new elevations and 
therefore maintain the number of structures that can be damaged by floods at the current level. 

Mecklenburg County has modified their building ordinances using the increased 
discharges to establish development standards that specify first floor elevations with a freeboard 
and a drainage buffer to slow the discharge into the receiving waters. 

5.2.1 Flood Data 
Mecklenburg County provided the flood data used for the development of their FIRM 
maps that were certified around 1975. Their flood data included digital representations of their 
100-year flood boundary and the stream cross sections attributed with the flood elevation and 
discharges for several return periods. Likewise, for the future conditions flooding, 
Mecklenburg County provided another dataset of cross sections attributed with the increased 
discharges and flood elevations generated by their hydrologic and hydraulic (H&H) modeling. 
This dataset was generated from a series of recent investigations performed by the county. The 
studies included using high resolution digital elevation models, comprehensive building and 
flood surveys, and GIS analyses. Mecklenburg was one of just two counties that were able to 
provide the digital elevation model (DEM) that was used in the development of a portion of their 
flood studies (the other was DuPage County). The DEM provided by Mecklenburg County was 
initially such high resolution that the project team needed to reduce the resolution to improve 

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processing time and ease data storage requirements. That is, we reduced the resolution from a 
grid with cells representing a few inches on a side to a grid representing 20-feet per side. 

Figure 1 and Figure 2 on the next page shows a side-by-side comparison of the resulting 
flood depth grids produced for the analysis. The differences in the flood conditions are difficult 
to visualize for a couple of reasons. The first is that improvements in the digital elevation model 
resolution may offset increases in the modeled flooding. The older flood analysis was performed 
around 1975 using more coarse data causing the flood surface to be too high in spots and too low 
in others. Second, the natural terrain of Mecklenburg County may also play a role in that most of 
the streams have relatively steep channels that constrain the increased discharge. Ultimately the 
reader can ascertain that the highest flood depth increased from 23 to 25 feet. 

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FIGURE 1. Existing Conditions Flood Depth Grid for Mecklenburg County FIGURE 2. Future Build-out Flood Conditions for Mecklenburg County 

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A better way to view the difference between the two flood depth grids is to perform a 
direct subtraction of the two datasets. Figure 3 shows the net difference and shows areas where 
the flooding will remain constant and areas where the flood levels can be expected to increase 
substantially. According to Figure 3, structures along Sugar Creek would experience a 1-3 foot 
increase in flood depths with pockets of higher flood depths up to a maximum of 5 feet. This 
could create substantial increases in damage within existing structures, a hypothesis that is tested 
in the following HAZUS model runs. 

FIGURE 3. Flood Depth Comparison (feet) Between Future and Existing Flood Grids for Mecklenburg County 
5.2.2 Inventory Data 
Mecklenburg County also provided detailed county assessors data. The data contained 
critical fields such as the total floor area of the structures, the occupancy class (in sufficient 
detail for easy mapping to the HAZUS occupancy classes), year built, assessed value, vacant 
parcel zoning, number of stories and the foundation type. In fact, Mecklenburg County was the 
only county to provide an assessor file that included this level of detailed data. Because the 
foundation type was provided, the project team chose to build a foundation distribution based on 
local data for application to the projected development of currently vacant parcels rather than 
using the HAZUS baseline foundation distribution, which is based on national level data. 
Mecklenburg County provided other data that proved useful to the analysis for their county. For 
many of the structures in the floodplain, Mecklenburg County was able to provide the master-

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elevation-certificate data in a digital format that could be merged with the assessors dataset, 
providing very good information for the analysis. 

Because Mecklenburg County also provided a GIS layer that included all of the building 
footprints in the county, the project team believed it would be more accurate to use the centroid 
of the building footprint rather than the centroid of the parcel when performing the loss 
estimation analysis for the existing buildings. However to determine the losses for the 
undeveloped parcels, the project team needed to revert to using the centroid of the parcel as the 
assumed location of the building. Figure 4 below provides a view of some of the data provided 
by Mecklenburg County and the future condition 100-year flood inundation area. The figure 
shows the parcels with existing structures and several parcels where no development has 
occurred. It clearly indicates which structures are likely to get inundated (those footprints within 
the water). The figure also shows the extension of the parcels to the centerline of the river, as 
defined by the county in its plat maps. 

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FIGURE 4. Sample of Mecklenburg County Data Used in This Analysis 

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Some problems did arise with the data, including typical concerns related to multi-family 
apartments and condominium projects for such a research effort. Determining the total value for 
large multi-building projects in a parcel can be problematic. The value of multi-family dwellings 
was determined by dividing the total floor area and value in the assessors data for the parcel and 
dividing it by the building footprints as seen on the parcel. Condominium units have individual 
parcel numbers but appear as single building footprints on the GIS data. However, since the 
assessors database included latitude and longitude information, and the project team was able to 
merge and attach the condominium units to a given building footprint. Without the latitude and 
longitude, this would have been a much more complicated process. 

For this analysis, the project team chose to perform the loss estimation modeling analysis 
at Sugar Creek. This stream was chosen because it is one of the longer rivers in Mecklenburg 
County and because nearly 37% of the parcels within the watershed are currently vacant, 
allowing for a significant increase in the total amount of impervious soils within the watershed 
when developed. For the loss estimation analysis, the project team added a 50-foot buffer to the 
100-year future condition flood boundary and intersected this with the parcel boundaries to 
ensure that all parcels that might get flooded were selected (this was a conservative approach to 
ensure the entire exposure was captured). There are more than 1,115 parcels that intersect the 
floodplain boundary (including the 100-foot buffer) surrounding Sugar Creek. Some duplicate 
parcels were identified and reviewed. In all cases, these turned out to be parcel slivers in the GIS 
data, most likely right-of-ways that have been either returned to the parcel, or offers of 
dedication that were never acted upon. In all cases, for the purpose of this analysis, the slivers 
were merged back into the main parcel. 

There are more than 1,115 parcels that intersect the floodplain boundary (including a 
100-foot buffer) surrounding Sugar Creek. Of those 1,115 parcels, 416 (37%) were identified as 
vacant, although 39 parcels had existing structures as seen in the building footprint file. 
Typically, these were large structures that physically covered more than one parcel. An 
additional six parcels had information in the effective area field in the county assessors data, 
indicating that a structure should have been found on the parcel. 

1,234 building footprints intersect the floodplain boundary. Of these, 216 could be 
assigned a first floor elevation and lowest adjacent grade from the survey data available. For the 
analysis, the six parcels without a building footprint that were identified as non-vacant based on 
the square footage in the assessors data were analyzed as single structures at the centroid of the 
parcel, for a total of 1,240 buildings in the floodplain (including the 100-foot buffer). Those 
structures without first floor elevation data were assigned a first floor elevation based on the 
HAZUS first floor elevations shown in Table 2 in Appendix 3. 

The project team used the default depth-damage functions within HAZUS for the damage 
analysis. These functions can be found in the HAZUS-MH MR2 Technical Manual (NIBS, 
2006) and within the model itself. These functions were used for every community analyzed for 
this project. For Mecklenburg County, the project team examined the existing buildings within 
the 1975 flood study depth grid, and the built-out condition within the future flood grid. 

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The results for buildings can be seen in Table 4 below. The results are shown for each of 
the specific HAZUS occupancy classes found within the watershed, such as COM1 (retail sales 
sites). Definitions of the occupancy classes are available in Table 3 in Appendix 3. The table 
shows the damages associated with the current inventory and the 1975 100-year flood study 
inundation area (representing the current flood). The table then shows what would happen to the 
existing inventory with the future conditions 100-year flood. This is shown because it clearly 
demonstrates the increased damage within the current inventory caused by the changing flood 
conditions. Finally, the table shows the built-out or the future inventory with the associated 
future conditions 100-year flood. 

The table shows that the overall damage can be expected to increase to nearly $33 million 
from $3 million should the 100-year flood occur in the future, built-out condition. Because of 
the increasing flood depths, and the relative constraining of the flooded area due to the steep 
terrain, the average dollar value damage within the affected structures is anticipated to increase 
from approximately $35,000 to nearly $130,000 after the area is built-out. The table also shows 
that the damage within the existing structures will double from $3 million to $6 million with the 
changing flood conditions and the average loss per structure will increase by nearly $13,000. 
This is an important finding in that it shows that the damage to existing structures represents an 
increase of 93% caused by the changing flood conditions. 

TABLE 4. Mecklenburg County Structure Dollar Damage by HAZUS Occupancy Type by Floodplain Analysis 

HAZUS Current Inventory  Current Inventory  Future Inventory  Future 
Occupancy Current Flood Conditions Future Flood Conditions Flood Conditions 
Code* Total ($) Average.($) Total ($) Average.($) Total ($) Average.($) 

COM1/COM4 17,869 17,869 17,869 17,869 2,383,879 170,277 
COM2 78,085 19,521 299,022 59,804 1,108,184 184,697 
COM3 10,031 3,344 16,488 4,122 16,488 4,122 
COM4 1,317,996 146,444 2,272,909 227,291 6,187,158 412,477 
COM7 0 0 9,193 9,193 9,193 9,193 
COM8 0 0 0 0 162,225 162,225 
GOV1 1,492 1,492 2,375 2,375 2,375 2,375 
IND2/IND6 4,491 2,246 8,247 4,124 16,169,840 414,611 
RES1A 708,186 16,862 1,295,099 18,770 3,967,198 31,238 
RES1B 309,012 22,072 400,183 26,679 400,183 26,679 
RES1D 48,455 12,114 69,048 17,262 69,048 17,262 
RES1E 342,341 57,057 353,753 70,751 600,265 60,027 
RES3 244,414 61,103 1,266,129 105,511 1,644,016 109,601 
RES4 85,273 85,273 107,526 107,526 107,526 107,526 
Total 3,167,644 34,809 6,117,840 47,060 32,827,578 129,753 
* See Table 3-3 of Appendix 3 for definitions of HAZUS Occupancy Types 
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Table 5 presents the contents and inventory damage for the structures that are damaged 
in the same modeled flood. The pattern seen in Table 4 (above) is repeated here, where the 
contents damages are estimated to increase to nearly $35 million from the almost $3.5 million in 
the current conditions. The average building damage increases from $38,000 to $137,000. This 
is due to large increases in damage to commercial uses such as professional services/offices 
(COM4) and heavy and light industrial development (IND1 and IND2). These increases are 
driven in part by the changes in the inundation area (more buildings getting wet as seen in the 
COM7 numbers) and new structures being built in areas that get wet (as seen in the increases of 
IND2/IND4). Like Table 4, Table 5 also shows the increase in damage to the existing (current) 
building contents caused by the increased inundation of the future conditions flood. That is, the 
damage to the current inventory increases from nearly $3.5 million to $7 million as the flood 
conditions change. 

TABLE 5. Mecklenburg County Contents Dollar Damage by HAZUS Occupancy Type by Floodplain Analysis 

HAZUS Current Inventory  Current Inventory  Future Inventory  Future 
Occupancy Current Flood Conditions Future Flood Conditions Flood Conditions 
Code* Total ($) Average ($) Total ($) Average ($) Total ($) Average ($) 

COM1/COM4 35,970 35,970 35,970 35,970 4,798,750 342,768 
COM2 270,954 67,739 1,037,608 207,522 3,845,399 640,900 
COM3 21,613 7,204 33,626 8,406 33,626 8,406 
COM4 2,092,977 232,553 3,609,380 360,938 9,825,207 655,014 
COM7 0 0 22,982 22,982 22,982 22,982 
COM8 0 0 0 0 360,139 360,139 
GOV1 56,200 56,200 67,841 67,841 67,841 67,841 
IND2/IND6 3,498 1,749 5,532 2,766 11,504,545 294,988 
RES1A 414,294 9,864 782,318 11,338 2,208,445 17,389 
RES1B 197,261 14,090 271,847 18,123 271,847 18,123 
RES1D 30,272 7,568 40,167 10,042 40,167 10,042 
RES1E 78,504 13,084 85,758 17,152 218,376 21,838 
RES3 116,134 29,033 903,858 75,321 1,341,746 89,450 
RES4 180,097 180,097 227,095 227,095 227,095 227,095 

Total 3,497,774 38,437 7,123,981 54,800 34,766,164 137,416 

* See Table 3-3 of Appendix 3 for definitions of HAZUS Occupancy Types 
Figure 5 shows that the difference in total losses (buildings plus contents) between the 
built-out conditions and the current conditions is nearly $61 million. The chart shows that most 
of the additional losses come from new development in the flood pone areas and that only $7 
million of the additional losses come from older development that is affected by higher flood 
depths. It is important to caveat the future inventory damage estimates since the building was 
placed at the centroid of the parcel. It is anticipated that developers and the countys storm water 
management team will attempt to keep future development from being placed within the 

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inundation areas. For this reason, the future-future bar may be considered an upper bound on the 
potential losses within this watershed. 

FIGURE 5. Comparison of Flood Losses at Build-Out vs Flood Losses for the Existing Inventory, Mecklenburg County 
The project team reviewed other available reports for Mecklenburg County (April 2003) 
and found this trend to be consistent throughout the other watersheds. In fact, for all watersheds, 
the future inventory losses varied between two and ten times those experienced by the existing 
inventory with the future conditions flood. This information leads the project team to infer that 
development within Mecklenburg County has traditionally been outside of the floodplain and is 
only now starting to encroach into higher risk areas. 

5.3 Larimer County (Fort Collins), Colorado 
The project team focused on Fort Collins, the largest city within Larimer County. 
Because of prior working relationships with the floodplain manager, the project team was able to 
obtain a significant amount of data that added value to the project. Fort Collins and Larimer 
County are pursuing the more common No Adverse Impact approach by requiring all future 
development to retain flood waters on site, and delay the discharge into the watershed over a 
period of time to prevent a large peak of flow and increased discharges. The concept asserts that 
if runoff up to the 100-year level is retained on site and discharged into the streams slowly, the 
hydrograph peak will have passed and downstream buildings will not see any increase in flood 
elevation from the upstream development. This means that any development will not have an 

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adverse impact on the downstream structures until the 100-year retention capabilities have been 
exceeded. 

Interestingly, if the development occurs according to plan, the current 100-year flood 
would remain unchanged as development progresses, since all additional discharge from the 
increased impervious soil would be contained on site and discharged slowly into the river 
thereby not increasing the 100-year flood. Naturally, there is always the chance that a greater 
event, such as the 500-year flood, would occur and exceed the capacity of the storage facilities, 
but that is beyond the scope of this project. In essence, the approach taken by the community 
would maintain the current discharge peak at the 100-year event, but the river would have a 
longer net discharge period. 

5.3.1 Flood Data 
To examine the difference between the existing and future conditions, Fort Collins ran its 
hydrologic and hydraulic models to develop a projection of the future conditions flooding 
assuming that no on site storage occurred and the 100-year discharges resulting from the future 
development was entering the watershed unabated. This is essentially reflective of the future 
flood conditions seen in the Mecklenburg County analysis above. This approach was taken 
because this allows the analysis to determine the benefit associated with storing the water on-site 
and managing the 100-year discharge peak. Discussions with the city provided the project team 
with a river reach that provided the best opportunity to test the effectiveness of the floodplain 
management program. The selected area had a number of vacant parcels slated for development 
although the mix was predominantly residential in nature. This development pressure is the 
reason for the floodplain management program established by the city. 

Fort Collins provided its flood data in a format that was easily readable in the Flood 
Information Tool (FIT) created for HAZUS. The data included a historic flood boundary, a 
digital elevation model for the region (the USGS 1/3 Arc-second data), and cross sections with 
flood elevations and discharges for the 100-year existing conditions flood and 100-year flood 
under future conditions (assuming no on-site storage). Figure 6 and Figure 7 on the next page 
show a side-by-side comparison of the two flood depth grids. Although it is difficult to observe 
visually, the future conditions flood depth grid does have areas where the inundation area is 
greater than the existing conditions grid. This generated a slight increase in the number of 
structures that were flooded. 

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FIGURE 6. Existing Conditions Flood Depth Grid for Fort Collins FIGURE 7. Future Build-out Flood Conditions for Fort Collins 

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Figure 8 below shows the net difference between the existing conditions flood grid and 
the future conditions flood grid. Generally, it can be seen that the majority of the flood area 
would see a net increase of six inches or less. There are a few areas where the inundation would 
increase by slightly more than a foot. This nominal increase in flooding is reflected in the 
analysis results discussed below. 

FIGURE 8. Flood Depth Comparison (feet) Between Future and Existing Flood Grids for Fort Collins 
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5.3.2 Inventory Data 
County assessor data was purchased from a commercial site, the data included detailed 
information on the total floor area of the improvements, the value of the buildings, occupancy 
classifications that were easily mapped to the HAZUS occupancy classes, whether a structure 
had a basement, the number of stories for each structure, and the year built. With this 
information, the project team used the HAZUS foundation distributions to provide foundation 
types for those structures that did not have a basement. The project team also used the HAZUS 
distribution data to estimate the first floor heights for all of the structures, since that information 
could not otherwise be determined. Figure 9 provides a graphic view of the data received by the 
project team with respect to the future conditions flooding. 

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FIGURE 9. Example of Larimer County Data Used in This Analysis 

Fort Collins did provide the parcel boundaries with the corresponding parcel number, 
allowing the commercial dataset and the geographic parcel data to be joined. Of the total 
number of parcels in the dataset, 355 intersected the flood grids generated from the Fort Collins 
hazard data. Nearly all of the parcels were residential in nature, either single-family or multi-

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family, with one commercial retail parcel, two churches or other religious use parcels, one 
agricultural, and one industrial parcel. Of the 355 parcels, 306 of them had already been 
developed and 49, or 14%, were currently vacant. Again, because the city needed to perform 
work in developing the H & H analysis, and because the selected area is currently undergoing 
development, the project team and the city established this river reach as a reasonable test of 
potential increased losses within the city. 

Since building footprints were not available, all of the structures were assumed to be 
located at the centroid of the parcels. This meant that the full value of the multi-family structures 
was applied at the centroid, whether there were multiple buildings or not. Table 6 shows the 
results of the analysis of direct building damage. The table shows that the current conditions 
would result in nearly $2.4 million in damage to the buildings along the river. Should 
development have occurred without on-site storage, the damage would increase to $3.3 million at 
build-out. The changing flood conditions would increase the losses to existing structures by 
about $450,000 from $ 2.4 million to $2.8 million. The average damage to an existing structure 
would increase by about $1,000. This stream is a good example of the risk in the city as it is 
likely to be representative of the small watersheds throughout the country and shows the 
incremental increase in damages that can occur. 

TABLE 6. Larimer County (Fort Collins) Structure Dollar Damage by HAZUS Occupancy Type 
Current Inventory  

HAZUS 
Occupancy* 
Current Inventory - Current 
Flood Conditions Future Flood Conditions 
No Detention 
Future Inventory  Future 
Flood Conditions No Detention 
Total ($) Average ($) Total ($) Average ($) Total ($) Average ($) 

AGR1 0 0 223 223 223 223 
COM1 21,694 21,694 23,054 23,054 23,054 23,054 
IND2 11,185 11,185 11,633 11,633 11,633 11,633 
REL1 60,285 30,142 60,757 30,379 60,757 30,379 
RES1 1,892,741 15,264 2,221,473 16,025 2,660,677 19,421 
RES2 281,604 93,868 301,926 75,482 301,926 75,481 
RES3A 63,501 12,700 142,903 20,415 142,903 20,415 
RES3C 81,758 27,253 104,279 34,760 104,279 34,760 
Total 2,412,767 17,234 2,856,248 18,193 3,305,451 21,054 

* See Table 3-3 of Appendix 3 for definitions of HAZUS Occupancy Types 
Table 7 shows similar results for the contents and inventory (goods for resale) for the 
structures within the study. An existing conditions flood could be expected to create nearly $1.7 
million in content and inventory damage, but this would increase to nearly $2.2 million if the 
regulatory requirement was not in place. Similar to the building damage, the changing flood 
conditions will increase the damage to existing contents and inventory by about $250,000. As 
this watershed is primarily residential, this means increased damage to personal property for the 
residents. 

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TABLE 7. Larimer County (Fort Collins) Contents Dollar Damage by HAZUS Occupancy Type 

Current Inventory - Current Current Inventory  Future Inventory  Future Flood 

HAZUS Future Flood Conditions 

Occupancy* 
Flood Conditions No Detention Conditions No Detention 

Total ($) Average ($) Total ($) Average ($) Total ($) Average ($) 
AGR1 0 0 2,388 2,388 2,387 2,387 
COM1 137,022 137,022 145,687 145,687 145,688 145,688 
IND2 45,338 45,338 48,922 48,922 48,922 48,922 
REL1 436,290 218,145 445,732 222,866 445,732 222,866 
RES1 887,046 7,154 1,060,862 7,687 1,336,497 9,755 
RES2 88,873 29,624 97,292 24,323 97,292 24,323 
RES3A 25,400 5,080 60,754 8,679 60,754 8,679 
RES3C 45,265 15,088 56,301 18,767 56,301 18,767 
Total 1,665,234 11,895 1,917,938 12,216 2,193,572 13,972 

* See Table 3-3 of Appendix 3 for definitions of HAZUS Occupancy Types 
Looking at the results graphically, Figure 10 shows the difference in total losses for both 
buildings and contents. The difference between the built out conditions and the current 
conditions is nearly $1.5 million. The relatively small savings is not surprising because of the 
overall minor increase in flood depths (as seen in Figure 8), and the fact that the area examined 
would have primarily residential development and the number of total parcels is smaller than the 
Mecklenburg County example. Because this analysis examines what would happen if there were 
no retention when new development occurred, it is assumed that the $1.5 million is the losses 
avoided should a 100-year event happen at build-out with detention, and assuming that none of 
the zoning changes as development expands. 

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FIGURE 10. Comparison of Flood Losses at Build-out vs Flood Losses With the Existing Inventory, Fort Collins 

5.4 Harris County, Texas 
Like Larimer County, Harris County and the City of Houston have also adopted the 
approach of requiring on-site detention of storm waters. The regulations require developers to 
store the equivalent amount of excess flow from their property in some form of on-site storage, 
and the release of the water must be over a period of several days. Because of the similarity to 
Larimer County, the project chose the same approach in analyzing the benefit of the management 
approach. As in Larimer County, the project team and the county chose an area where 
development pressure is relatively high and has enough vacant parcels whose development 
without on-site storage could lead to sufficient changes in the discharge of the watershed. 

While the smaller analysis area might limit the generalization across all the watersheds in 
the nation, the planning approach (i.e. development pressure in small planning areas) is typical of 
that seen in many cities so the analysis is actually relevant to the manner in which other cities 
might perform the assessment. 

When selecting the river reaches, the availability of data drove the number of reaches and 
the area within the community analyzed. In order for the project team to perform the analysis in 
Harris County, additional data development was needed. It was agreed that the analysis area 
would be limited. Additionally, Harris County was also very concerned about potential political 
consequences and wanted to keep the analysis area very constrained. 

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5.4.1 Flood Data 
The project team received data for a portion of Dry Creek for the existing 100-year flood 
and for the 100-year flood that would potentially occur when build-out was achieved, 
assuming that there is no detention. The data included cross sections for the study reach, 
attributed with both the 100-year existing conditions elevation and the future condition 100year 
flood assuming no detention. A sample floodplain boundary was provided. Harris County 
provided sufficient terrain data for the FIT analysis. Once the data was processed through the 
FIT, it was imported into the Flood Model for subsequent damage analysis. 

Figure 11 and Figure 12 provide a direct side-by-side comparison of the two grids 
produced by the analysis of the county supplied data. The most obvious conclusion is that the 
future conditions flood would inundate a significantly larger area than the current condition 
flooding. This may be due in part to the age of the current condition data (approximately 1975), 
the relatively flat terrain, and the large amount of development that has occurred and can still 
occur within very close proximity to the river. 

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FIGURE 11. Existing Conditions Flood Depth Grid for Harris County 

FIGURE 12. Future Build-out Flood Conditions for Harris County 

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Further comparison of the two depth grids by directly subtracting one from the other, 
Figure 13 shows the difference between the existing conditions flooding and the future flooding 
assuming no on-site detention. Overall, the majority of the area would be subjected to 0-3 feet of 
additional flooding, with pockets of areas that could experience between 3 to 6 feet or more 
increased flooding. 

FIGURE 13. Flood Depth Comparison (feet) Between Future and Existing Flood Grids for Harris County 
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5.4.2 Inventory Data 
County assessor data was purchased from the same commercial data source that provided 
the data on Larimer County. The purchased data included detailed information on the total floor 
area of the improvements, the value of the buildings, occupancy classifications that were easily 
mapped to the HAZUS occupancy classes, and number of stories and year built. The database 
did not include any information that would assist with determining the foundation type. These 
values were supplemented with the HAZUS regional averages by occupancy and this was 
assigned in a semi-random fashion ensuring that the percentages were maintained as closely as 
possible. The project team used the HAZUS default assumption of first floor height for each 
foundation type to estimate first floor heights for all of the structures. 

Figure 14 provides a graphical view of the data collected for the project with respect to 
the future conditions flood depth grid. The assessors data, stored in the User Defined 
Facilities table within HAZUS and shown as black triangles in the image, readily demonstrates 
why the resulting damage increases so significantly in the Harris County analysis. The 
expansion of the flooded area and the increased depth of flooding, as well as the heavy 
concentration of structures in the area, greatly increase the overall exposure and the resulting 
damage. 

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FIGURE 14. Example of Harris County Data Used in This Analysis 

Again, as for Larimer County, GIS data showing the parcel boundaries for Harris County 
was obtained, and all structures were assumed to be at the centroid of the parcel. Of the total 
dataset for Harris County (over 1.1 million parcels), 3,835 parcels intersected the floodplain 
boundaries developed by the Flood Information Tool, with 735 or 21% defined as currently 
vacant. The assessors file did provided approved zoning for the parcels, allowing the project 

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team to assume the typical HAZUS configuration for projected future development for the 
occupancies as seen in Appendix 3, Table 3. Table 8 shows the estimated structural damage to 
the buildings in the study area. The estimated damage for the future conditions is estimated to be 
approximately $62 million, up from the current conditions of slightly over $3.3 million. Most of 
the increase is seen in the agricultural (AGR1), government (GOV1), and single-family 
residential (RES1) occupancies. In this analysis, the impact to the existing structures is 
significant representing over a 1,300% increase. As seen in Figure 11 and Figure 12 without the 
use of on-site storage, a number of structures that are currently outside of the floodplain would 
be affected. The changing flood conditions with the future development would increase damage 
to existing buildings from $3.3 million to over $46 million. This increase represents nearly 75% 
of the total increased losses that potentially could occur after build-out if on site storage was not 
mandated. 

TABLE 8. Harris County Structure Dollar Damage by HAZUS Occupancy Type 

Current Inventory - Current Current Inventory  Future Future Inventory  Future Flood 
HAZUS Flood Conditions Flood Conditions No Detention Conditions No Detention 

Occupancy* 
Total ($) Average ($) Total ($) Average ($) Total ($) Average ($) 

AGR1 954,174 95,417 18,129,716 142,754 18,129,716 141,638 
COM1 4,348 4,348 2,860,952 286,095 7,152,675 246,644 
COM2 0 0 236,834 236,834 236,834 236,834 
COM3 0 0 29,612 29,612 29,612 29,612 
COM5 0 0 82,551 82,551 82,551 82,551 
COM8 0 0 241,020 120,510 241,020 120,510 
GOV1 5,389 5,389 1,942,885 242,861 6,187,602 309,380 
IND1 2,050,121 227,791 6,854,242 263,625 6,854,242 274,170 
IND2 0 0 26,539 2,413 26,539 2,654 
REL1 0 0 0 0 370,439 123,480 
RES1 290,777 10,770 16,379,057 13,559 22,634,263 10,924 
RES2 0 0 18,464 18,464 18,464 18,464 
Total 3,304,808 70,246 46,801,872 33,526 61,963,956 27,023 

* See Table 3-3 of Appendix 3 for definitions of HAZUS Occupancy Types 
The content and inventory damage for the analyzed portion of Harris County is shown in 
Table 9 below and the results shows that changes in the flood grids causes the estimate to soar. 
The future conditions estimation for the contents is anticipated to approach $289 million, up 
from the current conditions of under $18 million. This is driven by large increases in content 
damages to the agriculture occupancy (AGR1), retail commercial uses (COM1), government 
(GOV1), and single-family residential (RES1) occupancies. While the overall damage increases 
dramatically, the average content damage estimated per structure actually decreases by a third 
indicating a large increase in the number of flooded structures, many of which have lower 
average losses thus lowering the overall average. The content losses reflect the losses associated 
with the buildings with nearly 78% of the $289 million in losses coming from existing structures 
that are affected by the future flood conditions assuming no detention. 

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TABLE 9. Harris County Contents Dollar Damage by HAZUS Occupancy Type 
HAZUS 
Occupancy* 
Current Inventory - Current 
Flood Conditions 
Total ($) Average ($) 
Current Inventory  Future 
Flood Conditions No Detention 
Total ($) Average ($) 
Future Inventory  Future Flood 
Conditions No Detention 
Total ($) Average ($) 
AGR1 7,817,077 781,708 151,772,027 1,195,055 151,772,027 1,185,719 
COM1 30,052 30,052 20,519,469 2,051,947 50,172,683 1,730,093 
COM2 0 0 1,760,908 1,760,908 1,760,908 1,760,908 
COM3 0 0 104,734 104,734 104,734 104,734 
COM5 0 0 394,039 394,039 394,039 394,039 
COM8 0 0 1,276,635 638,318 1,276,635 638,317 
GOV1 32,332 32,332 11,896,421 1,487,053 34,261,767 1,713,088 
IND1 9,961,296 1,106,811 36,621,135 1,408,505 36,621,135 1,464,845 
IND2 0 0 64,728 5,884 6,699 6,470 
REL1 0 0 0 0 2,214,086 738,029 
RES1 105,985 3,925 7,276,741 6,024 10,678,623 5,154 
RES2 0 0 7,147 7,147 7,147 7,147 
Total 17,946,743 390,147 231,693,984 165,970 289,328,482 195,624 

* See Table 3-3 of Appendix 3 for definitions of HAZUS Occupancy Types 
Looking at the results graphically, Figure 15 present the difference in total losses, for 
both buildings and contents. The difference in loss between the built-out conditions and the 
current conditions is nearly $330 million. There are several reasons for the very large difference. 
The difference between the two flood grids probably has the most profound affect. The analysis 
performed with the current conditions flood grid inundates the centroid of 51 parcels, while that 
performed with the future conditions and no-detention flood grid inundates approximately 1,560 
parcels or an increase of 300% in total parcels effected. The other principle reason is the content 
damage on the parcels that are flooded. Many of the additional parcels in the floodplain are nonresidential 
parcels that are zoned for agricultural, commercial, industrial, and government uses. 
By definition the content value of such parcels is greater than that of the structures housing the 
contents (Table 4 in Appendix 3). This is because the structures themselves are typically pretty 
simple structures with limited internal finishes or beautification compared to that seen in 
residential structures, and because the contents are generally either expensive equipment or a 
commodity that may be for sale. 

The project team noted that the greatest content damage is occurs on agricultural parcels. 
The project team believes there is a strong possibility that the agriculture zoning will be changed 
to support residential or commercial development, as is typical when development expands. 
Such a change would impact the net results by shifting damage from a few structures with high 
value contents to a larger number of residential structures and the associated contents. The exact 
impact would be hard to determine since the project team cannot know the number of homes or 
types of home (luxury, custom, average, or economy) that might be developed. Therefore the 
project assumed that the current zoning dictates the future development. Because this analysis 
looks at what would happen if there is no retention when the new development takes place, it 
may be safe to assume that the $330 million represents the losses avoided should a 100-year 
event happen at build-out with detention, assuming that none of the zoning changes as 
development expands. 

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FIGURE 15. Comparison of Flood Losses at Build-out vs Flood Losses With the Existing Inventory, Harris County 

5.5 DuPage County, Illinois 
Like Larimer County and Harris County, DuPage County and the cities within that 
county have adopted the approach of requiring on-site detention of storm water. The regulations 
require developers to store the equivalent amount of excess flow from their property in some 
form of on-site storage and then release it over a period of several days. Because of the 
similarity to Larimer and Harris counties, the project chose the same approach in analyzed the 
benefit of the management approach in the same manner. The county staff recommended a 
series of reaches for the analysis based on the quality of the flood data and the number of vacant 
parcels. The parcels within the study area are a mix of residential and non-residential uses, and 
the non-residential uses vary from commercial to government uses. 

When selecting the river reaches, the availability of data drove the number of reaches and 
the area within the community analyzed. In order for the project team to perform the analysis in 
DuPage County, additional data needed to be developed, therefore it was agreed that the analysis 
area would be limited. 

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5.5.1 Flood Data 
Unlike the other communities researched for this report, DuPage County provided the 
H&H model for a historic storm and the associated discharges that were widely considered a 
close approximation of the 100-year event. For this historic event, the project team received 
flood related data for the reaches for the current development and for the built out conditions 
assuming that there is no detention. This data included cross sections and discharges assuming 
the same level of rainfall and differing soil conditions to account for development. The county 
provided the data assuming the current conditions of development, the future development with 
detention per code and without any detention for all future development. The data also included 
a floodplain boundary. DuPage County also provided the terrain data for the FIT analysis. The 
riverine hazard data was processed through the FIT for subsequent import into the Flood Model, 
where the loss estimation was performed. 

Figure 16 and Figure 17 provide a direct side-by-side comparison of the resulting depth 
grids. Like Mecklenburg County and Larimer County, the difference in the two grids is not 
readily apparent when presented in this fashion, as there appears to be relatively small increases 
in the overall inundated area. There are a few specific pockets of inundation in the future 
conditions depth grid that are not flooded in the existing conditions grid. 

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FIGURE 16. Existing Conditions Flood Depth Grid for DuPage County FIGURE 17. Future Build-out Flood Conditions for DuPage County 

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Direct comparison of the existing conditions flooding and the future conditions flooding 
(Figure 18), shows an increase of 0 to 4 feet over most of the inundated. There are some areas 
where the flood conditions are expected to improve, although this may be due to the modeling 
techniques used to develop. Overall, the rather sharp terrain constrains the storm waters into 
tightly defined inundation zones. 

FIGURE 18. Flood Depth Comparison (feet) Between Future and Existing Flood Grids for DuPage County 
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5.5.2 Inventory Data 
The DuPage County assessor provided data from which the occupancies could be 
determined and mapped to the HAZUS occupancy classes as well as detailed parcel boundaries 
for the county, from which the centroids for the analysis could be determined. The county 
assessor could not provide the total floor area for the parcels but did provide the valuations for 
all developed parcels. Although the project team needed to assume a floor area for each of the 
parcels to ensure that the flood model would operate correctly, the assumption is not expected to 
have an effect on resulting losses, because damage is determined as a percent of exposed value, 
not relative to exposed square footage. If the zoning was unknown, as was the case in DuPage 
County, the project team looked at the development patterns surrounding the parcel to identify 
the likely approved use. This was the exception rather than the rule. The project team also 
needed to assign the regional HAZUS foundation distribution, number of stories, and first floor 
elevations to all of the developed parcels as this information was missing from the assessors 
files. Figure 19 contains data collected by the project team with respect to the future flood 
conditions. 

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FIGURE 19. Example of DuPage County Data Used in This Analysis 

For the undeveloped parcels, the project team once again assumed that a typically 
configured building of the identified occupancy would be located at the centroid of the parcel, 
and estimated the valuation using the HAZUS parameters (see Table 3 in Appendix 3). 
Depending on the occupancy class, the project team also assigned the distribution of foundation 
types, number of stories, and first floor elevations. The assignments were semi-random in the 
sense that the project team attempted to ensure that the regional distribution as seen in Appendix 
3, Table 1 was maintained. 

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GIS data showing the parcel boundaries for DuPage County was obtained. Nearly 2,318 
parcels intersected the floodplain boundaries developed by the Flood Information Tool, with 42 
or 2% of those defined as currently vacant. The assessors file did provide approved zoning for 
the parcels, allowing the project team to assume the typical HAZUS configuration for projected 
future development for the occupancies as seen in Appendix 3, Table 3. 

Table 10 shows the estimated structural damage to buildings within the study area. The 
Current Inventory is based on the assessors data file provided by the county and reflects the 
conditions as of 2005. The Future Inventory in the table assumes that all of the vacant parcels 
are developed to their current zoning, as defined in the assessors file. The table shows that the 
overall structural damage will increase by nearly $3 million, but the average damage to each 
structure will be approximately half of the current conditions flooding. This is due primarily to 
the fact that more structures are damaged and many of additional structures are not as heavily 
flooded. Of the $5.8 million in estimated losses for the future conditions flooding, 84% of the 
increased losses are in the existing building stock. 

TABLE 10. DuPage County Structure Dollar Damage by HAZUS Occupancy Type 

Current Inventory - Current Current Inventory  Future Future Inventory - Flood 
HAZUS Flood Conditions Flood Conditions No Detention Conditions No Detention 

Occupancy* 
Total ($) Average ($) Total ($) Average ($) Total ($) Average ($) 

AGR1 0 0 385,751 192,876 385,751 192,875 
COM1 0 0 165,634 82,817 165,634 82,817 
GOV1 2,382,447 340,350 4,289,567 225,767 4,289,567 285,971 
IND1 0 0 0 0 379,229 189,615 
IND2 0 0 0 0 143,324 143,324 
REL1 0 0 338,423 169,212 338,423 169,211 
RES1 12,317 1,760 112,360 775 112,360 2,247 
Total 2,394,764 149,673 5,291,735 31,128 5,814,288 78,571 

* See Table 3-3 of Appendix 3 for definitions of HAZUS Occupancy Types 
Table 11 provides the estimated damage to the contents and commercial inventory of the 
structures analyzed above. Like the structural damage above, the overall content and inventory 
damage increases yet the average content damage per structure drops by half. This usually 
indicates an increase in the overall number of structures being damaged at lower depths of 
flooding. Significant increases in content damage can be seen in nearly all occupancy classes, 
but predominantly government (GOV1). Reflective of the building damage, the content damage 
for the existing structures, when subjected to the future conditions flooding represents nearly 
88% of the potential losses at build-out. 

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TABLE 11. DuPage County Contents Dollar Damage by HAZUS Occupancy Type 
Current Inventory - Current Current Inventory  Future Flood Future Inventory - Flood 

HAZUS Flood Conditions Conditions No Detention Conditions No Detention 
Occupancy* 
Total ($) Average ($) Total ($) Average ($) Total ($) Average ($) 

AGR1 28,077 28,077 3,259,610 1,629,805 3,259,611 1,629,805 
COM1 0 0 1,044,320 522,160 1,044,320 522,160 
GOV1 14,960,004 2,137,143 26,234,895 1,380,784 26,234,895 1,748,993 
IND1 0 0 0 0 1,795,791 897,895 
IND2 0 0 0 0 428,832 428,832 
REL1 4,261 4,261 1,851,232 925,616 1,851,232 925,616 
RES1 8,217 1,174 56,576 390 56,576 1,132 

Total 15,000,559 937,535 32,446,633 190,863 34,671,257 468,530 

* See Table 3-3 of Appendix 3 for definitions of HAZUS Occupancy Types 
Looking at the results graphically, Figure 20 shows the net difference between the 
existing flooding with the existing inventory, and the future flooding with no detention for the 
historic rainfall for buildings and contents. The net difference between the results of the current 
inventory / current flood conditions and the future inventory / flood conditions is nearly $23 
million. The majority of the difference is in the estimation of content damage. Because this 
analysis is looking at what would happen if there is no retention when the new development 
occurs, it may be safe to assume that the $23 million represents the losses avoided should a 100year 
event happen at build-out with detention, and assuming that none of the zoning changes as 
development expands. 

Like Harris County, this is driven by estimated damage to non-residential buildings that 
will be developed in the future. Key drivers for the increased losses include the current zoning 
that identifies agricultural and industrial uses. There are also a number of parcels that are zoned 
government use that also contributes to the estimated loss increase. 

The project team believes that most of the parcels currently zoned for agricultural 
production are likely to be rezoned to other uses such as commercial, residential or industrial, as 
the development pressure increases on those parcels. Exactly what those parcels would become 
is unknown. Another question arises as to whether all of the parcels designated as government 
would actually be developed as such, or if they may be converted to other uses or kept as open 
space. 

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FIGURE 20. Comparison of Flood Losses at Build-out vs Flood Losses With the Existing Inventory, DuPage County 

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6. CONCLUSIONS AND RECOMMENDATIONS 
6.1 Conclusions 
As previously stated, the goal of this project was to determine the consequence (cost and 
benefits) of managing future conditions in the watershed. The intent of the interviews is to 
provide a qualitative sampling which could be used to demonstrate first hand experience with 
costs associated with managing future conditions in the watershed. The benefits of managing 
future conditions were demonstrated by using HAZUS to quantify the avoided losses between 1) 
Current Inventory with Current Flood Conditions; 2) Current Inventory with Future Flood 
Conditions; and, 3) Anticipated Inventory with Future Flood Conditions. 

6.1.1 Interviews 
For this study, the project team interviewed community officials, developers, floodplain 
managers, insurance agents, mortgage lenders, federal officials, and state officials. The results 
from the interviews suggested that the costs associated with compliance with the regulations are 
negligible. Many interviewees felt that other market forces have a more predominant effect on 
the value of property other than the cost of floodplain regulation compliance. This conclusion is 
also supported by Evaluation of the National Flood Insurance Programs Building Standards 
report (Jones et al, 2006). The interviewees indicated that aside from the potential cost of 
performing hydrologic and hydraulic (H & H) studies to demonstrate potential changes in the 
inundation areas, the cost for future conditions floodplain management are primarily the cost of 
implementing new regulations. 

As for cost associated with the implementation of the regulations, many of the 
interviewees indicated that they too are negligible. While some interviewees discussed law suits 
associated with floodplain management, there was no indication that the number of law suits 
have increased because of the implementation of the regulations. The types of issues that they 
have encountered are called takings. This study cannot extrapolate the legal activity to other 
communities, but these communities did not experience a major increase in legal activity. This 
study cannot predict what might happen should the final build-out conditions not match the 
assumed build-out conditions used in the models. 

The NFIP has provided communities a backbone to regulate floodplain areas. There was 
agreement that because of the implementation of the NFIP and other watershed programs, they 
have a foundation on which they can negotiate with developers who do not have the long-range 
interest in mind. The location and building of a structure is more carefully planned. If a building 
has the potential to be flooded, extra flood proofing measures have been taken to mitigate the 
flood hazard. This has allowed for development in higher risk areas, which is beneficial to 
communities that have little land outside the floodplain. Thus the health and safety of the people 
in the communities is better protected. 

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6.1.2 Computational Analysis 
Using HAZUS and HAZUS-type methodologies, the computational analysis for this 
project was accomplished by comparing the results from the 100-year flood for: 1) Current 
Inventory with Current Flood Conditions; 2) Current Inventory with Future Flood Conditions; 
and, 3) Anticipated Inventory with Future Flood Conditions. 

An obvious observation is that building in the watershed does change the flood 
conditions and can greatly increase the damage to structures. Increasing the flood conditions 
will change the design level base flood elevation (BFE) and/or alter the 100-year floodplain. 
This in turn can expose more buildings, especially existing buildings, to damage. As this study 
demonstrates the increased damage to buildings can be multiple orders of magnitude depending 
on the flood conditions. Even in the areas with only minor differences in the flood elevations 
and subsequent flood depth, estimated savings could easily be in the millions of dollars. This is 
consistent with the No Adverse Impact thinking currently espoused by the Association of State 
Floodplain Managers. 

In Mecklenburg County, for example, the future conditions flooding represents a nearly 
440% increase from the existing conditions flooding. Even in Fort Collins (Larimer County), 
where the increased flooding is relatively minor, the future conditions flooding may result in a 
37% increase in flood damages from the 100-year event. 

The analysis shows that in most cases, the existing buildings will suffer increasing 
damage as the watershed is built out unless the floodplain management program includes on site 
storage. 

6.2 Recommendations 
The primary recommendation of this study is that communities should be encouraged to 
manage future conditions of the watershed. As previously mentioned, this study identified two 

(2) approaches to managing future conditions: 1) managing the watershed to the anticipated 100year 
build-out flood conditions; and, 2) attempting to avoid or delay the flood waters from 
entering the stream system thereby reducing or preventing an increase in the hydrograph. This 
study is not making a recommendation or distinction between the two (2) approaches; but it does 
recommend that communities should manage future conditions in the watershed using the 
approach that best suits their needs. 
Below is a brief description of other recommendations suggested by the project team. 
This list of recommendations is a direct result of the analysis performed for this project. These 
recommendations will aid in the implementation, maintenance, and enforcement of watershed 
management programs. 

Flood Studies 

The project team recommends that detailed flood studies, such as those currently being 
conducted with the Map Modernization program, should examine multiple return periods besides 

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just the 100-year event. Three (3) return periods (e.g. 50-, 100-, and 500-year) are the minimum 
necessary to develop a loss curve that can be integrated to estimate the average annual loss. This 
provides a number of benefits. Current FEMA guidelines and specifications state the FISs 
should include 10-, 50-, 100- and 500-year return periods. Most FISs include those return 
periods for the existing conditions flooding. 

First the community could use the multiple return periods to perform an annualized 
assessment on their flood areas which leads to an improved picture of the potential benefits and 
costs associated with their floodplain management strategy. Second, improving peoples 
understanding that the 100-year flood event is simply a line drawn on a map and not a limitation 
in nature may cause more consumers to consider purchasing flood insurance even if they are 
outside the regulatory floodplain. Recent events have demonstrated that nature can always 
surpass the worst event we ever have experienced with another storm or flood. 

Establishing and Maintaining Flood Maps 

Determining how communities can effectively maintain their maps and keep them up to 
date is crucial to developing an effective management program. Approval of the use of HAZUS 
at the Level 110 analysis for communities that cannot afford detailed flood studies may be a 
good start to achieving 100% flood mapping throughout the United States. That is, use HAZUS 
to develop non-engineered floodplains for communities with no flood maps or where flood 
studies do not exist. 

Promote loss estimation along with mapping efforts. Whenever a Draft Flood Insurance 
Rate Map (DFIRM) has been completed, perform the loss estimation to determine the overall 
flood risk. Programs such as HAZUS provide an opportunity for the floodplain manager and 
building and safety officials to present scientifically based information to the elected officials to 
help them in the decision making process. This was the process used in Mecklenburg County to 
help generate the political will. 

Floodplain Management Programs 

The analysis suggests that the No Adverse Impact initiative launched by ASFPM and 
FEMA should continue to be supported with credits in the Community Rating System since these 
activities are an excellent way to reduce future losses and analysis efforts such as this can assist 
in promoting such initiatives. 

As noted above, loss estimation can help develop political will and can aid public 
outreach. This is complicated by the fact that capturing the necessary data is a complicated 
process that takes significant time and resources. It is recommended that FEMA consider 
encouraging these efforts within the Hazard Mitigation Grant Program (HMGP) or other FEMA 
mitigation grant activities. 

10 HAZUS allows three levels of analysis. Level 1 uses primarily baseline data provided with the program, Level 2 
analysis occurs when users replace the baseline data with improved local data (such as was done in this report), and 
Level 3 analysis occurs when outside experts are used to modify components such as damage functions, mapping 
schemes, and other crucial analysis parameters. 

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The project team believes that the concept of on-site storage may need further research 
and analysis to demonstrate that the concept not only reduces the 100-year flood losses as seen in 
this analysis, but that it is effective in reducing the average annual losses. It is recommended 
that the communities using on-site storage in this project might be given grants to develop 
sufficient flood data to perform an annualized assessment for their current and future flood 
conditions. This will allow the communities and FEMA to determine how effective this 
approach really is. 

Data Collection Guidelines 

The project team recommends that FEMA develop a guide for local governments to 
provide outreach and training to local governments discussing potential data sources and data 
collection activities that can help the community define its overall risk. Assuming the 
communities in the study are typical, the project team believes there is a large amount of data 
available in most communities, but much of it is not coordinated, contains inaccuracies, is 
incomplete, and/or may be difficult to obtain. 

These guidelines are necessary to inform communities of the data needs. With this 
knowledge communities can assess the effort needed to complete this type of work. Without 
these guidelines communities may underestimate the cost to perform such an effort and may get 
discouraged as assumptions need to be made. 

Further, given the proper guidelines, communities throughout the U.S. could use the tools 
applied by the project team to determine the most effective floodplain management approach for 
their community. 

Lack of Knowledge 

The interviewees felt that lack of knowledge was a major barrier in the implementation of 
the NFIP and future floodplain condition programs. There are many reasons that this barrier has 
not been able to be overcome. Below is a sample of the reasons provided by the interviewees: 

 Community (elected) officials only stay in office for a few years, so they do not 
necessarily familiarize themselves with the details of the NFIP. 
 Developers only know the regulations of what they can or cannot do when building a 
structure. They see it as an annoyance because they do not have free reign in their 
designs and they believe that their engineers do not know what flood proofing is. 
 The floodplain managers know all about the NFIP, but they have not been able to 
completely educate the community, due to what they perceive as a lack of interest. 
In order for the project team to define the losses avoided and completely understand the 
potential impacts and subsequent savings, each community had to obtain additional hydrologic 
and hydraulic (H & H) modeling assuming that on-site storage would not be effective. 
Preserving the baseline H & H analysis for built-out conditions could be useful to communities 

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in educating their elected officials, developers, and public regarding the reasoning behind 
floodplain management for future conditions. 

Special Flood Hazard Area (SFHA) 

The interviewees believe that the flood insurance maps create a false sense of security 
amongst the general population caused in part by the line on the map being called the 100-year 
floodplain (also know as the SFHA). They felt that the public interprets it as the chance of 
getting flooded once every 100 years. However, this is misleading because they do not 
understand that the 100-year floodplain is the area that is affected should such a storm occur. 
Although difficult, the project team recommends using the available tools to educate 
communities on total flood risk and not just the 100-year flood. 

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7. APPENDICES 
Appendix 1: Community Data 
1.1 DuPage County, Illinois 
Date of Incorporation 1833 
Population 
County 904,161 
Municipalities 
Cities 40 
Annual Budget $632 million 
Number of Policy Holders 
FEMA Report 5,335 
Site Reports 642 
CRS Not participating 

1.2 Escambia County, Florida 
Date of Incorporation March 13, 1824 
Population 
City of Pensacola 56,255 
County 294,410 
Municipalities 
Cities 8 
Annual Budget $350 million 
Number of Policy Holders 
FEMA Report 638 
Site Reports 647 
CRS (County) 7 

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1.3 Grand Forks County, North Dakota 
Date of Incorporation February 22, 1881 
Population 
City of Grand Forks 49,366 
County 66,109 
Municipalities 
Townships 41 
Cities 12 
Districts 9 
Annual Budget $133 million 
Number of Policy Holders 
FEMA Report 1,917 
Site Reports 2,017 
CRS (City) 5 

1.4 Harris County, Texas 
Date of Incorporation 1836 
Population 
City of Houston 1,953,631 
County 3,400,578 
Municipalities 
Cities 38 
Annual Budget $2.623 billion 
Number of Policy Holders 
FEMA Report 99,505 
Site Reports 98,138 
CRS (County) 8 

1.5 Larimer County, Colorado 
Date of Incorporation 1873 
Population 
City of Fort Collins 126,848 
County 273,965 
Municipalities 9 
Annual Budget $438.4 million 
Number of Policy Holders 
FEMA Report 361 
Site Reports 361 
CRS (City) 4 

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1.6 Mecklenburg County, North Carolina 
Date of Incorporation December 11, 1762 
Population 
City of Charlotte 540,828 
County 746,427 
Municipalities 
Cities 7 
Annual Budget $1.1 billion 
Number of Policy Holders 
FEMA Report 26,509 
Site Reports 1,618 
CRS (County) 6 

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Appendix 2: Interview Discussion Guide 

I. RESPONDENT-RELATED INFORMATION 
A. Respondents professional background and job position 
B. Respondents self-perceived level of knowledge of NFIP 
C. How respondents job relates to or is part of NFIP 
II. COMMUNITY HISTORY 
Discuss your knowledge of major flooding in your community, the history and impacts, 
problematic areas? 
III. COMMUNITY IMPACTS OF THE NATIONAL FLOOD INSURANCE PROGRAM 
Since your community has enacted ordinances managing flood conditions, discuss your 
impression of the impacts on land use, development trends, and cost. 
IV. FUTURE CONDITIONS PROGRAM IMPLEMENTATION 
A. Are you aware of any legal issues that have arisen related to higher level of floodplain 
regulations? 
B. What do you perceive are the benefits of managing to future conditions? 
C. What are some of the barriers? 
V. BUILDING STANDARDS 
A. How well do you think current standards will meet anticipated/future needs in your 
community? 
VI. OVERALL OPINION OF NFIP 
A. What do you perceive as the benefits to your community because of NFIP? 
B. What do you perceive as barriers to implementing and/or benefiting from NFIP? 

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Appendix 3: HAZUSMH Flood Model Distribution Tables 

TABLE-1. HAZUS Distribution of Foundation Types for Single Family and Multi-Family Residences 

Foundation Types 
US Census States within 
Region the Region Pile Solid Pier/ Basement/ Crawl- Fill Slab-on-
Wall post Garden Level space Grade 

Northeast  New 
England 
CT, MA, ME, NH, 
RI, VT 0 0 0 81 10 0 9 
Northeast  Mid 
Atlantic NJ, NY, PA 0 0 0 76 10 0 14 
Midwest  East 
North Central IL, IN, MI, OH, WI 0 0 0 68 21 0 11 
Midwest  West 
North Central 
IA, KS, MN, MO, 
NE, ND, SD 0 0 0 75 13 0 12 
South  South 
Atlantic 
DE, DC, FL, GA, 
MD, NC, SC, VA, 
WV 
0 0 0 23 35 0 42 
South  East South 
Central AL, KY, MS, TN 0 0 0 25 49 0 26 
South  West 
South Central AR, LA, OK, TX, 0 0 0 5 38 0 57 
West- Mountain AZ, CO, ID, MT, NV, 
NM, UT, WY 0 0 0 32 29 0 39 
West  Pacific AK, CA, HI, OR, WA 0 0 0 13 45 0 42 

TABLE-2. HAZUS Default Floor Heights Above Grade to Top of Finished Floor (Riverine) 
Foundation Type Pre-Firm Post-FIRM 

Slab 1 ft 1 ft1 
Fill 2 ft 2 ft 
Crawlspace 3 ft 4 ft 
Basement (or Garden Level) 4ft 4 ft1 
Pier (or post and beam) 5 ft 6 ft 
Solid Wall 7 ft 8 ft 
Pile 7 ft 8 ft 
SOURCE: Expert Opinion 
Notes: 
1 Typically not allowed, but may exist 

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TABLE-3. HAZUS Default Full Replacement Cost Models (Means, 2002) 
HAZUS Occupancy Class 
Description Sub-category Means Model Description (Means Model 
Number) 
Means 
Cost/SF 
(2002) 
RES1 Single Family Dwelling See Table 14.2 SFR Avg 1 story 1,600 SF $79.88 
RES2 Manufactured Housing Manufactured Housing Manufactured Housing (N/A)1 $30.90 
Multi Family Dwelling  Duplex SFR Avg 2 St., MF adj, 3000 SF $67.24 
small Triplex/Quads SFR Avg 2 St., MF adj, 3000 SF $73.08 
Multi Family Dwelling  5-9 units Apt, 1-3 st, 8,000 SF (M.010) $125.63 
RES3 medium 10-19 units Apt., 1-3 st., 12,000 SF (M.010) $112.73 

Multi Family Dwelling  
large 
20-49 units 
50+ units 
Apt., 4-7 st., 40,000 SF (M.020) 
Apt., 4-7 st., 60,000 SF (M.020) 
Apt., 8-24 st., 145,000 SF (M.030) 
$108.86 
$106.13 
$111.69 
Hotel, medium Hotel, 4-7 st., 135,000 SF(M.350) $104.63 
RES4 Temp. Lodging 
Hotel, large 
Motel, small 
Hotel, 8-24 st., 450,000 SF (M.360) 
Motel, 1 st., 8,000 SF (M.420) 
$93.47 
$94.13 
Motel, medium Motel, 2-3 st., 49,000 SF (M.430) $110.03 
RES5 Institutional Dormitory 
Dorm, medium 
Dorm, large 
College Dorm, 2-3 st, 25,000 SF (M.130) 
College Dorm, 4-8 st, 85,000 SF (M.140) 
$118.82 
$113.31 
Dorm, small Frat House, 2 st., 10,000 SF (M.240) $99.50 
RES6 Nursing Home Nursing home Nursing Home, 2 st., 25,000 SF (M.450) $104.62 
COM1 
COM2Retail Trade 
Wholesale Trade 
Dept Store, 1 st 
Dept Store, 3 st 
Store, small 
Store, medium 
Store, convenience 
Auto Sales 
Warehouse, medium 
Warehouse, large 
Warehouse, small 
Store, Dept., 1 st., 110,000 SF (M.610) 
Store, Dept., 3 st., 95,000 SF (M.620) 
Store, retail, 8,000 SF (M.630) 
Supermarket, 44,000 SF (M.640) 
Store, Convenience, 4,000 SF (M.600) 
Garage, Auto Sales, 21,000 SF (M.260) 
Warehouse, 30,000 SF (M.690) 
Warehouse, 60,000 SF (M.690) 
Warehouse, 15,000 SF (M.690) 
$71.54 
$88.73 
$79.23 
$69.09 
$83.59 
$70.84 
$61.91 
$56.58 
$70.43 

Garage, Repair Garage, Repair, 10,000 SF (M.290) $86.81 
COM3 Personal and Repair 
Services 
Garage, Service sta. 
Funeral Home 
Laundromat 
Garage, Service sta., 1,400 SF (M.300) 
Funeral home, 10,000 SF (M.250) 
Laundromat 3,000 SF (M.380) 
$113.91 
$97.66 
$135.64 
Car Wash Car Wash, 1 st., 800 SF (M.080) $198.28 
COM4 Prof./ Tech./Business 
Services 
Office, Medium 
Office, Small 
Office, Large 
Office, 5-10 st., 80,000 SF (M.470) 
Office, 2-4 st., 20,000 SF (M.460) 
Office, 11-20 st., 260,000 SF (M.480) 
$98.96 
$102.69 
$88.21 
COM5 Banks Bank Bank, 1 st., 4100 SF (M.050) $153.97 

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TABLE-3 (Continued). HAZUS Default Full Replacement Cost Models (Means, 2002) 
HAZUS Occupancy Class 
Description Sub-category Means Model Description (Means Model 
Number) 
Means 
Cost/SF 
(2002) 
COM6 Hospital 
Hospital, Medium 
Hospital, Large 
Hospital, 2-3 st., 55,000 SF (M.330) 
Hospital, 4-8 st., 200,000 SF (M.340) 
$144.60 
$125.60 
COM7 Medical Office/Clinic 
Med. Office, medium 
Med. Office, small 
Medical office, 2 st., 7,000 SF (M.410) 
Medical office, 1 st., 7,000 SF (M.400) 
$129.82 
$118.01 
Restaurant Restaurant, 1 st., 5,000 SF (M.530) $137.02 
Restaurant, Fast food Restaurant, fast food, 4,000 SF (M.540) $121.49 
COM8 Entertainment & 
Recreation 
Bowling Alley 
Country Club 
Bowling Alley, 20,000 SF (M.060) 
Club, Country, 1 st., 6,000 SF (M.100) 
$72.31 
$135.23 
Social Club Club, Social, 1 st., 22,000 SF (M.110) $95.39 
Racquetball Court Racquetball Court, 30,000 SF (M.510) $111.23 
Hockey Rink Hockey Rink 30,000 SF (M.550) $115.13 
COM9 Theaters 
Movie Theatre 
Auditorium 
Movie Theatre, 12,000 SF (M.440) 
Auditorium, 1 st., 24,000 SF (M.040) 
$102.35 
$109.60 
Parking Garage Garage, Pkg, 5 st., 145,000 SF (M.270) $34.78 
COM10 Parking Parking Garage, 
Underground Garage, UG Pkg, 100,000 SF (M.280) $49.20 

IND1 
IND2 
IND3 
Heavy 
Light 
Food/Drugs/Chemicals 
Factory, small 
Factory, large 
Warehouse, medium 
Factory, small 
Factory, large 
College Laboratory 
Factory, small 
Factory, large 
Factory, 1 st., 30,000 SF (M.200) 
Factory, 3 st., 90,000 SF (M.210) 
Warehouse, 30,000 SF (M.690) 
Factory, 1 st., 30,000 SF (M.200) 
Factory, 3 st., 90,000 SF (M.210) 
College Lab, 1 st., 45,000 SF (M.150) 
Factory, 1 st., 30,000 SF (M.200) 
Factory, 3 st., 90,000 SF (M.210) 
$73.82 
$78.61 
$61.91 
$73.82 
$78.61 
$119.51 
$73.82 
$78.61 
IND4 
IND5 
Metals/Minerals ProcessinHigh Technology 
College Laboratory 
Factory, small g 
Factory, large 
College Laboratory 
Factory, small 
Factory, large 
College Lab, 1 st., 45,000 SF (M.150) 
Factory, 1 st., 30,000 SF (M.200) 
Factory, 3 st., 90,000 SF (M.210) 
College Lab, 1 st., 45,000 SF (M.150) 
Factory, 1 st., 30,000 SF (M.200) 
Factory, 3 st., 90,000 SF (M.210) 
$119.51 
$73.82 
$78.61 
$119.51 
$73.82 
$78.61 

Warehouse, medium Warehouse, 30,000 SF (M.690) $61.91 
IND6 Construction Warehouse, large Warehouse, 60,000 SF (M.690) $56.58 
Warehouse, small Warehouse, 15,000 SF (M.690) $70.43 
Warehouse, medium Warehouse, 30,000 SF (M.690) $61.91 
AGR1 Agriculture Warehouse, large Warehouse, 60,000 SF (M.690) $56.58 
Warehouse, small Warehouse, 15,000 SF (M.690) $70.43 
REL1 Church Church Church, 1 st., 17,000 SF (M.090) $114.08 

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TABLE-3 (Continued). HAZUS Default Full Replacement Cost Models (Means, 2002) 
HAZUS Occupancy Class 
Description Sub-category Means Model Description (Means 
Model Number) 
Means 
Cost/SF 
(2002) 

Town Hall, small Town Hall, 1 st., 11,000 SF (M.670) $90.30 
GOV1 General Services 
Town Hall, medium 
Courthouse, small 
Town Hall, 2-3 st., 18,000 SF (M.680) 
Courthouse, 1 st., 30,000 SF (M.180) 
$112.94 
$130.71 
Courthouse, medium Courthouse, 2-3 st., 60,000 SF (M.190) $136.81 
Post Office Post Office, 13,000 SF (M.500) $86.83 
GOV2 Emergency Response 
Police Station 
Fire Station, small 
Police Station, 2 st., 11,000 SF (M.490) 
Fire Station, 1 st., 6,000 SF (M.220) 
$136.10 
$105.53 
Fire Station, medium Fire Station, 2 st., 10,000 SF (M.230) $110.34 

High School School, High, 130,000 SF (M.570) $92.80 
Elementary School School, Elementary, 45,000 SF (M.560) $90.22 
EDU1 Schools/Libraries Jr. High School School, Jr. High, 110,000 SF (M.580) $95.21 
Library Library, 2 st., 22,000 SF (M.390) $103.94 
Religious School Religious Educ, 1 st., 10,000 SF (M.520) $112.19 
College Classroom College Class. 2-3 st, 50,000 SF (M.120) $114.68 
EDU2 Colleges/Universities College Laboratory College Lab, 1 st., 45,000 SF (M.150) $119.51 
Vocational school School, Vocational, 40,000 SF (M.590) $93.96 
Notes: 
1 Manufactured Housing Institute, 2000 cost for new manufactured home 

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TABLE-4. Default HAZUS Contents Value Percent of Structure Value 

No. Label Occupancy Class Contents Value (%) 
Residential 

1 RES1 Single Family Dwelling 50 
2 RES2 Mobile Home 50 
3 RES3 Multi Family Dwelling 50 
4 RES4 Temporary Lodging 50 
5 RES5 Institutional Dormitory 50 
6 RES6 Nursing Home 50 

Commercial 

7 COM1 Retail Trade 100 
8 COM2 Wholesale Trade 100 
9 COM3 Personal and Repair Services 100 
10 COM4 Professional/Technical/ 100 

Business Services 
11 COM5 Banks 100 
12 COM6 Hospital 150 
13 COM7 Medical Office/Clinic 150 
14 COM8 Entertainment & Recreation 100 
15 COM9 Theaters 100 
16 COM10 Parking 50 

Industrial 

17 IND1 Heavy 150 
18 IND2 Light 150 
19 IND3 Food/Drugs/Chemicals 150 
20 IND4 Metals/Minerals Processing 150 
21 IND5 High Technology 150 
22 IND6 Construction 100 

Agriculture 

23 AGR1 Agriculture 100 
Religion/Non/Profit 

24 REL1 Church/Membership Organization 100 
Government 

25 GOV1 General Services 100 
26 GOV2 Emergency Response 150 

Education 

27 EDU1 Schools/Libraries 100 
28 EDU2 Colleges/Universities 150 

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8. DATA DICTIONARY 
8.1 Inventory Data 
DuPage County, Illinois 
Field Source Format Description Used in Analysis (Y 
or N) 
PARCEL_ Boundary Number Parcel Number N 
Solutions Inc. 
PARCEL_ID Boundary Number Parcel Number N 
Solutions Inc. 
PIN Boundary Number Parcel Identification Y  UDEF 
Solutions Inc. Number 
MUNZCODE Boundary Number Municipal Code N 
Solutions Inc. 
USE00 Boundary Number Land Use Code Y  UDEF 
Solutions Inc. 
LUP97 Boundary Number Land Use Plan 97 Y  UDEF 
Solutions Inc. 
ACRES Boundary Number Parcel Acreage Y  UDEF 
Solutions Inc. 
BILLNAME Boundary Text Billing data Y  UDEF 
Solutions Inc. 
BILLSTNUM Boundary Text Billing data Y  UDEF 
Solutions Inc. 
BILLSTDIR Boundary Text Billing data Y  UDEF 
Solutions Inc. 
BILLSTNAME Boundary Text Billing data Y  UDEF 
Solutions Inc. 
BILLAPT Boundary Text Billing data Y  UDEF 
Solutions Inc. 
BILLCITY Boundary Text Billing data Y  UDEF 
Solutions Inc. 
BILLSTATE Boundary Text Billing data Y  UDEF 
Solutions Inc. 
BILLZIP Boundary Text Billing data Y  UDEF 
Solutions Inc. 
PROPNAME Boundary Text Property data N 
Solutions Inc. 
PROPSTNUM Boundary Text Property data N 
Solutions Inc. 

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Field Source Format Description Used in Analysis (Y 
or N) 
PROPSTDIR Boundary Text Property data N 
Solutions Inc. 
PROPSTNAME Boundary Text Property data N 
Solutions Inc. 
PROPAPT Boundary Text Property data N 
Solutions Inc. 
PROPCITY Boundary Text Property data N 
Solutions Inc. 
PROPZIP Boundary Text Property data N 
Solutions Inc. 
BILLVALUE Boundary Number Bill Value N 
Solutions Inc. 
TAXAMOUNT Boundary Number Tax Amount N 
Solutions Inc. 
FCVLAND Boundary Number 2005 fair-cash N 
Solutions Inc. assessed value of 
land 
FCVIMP Boundary Number 2005 fair-cash Y  UDEF 
Solutions Inc. assessed value of 
improvements 
FCVTOTAL Boundary Number 2005 fair-cash N 
Solutions Inc. assessed value of 
land and 
improvements 
TAXCODE Boundary Text Tax Identification N 
Solutions Inc. Number 
EXEMPTCODE Boundary Text Exempt Code N 
Solutions Inc. 
PROPCLASS Boundary Text Class of Property Tax N 
Solutions Inc. 
X_COORD Boundary Number X-Coordinate Y  UDEF 
Solutions Inc. 
Y_COORD Boundary Number Y-Coordinate Y  UDEF 
Solutions Inc. 

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79 

Escambia County, Florida 

Field Source Format Description Used in Analysis (Y 
or N) 
AREA Boundary Number Area of Parcel Y  UDEF 
Solutions Inc. 
PERIMETER Boundary Number Perimeter of Parcel N 
Solutions Inc. 
PARCEL_ Boundary Number Parcel N 
Solutions Inc. 
PARCEL_ID Boundary Number Parcel N 
Solutions Inc. Identification 
PARCEL_NUM Boundary Text Parcel N 
Solutions Inc. Identification 
Number 
APN Boundary Text Parcel N 
Solutions Inc. Identification 
Number 
ACCOUNT Boundary Number Account N 
Solutions Inc. 
LOCATION Boundary Text Property data Y  UDEF 
Solutions Inc. 
NAME1 Boundary Text Billing data Y  UDEF 
Solutions Inc. 
NAME2 Boundary Text Billing data Y  UDEF 
Solutions Inc. 
NAME3 Boundary Text Billing data Y  UDEF 
Solutions Inc. 
NAME4 Boundary Text Billing data Y  UDEF 
Solutions Inc. 
NAME5 Boundary Text Billing data Y  UDEF 
Solutions Inc. 
NAME6 Boundary Text Billing data Y  UDEF 
Solutions Inc. 
ZIPCODE Boundary Text Billing data Y  UDEF 
Solutions Inc. 
LEGAL1 Boundary Text Property data N 
Solutions Inc. 
LEGAL2 Boundary Text Property data N 
Solutions Inc. 
LEGAL3 Boundary Text Property data N 
Solutions Inc. 

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Field Source Format Description Used in Analysis (Y 
or N) 
LEGAL4 Boundary Text Property data N 
Solutions Inc. 
LEGAL5 Boundary Text Property data N 
Solutions Inc. 
LEGAL6 Boundary Text Property data N 
Solutions Inc. 
CITY_CODE Boundary Text City Code N 
Solutions Inc. 
USE_CODE Boundary Text Land Use Code Y  UDEF 
Solutions Inc. 
TOTAL_ASSM Boundary Number Assessed Value of N 
Solutions Inc. Building and Land 
BUILDING_A Boundary Number Assessed Building Y  UDEF 
Solutions Inc. Value 
LAND_ASSMN Boundary Number Assessed Land N 
Solutions Inc. Value 
SALEMONTH Boundary Number Month of Sale N 
Solutions Inc. 
SALEYEAR Boundary Number Year of Sale N 
Solutions Inc. 
SALEPRICE Boundary Number Price of Sale N 
Solutions Inc. 
ACTUAL_YEA Boundary Number Year Built Y  UDEF 
Solutions Inc. 
EFFECTIVE_ Boundary Number Year Sold Y  UDEF 
Solutions Inc. 
BATH_COUNT Boundary Number Number of N 
Solutions Inc. Bathrooms 
BEDROOM_CO Boundary Number Number of N 
Solutions Inc. Bedrooms 
ROOM_COUNT Boundary Number Number of Rooms N 
Solutions Inc. 
NUMBER_STO Boundary Number Number of Stories Y  UDEF 
Solutions Inc. 
NOTE1 Boundary Text Misc. data N 
Solutions Inc. 
NOTE2 Boundary Text Misc. data N 
Solutions Inc. 

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Field 
NOTE3 
Source 
Boundary 
Solutions Inc. 
Format 
Text 
Description 
Misc. data 
Used in Analysis (Y 
or N) 
N 
NOTE4 Boundary 
Solutions Inc. 
Text Misc. data N 
NOTE5 Boundary 
Solutions Inc. 
Text Misc. data N 
NOTE6 Boundary 
Solutions Inc. 
Text Misc. data N 

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Grand Forks County, North Dakota 

Field Source Format Description Used in Analysis (Y 
or N) 
AREA Boundary Number Area of Parcel Y  UDEF 
Solutions Inc. 
PERIMETER Boundary Number Perimeter of Parcel N 
Solutions Inc. 
PIN Boundary Text Parcel N 
Solutions Inc. Identification 
Number 
PARCEL Boundary Text Parcel Number N 
Solutions Inc. 
ASSESSOR Boundary Text Assessor N 
Solutions Inc. 
PCODE2 Boundary Text Parcel Number N 
Solutions Inc. 
BASE_ZONIN Boundary Text Base Zoning N 
Solutions Inc. 
ZONING_TEX Boundary Text Zoning Description N 
Solutions Inc. 
LANDUSE_CO Boundary Number Land Use Code Y  UDEF 
Solutions Inc. 
LU_DESIGNA Boundary Text Land Use Y  UDEF 
Solutions Inc. Designation 
APN Boundary Text Parcel N 
Solutions Inc. Identification 
Number 
HOUSE Boundary Text Property Data Y  UDEF 
Solutions Inc. 
STREET Boundary Text Property Data Y  UDEF 
Solutions Inc. 
MISC Boundary Text Property Data Y  UDEF 
Solutions Inc. 
YEARBLT Boundary Text Year Built Y  UDEF 
Solutions Inc. 
AREA_1 Boundary Text Square Footage of Y  UDEF 
Solutions Inc. Building 
HEIGHT Boundary Text Number of Stories Y  UDEF 
Solutions Inc. 
CONST Boundary Text Construction Type Y  UDEF 
Solutions Inc. 

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Harris County, Texas 
Field Source 
AREA Boundary 
Solutions Inc. 
Format 
Number 
Description 
Area of Parcel 
Used in Analysis (Y 
or N) 
Y - UDEF 
PERIMETER Boundary 
Solutions Inc. 
Number Perimeter of Parcel N 
PARCEL_ Boundary 
Solutions Inc. 
Number Parcel N 
PARCEL_ID Boundary 
Solutions Inc. 
Number Parcel Identification N 
HCAD_NUM Boundary 
Solutions Inc. 
Text HCAD Account 
Number 
N 
HCAD_ACCOU Boundary 
Solutions Inc. 
Text HCAD Account 
Number 
N 
REGION_NUM Boundary 
Solutions Inc. 
Text Region Number N 
PARCEL_I_1 Boundary 
Solutions Inc. 
Text N 
TAX_YEAR Boundary 
Solutions Inc. 
Number Tax Year N 
OWNER_NAME Boundary 
Solutions Inc. 
Text Billing Data Y  UDEF 
OWNER_MAIL Boundary 
Solutions Inc. 
Text Billing Data Y  UDEF 
OWNER_MA_1 Boundary 
Solutions Inc. 
Text Billing Data Y  UDEF 
OWNER_MA_2 Boundary 
Solutions Inc. 
Text Billing Data Y  UDEF 
MAIL_CITY_ Boundary 
Solutions Inc. 
Text Billing Data Y  UDEF 
MAIL_ZIP_C Boundary 
Solutions Inc. 
Text Billing Data Y  UDEF 
MAIL_ZIP_1 Boundary 
Solutions Inc. 
Text Billing Data Y - UDEF 
OWNER_PUR_ Boundary 
Solutions Inc. 
Number Owner Purchasing N 
OWNER_PUR1 Boundary 
Solutions Inc. 
Number Owner Purchasing 
(Owner 2) 
N 
OWNER_PU_1 Boundary 
Solutions Inc. 
Number Owner Purchasing 
(Owner 1) 
N 

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Field 
PROPERTY_D 
Source 
Boundary 
Solutions Inc. 
Format 
Text 
Description 
Property Data 
Used in Analysis (Y 
or N) 
N 
PROPERTY_1 Boundary 
Solutions Inc. 
Text Property Data N 
PROPERTY_2 Boundary 
Solutions Inc. 
Text Property Data N 
PROPERTY_3 Boundary 
Solutions Inc. 
Text Property Data N 
PROPERTY_R Boundary 
Solutions Inc. 
Text Property Data N 
ACREAGE Boundary 
Solutions Inc. 
Number Acreage of Parcel N 
CURRENT_LA Boundary 
Solutions Inc. 
Number Current Land Value N 
CURRENT_IM Boundary 
Solutions Inc. 
Number Current Improvement 
Value 
N 
CURRENT_PR Boundary 
Solutions Inc. 
Number Current Market Price N 
PREVIOUS_L Boundary 
Solutions Inc. 
Number Previous Land Value N 
PREVIOUS_I Boundary 
Solutions Inc. 
Number Previous 
Improvement Value 
N 
PREVIOUS_P Boundary 
Solutions Inc. 
Number Previous Market 
Price 
N 
PRODUCTIVI Boundary 
Solutions Inc. 
Text Productivity N 
STATE_REPO Boundary 
Solutions Inc. 
Text State Report N 
EXEMPTION_ Boundary 
Solutions Inc. 
Text Exemption N 
VET_DISABI Boundary 
Solutions Inc. 
Number Veterans Disability 
Code 
N 
VET_DISA_1 Boundary 
Solutions Inc. 
Number Veterans Disability 
Code 
N 
AGENT_CODE Boundary 
Solutions Inc. 
Text Agent Code N 
AGENT_ADDR Boundary 
Solutions Inc. 
Text Agent Data N 

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Field Source Format Description Used in Analysis (Y 
or N) 
AGENT_AD_1 Boundary Text Agent Data N 
Solutions Inc. 
AGENT_AD_2 Boundary Text Agent Data N 
Solutions Inc. 
AGENT_AD_3 Boundary Text Agent Data N 
Solutions Inc. 
AGENT_CITY Boundary Text Agent Data N 
Solutions Inc. 
AGENT_ZIP_ Boundary Text Agent Data N 
Solutions Inc. 
AGENT_ZIP1 Boundary Text Agent Data N 
Solutions Inc. 
AGENT_TEL_ Boundary Text Agent Data N 
Solutions Inc. 
AGENT_TEL1 Boundary Text Agent Data N 
Solutions Inc. 
LOCATION_S Boundary Text Property Data N 
Solutions Inc. 
LOCATION_1 Boundary Text Property Data N 
Solutions Inc. 
LOCATION_C Boundary Text Property Data N 
Solutions Inc. 
LOCATION_Z Boundary Text Property Data N 
Solutions Inc. 
LOCATION_2 Boundary Text Property Data N 
Solutions Inc. 
MAP_FACET Boundary Text Map Grid Location N 
Solutions Inc. 
PARCEL_KEY Boundary Text Parcel Key N 
Solutions Inc. 
STORY_HEIG Boundary Text Number of Stories Y  UDEF 
Solutions Inc. 
BLD_YEAR_B Boundary Text Year Built Y  UDEF 
Solutions Inc. 
CAMA_Commo Boundary Text (same as Parcel_I_1) N 
Solutions Inc. 
BLDG_SQFT Boundary Text Building Square Y  UDEF 
Solutions Inc. Footage 

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Field Source Format Description Used in Analysis (Y 
or N) 
TOT_LAND_V Boundary Text Total Land Value N 
Solutions Inc. 
TOT_IMPR_V Boundary Text Total Improvement Y  UDEF 
Solutions Inc. Value 
FIN_VAL Boundary Text Total Value of Land N 
Solutions Inc. and Improvement 
VALUE_TYPE Boundary Text Value Type N 
Solutions Inc. 
LAND_USE Boundary Text Land Use Code Y  UDEF 
Solutions Inc. 
STREET_NO Boundary Text Property Data N 
Solutions Inc. 
STREET_DIR Boundary Text Property Data N 
Solutions Inc. 
STREET_NAM Boundary Text Property Data N 
Solutions Inc. 
CAMA_Resid Boundary Text (same as Parcel_I_1) N 
Solutions Inc. 
STORY_HE_1 Boundary Text Number of Stories Y - UDEF 
Solutions Inc. 
STYLE Boundary Text Style N 
Solutions Inc. 
EXTERIOR_W Boundary Text Exterior Wall N 
Solutions Inc. 
DATE_ERECT Boundary Text Date Building N 
Solutions Inc. Erected 
SALE_YEAR_ Boundary Text Sale Year N 
Solutions Inc. 
SALE_TYPE_ Boundary Text Sale Type N 
Solutions Inc. 
SALE_PRICE Boundary Text Sale Price N 
Solutions Inc. 
SALE_YEAR1 Boundary Text Sale Year (Owner 2) N 
Solutions Inc. 
SALE_TYPE1 Boundary Text Sale Type (Owner 2) N 
Solutions Inc. 
SALE_PRI_1 Boundary Text Sale Price (Owner 2) N 
Solutions Inc. 

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Field 
SALE_YEA_1 
Source 
Boundary 
Solutions Inc. 
Format 
Text 
Description 
Sale Year (Owner 1) 
Used in Analysis (Y 
or N) 
N 
SALE_TYP_1 Boundary 
Solutions Inc. 
Text Sale Type (Owner 1) N 
SALE_PRI_2 Boundary 
Solutions Inc. 
Text Sale Price (Owner 1) N 
HAZUSOCCUP Boundary 
Solutions Inc. 
Text HAZUS Occupancy 
Code 
Y - UDEF 

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Larimer County, Colorado 
Field Source Format Description Used in Analysis (Y 
or N) 
LINK_ID County Number Link Identification N 
PARCELNO County Text Parcel Number 
APN County Text Parcel Number 
PARCELNB County Number Parcel Number 
ACCOUNTNO County Text Account Number N 
SCHEDULENU County Number Schedule Number N 
IMPACTUALV County Number Improvement Actual Y  UDEF 
Value 
BLDGID County Number Building N 
Identification 
Number 
PROPTYPE County Text Property Type N 
OCCCODE County Number Occupancy Code Y  UDEF 
OCCDESCR County Text Occupancy Y  UDEF 
Description 
BLTASID County Number Built As N 
Identification 
BLTASDESCR County Text Built As Description N 
SF County Number Square Footage Y  UDEF 
CONDOSF County Number Condo Square N 
Footage 
BSMNTSF County Number Basement Square N 
Footage 
BSMNTSFFIN County Number Basement Finished N 
Square Footage 
GARAGESF County Number Garage Square N 
Footage 
CARPORTSF County Number Carport Square N 
Footage 
BALCONYSF County Number Balcony Square N 
Footage 
PORCHSF County Number Porch Square Footage N 
PERIM County Number Perimeter N 
PERCCOMP County Number Percent Complete N 
CONDITION County Text Condition N 
QUALITY County Text Quality N 

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Field Source Format Description Used in Analysis (Y 
or N) 
HVACID County Number HVAC Identification N 
HVACDESCR County Text HVAC Description N 
EXTERIOR County Text Exterior N 
INTERIOR County Text Interior N 
UNITTYPE County Text Unit Type N 
STORIES County Number Number of Stories Y  UDEF 
SPRINKLERS County Number Sprinkler Square N 
Footage 
ROOFTYPE County Text Roof Type N 
ROOFCOVER County Text Roof Cover N 
FLOORCOVER County Text Floor Cover N 
FOUNDATION County Text Foundation Y  UDEF 
ROOMS County Number Number of Rooms N 
BEDROOMS County Number Number of Bedrooms N 
BATHS County Number Number of Baths N 
UNITS County Number Number of Units N 
CLASSID County Text Class Identification Y  UDEF 
CLASSDESCR County Text Class Description Y  UDEF 
YRBLT County Number Year Built Y  UDEF 
YRREM County Number Year Remodeled N 
PERCREM County Number Percent Remodeled N 
ADJYRBLT County Number Adjusted Year Built N 
AGE County Number Age N 
MHTITLENO County Text Mobile Home Title N 
Number 
MHSERIALNO County Text Mobile Home Serial N 
Number 
MHLENGTH County Number Mobile Home Length N 
MHWIDTH County Number Mobile Home Width N 
MHMAKE County Text Mobile Home Make N 

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Mecklenburg County, North Carolina 
Field Source Format Description Used in Analysis (Y 
or N) 
PID County Text Parcel Y  UDEF 
Identification 
ALPHA_EXT County Text Alpha Extension N 
CARD_NO County Text Card Number N 
USE_CODE County Text Use Code Y  UDEF 
STYLE County Text Style N 
MODEL County Text Model N 
HEATED_ARE County Number Heated Area N 
EFFECTIVE_ County Number Effective Area N 
YEAR_BUILT County Number Year Built Y  UDEF 
BEDROOMS County Number Number of N 
Bedrooms 
BATHROOMS County Number Number of N 
Bathrooms 
RESTROOMS County Number Number of N 
Restrooms 
BASEMENT County Text Basement N 
GARAGE County Text Garage N 
FIREPLACE County Text Fireplace N 
AC County Text Air Conditioning N 
EXT_WALL County Text Exterior Wall N 
HEATING_TY County Text Heating Type N 
NET_BLDG_V County Number Total Building N 
Value 
NET_OBXF_V County Number Total Out Building N 
Value 
UNITS County Number Number of Units N 
QUALITY County Number Quality N 
MRK_DESIGN County Number Market Design N 
ABLDG_KEY County Text Building N 
Identification 
PID_EXT County Text Parcel Y  UDEF 
Identification 
OWNER_NAME County Text Billing Data Y  UDEF 
NAME_OVERF County Text Billing Data Y  UDEF 

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Field Source Format Description Used in Analysis (Y 
or N) 
MAILADDR1 County Text Billing Data Y  UDEF 
MAILADDR2 County Text Billing Data Y  UDEF 
CITY County Text Billing Data Y  UDEF 
STATE County Text Billing Data Y  UDEF 
ZIP_CODE County Text Billing Data Y  UDEF 
PERCENT_IN County Number Billing Data N 
CAMA_ACREA County Number Parcel Acreage N 
LEGAL_ACRE County Number Legal Acreage Y  UDEF 
CAMA_ACRE_ County Text Parcel Acreage N 
Description 
LGLACSRC County Text Legal Acreage N 
LEGAL_DESC County Text Legal Description N 
ACCOUNT_TY County Text Account Type N 
FARM_FLAG County Text Farm Flag N 
HIST_FLAG County Text Historical Flag N 
MUNIC_CODE County Text Municipality Code N 
TOWNSHIP County Text Township Code N 
FIRE_DIST County Text Fire District N 
SP_DIST County Text Special District N 
DEED_BOOK County Text Deed Book N 
DEED_PAGE County Text Deed Page N 
FILE_DATE County Date/Time File Date N 
QUALIFIED_ County Text Qualified N 
SALES_PRIC County Number Sales Price N 
SALE_DATE County Date/Time Sale Date N 
HOUSE_NO County Text Property Data N 
HOUSE_NO_num County Number Property Data N 
HOUSE_UNIT County Text Property Data N 
HOUSEUNDES County Text Property Data N 
ST_DIR County Text Property Data N 
ST_NAME County Text Property Data N 
ST_TYPE County Text Property Data N 
ST_SUFFIX County Text Property Data N 

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Field Source Format Description Used in Analysis (Y 
or N) 
NEIGHBORHO County Text Property Data N 
NEIGHBRHD_ County Text Property Data N 
USE_CODE_T County Text Use Code Y  UDEF 
AC_TYPE County Text Air Conditioning N 
Type 
EXT_WALL_T County Text Exterior Wall Type N 
FIREPLACE County Text Fireplace N 
HEATINGTYP County Text Heating Type N 
MODEL_TYPE County Text Model Type N 
STYLE_TYPE County Text Number of Stories Y  UDEF 
MUNIC County Text Municipality N 
NEWPID County Text New Parcel N 
Identification 
ZONING County Text Zoning N 
LAND_VALUE County Number Land Value N 
DEF_VALUE County Number Unknown N 
TOT_IMP_VA County Number Total Improvement Y  UDEF 
Value 
TOT_MRK_VA County Number Total Market Value N 
SALES98 County Number Sales 98 N 

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8.2 Hazard Data 
DuPage County, Illinois 
Feature 
Name of the file Source Format Class Description Used? 
Type 
98MrSID_County27 County MrSID 1998 Dupage County, IL 0.5 No 
pixel res, Ortho imagery - 
MrSID - NAD27 

03MrSID_County27.sid County MrSID 2002_2003 Dupage county, No 
IL, 0.5 pix, Orthoimagery -
MrSID NAD27 

cs_af_grad_y.e00 County Interchange 1 story masonry commercial No 
building finished floor plan 
cs_gk_grad_y.e00 County Interchange Grading and erosion control No 
plan 
cs_gke_grad_y.e00 County Interchange Grading and erosion control No 
plan 
cs_gks_grad_y.e00 County Interchange Grading and erosion control No 
plan 
cs_gla_grad_y.e00 County Interchange Grading and erosion control No 
plan 
cs_ip_grad_y.e00 County Interchange Grading and erosion control No 
plan 
cs_kcbus_y.e00 County Interchange Grading plan No 
cs_ke_det_y.e00 County Interchange Pond elevations overflow No 

cross sections 
450 kchoc boulevard, Carol 
stream, Illinois 

cs_nh_topo_y.e00 County Interchange Topology No 
cs_rand_y.e00 County Interchange No 
cs_satdet_y.e00 County Interchange Building floor elevation No 
cs_sf_grad_y.e00 County Interchange Building floor plan No 
cs_shire_y.e00 County Interchange No 
cs_tc_grad_y.e00 County Interchange Village of Carol stream No 
hydrocl.e00 County Interchange Stream network No 
lclrdan_blm.e00 County Interchange Street names No 
lclrdan_mil.e00 County Interchange Street names No 
lclrdan_way.e00 County Interchange Street names No 
lclrdan_win.e00 County Interchange Street names No 
lomc_ud_xtnts.e00 County Interchange No 
munic04.e00 County Interchange Municipality  Towns No 
topo_ud_xtnts.e00 County Interchange Municipality - Places No 
wbkc_1b.e00 County Interchange Riverine cross sections Yes  FIT data 
wbkc_2b.e00 County Interchange Riverine cross sections Yes - FIT data 

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Name of the file Source Format Feature 
Class Type Description Used? 
wbkc_3b.e00 County Interchange Riverine cross sections Yes - FIT data 
wbkc_5b.e00 County Interchange Riverine cross sections Yes - FIT data 
wbkc_6b.e00 County Interchange Riverine cross sections Yes - FIT data 
wbkc_bdy.e00 County Interchange Riverine area buffer region Yes - Reference 
wbkc_c100_rfm.e00 County Interchange Riverine cross sections Yes - Reference 
wbkc_fw_rfm.e00 County Interchange Riverine mainstream No 
polygon 
wbkc_m100_rfm.e00 County Interchange Riverine cross sections Yes  Reference 
wbkc_p100_rfm.e00 County Interchange Riverine stream polygon Yes  Reference 
wbkc_p500_rfm.e00 County Interchange Riverine stream polygon Yes - Reference 
wbkc_rte.e00 County Interchange Riverine stream network Yes  Reference 
wbkc_topo.e00 County Interchange Topology Yes  Reference 
wbkc_xs_rfm.e00 County Interchange Riverine cross sections Yes  Reference 
wtrsheds.e00 County Interchange Watersheds polygon No 
lclrdln_blm_shp County Shapefile Polygon Streets name No 
lclrdln_mil_shp County Shapefile Polygon Streets name No 
lclrdln_way_shp County Shapefile Polygon Streets name No 
lclrdln_win_shp County Shapefile Polygon Streets name No 
pclpy_blm_shp County Shapefile Polygon Parcels Yes - 
Presentation 
layer/ Centroid 
of parcel 
pclpy_mil_shp County Shapefile Polygon Parcels Yes - 
Presentation 
layer/ Centroid 
of parcel 
pclpy_way_shp County Shapefile Polygon Parcels Yes - 
Presentation 
layer/ Centroid 
of parcel 
pclpy_win_shp County Shapefile Polygon Parcels Yes - 
Presentation 
layer/ Centroid 
of parcel 
Klein creek DEM County DEM Klein creek DEM No 

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Escambia County, Florida 
Feature 
Name of the file Source Format Class Description Used? 
Type 
County.shp County Shapefile Polygon County No 
Parcels.shp County Shapefile Polygon Parcels Yes - 
Presentation 
layer/ Centroid of 
parcel 
Roads.shp County Shapefile Polyline Roads No 
Grand Forks County, North Dakota 
Feature 
Name of the file Source Format Class Description Used? 
Type 
County.shp County Shapefile Polygon County No 
Parcels.shp County Shapefile Polygon Parcels Yes - 
Presentation 
layer/ Centroid of 
parcel 
Roads.shp County Shapefile Polyline Roads No 
Road.shp County Shapefile Polyline Roads  Whatcom No 
county 

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96 
Harris County, Texas 
Name of the file 
County.shp 
Source 
County 
Format 
Shapefile 
Feature 
Class 
Type 
Polygon 
Description 
County polygon No 
Used? 
Parcels.shp County Shapefile Polygon Parcels Yes - 
Presentation 
layer/ Centroid 
of parcel 
Roads.shp County Shapefile Polyline Roads No 
Exist County Raster Existing 
conditions depth 
grid 
Yes - As 
depthgrid 
RPD_100 in 
HAZUS 
Future County Raster Future conditions 
depth grid 
Yes - As 
depthgrid 
RPD_100 in 
HAZUS 
FloodAreaWithParcels.mxd County Arcmap 
document 
Zoomed view of 
the area 
Yes - Reference 

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97 
Larimer County, Colorado 
Feature 
Name of the file Source Format Class Description Used? 
Type 
county.shp County Shapefile Polygon County polygon No 
Flooded_Parcels.shp ABS Shapefile Polygon Flooded parcels Yes 
parcels.shp County Shapefile Polygon Parcels Yes - 
Presentation 
layer/ Centroid 
of parcel 
Parcels_Existing.shp ABS Shapefile Polygon Existing parcels Yes 
Roads.shp County Shapefile Polyline Roads No 
Depth100 HAZUS-Raster Existing conditions Yes - As 
MH - FIT depth grid depthgrid 
RPD_100 in 
HAZUS 
Depth101 HAZUS-Raster Future conditions Yes - As 
MH - FIT depth grid depthgrid 
RPD_100 in 
HAZUS 
Elev100 County Raster Existing conditions Yes  
elevation grid Reference 
Elev101 County raster Future conditions Yes - Reference 
elevation grid 

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98 

Mecklenburg County, North Carolina 
Name of the file Source Format 
County.shp County Shapefile 
Feature 
Class Type 
Polygon 
DescriptionCounty No 
Used? 
FloodedParcels.shp County Shapefile Polygon Flooded parcels Yes 
Mecklenburg.mxd County Arcmap 
document 
Yes - Reference 
Parcels.shp County Shapefile Polygon Parcels Yes - Presentation 
layer/ Centroid of 
parcel 
Roads.shp County Shapefile Polyline Roads No 
Trainingparcels.shp County Shapefile Polygon Training parcels Yes - Presentation 
layer/ Centroid of 
parcel 
sugar_parcels.shp County Shapefile Polygon Parcels Yes - Presentation 
layer/ Centroid of 
parcel 
sugar_footprints.shp County Shapefile Polygon Foot prints Yes 
Futuredepth County Raster Future conditions 
depth grid 
Yes - As depthgrid 
RPD_100 in HAZUS 
Existdepth County Raster Existing conditions 
depth grid 
Yes - As depthgrid 
RPD_100 in HAZUS 
footprints_over500sqft.shp County Shapefile Polygon Footprints Yes  Reference 
gpsbfe.shp County Shapefile Point Yes 
master.shp County Shapefile Point Elevation points Yes 
master_elev_certs.shp County Shapefile Point Elevation points Yes 
xsects.shp County Shapefile Polyline Stream intersections Yes 
future100yr_combined.shp County Shapefile Polygon Yes 
flum_stream_centerline.shp County Shapefile Polyline Stream centerline Yes 
floodway_oldareas.shp County Shapefile Polygon Floodway Yes 
floodplain_oldareas.shp County Shapefile Polygon Flood plain Yes 
existing100yr.shp County Shapefile Polygon Existing conditions - 
100 year flood streams 
Yes  Reference 
parcels_withpid.shp County Shapefile Polygon Parcels Yes - Presentation 
layer/ Centroid of 
parcel 
masteraddress.shp County Shapefile Point Address data for 
elevation points 
Yes 

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9. ACRONYMS 
AGR1 Agriculture Occupancy 
AIR American Institutes for Research 
ASFPM Association of Floodplain Mangers 
BFE Base Flood Elevation 
COM1 Retail Commercial Uses 
CRS Community Rating System 
DEM Digital Elevation Model 
DFIRM Draft Flood Insurance Rate Map 
DHS Department of Homeland Security 
FEMA Federal Emergency Management Agency 
FIMA Federal Insurance and Mitigation Administration 
FIRM Flood Insurance Rate Maps 
FIT Flood Information Tool 
GIS Geographical Information Systems 
GOV1 Government 
HAZUS HAZUS-MH Flood Model 
HMGP Hazard Mitigation Grant Program 
LIDAR Laser Imaging Detection and Ranging 
NFIP National Flood Insurance Program 
NIBS National Institute for Building Sciences 
RES1 Single-family Residential 
RUNUP Wave Run-up Computation 
SFHA Special Flood Hazard Areas 
WHAFIS Wave Height Analysis for Flood Insurance Studies 

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10. REFERENCES 
Dewberry & Davis. January 1999. Revised Draft  Examples of Flood Hazard Changes with 
Long Term Erosion. 

NIBS 2006. Multi-hazard Loss Estimation Methodology: Flood Model, HAZUS-MH MR1 
Technical Manual, prepared by the National Institute of Building Sciences (NIBS) for the 
Department of Homeland Security, Emergency Preparedness and Response Directorate, 
Federal Emergency Management Agency, Mitigation Division, Washington DC. 

NIBS 2006. Multi-hazard Loss Estimation Methodology: Flood Model, HAZUS-MH MR1 User 
Manual, prepared by the National Institute of Building Sciences (NIBS) for the 
Department of Homeland Security, Emergency Preparedness and Response Directorate, 
Federal Emergency Management Agency, Mitigation Division, Washington DC. 

Galvin, C. 1983. Sea Level Rise and Shoreline Recession. ASCE Proceedings of Coastal Zone 
'83. pp. 2684-2705 

FEMA. April 2003. Guidelines and Specifications for Flood Hazard Mapping Partners, 
Appendix D  Guidance for Coastal Flooding Analysis and Mapping. 

Mecklenburg County, April 2003. Determination of Financial Impacts from Flood Studies, 
prepared by Mecklenburg County Water and Land Resources Division for the 
Engineering and Building Standards Department, Charlotte, NC. 

Heinz Center. April 2000. Evaluation of Erosion Hazards. 

RSMeans, 2001, RSMeans Square Foot Costs 23rd Annual Edition 2002. RSMeans Company, 
Inc. Construction Publisher and Consultants, Kingston, MA. 

Evaluation of the National Flood Insurance Program D R A F T 

Managing Future Development Conditions in the National Flood Insurance Program 


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